As a central organizing principle of biology, bacteria and archaea are classified into a hierarchical structure across taxonomic ranks from kingdom to subspecies. Traditionally, this organization was based on observable characteristics of form and chemistry but recently, bacterial taxonomy has been robustly quantified using comparisons of sequenced genomes, as exemplified in the Genome Taxonomy Database (GTDB). Such genome-based taxonomies resolve genomes down to genera and species and are useful in many contexts yet lack the flexibility and resolution of a fine-grained approach. The Life Identification Number (LIN) approach is a common, quantitative framework to tie existing (and future) bacterial taxonomies together, increase the resolution of genome-based discrimination of taxa, and extend taxonomic identification below the species level in a principled way. Utilizing LINgroup as an organizational concept helps resolve some of the confusion and unforeseen negative effects resulting from nomenclature changes of microorganisms that are closely related by overall genomic similarity (often due to genome-based reclassification). Our experimental results demonstrate the value of LINs and LINgroups in mapping between taxonomies, translating between different nomenclatures, and integrating them into a single taxonomic framework. They also reveal the robustness of LIN assignment to hyper-parameter changes when considering within-species taxonomic groups.

BACKGROUND: Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance. RESULTS: Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis. CONCLUSION: AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/ .

Antimicrobial resistance (AMR) has been identified by the World Health Organization (WHO) as a major global health threat. It is projected that AMR will increase exponentially by 2050, leading to substantial human morbidity and mortality (O’Neill, 2016; Pires et al., 2017). Therefore, swift action is required to enable enhanced monitoring and to help tackle the spread of AMR, including: understanding the mechanisms controlling dissemination of antibiotic resistance genes (ARGs) via environmental sources and pathways (Bengtsson-Palme et al., 2018; Martínez, 2008; Pruden et al., 2013), discovering novel ARGs before they are found to be problematic in the clinic (Berglund et al., 2017), developing new computational strategies for ARG annotation (Arango-Argoty et al., 2018; Gibson et al., 2015; Lakin et al., 2017; McArthur et al., 2013; Yang et al., 2016) and expansion of current ARG repositories (Arango-Argoty et al., 2018; Lakin et al., 2017). Metagenomic sequencing has provided a powerful means for accessing the diverse array of ARGs, or ‘resistomes’, (Sello, 2012) characteristic of various environments (Bengtsson-Palme et al., 2016; Garner et al., 2016; Li et al., 2017; Pal et al., 2016) and has supported the discovery of novel ARGs and their interactions (Forsberg et al., 2014; Pehrsson et al., 2016). Existing metagenomic approaches are largely dependent upon predicting antibiotic resistance attributes through sequence similarity computation, which is subject to major limitations. First, such similarity computations require a high quality and up-to-date ARG reference/annotation database to enable consistent and accurate ARG identification. Second, the scope of such analyses is limited to previously characterized ARGs by the lack of a comprehensive target gene for alignment (Yang et al., 2016). However, computational efforts have been made to annotate novel ARGs to increment the repertoire of sequences by using structurally (3D) close analogs (Ruppé et al., 2019). To improve the capacity of metagenomic-based approaches to broadly and accurately detect the full range of ARGs present in a given sample, it is necessary to continuously expand and improve curation of corresponding databases (Arango-Argoty et al., 2018). However, the risk of incorporation of false positives, i.e. ‘ARG-like’ genes that do not necessarily induce an AMR phenotype, stands as a major impediment to expanded curation efforts. Therefore, manual inspection and validation of potential ARG entries is a critical aspect of ensuring the validity of AMR databases and their application. Manual curation of ARGs is typically carried out by a few experts associated with research groups committed to maintaining public databases. This process is complex, tedious and time consuming. For instance, the last update of the Antibiotic Resistance Database (ARDB) was in 2009 (Liu and Pop, 2009), and, therefore, it does not contain any recently discovered ARGs, such as blaNDM-1 or mcr-1. The MEGARes database (Lakin et al., 2017), which was designed to simplify the organization of ARG annotation, has not been updated since December 2016. The resqu database, which contains genes for which there is evidence of having been transferred via Mobile Genetic elements (MGEs), has not been updated since 2013 (Bengtsson-Palme et al., 2017). The Comprehensive Antibiotic Resistance Database (CARD) (McArthur et al., 2013) is widely considered to be the most up-to-date ARG resource. First introduced in 2016, CARD has been updated more than 21 times, with corresponding changes to the ARG sequences and metadata (e.g. antibiotic class, gene name and mechanism). This acutely illustrates how complex and time-consuming ARG database curation is, even for domain experts. Attempts have been made to address limitations of currently available databases, understand the definition of ARGs (Martínez et al., 2015) and introduce new databases, such as the Structured Antibiotic Resistance Database (SARG), which employed intense manual curation to address issues such as inconsistencies in nomenclature and elimination of single-nucleotide polymorphisms and housekeeping genes (Yang et al., 2016). In our own research group, we previously introduced DeepARG: a computational approach for predicting ARGs using deep learning (Arango-Argoty et al., 2018). Along with the machine learning models, we also released a curated database named DeepARG-DB. This database employs manual curation, literature review of ARGs and annotation of ARGs using sequence alignments. DeepARG-DB was first released in July 2017 and most recently updated in August 2018. However, the DeepARG database depends on annotations from multiple resources, making it sensitive to the propagation of errors from other databases. This highlights the need of enabling a specialized