Machine learning (ML) models, such as SVM, for tasks like classification and clustering of sequences, require a definition of distance/similarity between pairs of sequences. Several methods have been proposed to compute the similarity between sequences, such as the exact approach that counts the number of matches between k-mers (sub-sequences of length k) and an approximate approach that estimates pairwise similarity scores. Although exact methods yield better classification performance, they pose high computational costs, limiting their applicability to a small number of sequences. The approximate algorithms are proven to be more scalable and perform comparably to (sometimes better than) the exact methods - they are designed in a "general" way to deal with different types of sequences (e.g., music, protein, etc.). Although general applicability is a desired property of an algorithm, it is not the case in all scenarios. For example, in the current COVID-19 (coronavirus) pandemic, there is a need for an approach that can deal specifically with the coronavirus. To this end, we propose a series of ways to improve the performance of the approximate kernel (using minimizers and information gain) in order to enhance its predictive performance pm coronavirus sequences. More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e.g., Alpha, Beta, Gamma). We report results using different classification and clustering algorithms and evaluate their performance using multiple evaluation metrics. Using two datasets, we show that our proposed method helps improve the kernel's performance compared to the baseline and state-of-the-art approaches in the healthcare domain.

The opioid abuse epidemic represents a major public health threat to global populations. The role social media may play in facilitating illicit drug trade is largely unknown due to limited research. However, it is known that social media use among adults in the US is widespread, there is vast capability for online promotion of illegal drugs with delayed or limited deterrence of such messaging, and further, general commercial sale applications provide safeguards for transactions; however, they do not discriminate between legal and illegal sale transactions. These characteristics of the social media environment present challenges to surveillance which is needed for advancing knowledge of online drug markets and the role they play in the drug abuse and overdose deaths. In this paper, we present a computational framework developed to automatically detect illicit drug ads and communities of vendors. The SVM- and CNN- based methods for detecting illicit drug ads, and a matrix factorization based method for discovering overlapping communities have been extensively validated on the large dataset collected from Google+, Flickr and Tumblr. Pilot test results demonstrate that our computational methods can effectively identify illicit drug ads and detect vendor-community with accuracy. These methods hold promise to advance scientific knowledge surrounding the role social media may play in perpetuating the drug abuse epidemic.

The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. However, NGS data require sophisticated analysis dealing with millions of error-prone short reads per patient. Prior to the NGS era, epidemiological and phylogenetic analyses were geared toward Sanger sequencing technology; now, they must be redesigned to handle the large-scale NGS datasets and properly model the evolution of heterogeneous rapidly mutating viral populations. Additionally, dedicated epidemiological surveillance systems require big data analytics to handle millions of reads obtained from thousands of patients for rapid outbreak investigation and management. We survey bioinformatics tools analyzing NGS data for (i) characterization of intra-host viral population complexity including single nucleotide variant and haplotype calling; (ii) downstream epidemiological analysis and inference of drug-resistant mutations, age of infection and linkage between patients; and (iii) data collection and analytics in surveillance systems for fast response and control of outbreaks.

One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.

SUMMARY: Intra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. AVAILABILITY AND IMPLEMENTATION: Its source code is available at https://github.com/compbel/SCIFIL.

The papers in this special section were presented at the 12th International Symposium on Bioinformatics Research and Application (ISBRA), which was held at Belarusian State University in Minsk, Belarus on June 5-8, 2016.

Summary: Genomic sequences are assembled into a variable, but large number of contigs that should be scaffolded (ordered and oriented) for facilitating comparative or functional analysis. Finding scaffolding is computationally challenging due to misassemblies, inconsistent coverage across the genome and long repeats. An accurate assessment of scaffolding tools should take into account multiple locations of the same contig on the reference scaffolding rather than matching a repeat to a single best location. This makes mapping of inferred scaffoldings onto the reference a computationally challenging problem. This paper formulates the repeat-aware scaffolding evaluation problem, which is to find a mapping of the inferred scaffolding onto the reference maximizing number of correct links and proposes a scalable algorithm capable of handling large whole-genome datasets. Our novel scaffolding validation framework has been applied to assess the most of state-of-the-art scaffolding tools on the representative subset of Genome Assembly Golden-Standard Evaluations (GAGE) datasets and some novel simulated datasets. Availability and implementation: The source code of this evaluation framework is available at https://github.com/mandricigor/repeat-aware. The documentation is hosted at https://mandricigor.github.io/repeat-aware. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use. Results: The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN. Contact: [email protected] or [email protected] or [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Molecular surveillance and outbreak investigation are important for elimination of hepatitis C virus (HCV) infection in the United States. A web-based system, Global Hepatitis Outbreak and Surveillance Technology (GHOST), has been developed using Illumina MiSeq-based amplicon sequence data derived from the HCV E1/E2-junction genomic region to enable public health institutions to conduct cost-effective and accurate molecular surveillance, outbreak detection and strain characterization. However, as there are many factors that could impact input data quality to which the GHOST system is not completely immune, accuracy of epidemiological inferences generated by GHOST may be affected. Here, we analyze the data submitted to the GHOST system during its pilot phase to assess the nature of the data and to identify common quality concerns that can be detected and corrected automatically. RESULTS: The GHOST quality control filters were individually examined, and quality failure rates were measured for all samples, including negative controls. New filters were developed and introduced to detect primer dimers, loss of specimen-specific product, or short products. The genotyping tool was adjusted to improve the accuracy of subtype calls. The identification of "chordless" cycles in a transmission network from data generated with known laboratory-based quality concerns allowed for further improvement of transmission detection by GHOST in surveillance settings. Parameters derived to detect actionable common quality control anomalies were incorporated into the automatic quality control module that rejects data depending on the magnitude of a quality problem, and warns and guides users in performing correctional actions. The guiding responses generated by the system are tailored to the GHOST laboratory protocol. CONCLUSIONS: Several new quality control problems were identified in MiSeq data submitted to GHOST and used to improve protection of the system from erroneous data and users from erroneous inferences. The GHOST system was upgraded to include identification of causes of erroneous data and recommendation of corrective actions to laboratory users.

BACKGROUND: Many biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious naïeve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets. RESULTS: In this paper, we present fast and scalable k-mer-based framework to perform such sequence similarity queries efficiently, which specifically targets data produced by deep sequencing of heterogeneous populations such as viruses. It shows better filtering quality and time performance when comparing to other tools. The tool is freely available for download at https://github.com/vyacheslav-tsivina/signature-sj CONCLUSION: The proposed tool allows for efficient detection of genetic relatedness between genomic samples produced by deep sequencing of heterogeneous populations. It should be especially useful for analysis of relatedness of genomes of viruses with unevenly distributed variable genomic regions, such as HIV and HCV. For the future we envision, that besides applications in molecular epidemiology the tool can also be adapted to immunosequencing and metagenomics data.

SUMMARY: This note presents IsoEM2 and IsoDE2, new versions with enhanced features and faster runtime of the IsoEM and IsoDE packages for expression level estimation and differential expression. IsoEM2 estimates fragments per kilobase million (FPKM) and transcript per million (TPM) levels for genes and isoforms with confidence intervals through bootstrapping, while IsoDE2 performs differential expression analysis using the bootstrap samples generated by IsoEM2. Both tools are available with a command line interface as well as a graphical user interface (GUI) through wrappers for the Galaxy platform. AVAILABILITY AND IMPLEMENTATION: The source code of this software suite is available at https://github.com/mandricigor/isoem2. The Galaxy wrappers are available at https://toolshed.g2.bx.psu.edu/view/saharlcc/isoem2_isode2/. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: For fighting cancer, earlier detection is crucial. Circulating auto-antibodies produced by the patient's own immune system after exposure to cancer proteins are promising bio-markers for the early detection of cancer. Since an antibody recognizes not the whole antigen but 4-7 critical amino acids within the antigenic determinant (epitope), the whole proteome can be represented by a random peptide phage display library. This opens the possibility to develop an early cancer detection test based on a set of peptide sequences identified by comparing cancer patients' and healthy donors' global peptide profiles of antibody specificities. RESULTS: Due to the enormously large number of peptide sequences contained in global peptide profiles generated by next generation sequencing, the large number of cancer and control sera is required to identify cancer-specific peptides with high degree of statistical significance. To decrease the number of peptides in profiles generated by nextgen sequencing without losing cancer-specific sequences we used for generation of profiles the phage library enriched by panning on the pool of cancer sera. To further decrease the complexity of profiles we used computational methods for transforming a list of peptides constituting the mimotope profiles to the list motifs formed by similar peptide sequences. CONCLUSION: We have shown that the amino-acid order is meaningful in mimotope motifs since they contain significantly more peptides than motifs among peptides where amino-acids are randomly permuted. Also the single sample motifs significantly differ from motifs in peptides drawn from multiple samples. Finally, multiple cancer-specific motifs have been identified.

