A report of the 12th International Conference on Systems Biology (ISB2018), 18-21 August, Guiyang, China.

MOTIVATION: In prognosis and survival studies, an important goal is to identify multi-biomarker panels with predictive power using molecular characteristics or clinical observations. Such analysis is often challenged by censored, small-sample-size, but high-dimensional genomic profiles or clinical data. Therefore, sophisticated models and algorithms are in pressing need. RESULTS: In this study, we propose a novel Area Under Curve (AUC) optimization method for multi-biomarker panel identification named Nearest Centroid Classifier for AUC optimization (NCC-AUC). Our method is motived by the connection between AUC score for classification accuracy evaluation and Harrell's concordance index in survival analysis. This connection allows us to convert the survival time regression problem to a binary classification problem. Then an optimization model is formulated to directly maximize AUC and meanwhile minimize the number of selected features to construct a predictor in the nearest centroid classifier framework. NCC-AUC shows its great performance by validating both in genomic data of breast cancer and clinical data of stage IB Non-Small-Cell Lung Cancer (NSCLC). For the genomic data, NCC-AUC outperforms Support Vector Machine (SVM) and Support Vector Machine-based Recursive Feature Elimination (SVM-RFE) in classification accuracy. It tends to select a multi-biomarker panel with low average redundancy and enriched biological meanings. Also NCC-AUC is more significant in separation of low and high risk cohorts than widely used Cox model (Cox proportional-hazards regression model) and L1-Cox model (L1 penalized in Cox model). These performance gains of NCC-AUC are quite robust across 5 subtypes of breast cancer. Further in an independent clinical data, NCC-AUC outperforms SVM and SVM-RFE in predictive accuracy and is consistently better than Cox model and L1-Cox model in grouping patients into high and low risk categories. CONCLUSION: In summary, NCC-AUC provides a rigorous optimization framework to systematically reveal multi-biomarker panel from genomic and clinical data. It can serve as a useful tool to identify prognostic biomarkers for survival analysis. AVAILABILITY AND IMPLEMENTATION: NCC-AUC is available at http://doc.aporc.org/wiki/NCC-AUC. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. Therefore, discovering driver pathways is becoming an important step to understand the molecular mechanisms underlying cancer and design efficient treatments for cancer patients. Previous studies have focused mainly on observation of the alterations in cancer genomes at the individual gene or single pathway level. However, a great deal of evidence has indicated that multiple pathways often function cooperatively in carcinogenesis and other key biological processes. RESULTS: In this study, an exact mathematical programming method was proposed to de novo identify co-occurring mutated driver pathways (CoMDP) in carcinogenesis without any prior information beyond mutation profiles. Two possible properties of mutations that occurred in cooperative pathways were exploited to achieve this: (1) each individual pathway has high coverage and high exclusivity; and (2) the mutations between the pair of pathways showed statistically significant co-occurrence. The efficiency of CoMDP was validated first by testing on simulated data and comparing it with a previous method. Then CoMDP was applied to several real biological data including glioblastoma, lung adenocarcinoma, and ovarian carcinoma datasets. The discovered co-occurring driver pathways were here found to be involved in several key biological processes, such as cell survival and protein synthesis. Moreover, CoMDP was modified to (1) identify an extra pathway co-occurring with a known pathway and (2) detect multiple significant co-occurring driver pathways for carcinogenesis. CONCLUSIONS: The present method can be used to identify gene sets with more biological relevance than the ones currently used for the discovery of single driver pathways.

MOTIVATION: The first step for clinical diagnostics, prognostics and targeted therapeutics of cancer is to comprehensively understand its molecular mechanisms. Large-scale cancer genomics projects are providing a large volume of data about genomic, epigenomic and gene expression aberrations in multiple cancer types. One of the remaining challenges is to identify driver mutations, driver genes and driver pathways promoting cancer proliferation and filter out the unfunctional and passenger ones. RESULTS: In this study, we propose two methods to solve the so-called maximum weight submatrix problem, which is designed to de novo identify mutated driver pathways from mutation data in cancer. The first one is an exact method that can be helpful for assessing other approximate or/and heuristic algorithms. The second one is a stochastic and flexible method that can be employed to incorporate other types of information to improve the first method. Particularly, we propose an integrative model to combine mutation and expression data. We first apply our methods onto simulated data to show their efficiency. We further apply the proposed methods onto several real biological datasets, such as the mutation profiles of 74 head and neck squamous cell carcinomas samples, 90 glioblastoma tumor samples and 313 ovarian carcinoma samples. The gene expression profiles were also considered for the later two data. The results show that our integrative model can identify more biologically relevant gene sets. We have implemented all these methods and made a package called mutated driver pathway finder, which can be easily used for other researchers. AVAILABILITY: A MATLAB package of MDPFinder is available at http://zhangroup.aporc.org/ShiHuaZhang. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Mycobacterium tuberculosis is an infectious bacterium posing serious threats to human health. Due to the difficulty in performing molecular biology experiments to detect protein interactions, reconstruction of a protein interaction map of M. tuberculosis by computational methods will provide crucial information to understand the biological processes in the pathogenic microorganism, as well as provide the framework upon which new therapeutic approaches can be developed. RESULTS: In this paper, we constructed an integrated M. tuberculosis protein interaction network by machine learning and ortholog-based methods. Firstly, we built a support vector machine (SVM) method to infer the protein interactions of M. tuberculosis H37Rv by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying its protein interactions to M. tuberculosis. Moreover, the documented interactions of 14 other species were mapped to the interactome of M. tuberculosis by the interolog method. The ensemble protein interactions were validated by various functional relationships, i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. The accuracy and validation demonstrate the effectiveness and efficiency of our framework. CONCLUSIONS: A protein interaction map of M. tuberculosis is inferred from genetic codons and interologs. The prediction accuracy and numerically experimental validation demonstrate the effectiveness and efficiency of our method. Furthermore, our methods can be straightforwardly extended to infer the protein interactions of other bacterial species.