tool that brings all the ARG information from different resources to easily integrate new ARGs or to validate the annotations of current ARGs. To overcome the difficulties in curation and manual validation of an extensive number of ARGs, a novel approach that breaks down this complex task into simpler and smaller microtasks is proposed. The core of this methodology consists of aggregating a compendium of AMR resources and deploying a crowdsourcing strategy, which simplifies the ARG information to allow non-experts, i.e. the general public, and domain experts collectively to execute curation of the ARG database. Application of crowdsourcing in biology, particularly for data curation, is not new and comprises a variety of areas including: name entity recognition (NER) for drugs and diseases (Islamaj Dogan et al., 2009; Khare et al., 2016; Lu et al., 2009), identification of medically relevant terms from patient online posts (MacLean and Heer, 2013), annotation of diseases described in PubMed (Good et al., 2014), and systematic examination of databases and other resources for drug indications, biomedical ontologies, and gene–disease interactions (Arighi et al., 2013; Khare et al., 2016; Lu and Hirschman, 2012; Wei et al., 2012; Wei et al., 2013). Interestingly, in most of the studies, crowdsourcing has proven to be as effective as expert curation (Good and Su, 2013; Khare et al., 2016). A major problem that encompasses all ARG resources is the lack of a standardized gene nomenclature. In particular, naming ARGs does not follow the general nomenclature for naming bacterial genes (Demerec et al., 1966). For instance, macrolide resistance genes are structured so that the class is indicated by brackets [e.g. ole(B), srm(B), vga(B) or ere(B)] (Levy et al., 1999). When compared to tetracycline genes, this gene nomenclature differs radically, because, in tetracycline genes, the determinant is placed as a capital letter after the gene name (e.g. tetA, tetB, tetC) (Levy et al., 1999). At the same time, those nomenclatures differ from the gene convention proposed to annotate beta-lactamase genes (Hall and Schwarz, 2016). Other examples to highlight these differences include the aminoglycoside gene (Vanhoof et al., 1998) aadA1 found under different names across the available ARG databases [ANT(3″)-I, aadA1-pm, ANT3-DPRIME and ant3ia]. Therefore, the diversity and variation in the ARG nomenclature and naming conventions complicate and greatly hinder consistent ARG curation. Here we introduce ARGminer, an online platform to enhance manual curation of ARGs. ARGminer enables users to curate and retrieve all of the information available from several ARG resources, including CARD (McArthur et al., 2013), DeepARG-DB (Arango-Argoty et al., 2018), ARDB (Liu and Pop, 2009), MEGARes (Lakin et al., 2017), UniProt (Leplae et al., 2004), the National Database of Antibiotic Resistant Organisms (NDARO) (https://www.ncbi.nlm.nih.gov/pathogens/antimicrobial-resistance/), the SARG (Yang et al., 2016), ResFinder (Zankari et al., 2012) and the ARG-ANNOT (Gupta et al., 2014) databases. Manual crowdsource-based curation is enhanced by a machine learning model based on word embeddings (Goldberg and Levy, 2014; Turian et al., 2010), a technique widely used in natural language processing (NLP) to aid in validation and achieve consistency in ARG nomenclature. On the other hand, MGEs such as plasmids phages and viruses play an important role in the dissemination of ARGs (Bengtsson-Palme et al., 2017; Gillings, 2014; Leplae et al., 2004). Therefore, ARGminer also interfaces with the PATRIC (Wattam et al., 2014) and Classification of Mobile Genetic Elements (ACLAME) (Leplae et al., 2004) databases, which provide information on potential carriage of ARGs by pathogens or MGEs, respectively. The ARGminer platform is designed, built and implemented as an open-source project facilitating a collaborative and integrative approach for the standardization of ARG annotation by the broad community of scientists and citizens motivated by a common desire to contribute toward combating the spread of AMR. ARGminer also includes a community blog for users to post questions and share solutions/discussion regarding AMR with the objective to keep the scientific community actively engaged in the latest updates and development of ARG databases (see Supplementary Fig. S1). All data associated with ARGminer, as well as the source code, is freely available under a public repository at http://bench.cs.vt.edu/argminer. ARGs were downloaded from the following resources: CARD (McArthur et al., 2013), which contains ARG information; the ARDB (Liu and Pop, 2009) database, which comprises a vast number of homology-predicted ARGs; DeepARG-DB (Arango-Argoty et al., 2018), which integrates ARGs from UniProt (UniProt Consortium, 2014), CARD and ARDB; MEGARes (Lakin et al., 2017) database, which incorporates genes from the ARG-ANNOT (Gupta et al., 2014), ResFinder (Zankari et al., 2012), the Lahey Clinic beta-lactamase archive (Bush and Jacoby, 2010) available from the National Center for Biotechnology Information (NCBI), the SARG database (Yang et al., 2016) and the NDARO database version 2. To obtain a comprehensive collection of ARGs, the DeepARG-DB database was updated with a more recent version of the CARD (v 2.0.4) and UniProt databases using their corresponding sequence identifiers. 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This special issue introduces current developments in bioinformatics, the use of computational techniques in the service of the life sciences, in the specific context of the analysis and interpretation of DNA molecules, where DNA is the primary informational molecule of all life. To fully appreciate the papers in this issue, some background knowledge in biology is required. The DNA in a cell is organized into a genome consisting of one or more chromosomes, each of which is a DNA molecule, which can be thought of as a long string over the alphabet of nucleotides, A, C, G, and T. Within a DNA string or sequence are substrings that are genes that normally encode the recipe for the functional proteins in the cell. The human genome is organized into 23 pairs of chromosomes, one maternal and one paternal chromosome set, containing a total of approximately six billion nucleotides. Amazingly, all ~1 trillion cells in one’s body, of diverse form and function, contain the same “molecular blueprint” with a near perfect copy of their genome.