MOTIVATION: Next-generation high-throughput sequencing has become a state-of-the-art technique in genome assembly. Scaffolding is one of the main stages of the assembly pipeline. During this stage, contigs assembled from the paired-end reads are merged into bigger chains called scaffolds. Because of a high level of statistical noise, chimeric reads, and genome repeats the problem of scaffolding is a challenging task. Current scaffolding software packages widely vary in their quality and are highly dependent on the read data quality and genome complexity. There are no clear winners and multiple opportunities for further improvements of the tools still exist. RESULTS: This article presents an efficient scaffolding algorithm ScaffMatch that is able to handle reads with both short (<600 bp) and long (>35 000 bp) insert sizes producing high-quality scaffolds. We evaluate our scaffolding tool with the F score and other metrics (N50, corrected N50) on eight datasets comparing it with the most available packages. Our experiments show that ScaffMatch is the tool of preference for the most datasets. AVAILABILITY AND IMPLEMENTATION: The source code is available at http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Next-generation sequencing (NGS) allows for analyzing a large number of viral sequences from infected patients, providing an opportunity to implement large-scale molecular surveillance of viral diseases. However, despite improvements in technology, traditional protocols for NGS of large numbers of samples are still highly cost and labor intensive. One of the possible cost-effective alternatives is combinatorial pooling. Although a number of pooling strategies for consensus sequencing of DNA samples and detection of SNPs have been proposed, these strategies cannot be applied to sequencing of highly heterogeneous viral populations. RESULTS: We developed a cost-effective and reliable protocol for sequencing of viral samples, that combines NGS using barcoding and combinatorial pooling and a computational framework including algorithms for optimal virus-specific pools design and deconvolution of individual samples from sequenced pools. Evaluation of the framework on experimental and simulated data for hepatitis C virus showed that it substantially reduces the sequencing costs and allows deconvolution of viral populations with a high accuracy. AVAILABILITY AND IMPLEMENTATION: The source code and experimental data sets are available at http://alan.cs.gsu.edu/NGS/?q=content/pooling.

Single nucleotide polymorphisms, a dominant type of genetic variants, have been used successfully to identify defective genes causing human single gene diseases. However, most common human diseases are complex diseases and caused by gene-gene and gene-environment interactions. Many SNP-SNP interaction analysis methods have been introduced but they are not powerful enough to discover interactions more than three SNPs. The paper proposes a novel method that analyzes all SNPs simultaneously. Different from existing methods, the method regards an individual's genotype data on a list of SNPs as a point with a unit of energy in a multi-dimensional space, and tries to find a new coordinate system where the energy distribution difference between cases and controls reaches the maximum. The method will find different multiple SNPs combinatorial patterns between cases and controls based on the new coordinate system. The experiment on simulated data shows that the method is efficient. The tests on the real data of age-related macular degeneration (AMD) disease show that it can find out more significant multi-SNP combinatorial patterns than existing methods.

MOTIVATION: Next-generation sequencing technologies sequence viruses with ultra-deep coverage, thus promising to revolutionize our understanding of the underlying diversity of viral populations. While the sequencing coverage is high enough that even rare viral variants are sequenced, the presence of sequencing errors makes it difficult to distinguish between rare variants and sequencing errors. RESULTS: In this article, we present a method to overcome the limitations of sequencing technologies and assemble a diverse viral population that allows for the detection of previously undiscovered rare variants. The proposed method consists of a high-fidelity sequencing protocol and an accurate viral population assembly method, referred to as Viral Genome Assembler (VGA). The proposed protocol is able to eliminate sequencing errors by using individual barcodes attached to the sequencing fragments. Highly accurate data in combination with deep coverage allow VGA to assemble rare variants. VGA uses an expectation-maximization algorithm to estimate abundances of the assembled viral variants in the population. RESULTS on both synthetic and real datasets show that our method is able to accurately assemble an HIV viral population and detect rare variants previously undetectable due to sequencing errors. VGA outperforms state-of-the-art methods for genome-wide viral assembly. Furthermore, our method is the first viral assembly method that scales to millions of sequencing reads. AVAILABILITY: Our tool VGA is freely available at http://genetics.cs.ucla.edu/vga/

BACKGROUND: Interest in de novo genome assembly has been renewed in the past decade due to rapid advances in high-throughput sequencing (HTS) technologies which generate relatively short reads resulting in highly fragmented assemblies consisting of contigs. Additional long-range linkage information is typically used to orient, order, and link contigs into larger structures referred to as scaffolds. Due to library preparation artifacts and erroneous mapping of reads originating from repeats, scaffolding remains a challenging problem. In this paper, we provide a scalable scaffolding algorithm (SILP2) employing a maximum likelihood model capturing read mapping uncertainty and/or non-uniformity of contig coverage which is solved using integer linear programming. A Non-Serial Dynamic Programming (NSDP) paradigm is applied to render our algorithm useful in the processing of larger mammalian genomes. To compare scaffolding tools, we employ novel quantitative metrics in addition to the extant metrics in the field. We have also expanded the set of experiments to include scaffolding of low-complexity metagenomic samples. RESULTS: SILP2 achieves better scalability throughg a more efficient NSDP algorithm than previous release of SILP. The results show that SILP2 compares favorably to previous methods OPERA and MIP in both scalability and accuracy for scaffolding single genomes of up to human size, and significantly outperforms them on scaffolding low-complexity metagenomic samples. CONCLUSIONS: Equipped with NSDP, SILP2 is able to scaffold large mammalian genomes, resulting in the longest and most accurate scaffolds. The ILP formulation for the maximum likelihood model is shown to be flexible enough to handle metagenomic samples.

BACKGROUND: Highly mutable RNA viruses exist in infected hosts as heterogeneous populations of genetically close variants known as quasispecies. Next-generation sequencing (NGS) allows for analysing a large number of viral sequences from infected patients, presenting a novel opportunity for studying the structure of a viral population and understanding virus evolution, drug resistance and immune escape. Accurate reconstruction of genetic composition of intra-host viral populations involves assembling the NGS short reads into whole-genome sequences and estimating frequencies of individual viral variants. Although a few approaches were developed for this task, accurate reconstruction of quasispecies populations remains greatly unresolved. RESULTS: Two new methods, AmpMCF and ShotMCF, for reconstruction of the whole-genome intra-host viral variants and estimation of their frequencies were developed, based on Multicommodity Flows (MCFs). AmpMCF was designed for NGS reads obtained from individual PCR amplicons and ShotMCF for NGS shotgun reads. While AmpMCF, based on covering formulation, identifies a minimal set of quasispecies explaining all observed reads, ShotMCS, based on packing formulation, engages the maximal number of reads to generate the most probable set of quasispecies. Both methods were evaluated on simulated data in comparison to Maximum Bandwidth and ViSpA, previously developed state-of-the-art algorithms for estimating quasispecies spectra from the NGS amplicon and shotgun reads, respectively. Both algorithms were accurate in estimation of quasispecies frequencies, especially from large datasets. CONCLUSIONS: The problem of viral population reconstruction from amplicon or shotgun NGS reads was solved using the MCF formulation. The two methods, ShotMCF and AmpMCF, developed here afford accurate reconstruction of the structure of intra-host viral population from NGS reads. The implementations of the algorithms are available at http://alan.cs.gsu.edu/vira.html (AmpMCF) and http://alan.cs.gsu.edu/NGS/?q=content/shotmcf (ShotMCF).

This special section includes a selection of papers presented at the Eighth International Symposium on Bioinformatics Research and Application (ISBRA), which was held in Dallas, Texas, on 21-23 May 2012. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. In 2012, 66 papers were submitted in response to the call for papers, out of which 26 papers appeared in the ISBRA proceedings published as volume 7292 of Springer Verlag's Lecture Notes in Bioinformatics series. Extended versions of nine symposium papers were invited and accepted for publication in this special section following a rigorous review process. The selected papers cover a broad range of bioinformatics topics, including biological networks, computational complexity of problems in structural biology and genomics, and phylogenetic inference and analysis. Herein, we briefly introduce each of them.

This Supplement includes a selection of papers presented at the 7th International Symposium on Bioinformatics Research and Application (ISBRA), which was held on May 27-29, 2011 at Central South University in Changsha, China.The technical program of the symposium included 36 extended abstracts presented orally and published in volume 6674 of Springer Verlag's Lecture Notes in Bioinformatics series.Additionally, the program included 38 short abstracts presented either orally or as posters.Authors of both extended and short abstracts presented at the symposium were invited to submit full versions of their work to this Supplement.Following a rigorous review process, 19 of the 40 full papers submitted were selected for publication.Selected papers cover a broad range of bioinformatics topics, ranging from algorithms for structural biology to phylogenetics and biological networks.The first two papers of the Supplement address two important problems in structural biology.Improved methods for predicting protein-protein and protein-DNA binding and identification of binding sites are critical components of rational drug design and functional annotation pipelines.The paper by Guo and Wang proposes an efficient algorithm for finding similar binding sites on the protein surfaces based on sequence alignment, protein surface detection, and 3D structure comparison.Validation experiments show significantly improved average recall and precision values compared with existing approaches.Szabóová et al. propose methods for predicting protein-DNA binding propensity from spatial structure information without the use of evolutionary information.Such methods are particularly useful for optimizing DNA-binding of engineered proteins, for which evolutionary information is not available.Unlike previous approaches that rely on ad-hoc sets of physicochemical

Recent advances in DNA sequencing have made it possible to sequence the whole transcriptome by massively parallel sequencing, commonly referred as RNA-Seq. RNA-Seq is quickly becoming the technology of choice for transcriptome research and analyses. RNA-Seq allows to reduce the sequencing cost and significantly increase data throughput, but it is computationally challenging to use such RNA-Seq data for reconstructing of full length transcripts and accurately estimate their abundances across all cell types. A number of recent works have addressed the problem of transcriptome reconstruction from RNA-Seq reads. These methods fall into three categories: genome-guided, genome-independent and annotation-guided. In this work, we propose a novel statistical genome-guided method called “ T ranscriptome R econstruction using I nteger P rograming” (TRIP) that incorporates fragment length distribution into novel transcript reconstruction from paired-end RNA-Seq reads. To reconstruct novel transcripts, we create a splice graph based on inferred exon boundaries and RNA-Seq reads. A splice graph is a directed acyclic graph (DAG), whose vertices represent exons and edges represent splicing events. We enumerate all maximal paths in the splice graph using a depth-first-search (DFS) algorithm. These paths correspond to putative transcripts and are the input for the TRIP algorithm. To solve the transcriptome reconstruction problem we must select a set of putative transcripts with the highest support from the RNA-Seq reads. We formulate this problem as an integer program. The objective to select the smallest set of putative transcripts that yields a good statistical fit between the fragment length distribution empirically determined during library preparation and fragment lengths implied by mapping read pairs to selected transcripts. Preliminary experimental results on synthetic datasets generated with various sequencing parameters and distribution assumptions show that TRIP has increased transcriptome reconstruction accuracy compared to previous methods that ignore fragment length distribution information.