BACKGROUND: Gene expression profiling technologies have gradually become a community standard tool for clinical applications. For example, gene expression data has been analyzed to reveal novel disease subtypes (class discovery) and assign particular samples to well-defined classes (class prediction). In the past decade, many effective methods have been proposed for individual applications. However, there is still a pressing need for a unified framework that can reveal the complicated relationships between samples. RESULTS: We propose a novel convex optimization model to perform class discovery and class prediction in a unified framework. An efficient algorithm is designed and software named OTCC (Optimization Tool for Clustering and Classification) is developed. Comparison in a simulated dataset shows that our method outperforms the existing methods. We then applied OTCC to acute leukemia and breast cancer datasets. The results demonstrate that our method not only can reveal the subtle structures underlying those cancer gene expression data but also can accurately predict the class labels of unknown cancer samples. Therefore, our method holds the promise to identify novel cancer subtypes and improve diagnosis. CONCLUSIONS: We propose a unified computational framework for class discovery and class prediction to facilitate the discovery and prediction of subtle subtypes of cancers. Our method can be generally applied to multiple types of measurements, e.g., gene expression profiling, proteomic measuring, and recent next-generation sequencing, since it only requires the similarities among samples as input.

MOTIVATION: A large amount of biomolecular network data for multiple species have been generated by high-throughput experimental techniques, including undirected and directed networks such as protein-protein interaction networks, gene regulatory networks and metabolic networks. There are many conserved functionally similar modules and pathways among multiple biomolecular networks in different species; therefore, it is important to analyze the similarity between the biomolecular networks. Network querying approaches aim at efficiently discovering the similar subnetworks among different species. However, many existing methods only partially solve this problem. RESULTS: In this article, a novel approach for network querying problem based on conditional random fields (CRFs) model is presented, which can handle both undirected and directed networks, acyclic and cyclic networks and any number of insertions/deletions. The CRF method is fast and can query pathways in a large network in seconds using a PC. To evaluate the CRF method, extensive computational experiments are conducted on the simulated and real data, and the results are compared with the existing network querying methods. All results show that the CRF method is very useful and efficient to find the conserved functionally similar modules and pathways in multiple biomolecular networks.

BACKGROUND: With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. RESULTS: In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. CONCLUSIONS: By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.

MOTIVATION: Protein-RNA interactions play a key role in a number of biological processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function and eukaryotic spliceosomes. As a result, a reliable identification of RNA binding site of a protein is important for functional annotation and site-directed mutagenesis. Accumulated data of experimental protein-RNA interactions reveal that a RNA binding residue with different neighbor amino acids often exhibits different preferences for its RNA partners, which in turn can be assessed by the interacting interdependence of the amino acid fragment and RNA nucleotide. RESULTS: In this work, we propose a novel classification method to identify the RNA binding sites in proteins by combining a new interacting feature (interaction propensity) with other sequence- and structure-based features. Specifically, the interaction propensity represents a binding specificity of a protein residue to the interacting RNA nucleotide by considering its two-side neighborhood in a protein residue triplet. The sequence as well as the structure-based features of the residues are combined together to discriminate the interaction propensity of amino acids with RNA. We predict RNA interacting residues in proteins by implementing a well-built random forest classifier. The experiments show that our method is able to detect the annotated protein-RNA interaction sites in a high accuracy. Our method achieves an accuracy of 84.5%, F-measure of 0.85 and AUC of 0.92 prediction of the RNA binding residues for a dataset containing 205 non-homologous RNA binding proteins, and also outperforms several existing RNA binding residue predictors, such as RNABindR, BindN, RNAProB and PPRint, and some alternative machine learning methods, such as support vector machine, naive Bayes and neural network in the comparison study. Furthermore, we provide some biological insights into the roles of sequences and structures in protein-RNA interactions by both evaluating the importance of features for their contributions in predictive accuracy and analyzing the binding patterns of interacting residues. AVAILABILITY: All the source data and code are available at http://www.aporc.org/doc/wiki/PRNA or http://www.sysbio.ac.cn/datatools.asp CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The accumulation of high-throughput data greatly promotes computational investigation of gene function in the context of complex biological systems. However, a biological function is not simply controlled by an individual gene since genes function in a cooperative manner to achieve biological processes. In the study of human diseases, rather than to discover disease related genes, identifying disease associated pathways and modules becomes an essential problem in the field of systems biology. RESULTS: In this paper, we propose a novel method to detect disease related gene modules or dysfunctional pathways based on global characteristics of interactome coupled with gene expression data. Specifically, we exploit interacting relationships between genes to define a gene's active score function based on the kernel trick, which can represent nonlinear effects of gene cooperativity. Then, modules or pathways are inferred based on the active scores evaluated by the support vector regression in a global and integrative manner. The efficiency and robustness of the proposed method are comprehensively validated by using both simulated and real data with the comparison to existing methods. CONCLUSIONS: By applying the proposed method to two cancer related problems, i.e. breast cancer and prostate cancer, we successfully identified active modules or dysfunctional pathways related to these two types of cancers with literature confirmed evidences. We show that this network-based method is highly efficient and can be applied to a large-scale problem especially for human disease related modules or pathway extraction. Moreover, this method can also be used for prioritizing genes associated with a specific phenotype or disease.