MOTIVATION: Can we predict protein-protein interactions (PPIs) of a novel virus with its host? Three major problems arise: the lack of known PPIs for that virus to learn from, the cost of learning about its proteins and the sequence dissimilarity among viral families that makes most methods inapplicable or inefficient. We develop DeNovo, a sequence-based negative sampling and machine learning framework that learns from PPIs of different viruses to predict for a novel one, exploiting the shared host proteins. We tested DeNovo on PPIs from different domains to assess generalization. RESULTS: By solving the challenge of generating less noisy negative interactions, DeNovo achieved accuracy up to 81 and 86% when predicting PPIs of viral proteins that have no and distant sequence similarity to the ones used for training, receptively. This result is comparable to the best achieved in single virus-host and intra-species PPI prediction cases. Thus, we can now predict PPIs for virtually any virus infecting human. DeNovo generalizes well; it achieved near optimal accuracy when tested on bacteria-human interactions. AVAILABILITY AND IMPLEMENTATION: Code, data and additional supplementary materials needed to reproduce this study are available at: https://bioinformatics.cs.vt.edu/~alzahraa/denovo CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Alternative splicing (AS) is a post-transcriptional regulatory mechanism for gene expression regulation. Splicing decisions are affected by the combinatorial behavior of different splicing factors that bind to multiple binding sites in exons and introns. These binding sites are called splicing regulatory elements (SREs). Here we develop CoSREM (Combinatorial SRE Miner), a graph mining algorithm to discover combinatorial SREs in human exons. Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy. CoSREM is able to identify sets of SREs and is not limited to SRE pairs as are current approaches. RESULTS: We identified 37 SRE sets that include both enhancer and silencer elements. We show that our results intersect with previous results, including some that are experimental. We also show that the SRE set GGGAGG and GAGGAC identified by CoSREM may play a role in exon skipping events in several tumor samples. We applied CoSREM to RNA-Seq data for multiple tissues to identify combinatorial SREs which may be responsible for exon inclusion or exclusion across tissues. CONCLUSION: The new algorithm can identify different combinations of splicing enhancers and silencers without assuming a predefined size or limiting the algorithm to find only pairs of SREs. Our approach opens new directions to study SREs and the roles that AS may play in diseases and tissue specificity.