BACKGROUND: Next-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing. RESULTS: In this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones. CONCLUSIONS: Both algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses.The implementations of the algorithms and data sets used for their testing are available at: http://alan.cs.gsu.edu/NGS/?q=content/pyrosequencing-error-correction-algorithm.

The articles in this special section include selected papers from the Seventh International Symposium on Bioinformatics Research and Applications.

In a data collection sensor network, how to maximize the network lifetime through topology control remains an open research problem. Previous work has studied this problem by aiming to build a max-lifetime data collection tree, however, tree-based data collection does not necessarily yield maximum network lifetime. In this paper, we consider collaborative multipath data delivery and formulate the lifetime maximization problem as a max-fair-flow problem, then study how to collaboratively adjust the transmission power of sensor nodes to achieve the maxfair-flow, thus maximizing the network lifetime. We give both theoretical proofs and simulations to validate its correctness and performance.

BACKGROUND: RNA viruses infecting a host usually exist as a set of closely related sequences, referred to as quasispecies. The genomic diversity of viral quasispecies is a subject of great interest, particularly for chronic infections, since it can lead to resistance to existing therapies. High-throughput sequencing is a promising approach to characterizing viral diversity, but unfortunately standard assembly software was originally designed for single genome assembly and cannot be used to simultaneously assemble and estimate the abundance of multiple closely related quasispecies sequences. RESULTS: In this paper, we introduce a new Viral Spectrum Assembler (ViSpA) method for quasispecies spectrum reconstruction and compare it with the state-of-the-art ShoRAH tool on both simulated and real 454 pyrosequencing shotgun reads from HCV and HIV quasispecies. Experimental results show that ViSpA outperforms ShoRAH on simulated error-free reads, correctly assembling 10 out of 10 quasispecies and 29 sequences out of 40 quasispecies. While ShoRAH has a significant advantage over ViSpA on reads simulated with sequencing errors due to its advanced error correction algorithm, ViSpA is better at assembling the simulated reads after they have been corrected by ShoRAH. ViSpA also outperforms ShoRAH on real 454 reads. Indeed, 7 most frequent sequences reconstructed by ViSpA from a real HCV dataset are viable (do not contain internal stop codons), and the most frequent sequence was within 1% of the actual open reading frame obtained by cloning and Sanger sequencing. In contrast, only one of the sequences reconstructed by ShoRAH is viable. On a real HIV dataset, ShoRAH correctly inferred only 2 quasispecies sequences with at most 4 mismatches whereas ViSpA correctly reconstructed 5 quasispecies with at most 2 mismatches, and 2 out of 5 sequences were inferred without any mismatches. ViSpA source code is available at http://alla.cs.gsu.edu/~software/VISPA/vispa.html. CONCLUSIONS: ViSpA enables accurate viral quasispecies spectrum reconstruction from 454 pyrosequencing reads. We are currently exploring extensions applicable to the analysis of high-throughput sequencing data from bacterial metagenomic samples and ecological samples of eukaryote populations.

Digital microfluidic biochips (DMFBs) are rectangular arrays of electrodes, or cells, that enable precise manipulation of nanoliter-sized droplets of biological fluids and chemical reagents. Because of the safety-critical nature of their applications, biochips must be tested frequently, both off-line (e.g., postmanufacturing) and concurrent with assay execution. Under both scenarios, testing is accomplished by routing one or more test droplets across the chip and recording their arrival at the destination. In this paper, we formalize the DMFB-testing problem under the common objective of completion time minimization, including previously ignored constraints of droplet noninterference. Our contributions include a proof that the general version of the problem is NP-hard, tight lower bounds for both off-line and concurrent testing, optimal and approximation algorithms for off-line testing of commonly used rectangular shaped biochips, as well as a concurrent testing heuristic producing solutions within 23%–34% of the lower bound in experiments conducted on data sets simulating varying percentages of biochip cells occupied by concurrently running assays.

Some emerging applications deal with graph data and relie on graph matching and mining. The service-oriented graph matching and mining tool has been required. In this demo we present the web service tool WS-GraphMatching which supports the efficient and visualized matching of polytrees, series-parallel graphs, and arbitrary graphs with bounded feedback vertex set. Its embedded matching algorithms take in account the similarity of vertex-to-vertex and graph structures, allowing path contraction, vertex deletion, and vertex insertions. It provides one-to-one matching queries as well as queries in batch modes including one-to-many matching mode and many-to-many matching mode. It can be used for predicting unknown structured information, comparing and finding conserved patterns, and resolving ambiguous identification of vertices.

Optical Burst Switching (OBS) is considered to be a promising paradigm for bearing IP traffic in Wavelength Division Multiplexing (WDM) optical networks. In OBS networks, a key challenge is to reduce the data loss rate with efficient scheduling algorithms. In this work, we propose novel algorithms for batch scheduling in OBS networks with different optimization criteria. The algorithms effectively consider the strong correlations among the multiple bursts, and employ the proposed interval graphs and min-cost circular flow techniques to achieve optimized network performance in terms of data loss rate in the network. Simulation results show that our algorithms achieve a loss rate which is as much as 20% less than one of the best previously known algorithms, LAUC-VF, and suffer only a minor increase (about 1-hop link propagation) in the data latency.

SUMMARY: The accumulation of high-throughput genomic, proteomic and metabolical data allows for increasingly accurate modeling and reconstruction of metabolic networks. Alignment of the reconstructed networks can help to catch model inconsistencies and infer missing elements. In this note, we present the web service tool MetNetAligner which aligns metabolic networks, taking in account the similarity of network topology and the enzymes' functions. It can be used for predicting unknown pathways, comparing and finding conserved patterns and resolving ambiguous identification of enzymes. The tool supports several alignment options including allowing or forbidding enzyme deletion and insertion. It is based on a novel scoring scheme which measures enzyme-to-enzyme functional similarity and a fast algorithm which efficiently finds optimal mappings from a directed graph with restricted cyclic structure to an arbitrary directed graph. AVAILABILITY: MetNetAligner is available as web-server at: http://alla.cs.gsu.edu:8080/MinePW/pages/gmapping/GMMain.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The five papers in this special section were presented at the Fourth International Symposium on Bioinformatics Research and Application (ISBRA), held at Georgia State University in Atlanta, GA, on 6-9 May 2008.

Network alignments are extensively used for comparing, exploring, and predicting biological networks. Existing alignment tools are mostly based on isomorphic and homeomorphic embedding and require solving a problem that is NP-complete even when searching a match for a tree in acyclic networks. On the other hand, if the mapping of different nodes from the query network (pattern) into the same node from the text network is allowed, then trees can be optimally mapped into arbitrary networks in polynomial time.In this paper we present the first polynomial-time algorithm for finding the best matching pair consisting of a subtree in a given tree pattern and a subgraph in a given text (represented by an arbitrary network) when both insertions and deletions of degree-2 vertices are allowed on any path. Our dynamic programming algorithm is an order of magnitude faster than the previous network alignment algorithm when deletions are forbidden. The algorithm has been also generalized to pattern networks with cycles: with a modest increase in runtime it can handle patterns with the limited vertex feedback set.We have applied our algorithm to matching metabolic pathways of four organisms (E. coli, S. cerevisiae, B. subtilis and T. thermophilus species) and found a reasonably large set of statistically significant alignments. We show advantages of allowing pattern vertex deletions and give an example validating biological relevance of the pathway alignment.

Emerging microarray technologies allow affordable typing of very long genome sequences. A key challenge in analyzing of such huge amount of data is scalable and accurate computational inferring of haplotypes (i.e., splitting of each genotype into a pair of corresponding haplotypes). In this paper, we first phase genotypes consisting only of two SNPs using genotypes frequencies adjusted to the random mating model and then extend phasing of two-SNP genotypes to phasing of complete genotypes using maximum spanning trees. Runtime of the proposed 2SNP algorithm is O(nm (n + log m), where n and m are the numbers of genotypes and SNPs, respectively, and it can handle genotypes spanning entire chromosomes in a matter of hours. On datasets across 23 chromosomal regions from HapMap[11], 2SNP is several orders of magnitude faster than GERBIL and PHASE while matching them in quality measured by the number of correctly phased genotypes, single-site and switching errors. For example the 2SNP software phases entire chromosome (10(5) SNPs from HapMap) for 30 individuals in 2 hours with average switching error 7.7%. We have also enhanced 2SNP algorithm to phase family trio data and compared it with four other well-known phasing methods on simulated data from [15]. 2SNP is much faster than all of them while loosing in quality only to PHASE. 2SNP software is publicly available at http://alla.cs.gsu.edu/~software/2SNP.

The special section includes a selection of papers presented at the Third International Symposium on Bioinformatics Research and Application (ISBRA 2007). which took place at Georgia State University, Atlanta, 7-10 May 2007.