BACKGROUND: The Structural Classification of Proteins (SCOP) database uses a large number of hidden Markov models (HMMs) to represent families and superfamilies composed of proteins that presumably share the same evolutionary origin. However, how the HMMs are related to one another has not been examined before. RESULTS: In this work, taking into account the processes used to build the HMMs, we propose a working hypothesis to examine the relationships between HMMs and the families and superfamilies that they represent. Specifically, we perform an all-against-all HMM comparison using the HHsearch program (similar to BLAST) and construct a network where the nodes are HMMs and the edges connect similar HMMs. We hypothesize that the HMMs in a connected component belong to the same family or superfamily more often than expected under a random network connection model. Results show a pattern consistent with this working hypothesis. Moreover, the HMM network possesses features distinctly different from the previously documented biological networks, exemplified by the exceptionally high clustering coefficient and the large number of connected components. CONCLUSIONS: The current finding may provide guidance in devising computational methods to reduce the degree of overlaps between the HMMs representing the same superfamilies, which may in turn enable more efficient large-scale sequence searches against the database of HMMs.

Systems biology has made massive strides in recent years, with capabilities to model complex systems including cell division, stress response, energy metabolism, and signaling pathways. Concomitant with their improved modeling capabilities, however, such biochemical network models have also become notoriously complex for humans to comprehend. We propose network comprehension as a key problem for the KDD community, where the goal is to create explainable representations of complex biological networks. We formulate this problem as one of extracting temporal signatures from multi-variate time series data, where the signatures are composed of ordinal comparisons between time series components. We show how such signatures can be inferred by formulating the data mining problem as one of feature selection in rank-order space. We propose five new feature selection strategies for rank-order space and assess their selective superiorities. Experimental results on budding yeast cell cycle models demonstrate compelling results comparable to human interpretations of the cell cycle.

A multimodal network (MMN) is a novel graph-theoretic formalism designed to capture the structure of biological networks and to represent relationships derived from multiple biological databases. MMNs generalize the standard notions of graphs and hypergraphs, which are the bases of current diagrammatic representations of biological phenomena, and incorporate the concept of mode. Each vertex of an MMN is a biological entity, a biot, while each modal hyperedge is a typed relationship, where the type is given by the mode of the hyperedge. The semantics of each modal hyperedge e is given through denotational semantics, where a valuation function fe defines the relationship among the values of the vertices incident on e. The meaning of an MMN is denoted in terms of the semantics of a hyperedge sequence. A companion paper defines MMNs and concentrates on the structural aspects of MMNs. This paper develops MMN denotational semantics when used as a representation of the semantics of biological networks and discusses applications of MMNs in managing complex biological data.