Recent improvements in high-throughput genotyping technology make possible genome-wide association studies and status prediction (classification) for common complex diseases. This paper addresses three challenges commonly facing such studies: (i) searching an enormous amount of possible gene interactions, (ii) validating reproducibility of associations and (iii) reliably predicting disease status. These challenges have been traditionally addressed in statistics while here we apply computational approaches -optimization and cross-validation. A complex risk factor is modeled as a subset of SNP's with specified alleles and the optimization formulation asks for the one with the maximum odds ratio. When searching for disease associated risk factor, we show that greedy heuristics are much faster and lead to significantly better solutions than exhaustive heuristics in a reasonable amount of time. We propose a novel randomized complimentary greedy search method that is advantageous to the previously best search method. To measure and compare ability of search methods to find reproducible risk factors, we propose to apply a cross-validation scheme usually used for prediction validation. The proposed heuristic association search methods promise better reproducibility than exhaustive searches. We then show that k-fold cross-validation is more reliable than leave-one-out cross-validation for disease status prediction methods since it captures overtraining effect. We have applied known search methods with proposed enhancements as well as status prediction methods (based on these search methods) to real case-control studies for several diseases (Chron's disease, autoimmune disorder, tick-born encephalitis, lung cancer, and rheumatoid arthritis). 2-and 3-fold cross-validations show that the new methods find strongly associated risk factors and reliably predict disease status for considered case-control studies.

Network mapping is a convenient tool for comparing and exploring biological networks; it can be used for predicting unknown pathways, fast and meaningful searching of databases, and potentially establishing evolutionary relations. Unfortunately, existing tools for mapping paths into general networks (PathBlast) or trees into tree networks allowing gaps (MetaPathwayHunter) cannot handle large query pathways or complex networks. In this paper we consider homomorphisms, i.e., mappings allowing to map different enzymes from the query pathway into the same enzyme from the networks. Homomorphisms are more general than homeomorphism (allowing gaps) and easier to handle algorithmically. Our dynamic programming algorithm efficiently finds the minimum cost homomorphism from a multisource tree to directed acyclic graphs as well as general networks. We have performed pairwise mapping of all pathways for four organisms (E. coli, S. cerevisiae, B. subtilis and T. thermophilus species) and found a reasonably large set of statistically significant pathway similarities. Further analysis of our mappings identifies conserved pathways across examined species and indicates potential pathway holes in existing pathway descriptions.

This paper addresses the computational challenge facing association analysis of case-control studies - searching an enormous amount of possible gene interactions. A complex risk factor (RF) is proposed to be modeled as close (weighted) match to a diplotype (e.g., no more than k mismatches) and the optimization formulation asks for RF with the maximum odds ratio. We have applied and cross-validated previously known and two proposed search methods for finding basic RF's with large odds ratios on 5 real case-control studies. New proposed methods find RF's that are statistically significant on all data including two datasets where no significant RF's were found before. The found RF's explain 1.5-4 times more cases than previously known RF's. The new methods also have significantly higher leave-half-out cross-validation rate.

Alternating-aperture phase shift masking (AAPSM), a form of strong resolution enhancement technology, will be used to image critical features on the polysilicon layer at smaller technology nodes. This technology imposes additional constraints on the layouts beyond traditional design rules. Of particular note is the requirement that all critical features be flanked by opposite-phase shifters while the shifters obey minimum width and spacing requirements. A layout is called phase assignable if it satisfies this requirement. Phase conflicts have to be removed to enable the use of AAPSM for layouts that are not phase assignable. Previous work has sought to detect a suitable set of phase conflicts to be removed as well as correct them. This paper has two key contributions: 1) a new computationally efficient approach to detect a minimal set of phase conflicts, which when corrected will produce a phase-assignable layout, and 2) a novel layout modification scheme for correcting these phase conflicts with small layout area increase. Unlike previous formulations of this problem, the proposed solution for the conflict detection problem does not frame it as a graph bipartization problem. Instead, a simpler and more computationally efficient reduction is proposed. This simplification greatly improves the runtime while maintaining the same improvements in the quality of results obtained in Chiang (Proc. DATE, 2005, p. 908). An average runtime speedup of 5.9times is achieved using the new flow. A new layout modification scheme suited for correcting phase conflicts in large standard-cell blocks is also proposed. The experiments show that the percentage area increase for making standard-cell blocks phase assignable ranges from 1.7% to 9.1%

The aggressive scaling of very large-scale integration feature size and the pervasive use of advanced reticle enhancement technologies lead to dramatic increases in mask costs, pushing prototype and low-volume production designs to the limit of economic feasibility. Multiproject wafers (MPWs), or “shuttle” runs, provide an attractive solution for such designs by providing a mechanism to share the cost of mask tooling among up to tens of designs. However, MPW reticle floorplanning and wafer dicing introduce complexities that are not encountered in typical single-project wafers. Recent works on wafer dicing adopt one or more of the following assumptions to reduce problem complexity: 1) equal production volume requirement for all designs; 2) same dicing plan used for all wafers or for all rows/columns of reticle images on a wafer; 3) unrealistic wafer models such as a rectangular array of projections; and 4) fixed wafer shot-map. Although using one or more of the aforementioned assumptions makes the problem solvable, the performance of the solutions is degraded. In this paper, a comprehensive MPW flow aimed at minimizing the number of wafers needed to fulfill given die production volumes is proposed. The proposed flow includes two main steps: 1) multiproject reticle floorplanning and 2) wafer shot-map and dicing plan definition. For each of these steps, improved algorithms are proposed as follows. The proposed reticle floorplanner uses a hierarchical quadrisection combined with simulated annealing to generate “diceable” floorplans, observing given maximum reticle sizes. The proposed dicing planner allows multiple side-to-side dicing plans for different wafers and different reticle projection rows/columns within a wafer and further improves the dicing yield by partitioning each wafer into a small number of parts before individual die extraction. A wafer shot-map definition heuristic is also proposed in order to fully utilize round wafer real estate by extracting the maximum number of functional dies from both fully and partially printed reticle images. Experiments on industry test cases show that the proposed methods outperform significantly not only previous methods in the literature but also reticle floorplans manually designed by experienced engineers.

Shallow trench isolation (STI) is the mainstream CMOS isolation technology. It uses chemical mechanical planarization (CMP) to remove excess of deposited oxide and attain a planar surface for successive process steps. Despite advances in STI CMP technology, pattern dependencies cause large post-CMP topography variation that can result in functional and parametric yield loss. Fill insertion is used to reduce pattern variation and consequently decrease post-CMP topography variation. Traditional fill insertion is rulebased and is used with reverse etchback to attain desired planarization quality. Due to extra costs associated with reverse etchback, “single-step ” STI CMP in which fill insertion suffices is desirable. To alleviate the failures caused by imperfect CMP, we focus on two objectives for fill insertion: oxide density variation minimization and nitride density maximization. A linear programming based optimization is used to calculate oxide densities that minimize oxide density variation. Next a fill insertion methodology is presented that attains the calculated oxide density while maximizing the nitride density. Averaged over the two large testcases, the oxide density variation is reduced by 63 % and minimum nitride density is increased by 79 % compared to tiling-based fill insertion. To assess post-CMP planarization, we run CMP simulation on the layout filled with our approach and find the planarization window (time window in which polishing can be stopped) to increase by 17 % and maximum final step height (maximum difference in post-CMP oxide thickness) to decrease by 9%.

Energy consumption in monitoring and communication protocols for wireless sensor networks became one of the most important performance objective. We assume a commonly accepted sensor network model in which sensors can interchange idle and active modes both for monitoring and communicating. We introduce a reliability requirement for distributed target-monitoring protocols and prove that previously considered protocols (P. Berman et al., 2004) are reliable. In this paper we propose a new deterministic energy-efficient protocol for sensor networks (DEEPS) aimed at prolonging the lifetime. We prove that DEEPS is reliable and compare DEEPS with several known target-monitoring protocols in NS2 environment using LEACH (W. Heizelman et al., 2002) protocol for data delivery to the base. We implemented the full-fledged simulation of the monitoring protocols on NS2 combined with LEACH as a communication protocol, and performed extensive experimental study of several protocols showing almost 2 times increase in the lifetime for DEEPS over known protocols

In this paper, we consider the problem of maximizing the lifetime of a target-covering sensor network in which each sensor can adjust its sensing range. The network model consists of a large number of sensors with adjustable sensing ranges being deployed to monitor a set of targets. Since more than one sensor can cover a target, in order to be energy efficient, one can activate successive subsets of sensors that cover all targets. This paper addresses the problem of maximizing the total lifetime of such an activation schedule. In contrast to the approach taken by Cardei et al. (2005), our formulation directly maximizes the network lifetime rather than maximizing the number of sensor covers. We give a mathematical model of this problem using a linear program with exponential number of variables and solve this linear program using the approximation algorithm of Garg-Konemann (1998). Our experimental results on simulated data show a 4times increase in lifetime when compared with the previous approach taken by Cardei et al. (2005)

2SNP software package implements a new very fast scalable algorithm for haplotype inference based on genotype statistics collected only for pairs of SNPs. This software can be used for comparatively accurate phasing of large number of long genome sequences, e.g. obtained from DNA arrays. As an input 2SNP takes genotype matrix and outputs the corresponding haplotype matrix. On datasets across 79 regions from HapMap 2SNP is several orders of magnitude faster than GERBIL and PHASE while matching them in quality measured by the number of correctly phased genotypes, single-site and switching errors. For example, 2SNP requires 41 s on Pentium 4 2 Ghz processor to phase 30 genotypes with 1381 SNPs (ENm010.7p15:2 data from HapMap) versus GERBIL and PHASE requiring more than a week and admitting no less errors than 2SNP.

UNLABELLED: The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. For these studies, it is essential to use a small subset of informative SNPs accurately representing the rest of the SNPs. Informative SNP selection can achieve (1) considerable budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs or (2) necessary reduction of the huge SNP sets (obtained, e.g. from Affymetrix) for further fine haplotype analysis. A novel informative SNP selection method for unphased genotype data based on multiple linear regression (MLR) is implemented in the software package MLR-tagging. This software can be used for informative SNP (tag) selection and genotype prediction. The stepwise tag selection algorithm (STSA) selects positions of the given number of informative SNPs based on a genotype sample population. The MLR SNP prediction algorithm predicts a complete genotype based on the values of its informative SNPs, their positions among all SNPs, and a sample of complete genotypes. An extensive experimental study on various datasets including 10 regions from HapMap shows that the MLR prediction combined with stepwise tag selection uses fewer tags than the state-of-the-art method of Halperin et al. (2005). AVAILABILITY: MLR-Tagging software package is publicly available at http://alla.cs.gsu.edu/~software/tagging/tagging.html

DNA probe arrays, or DNA chips, have emerged as a core genomic technology that enables cost-effective gene expression monitoring, mutation detection, single nucleotide polymorphism analysis, and other genomic analyses. DNA chips are manufactured through a highly scalable process called very large-scale immobilized polymer synthesis (VLSIPS) that combines photolithographic technologies adapted from the semiconductor industry with combinatorial chemistry. Commercially available DNA chips contain more than half a million probes and are expected to exceed 100 million probes in the next generation. This paper is one of the first attempts to apply very large scale integration (VLSI) computer-aided design methods to the physical design of DNA chips, where the main objective is to minimize total border cost (i.e., the number of nucleotide mismatches between adjacent sites). By exploiting analogies between manufacturing processes for DNA arrays and for VLSI chips, the authors demonstrate the potential for transfer of methodologies from the 40-year-old field of electronic design automation to the newer DNA array design field. The main contributions of this paper are the following. First, it proposes several partitioning-based algorithms for DNA probe placement that improve solution quality by over 4% compared to best previously known methods. Second, it gives a new design flow for DNA arrays, which enhances current methodologies by adding flow awareness to each optimization step and introducing feedback loops. Third, it proposes solution methods for new formulations integrating multiple design steps, including probe selection, placement, and embedding. Finally, it introduces new techniques to experimentally evaluate the scalability and suboptimality of existing and newly proposed probe placement algorithms. Interestingly, the authors find that DNA placement algorithms appear to have better suboptimality properties than those recently reported for VLSI placement algorithms [C.C. Chang et al., Optimality and scalability study of existing placement algorithms, Proc. Asia South-Pacific Design Automation Conf., Kitakyushu, Japan, p.621-7, Jan. 2003; J. Cong et al., Optimality, scalability and stability study of partitioning and placement algorithms, Proc. Int. Symp. Physical Design (ISPD), Monterey, CA, p.88-94, 2003]

As feature sizes shrink, it will be necessary to use AAPSM (alternating-aperture phase shift masking) to image critical features, especially on the polysilicon layer. This imposes additional constraints on the layouts beyond traditional design rules. Of particular note is the requirement that all critical features be flanked by opposite-phase shifters, while the shifters obey minimum width and spacing requirements. A layout is called phase-assignable if it satisfies this requirement. If a layout is not phase-assignable, the phase conflicts have to be removed to enable the use of AAPSM for the layout. Previous work has sought to detect a suitable set of phase conflicts to be removed, as well as correct them. The contributions of this paper are the following: (1) a new approach to detect a minimal set of phase conflicts (also referred to as AAPSM conflicts), which when corrected will produce a phase-assignable layout; (2) a novel layout modification scheme for correcting these AAPSM conflicts. The proposed approach for conflict detection shows significant improvements in the quality of results and runtime for real industrial circuits, when compared to previous methods. To the best of our knowledge, this is the first time layout modification results are presented for bright-field AAPSM. Our experiments show that the percentage area increase for making a layout phase-assignable ranges from 0.7-11.8%.

Optimizing the energy consumption in monitoring and communication protocols for wireless sensor networks has become the most important performance objective. We explore the problem of maximizing sensor network lifetime, i.e., time during which the set of targets is covered. We propose centralized algorithms for lifetime maximization with provable approximation ratio for the realistic model studied. In this paper we introduce reliability requirement for distributed target-monitoring protocols and prove that previously considered protocols are reliable. A new deterministic energy-efficient protocol for sensor networks (DEEPS) aimed at prolonging lifetime is proposed. We prove that DEEPS is reliable and compare DEEPS with several known target-monitoring protocols in NS2 environment using LEACH (W. Heinzelman et al., 2000) for simulating monitoring data delivery to the base. Our contributions also include the first full-fledged simulation of the monitoring protocols on NS2 combined with LEACH (W. Heinzelman et al., 2000) as a communication protocol, and extensive experimental study of several protocols showing almost 2 times increase in the lifetime for DEEPS over known protocols

Group key management in mobile wireless networks faces new challenges due to the mobility of group members. Current proposed mobile GKM algorithms assume parts of fixed backbone and are completely centralized. This does not fit pure MANET requirements, that are decentralized and infrastructureless networks. To overcome this weakness, we present a protocol designed to provide a virtual backbone in order to use current mobile GKM protocols in MANET. Simulation results are presented, and the performance evaluation shows that the presented protocol quickly adapts to dynamic topologies.

Enhancing route request broadcasting protocols constitutes a substantial part of recent research in mobile ad hoc network (MANET) routing. In this paper, we introduce a novel node caching approach for constraining the route request protocol in ad hoc routing. We have implemented node caching enhancement AODV-NC of AODV which improves the original AODV in all three metrics - extensive simulations in NS-2 show average decrease by 90% in communication overhead as well as average decrease by 63% in the delay, and average increase by 20% in the delivery ratio. We have also proposed a new measure of fairness of ad hoc routing protocols which depends on distribution of the forwarding load among nodes. The AODV-NC protocols are shown to be unfair and make certain overused nodes exhaust their batteries prematurely. We also suggest a load-balancing scheme that improves fairness and lifetime of AODV-NC, sustaining considerable improvement in overhead, delivery ratio and delay over the standard AODV.

The classical Steiner tree problem in weighted graphs seeks a minimum weight connected subgraph containing a given subset of the vertices (terminals). We present a new polynomial-time heuristic that achieves a best-known approximation ratio of $1 + \frac{\ln 3}{2} \approx 1.55$ for general graphs and best-known approximation ratios of $\approx 1.28$ for both quasi-bipartite graphs (i.e., where no two nonterminals are adjacent) and complete graphs with edge weights 1 and 2. Our method is considerably simpler and easier to implement than previous approaches. We also prove the first known nontrivial performance bound ($1.5 \cdot$ OPT) for the iterated 1-Steiner heuristic of Kahng and Robins in quasi-bipartite graphs.

Control of variability and performance in the back end of the VLSI manufacturing line has become extremely difficult with the introduction of new materials such as copper and low-k dielectrics. To improve manufacturability, and in particular to enable more uniform chemical-mechanical planarization (CMP), it is necessary to insert area fill features into low-density layout regions. Because area fill feature sizes are very small compared to the large empty layout areas that need to be filled, the filling process can increase the size of the resulting layout data file by an order of magnitude or more. To reduce file transfer times, and to accommodate future maskless lithography regimes, data compression becomes a significant requirement for fill synthesis. In this paper, we make the following contributions. First, we define two complementary strategies for fill data volume reduction corresponding to two different points in the design-to-manufacturing flow: compressible filling and post-fill compression . Second, we compare compressible filling methods in the fixed-dissection regime when two different sets of compression operators are used: the traditional GDSII array reference (AREF) construct, and the new Open Artwork System Interchange Standard (OASIS) repetitions. We apply greedy techniques to find practical compressible filling solutions and compare them with optimal integer linear programming solutions. Third, for the post-fill data compression problem, we propose two greedy heuristics, an exhaustive search-based method, and a smart spatial regularity search technique. We utilize an optimal bipartite matching algorithm to apply OASIS repetition operators to irregular fill patterns. Our experimental results indicate that both fill data compression methodologies can achieve significant data compression ratios, and that they outperform industry tools such as Calibre V8.8 from Mentor Graphics. Our experiments also highlight the advantages of the new OASIS compression operators over the GDSII AREF construct.

Multi-project Wafers (MPW) are an efficient way to share the rising costs of mask tooling between multiple prototype and low production volume designs. Packing the different die images on a multi-project reticle leads to new and highly challenging floorplanning formulations, characterized by unusual constraints and complex objective functions. In this paper we study multi-project reticle floorplanning and wafer dicing problems under the prevalent side-to-side wafer dicing technology. Our contributions include practical mathematical programming algorithms and efficient heuristics based on interval-graph coloring which find side-to-side wafer dicing plans with maximum yield for a fixed multi-project reticle floorplan and given per-die maximum dicing margins. We also give novel shelf packing and simulated annealing reticle floorplanning algorithms for maximizing wafer-dicing yield. Experimental results show that our algorithms improve wafer-dicing yield significantly compared to existing industry tools and academic min-area floorplanners.

Optimizing the energy consumption in wireless sensor networks has recently become the most important performance objective. We assume the sensor network model in which sensors can interchange idle and active modes. Given monitoring regions, battery life and energy consumption rate for each sensor, we formulate the problem of maximizing sensor network lifetime, i.e., time during which the monitored area is (partially or fully) covered. Our contributions include (1) an efficient data structure to represent the monitored area with at most n/sup 2/ points guaranteeing the full coverage which is superior to the previously used approach based on grid points, (2) efficient provably good centralized algorithms for sensor monitoring schedule maximizing the total lifetime including (1+ln(1-q)/sup -1/)-approximation algorithm for the case when a q-portion of the monitored area is required to cover, e.g., for the 90% area coverage our schedule guarantees to be at most 3.3 times shorter than the optimum, (4) a family of efficient distributed protocols with trade-off between communication and monitoring power consumption, (5) extensive experimental study of the proposed algorithms showing significant advantage in quality, scalability and flexibility.