A multimodal network (MMN) is a novel graph-theoretic formalism designed to capture the structure of biological networks and to represent relationships derived from multiple biological databases. MMNs generalize the standard notions of graphs and hypergraphs, which are the bases of current diagrammatic representations of biological phenomena and incorporate the concept of mode. Each vertex of an MMN is a biological entity, a biot, while each modal hyperedge is a typed relationship, where the type is given by the mode of the hyperedge. The current paper defines MMNs and concentrates on the structural aspects of MMNs. A companion paper develops MMNs as a representation of the semantics of biological networks and discusses applications of the MMNs in managing complex biological data. The MMN model has been implemented in a database system containing multiple kinds of biological networks.

Genomic sequences display characteristic features at various scales ranging from oligonucleotide frequencies to large organizational units such as genes. The generation of such a sequence, defined as a string over the alphabet SigmaDNA={A C, T, G}, can be approximated by a formal machine, a Markov chain having strings as states, whose parameters lend unique characteristics to the sequence. We present a formal mathematical framework that analyzes this approximation in terms of transition probabilities of words at various scales. Within this framework, we present an algorithm that estimates the order of the Markov chain of order omega hypothesized to have generated a sequence S at hand. Consider the probability of transition from string alpha to string gamma both of length omega, computed using both order w and order omega-1 Markov chains. The expected difference of the two probabilities thus obtained, is zero, and demonstrates a sharp positive transition for values of omega between omega and omega+1. Both mathematical and experimental results are obtained that explain the general behavior of the algorithm.

BACKGROUND: Modeling of cis-elements or regulatory motifs in promoter (upstream) regions of genes is a challenging computational problem. In this work, set of regulatory motifs simultaneously present in the promoters of a set of genes is modeled as a biclique in a suitably defined bipartite graph. A biologically meaningful co-occurrence of multiple cis-elements in a gene promoter is assessed by the combined analysis of genomic and gene expression data. Greater statistical significance is associated with a set of genes that shares a common set of regulatory motifs, while simultaneously exhibiting highly correlated gene expression under given experimental conditions. METHODS: XcisClique, the system developed in this work, is a comprehensive infrastructure that associates annotated genome and gene expression data, models known cis-elements as regular expressions, identifies maximal bicliques in a bipartite gene-motif graph; and ranks bicliques based on their computed statistical significance. Significance is a function of the probability of occurrence of those motifs in a biclique (a hypergeometric distribution), and on the new sum of absolute values statistic (SAV) that uses Spearman correlations of gene expression vectors. SAV is a statistic well-suited for this purpose as described in the discussion. RESULTS: XcisClique identifies new motif and gene combinations that might indicate as yet unidentified involvement of sets of genes in biological functions and processes. It currently supports Arabidopsis thaliana and can be adapted to other organisms, assuming the existence of annotated genomic sequences, suitable gene expression data, and identified regulatory motifs. A subset of Xcis Clique functionalities, including the motif visualization component MotifSee, source code, and supplementary material are available at https://bioinformatics.cs.vt.edu/xcisclique/.

BACKGROUND: Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. RESULTS: The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. CONCLUSION: The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In one experiment, MIV is a better choice than IIV as input to data normalization and statistical analysis methods, as it yields as greater number of statistically significant differentially expressed genes; TM4 does not support the choice of MIV input data. Overall, the more flexible and extensive statistical models of Expresso achieve more accurate analytical results, when judged by the yardstick of qRT-PCR data, in the context of an experimental design of modest complexity.

The PMU placement problem is an optimization problem abstracted from an approach to supervising an electrical power system. The power system is modeled as a graph, and adequate supervision of the system requires that the voltage at each node and the current through each edge be observable. A phasor measurement unit (PMU) is a monitor that can be placed at a node to directly observe the voltage at that node, as well as the current and its phase through all incident edges. The PMU placement problem is to place PMUs at a minimum number of nodes so that the entire electric power system is observed. A new simpler definition of graph observability and several complexity results for the PMU placement problem are presented. The PMU placement problem is shown to be NP-complete even for planar bipartite graphs. Several fundamental properties of PMU placements are proven, including the property that a minimum PMU placement requires no more than 1/3 of the nodes in a connected graph of at least 3 nodes.

We present a new deterministic algorithm for constructing endomorphism rings of a finite dimensional module M, given via a vertex projective presentation, over finite dimensional quotients of path algebras. We use the theory of right Gröbner basis to encode M and to construct appropriate systems of equations for finding the endomorphism ring of M. The algorithm is implemented in the computer algebra system GAP and is included in HOPF, a computational package for noncommutative algebra. We compare the performance of our implementation with implementations of existing algorithms for computing endomorphism rings.

Stack layouts and queue layouts of undirected graphs have been used to model problems in fault tolerant computing and in parallel process scheduling. However, problems in parallel process scheduling are more accurately modeled by stack and queue layouts of directed acyclic graphs (dags). A stack layout of a dag is similar to a stack layout of an undirected graph, with the additional requirement that the nodes of the dag be in some topological order. A queue layout is defined in an analogous manner. The stacknumber (queuenumber) of a dag is the smallest number of stacks (queues) required for its stack layout (queue layout). This paper presents algorithmic results---in particular, linear time algorithms for recognizing 1-stack dags and 1-queue dags, and proofs of NP-completeness for the problem of recognizing a 4-queue dag and the problem of recognizing a 6-stack dag. The companion paper (Part I [SIAM J. Comput., 28 (1999), pp. 1510--1539.]) presents combinatorial results.

Stack layouts and queue layouts of undirected graphs have been used to model problems in fault-tolerant computing and in parallel process scheduling. However, problems in parallel process scheduling are more accurately modeled by stack and queue layouts of directed acyclic graphs (dags). A stack layout of a dag is similar to a stack layout of an undirected graph, with the additional requirement that the nodes of the dag be in some topological order. A queue layout is defined in an analogous manner. The stacknumber ( queuenumber) of a dag is the smallest number of stacks (queues) required for its stack layout (queue layout). In this paper, bounds are established on the stacknumber and queuenumber of two classes of dags: tree dags and unicyclic dags. In particular, any tree dag can be laid out in 1 stack and in at most 2 queues; and any unicyclic dag can be laid out in at most 2 stacks and in at most 2 queues. Forbidden subgraph characterizations of 1-queue tree dags and 1-queue cycle dags are also presented. Part II of this paper presents algorithmic results---in particular, linear time algorithms for recognizing 1-stack dags and 1-queue dags and proof of NP-completeness for the problem of recognizing a 4-queue dag and the problem of recognizing a 9-stack dag.

The stacknumber (queuenumber) of a poset is defined as the stacknumber (queuenumber) of its Hasse diagram viewed as a directed acyclic graph. Upper bounds on the queuenumber of a poset are derived in terms of its jumpnumber, its length, its width, and the queuenumber of its covering graph. A lower bound of $\Omega(\sqrt n)$ is shown for the queuenumber of the class of n-element planar posets. The queuenumber of a planar poset is shown to be within a small constant factor of its width. The stacknumber of n-element posets with planar covering graphs is shown to be $\Theta(n)$. These results exhibit sharp differences between the stacknumber and queuenumber of posets as well as between the stacknumber (queuenumber) of a poset and the stacknumber (queuenumber) of its covering graph.

Students in Computer Science are constantly asked to understand dynamic processes in the form of computer algorithms. Aside from a pseudocode or computer program implementation, a higher order description for the algorithm is usually

A digital library of computer science literature, Envision provides powerful information visualization by displaying search results as a matrix of icons, with layout semantics under user control.Envision's Graphic View interacts with an Item Summary Window giving users access to bibliographic information, and XMosaic provides access to complete bibliographic information, abstracts, and full content.While many visualization interfaces for information retrieval systems depict ranked query-document similarity, Envision graphically presents a variety of document characteristics and supports an extensive range of user tasks.Formative usability evaluation results show great user satisfaction with Envision's style of presentation and the document characteristics visualized.

The behavior of random graphs with respect to graph partitioning is considered. It is shown that, for a random graph with n vertices and with expected degree exceeding a constant times ln n, the graph cannot be partitioned well, i.e. a random partition is likely to be almost as good as an optimal partition.