The rectilinear Steiner minimum tree (RSMT) problem, which asks for a minimum-length interconnection of a given set of terminals in the rectilinear plane, is one of the fundamental problems in electronic design automation. Recently there has been renewed interest in this problem due to the need for highly scalable algorithms able to handle nets with tens of thousands of terminals. In this paper we give a practical O (n log2 n) heuristic for computing near-optimal rectilinear Steiner trees based on a batched version of the greedy triple contraction algorithm of Zelikovsky [21]. Experiments conducted on both random and industry testcases show that our heuristic matches or exceeds the quality of best known RSMT heuristics, e.g., on random instances with more than 100 terminals our heuristic improves over the rectilinear minimum spanning tree by an average of 11%. Moreover, our heuristic has very well scaling runtime, e.g., it can route a 34k-terminals net extracted from a real design in less than 25 seconds compared to over 86 minutes needed by the O(n2) edge-based heuristic of Borah, Owens, and Irwin [3]. Since our heuristic is graph-based, it can be easily modified to handle practical considerations such as routing obstacles, preferred directions, via costs, and octilinear routing - indeed, experimental results show only a small factor increase in runtime when switching from rectilinear to octilinear routing.

This paper describes the advantages of SyD (System on Mobile Devices), a middleware technology for mobile devices and e-services, in terms of technology and programming. Features of SyD are illustrated here through our prototype application, a complex communication system for a trucking fleet that operates an automated package delivery system. The fleet system has been implemented in three ways, with SOAP, with JDBC, and with SyD. Our implementation experience shows that SyD greatly simplifies coding by allowing heterogeneous devices, peer-to-peer communications, group transactions based on triggering events, and mobility support through proxies and directory service.

The QoS Steiner tree problem asks for the most cost-efficient way to multicast multimedia to a heterogeneous collection of users with different consumption rates. We assume that the cost of using a link is not constant, but rather depends on the maximum bandwidth routed through the link. Formally, given a graph with costs on the edges, a source node and a set of terminal nodes, each one with a bandwidth requirement, the goal is to find a Steiner tree containing the source and the cheapest assignment of bandwidth to each of its edges so that each source-to-terminal path in the tree has bandwidth at least as large as the bandwidth required by the terminal. Our main contributions are: (1) new covering-type integer linear program formulations for the problem; (2) two new heuristics based on the primal-dual framework; (3) a primal-dual constant-factor approximation algorithm; (4) an extensive experimental study of the new heuristics and of several previously proposed algorithms.

DNA probe arrays have recently emerged as one of the core genomic technologies. Exploiting analogies between manufacturing processes for DNA arrays and for VLSI chips, we demonstrate the potential for transfer of methodologies from the 40-year old field of electronic design automation to the newer DNA array design field. Our main contributions are the following. (1) We give a new design flow for DNA arrays which enhances current methodologies by adding flow-awareness to each optimization step and introducing feedback loops. (2) We propose solution methods for new formulations integrating multiple design steps, including probe selection, placement, and embedding. (3) We give results of a comprehensive experimental study showing that significant improvements in solution quality can be achieved by using the enhanced methodologies.

Design of DNA arrays for very large-scale immobilized polymer synthesis (VLSIPS) [8] seeks to minimize effects of unintended illumination during mask exposure steps. [9, 14] formulate this requirement as the Border Minimization Problem and give methods for placement (at array sites) and embedding (in the mask sequence) of probes in both synchronous and asynchronous regimes. These previous methods do not address several practical details of the application and, more critically, are not scalable to the O(108) probes contemplated for next-generation probe arrays. In this work, we make two main contributions:

We study the problem of assigning transmission ranges to the nodes of ad hoc wireless networks to minimize power consumption while ensuring network connectivity. We give an exact branch and cut algorithm based on a new integer linear program formulation solving instances with up to 35-40 nodes in 1 hour; a proof that min-power symmetric connectivity with asymmetric power requirements is inapproximable within factor (1 - /spl epsi/) ln |V| for any /spl epsi/ > 0 unless P = NP; an improved analysis for two approximation algorithms recently proposed by Calinescu et al. (TCS'02), decreasing the best known approximation factor to 5/3 + /spl epsi/; and a comprehensive experimental study comparing new and previously proposed heuristics with the above exact and approximation algorithms.

Bounding the load capacitance at gate outputs is a standard element in today's electrical correctness methodologies for high-speed digital very large scale integration design. Bounds on load caps improve coupling-noise immunity, reduce degradation of signal transition edges, and reduce delay uncertainty due to coupling noise (Kahng et al. 1998). For clock and test distribution, an additional design requirement is bounding the buffer skew, i.e., the difference between the maximum and the minimum number of buffers over all of the source-to-sink paths in the routing tree, since buffer skew is one of the main factors affecting delay skew (Tellez and Sarrafzadeh 1997). In this paper, we consider algorithms for buffering a given tree with the minimum number of buffers under given load cap and buffer skew constraints. We show that the greedy algorithm proposed by Tellez and Sarrafzadeh is suboptimal for nonzero buffer-skew bounds and give examples showing that no bottom-up greedy algorithm can achieve optimality. The main contribution of the paper is an optimal dynamic programming algorithm for the problem. Experiments on test cases extracted from recent industrial designs show that the dynamic programming algorithm has practical running time and saves up to 37.5% of the buffers inserted by Tellez and Sarrafzadeh's algorithm.

In high-speed digital VLSI design, bounding the load capacitance at gate outputs is a well-known methodology to improve coupling noise immunity, reduce degradation of signal transition edges, and reduce delay uncertainty due to coupling noise. Bounding load capacitance also improves reliability with respect to hot-carrier oxide breakdown and AC self-heating in interconnects, and guarantees bounded input rise/fall times at buffers and sinks. This paper introduces a new minimum-buffer routing problem (MBRP) formulation which requires that the capacitive load of each buffer, and of the source driver, be upper-bounded by a given constant. Our contributions are as follows: We give linear-time algorithms for optimal buffering of a given routing tree with a single (inverting or noninverting) buffer type. For simultaneous routing and buffering with a single noninverting buffer type, we prove that no algorithm can guarantee a factor smaller than 2 unless P=NP and give an algorithm with approximation factor slightly larger than 2 for typical buffers. For the case of a single inverting buffer type, we give an algorithm with approximation factor slightly larger than 4. We give local-improvement and clustering based MBRP heuristics with improved practical performance, and present a comprehensive experimental study comparing the runtime/quality tradeoffs of the proposed MBRP heuristics on test cases extracted from recent industrial designs.

Control of variability in the back end of the line, and hence in interconnect performance as well, has become extremely difficult with the introduction of new materials such as copper and low-k dielectrics. Uniformity of chemical-mechanical planarization (CMP) requires the addition of area fill geometries into the layout, in order to smoothen the variation of feature densities across the die. Our work addresses the following smoothness gap in the recent literature on area fill synthesis. (1)The very first paper on the filling problem (Kahng et al., ISPD98 [7]) noted that there is potentially a large difference between the optimum window densities in fixed dissections vs. when all possible windows in the layout are considered. (2)Despite this observation, all filling methods since 1998 minimize and evaluate density variation only with respect to a fixed dissection. This paper gives the first evaluation of existing filling algorithms with respect to "gridless" ("floating-window") mode, according to both the effective and spatial density models. Our experiments indicate surprising advantages of Monte-Carlo and greedy strategies over "optimal" linear programming (LP) based methods. Second, we suggest new, more relevant methods of measuring a local uniformity based on Lipschitz conditions, and empirically demonstrate that Monte-Carlo methods are inherently better than LP with respect to the new criteria. Finally, we propose new LP-based filling methods that are directly driven by the new criteria, and show that these methods indeed help close the "smoothness gap".

Chemical-mechanical polishing (CMP) and other manufacturing steps in very deep submicron very large scale integration have varying effects on device and interconnect features, depending on local characteristics of the layout. To improve manufacturability and performance predictability, the authors seek to make a layout uniform with respect to prescribed density criteria, by inserting "area fill" geometries into the layout. In this paper, they make the following contributions. First, the authors define the flat, hierarchical, and multiple-layer filling problems, along with a unified density model description. Secondly, for the flat filling problem, they summarize current linear programming approaches with two different objectives, i.e., the Min-Var and Min-Fill objectives. They then propose several new Monte Carlo-based filling methods with fast dynamic data structures. Thirdly, they give practical iterated methods for layout density control for CMP uniformity based on linear programming, Monte Carlo, and greedy algorithms. Fourthly, to address the large data volume and inherent lack of scalability of flat layout density control, the authors propose practical methods for hierarchical layout density control. These methods smoothly trade off runtime, solution quality, and output data volume. Finally, they extend the linear programming approaches and present new Monte Carlo-based methods for the multiple-layer filling problem. Comparisons with previous filling methods show the advantages of the new iterated Monte Carlo and iterated greedy methods for both flat and hierarchical layouts and for both density models (spatial density and effective density). The authors achieve near-optimal filling for flat layouts with respect to each of these objectives. Their experiments indicate that the hybrid hierarchical filling approach is efficient, scalable, accurate, and highly competitive with existing methods (e.g., linear programming-based techniques) for hierarchical layouts.

To implement high-performance global interconnect without impacting the placement and performance of existing blocks, the use of buffer blocks is becoming increasingly popular in structured-custom and block-based application specified integrated circuit methodologies. We address the problem of how to perform the buffering of global multiterminal nets given an existing buffer block plan. We give provably good and heuristic algorithms for this problem. The method routes connections using available buffer blocks, such that required upper and lower bounds on buffer intervals are satisfied. In addition, the algorithms allow more than one buffer to be inserted into any given connection and observe upper bounds and parity constraints on the number of buffers per connection. Most importantly, and unlike previous works on the problem, we take into account: 1) multiterminal nets; 2) multiple routing layers; 3) simultaneous buffered routing and compaction; and 4) buffer libraries. Our method outperforms existing algorithms for the problem, based on two-pin decompositions of the nets, and has been validated on top-level layouts extracted from a recent high-end microprocessor design.

To improve manufacturability and performance predictability, we seek to make a layout uniform with respect to prescribed density criteria, by inserting "fill" geometries into the layout. Previous approaches for at layout density control are not scalable due to the necessity of solving very large linear programs, the large data volume of the solution, and the impact of hierarchy-breaking on verification. In this paper, we give the first methods for hierarchical layout density control for process uniformity. Our approach trades off naturally between runtime, solution quality, and output data volume. We also allow generation of compressed GDSII of fill geometries. Our experiments show that this hybrid hierarchical filling approach saves data volume and is scalable, while yielding solution quality that is competitive with existing Monte-Carlo and linear programming based approaches.

To implement high-performance global interconnect without impacting the placement and performance of existing blocks, the use of buffer blocks is becoming increasingly popular in structured-custom and block-based ASIC methodologies. Recent works by Cong, Kong and Pan [5] and Tang and Wong [18] give algorithms to solve the buffer block planning problem. In this paper, we address the problem of how to perform buffering of global multiterminal nets given an existing buffer block plan. We give a provably good algorithm based on a recent approach of Garg and Könemann [8] and Fleischer [7] (see also Albrecht [1] and Dragan et al. [6]). Our method routes connections using available buffer blocks, such that required upper and lower bounds on buffer intervals - as well as wirelength upper bounds per connection - are satisfied. In addition, our algorithm allows more than one buffer to be inserted into any given connection and observes buffer parity constraints. Most importantly, and unlike previous works on the problem [5, 18, 6], we take into account multiterminal nets. Our algorithm outperforms existing algorithms for the problem [5, 6], which are based on 2-pin decompositions of the nets. The algorithm has been validated on top-level layouts extracted from a recent high-end microprocessor design.

We describe new graph bipartization algorithms for lay-out modification and phase assignment of bright-field alternating phase-shifting masks (AltPSM) [25]. The problem of layout modification for phase-assignability reduces to the problem of making a certain layout-derived graph bipartite (i.e., 2-colorable). Previous work [3] solves bipartization optimally for the dark field alternating PSMregime. Only one degree of freedom is allowed (and relevant) for such a bipartization: edge deletion, which corresponds to increasing the spacing between features in order to remove phase conflict. Unfortunately, dark-field PSM is used only for contact layers, due to limitations of negative photoresists. Poly and metal layers are actually created using positive photoresists and bright-field masks. In this paper, we define a new graph bipartization formulation that pertains to the more technologically relevant bright-field regime. Previous work [3] does not apply to this regime. This formulation allows two degrees of freedom for layout perturbation: (i) increasing the spacing between features, and (ii) increasing the width of critical features. Each of these corresponds to node deletion in a new layout-derived graph that we define, called the feature graph. Graph bipartization by node deletion asks for a minimum weight node set A such that deletion of A makes the graph bipartite. Unlike bipartization by edge deletion, this problem is NP-hard. We investigate several practical heuristics for the node deletion bipartization of planar graphs, including one that has 9/4 approximation ratio. Computational experience with industrial VLSI layout benchmarks shows promising results.

In high-speed digital VLSI design, bounding the load capacitance at gate outputs is a well-known methodology to improve coupling noise immunity, reduce degradation of signal transition edges, and reduce delay uncertainty due to coupling noise. Bounding load capacitance also improves reliability with respect to hot-carrier oxide breakdown and AC self-heating in interconnects, and guarantees bounded input rise/fall times at buffers and sinks. This paper introduces a new minimum-buffer routing problem (MBRP) formulation which requires that the capacitive load of each buffer, and of the source driver, be upper-bounded by a given constant. Our contributions include the following. (i) We give linear-time algorithms for optimal buffering of a given routing tree with a single (inverting or noninverting) buffer type. (ii) For simultaneous routing and buffering with a single noninverting buffer type, we give a factor 2(1+/spl epsiv/) approximation algorithm and prove that no algorithm can guarantee a factor smaller than 2 unless P=NP. For the case of a single inverting buffer type, we give a factor 4(1+/spl epsiv/) approximation algorithm. (iii) We give local-improvement and clustering based MBRP heuristics with improved practical performance, and present a comprehensive experimental study comparing the runtime/quality trade-offs of the proposed MBRP heuristics on test cases extracted from recent industrial designs.

We address a practical problem which arises in several areas, including network design and VLSI circuit layout. Given an undirected weighted graph G = (V, E and a family N = [N1, …, Nk] of k disjoint groups of nodes Ni ⊆ V, the Group Steiner Problem asks for a minimum-cost tree which contains at least one node from each group Ni. In this paper, we give polynomial-time O(kϵ-approximation algorithms for any fixed ϵ > 0. This result improves the previously known O(k)-approximation. We also apply our approximation algorithms to the Steiner problem in directed graphs, while guaranteeing the same performance ratio. © 2001 John Wiley & Sons, Inc.

The skew of an edge-weighted rooted tree is the maximum difference between any two root-to-leaf path weights. Zero- or bounded-skew trees are needed for achieving synchronization in many applications, including network multicasting [G. N. Rouskas and I. Baldine, IEEE J. on Selected Areas in Communication, 15 (1997), pp. 346--356] and VLSI clock routing [H. Bakoglu, Circuits, Interconnections, and Packaging for VSLI, Addison-Wesley, Reading, MA, 1990, A. B. Kahng and G. Robins, On Optimal Interconnections for VSLI, Kluwer Academic Publishers, Norwell, MA, 1995]. In these applications edge weights represent propagation delays, and a signal generated at the root should be received by multiple recipients located at the leaves (almost) simultaneously. The objective is to find zero- or bounded-skew trees of minimum total weight, since the weight of the tree is directly proportional to the amount of resources (bandwidth and buffers for network multicasting, power and chip area for clock routing in VLSI) that must be allocated to the tree. Charikar et al. in [Proceedings of the Tenth ACM-SIAM Symposium on Discrete Algorithms, Baltimore, MD, 1999, ACM, New York, 1999, pp. 177--184] have recently proposed the first strongly polynomial algorithms with proven constant approximation factors, $2e\approx 5.44 and 16.86, for finding minimum weight zero- and bounded-skew trees, respectively. In this paper we introduce a new approach to these problems, based on zero-skew "stretching" of spanning trees, and obtain algorithms with improved approximation factors of 4 and 14. For the case when tree nodes are points in the plane and edge weights are given by the rectilinear metric our algorithms find zero- and bounded-skew trees of length at most 3 and 9 times the optimum. This case is of special interest in VLSI clock routing. An important feature of our algorithms is their practical running time, which is asymptotically the same as the time needed for computing the minimum spanning tree.

Abstract| Chemical-mechanical polishing (CMP) and other manufacturing steps in very deep submicron VLSI have varying eects on devic e and inter connect features, dep ending on local char acteristics of the layout.T o enhance manufacturability and performance p r edictability, we seek to make the layout uniform with respect to prescribed densit ycriteria, by inserting \ ll" geometries into the layout.We propose several new Monte-Carlo based lling methods with fast dynamic data structures and report the tradeo between runtime and accuracy for the suggested methods.Compared to existing linear programming based a p p r oaches, our Monte-Carlo methods seem very promising as they produc enearly-optimal solutions within reasonable runtimes.

We propose practical iterated methods for layout density control for CMP uniformity, based on linear programming, Monte-Carlo and greedy algorithms. We experimentally study the tradeoffs between two main filling objectives: minimizing density variation, and minimizing the total amount of inserted fill. Comparisons with previous filling methods show the advantages of our new iterated Monte-Carlo and iterated greedy methods. We achieve near-optimal filling with respect to each of the objectives and for both density models (spatial density [3] and effective density [8]). Our new methods are more efficient in practice than linear programming [3] and more accurate than non-iterated Monte-Carlo approaches [1].

To implement high-performance global interconnect without impacting the performance of existing blocks, the use of buffer blocks is increasingly popular in structured-custom and block-based ASIC/SOC methodologies. Recent works by Cong et al. (1999) and Tang and Wong (2000) give algorithms to solve the buffer block planning problem. In this paper we address the problem of how to perform buffering of global nets given an existing buffer block plan. Assuming that global nets have been already decomposed into two-pin connections, we give a provably good algorithm based on a recent approach of Garg and Konemann (1998) and Fleischer (1999). Our method routes connections using available buffer blocks, such that required upper and lower bounds on buffer intervals-as well as wirelength upper bounds per connection-are satisfied. Our model allows more than one buffer to be inserted into any given connection. In addition, our algorithm observes buffer parity constraints, i.e., it will choose to use an inverter or a buffer (=co-located pair of inverters) according to source and destination signal parity. The algorithm outperforms previous approaches and has been validated on top-level layouts extracted from a recent high-end microprocessor design.