Lower boundsfor the dilation of a graph embedding of a

Project Envision aims to build a “user-centered database from the computer science literature,” initially using the publications of the Association for Computing Machinery (ACM). Accordingly, we have interviewed potential users, as well as experts in library, information, and computer science—to understand their needs, to become aware of their perception of existing information systems, and to collect their recommendations. Design and formative usability evaluation of our interface have been based on those interviews, leading to innovative query formulation and search results screens that work well according to our usability testing. Our development of the Envision database, system software, and protocol for client-server communication builds upon work to identify and represent “objects” that will facilitate reuse and high-level communication of information from author to reader (user). All these efforts are leading not only to a usable prototype digital library but also to a set of nine principles for digital libraries, which we have tried to follow, covering issues of representation, architecture, and interfacing. © 1993 John Wiley & Sons, Inc.

Our previous research on one-probe access to large collections of data indexed by alphanumeric keys has produced the first practical minimal perfect hash functions for this problem. Here, a new algorithm is described for quickly finding minimal perfect hash functions whose specification space is very close to the theoretical lower bound, i.e., around 2 bits per key. The various stages of processing are detailed, along with analytical and empirical results, including timing for a set of over 3.8 million keys that was processed on a NeXTstation in about 6 hours.

In 1979, Bernhart and Kainen conjectured that graphs of fixed genus g ≥ 1 have unbounded pagenumber. In this paper, it is proven that genus g graphs can be embedded in O ( g ) pages, thus disproving the conjecture. An Ω( g 1/2 ) lower bound is also derived. The first algorithm in the literature for embedding an arbitrary graph in a book with a non-trivial upper bound on the number of pages is presented. First, the algorithm computes the genus g of a graph using the algorithm of Filotti, Miller, Reif (1979), which is polynomial-time for fixed genus. Second, it applies an optimal-time algorithm for obtaining an O ( g )-page book embedding. Separate book embedding algorithms are given for the cases of graphs embedded in orientable and nonorientable surfaces. An important aspect of the construction is a new decomposition theorem, of independent interest, for a graph embedded on a surface. Book embedding has application in several areas, two of which are directly related to the results obtained: fault-tolerant VLSI and complexity theory.

The problem of laying out the edges of a graph using queues is studied. In a k-queue layout, vertices of the graph are placed in some linear order and each edge is assigned to exactly one of the k queues so that the edges assigned to each queue obey a first-in/first-out discipline. This layout problem abstracts a design problem of fault-tolerant processor arrays, a problem of sorting with parallel queues, and a problem of scheduling parallel processors. A number of basic results about queue layouts of graphs are established, and these results are contrasted with their analogues for stack layouts of graphs (the book-embedding problem). The 1-queue graphs (they are almost leveled-planar graphs) are characterized. It is proved that the problem of recognizing 1-queue graphs is NP-complete. Queue layouts for some specific classes of graphs are given. Relationships between the queuenumber of a graph and its bandwidth and separator size are presented. An apparent tradeoff between the queuewidth and the number of queues allowed in layouts of complete binary trees is indicated.

The relative powers of queues and stacks are compared as mechanisms for laying out the edges of a graph. In a k-queue layout, vertices of the graph are placed in some linear order (also called a linear arrangement), and each edge is assigned to exactly one of the k queues, so that the edges assigned to each queue obey a first-in/first-out (FIFO) discipline. As the vertices are scanned left to right, an edge is enqueued on its assigned queue when its left endpoint is encountered and is dequeued from its queue when its right endpoint is encountered. In a k-stack layout, vertices of the graph are placed in some linear order, and each edge is assigned to exactly one of the k stacks so that the edges assigned to each stack obey a last-in/first-out discipline. As the vertices are scanned left to right, an edge is pushed on its assigned stack when its left endpoint is encountered and is popped from its stack when its right endpoint is encountered. The paper has three main results. First, a tradeoff between queuenumber and stacknumber is shown for a fixed linear order of the vertices of G. In particular, for a fixed-order layout of a graph G, \[ {\text{queuenumber }} \times {\text{ stacknumber }} \geq {\text{ cutwidth/valence }}( G ).\] Second, it is shown that every 1-queue graph has a 2-stack layout and that every 1-stack graph has a 2-queue layout. Third, in a surprising display of the power of queues, it is shown that the ternary hypercube requires exponentially more stacks than queues. More precisely, an N-vertex ternary hypercube has a $( 2\log _3 N )$-queue layout but requires $\Omega ( N^{1/9 - \epsilon } )$ stacks, $\epsilon > 0$, in any stack layout. Also, some asymptotic bounds for the queuenumber of bounded-valence graphs are derived.