We describe new, efficient algorithms for layout modification and phase assignment for dark field alternating-type phase shifting masks in the single exposure regime. We make the following contributions. First, we suggest new two-coloring and compaction approach that simultaneously optimizes layout and phase assignment which is based on planar embedding of an associated conflict graph. We also describe additional approaches to cooptimization of layout and phase assignment for alternating PSM. Second, we give optimal and fast algorithms to minimize the number of phase conflicts that must be removed to ensure two colorability of the conflict graph. We reduce this problem to the T-join problem which asks for a minimum weight edge set A such that a node u is incident to an odd number of edges of A if u belongs to a given node subset T of a weighted graph. Third, we suggest several practical algorithms for the T-join problem. In sparse graphs, our algorithms are faster than previously known methods. Computational experience with industrial VLSI layout benchmarks shows the advantages of the new algorithms.

Previous literature on very large scale integration routing and wiring estimation typically assumes a one-to-one correspondence between terminals and ports. In practice, however, each "terminal" consists of a large collection of electrically equivalent ports, a fact that is not accounted for in layout steps such as wiring estimation. In this paper, we address the general problem of minimum-cost routing tree construction in the presence of multiport terminals, which gives rise to the group Steiner minimal tree problem. Our main result is the first known approximation algorithm for the group Steiner problem with a sublinear performance bound. In particular, for a net with k multiport terminals, previous heuristics have a performance bound of (k-1)/spl middot/OPT, while our construction offers an improved performance bound of 2/spl middot/(2+1n(k/2))/spl middot//spl radic/k/spl middot/OPT. Our Java implementation is available on the Web.

Certain manufacturing steps in very deep submicron VLSI involve chemical-mechanical polishing (CIMP) which has varying effects on device and interconnect features, depending on local layout characteristics. To reduce manufacturing variation due to CMP and to improve yield and performance predictability, the layout needs to be made uniform with respect to certain density criteria, by inserting "fill" geometries into the layout. This paper presents an efficient multilevel approach to density analysis that affords user-tunable accuracy. We also develop exact fill synthesis solutions based on combining multilevel analysis with a linear programming approach. Our methods apply to both flat and hierarchical designs.

We study a type of linear placement problem arising in detailed placement optimization of a given cell row in the presence of white-space (extra sites). In this single-row placement problem, the cell order is fixed within the row; all cells in other rows are also fixed. We give the first solutions to the single-row problem: (i) a dynamic programming technique with time complexity O(m/sup 2/) where m is the number of nets incident to cells in the given row, and (ii) an O(m log m) technique that exploits the convexity of the wirelength objective. We also propose an iterative heuristic for improving cell ordering within a row; this can be run optionally before applying either (i) or (ii). Experimental results show an average of 6.5% wirelength improvement on industry test cases when our methods are applied to the final output of a leading industry placement tool.

We introduce the associative skew clock routing problem, which seeks a clock routing tree such that zero skew is preserved only within identified groups of sinks. The associative skew problem is easier to address within current EDA frameworks than useful-skew (skew-scheduling) approaches, and defines an interesting tradeoff between the traditional zero-skew clock routing problem (one sink group) and the Steiner minimum tree problem (n sink groups). We present a set of heuristic building blocks, including an efficient and optimal method of merging two zero-skew trees such that zero skew is preserved within the sink sets of each tree. Finally, we list a number of open issues for research and practical application.

We describe new, efficient algorithms for layout modification and phase assignment for dark field alternating-type phase shifting masks in the single exposure regime. We make the following contributions. First, we suggest new two-coloring and compaction approach that simultaneously optimizes layout and phase assignment which is based on planar embedding of an associated conflict graph. We also describe additional approaches to cooptimization of layout and phase assignment for alternating PSM. Second, we give optimal and fast algorithms to minimize the number of phase conflicts that must be removed to ensure two colorability of the conflict graph. We reduce this problem to the -join problem which asks for a minimum weight edge set such that a node is incident to an odd number of edges of if belongs to a given node subset of a weighted graph. Third, we suggest several practical algorithms for the -join problem. In sparse graphs, our algorithms are faster than previously known methods. Computational experience with industrial VLSI layout benchmarks shows the advantages of the new algorithms.

Wirelength estimation in very large scale integration layout is fundamental to any predetailed routing estimate of timing or routability. In this paper, we develop efficient wirelength estimation techniques appropriate for wirelength estimation during top-down floorplanning and placement of cell-based designs. Our methods give accurate, linear-time approaches, typically with sublinear time complexity for dynamic updating of estimates (e.g., for annealing placement). Our techniques offer advantages not only for early on-line wirelength estimation during top-down placement, but also for a posteriori estimation of routed wirelength given a final placement. In developing these new estimators, we have made several contributions, including (1) insight into the contrast between region-based and bounding box-based rectilinear Steiner minimal tree (RStMT) estimation techniques; (2) empirical assessment of the correlations between pin placements of a multipin net that is contained in a block; and (3) new wirelength estimates that are functions of a block's complexity (number of cell instances) and aspect ratio.

In very deep-submicron very large scale integration (VLSI), manufacturing steps involving chemical-mechanical polishing (CMP) have varying effects on device and interconnect features, depending on local characteristics of the layout. To reduce manufacturing variation due to CMP and to improve performance predictability and yield, the layout must be made uniform with respect to certain density criteria, by inserting "fill" geometries into the layout. To date, only foundries and special mask data processing tools perform layout post-processing for density control. In the future, better convergence of performance verification flows will depend on such layout manipulations being embedded within the layout synthesis (place-and-route) flow. In this paper, we give the first realistic formulation of the filling problem that arises in layout optimization for manufacturability. Our formulation seeks to add features to a given process layer, such that (1) feature area densities satisfy prescribed upper and lower bounds in all windows of given size and (2) the maximum variation of such densities over all possible window positions in the layout is minimized. We present efficient algorithms for density analysis, notably a multilevel approach that affords user-tunable accuracy. We also develop exact solutions to the problem of fill synthesis, based on a linear programming approach. These include a linear programming (LP) formulation for the fixed-dissection regime (where density bounds are imposed on a predetermined set of windows in the layout) and an LP formulation that is automatically generated by our multilevel density analysis. We briefly review criteria for fill pattern synthesis, and the paper then concludes with computational results and directions for future research.

Given a weighted graph and a family of k disjoint groups of nodes, the group Steiner problem asks for a minimum-cost routing tree that contains at least one node from each group. We give polynomial-time O(k/sup /spl epsiv//)-approximation algorithms for arbitrarily small values of /spl epsiv/>0, improving on the previously known O(k/sup 1/2 /)-approximation. Our techniques also solve the graph Steiner arborescence problem with an O(k/sup /spl epsiv//) approximation bound. These results are directly applicable to a practical problem in VLSI layout, namely the routing of nets with multi-port terminals. Our Java implementation is available on the Web.

Wirelength estimation in VLSI layout is fundamental to any pre-detailed routing estimate of timing or routability. In this paper, we develop new wirelength estimation techniques appropriate for top-down floor-planning and placement synthesis of row-based VLSI layouts. Our methods include accurate, linear-time approaches, often with sublinear time complexity for dynamic updating of estimates (e.g., for annealing placement). The new techniques offer advantages not only for early on-line wirelength estimation during top-down placement, but also for a posteriori estimation of routed wirelength given a final placement. In developing these new estimators, we have made several theoretical contributions. Notably, we have resolved the long-standing discrepancy between region-based and bounding box-based RSMT estimation techniques; this leads to new estimates that are functions of instance size n and aspect ratio AR.

In very deep-submicron VLSI, certain manufacturing steps &mdash notably optical exposure, resist development and etch, chemical vapor deposition and chemical-mechanical polishing (CMP)&mdash have varying effects on device and interconnect features depending on local characteristics of the layout. To make these effects uniform and predictable, the layout itself must be made uniform with respect to certain density parameters. Traditionally, only foundries have performed the post-processing needed to achieve this uniformity, via insertion (“filling”) or partial deletion (“slotting”) of features in the layout. Today, however, physical design and verification tools cannot remain oblivious to such foundry post-processing. Without an accurate estimate of the filling and slotting, RC extraction, delay calculation, and timing and noise analysis flows will all suffer from wild inaccuracies. Therefore, future place-and-route tools must efficiently perform filling and slotting prior to performance analysis within the layout optimization loop. We give the first formulations of the filling and slotting problems that arise in layout post-processing or layout optimization for manufacturability. Such formulations seek to add or remove features to a given process layer, so that the local area or perimeter density of features satisfies prescribed upper and lower bounds in all windows of a given size. We also present efficient algorithms for density analysis as well as for filling/slotting synthesis. Our work provides a new unification between manufacturing and physical design, and captures a number of general requirements imposed on layout by the manufacturing process.

Previous literature on VLSI routing and wiring estimation typically assumes a one-to-one correspondence between terminals and ports. In practice, however (say, in a gridded routing regime), each "terminal" consists of a large collection of electrically equivalent ports, a fact that is not accounted for in layout steps such as wiring estimation. The presence of multiple ports for a given terminal gives rise to the group Steiner minimal tree problem. In this paper, we address the general problem of minimum-cost routing tree construction in the presence of multi-port terminals. Our main result is the first known heuristic with a sub-linear performance bound. In particular, for a net with k multi-port terminals, previous heuristics have a performance bound of (k \\Gamma 1) \\Delta OPT , while our construction offers an improved performance bound of (1 + ln k 2 ) \\Delta p k \\Delta OPT . Our Java implementation is available on the World Wide Web.

An instance of the Network Steiner Problem consists of an undirected graph with edge lengths and a subset of vertices; the goal is to find a minimum cost Steiner tree of the given subset (i.e., minimum cost subset of edges which spans it). An 11/6-approximation algorithm for this problem is given. The approximate Steiner tree can be computed in the time 0(¦V¦ ¦E¦ + ¦S¦ 4 ), where V is the vertex set, E is the edge set of the graph, and S is the given subset of vertices.