Rapid access to information is essential for a wide variety of retrieval systems and applications.Hashing has long been used when the fastest possible direct search is desired, but is generally not appropriate when sequential or range searches are also required.This paper describes a hashing method, developed for collections that are relatively static, that supports both direct and sequential access.The algorithms described give hash functions that are optimal in terms of time and hash table space utilization, and that preserve any a priori ordering desired, Furthermore, the resulting order-preserving minimal perfect hash functions (OPMPHFS) can be found using time and space that are linear in the number of keys involve~this is close to optimal,

Rapid access to information is essential for a wide variety of retrieval systems and applications. Hashing has long been used when the fastest possible direct search is desired, but is generally not appropriate when sequential or range searches are also required. This paper describes a hashing method, developed for collections that are relatively static, that supports both direct and sequential access. Indeed, the algorithm described gives hash functions that are optimal in terms of time and hash table space utilization, and that preserve any a priori ordering desired. Furthermore, the resulting order preserving minimal perfect hash functions (OPMPHFs) can be found using space and time that is on average linear in the number of keys involved.

The problem of realizing an idealized parallel architecture on a (possibly fault-laden) physical architecture is studied. Our formulation performs the mapping in the light of the algorithm that one wants to implement on the idealized architecture. A version of the mapping algorithm suggested by the DIOGENES methodology for designing fault-tolerant VLSI processor arrays is settled definitely. Two quality metrics for mappings are considered, the first embodying an idealized notion of average delay , which relates to power consumption, and the second being the length of the longest run of wire . For the average-delay measure, four algorithms that optimally assign the m vertices of the embedded graph to the n fault-free processors that have been fabricated are presented. The most general algorithm makes no assumptions about the structure of the array or the physical format of the processors; it runs in time O ( m · ( n - m ) 2 ). The other algorithms assume that the processors are laid out in such a way that interprocessor distances obey the triangle equality; they run in times ranging from time O (max{ m , n - m } · log min { m, n - m }) for certain array structures, including linear arrays, to time O (max{ m, n - m }) for a narrow class of array structures, including pyramid arrays. For the max-wire-run cost measure, it is shown that the problem of finding cost-optimal vertex-to-processor assignments is NP-complete. However, an algorithm is presented that yields, in time O ( m · ( n - m ) 2 ), vertex-to-processor assignments that are within a factor of 3 of optimal (they are optimal when the input graph-embedding is outplanar). This algorithm can easily be converted to one that yields, in time O ( m · ( n - m ) 3 ), vertex-to-processor assignments that are within a factor of 2 of optimal. Finally, an algorithm that yields optimal assignments when the interprocessor distances obey the triangle equality is presented; this algorithm operates in time O ( m · ( n - m ) · log( m · ( n - m )) · log M ), where M is the largest interprocessor distance.

This paper disproves the conjecture that graphs of fixed genus g ≤ 1 have unbounded pagenumber (Bernhart and Kainen, 1979). We show that genus g graphs can be embedded in Ο(g) pages, and derive an Ω(√g) lower bound. We present the first algorithm in the literature for embedding an arbitrary graph in a book with a non-trivial upper bound on the number of pages. We first compute the genus g of a graph using the algorithm of Filotti, Miller, Reif (1979), and then apply our (optimal-time) algorithm for obtaining an Ο(g) page embedding. An important aspect of our construction is a new decomposition theorem, of independent interest, for a graph embedded on a surface. Book embedding has application in several areas, two of which are directly related to the results we obtain: fault-tolerant VLSI and complexity theory.

This paper investigates the problem of embedding planar graphs in books of few pages. An efficient algorithm for embedding a planar graph in a book establishes an upper bound of seven pages for any planar graph. This disproves a conjecture of Bernhart and Kainen that the pagenumber of a planar graph can be arbitrarily large. It is also shown that the stellations of K/sub 3/ have pagenumber three, the best possible.