Alzheimer's disease usually causes dementia in the old people and the symptom progression of the disease phenotype displays certain patterns. One possible reason is that the nerve cells in the brains of the patients degenerate at different stages. Here, we analyze the dynamics of disease progression based on its biomolecular network. We develop a novel computational method to integrate an ensemble protein network and the hippocampal gene expression data. Specifically, we construct the induced dynamical pathways which present particular characteristics at different disease stages from the control to disease samples. Based on the network-based method, we reveal that the active pathways tend to be more complicated during the development of disease. Also we find that the disease proteins performing important functions are always located in the cooperations of the identified pathways. These results also demonstrate that the network-based analysis can provide knowledge and evidences on the dynamics and pathological pathways of the complex Alzheimer's disease.

BACKGROUND: Transcriptional regulation is a fundamental process in biological systems, where transcription factors (TFs) have been revealed to play crucial roles. In recent years, in addition to TFs, an increasing number of non-coding RNAs (ncRNAs) have been shown to mediate post-transcriptional processes and regulate many critical pathways in both prokaryotes and eukaryotes. On the other hand, with more and more high-throughput biological data becoming available, it is possible and imperative to quantitatively study gene regulation in a systematic and detailed manner. RESULTS: Most existing studies for inferring transcriptional regulatory interactions and the activity of TFs ignore the possible post-transcriptional effects of ncRNAs. In this work, we propose a novel framework to infer the activity of regulators including both TFs and ncRNAs by exploring the expression profiles of target genes and (post)transcriptional regulatory relationships. We model the integrated regulatory system by a set of biochemical reactions which lead to a log-bilinear problem. The inference process is achieved by an iterative algorithm, in which two linear programming models are efficiently solved. In contrast to available related studies, the effects of ncRNAs on transcription process are considered in this work, and thus more reasonable and accurate reconstruction can be expected. In addition, the approach is suitable for large-scale problems from the viewpoint of computation. Experiments on two synthesized data sets and a model system of Escherichia coli (E. coli) carbon source transition from glucose to acetate illustrate the effectiveness of our model and algorithm. CONCLUSION: Our results show that incorporating the post-transcriptional regulation of ncRNAs into system model can mine the hidden effects from the regulation activity of TFs in transcription processes and thus can uncover the biological mechanisms in gene regulation in a more accurate manner. The software for the algorithm in this paper is available upon request.

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system-based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases.

To unscramble the relationship between protein function and protein structure, it is essential to assess the protein similarity from different aspects. Although many methods have been proposed for protein structure alignment or comparison, alternative similarity measures are still strongly demanded due to the requirement of fast screening and query in large-scale structure databases. In this paper, we first formulate a novel representation of a protein structure, i.e., Feature Sequence of Surface (FSS). Then, a new score scheme is developed to measure the similarity between two representations. To verify the proposed method, numerical experiments are conducted in four different protein data sets. We also classify SARS coronavirus to verify the effectiveness of the new method. Furthermore, preliminary results of fast classification of the whole CATH v2.5.1 database based on the new macrostructure similarity are given as a pilot study. We demonstrate that the proposed approach to measure the similarities between protein structures is simple to implement, computationally efficient, and surprisingly fast. In addition, the method itself provides a new and quantitative tool to view a protein structure.

Transcriptional regulation is a fundamental process in biological systems, where transcription factors (TFs) play crucial roles. Except for TFs, an increasing number of small non-coding RNAs (ncRNAs) have been shown to mediate post-transcriptional processes in both prokaryotes and eukaryotes. In this work, we propose a novel approach to infer the activities of regulators including TFs and ncRNAs by exploring target gene expression profiles and (post) transcriptional regulatory relationships. The inference process is efficiently achieved by an iteration algorithm, in which two linear programming models are iteratively solved. In contrast to the existing works, for the first time, the effects of ncRNAs on transcription process are considered and thus more reasonable inference can be expected. Experiments on a model system of E. coli carbon source transition from glucose to acetate illustrate the effectiveness of our method.

An interesting problem in nonnegative matrix factorization (NMF) is to factorize the matrix X which is of some specific class, for example, binary matrix. In this paper, we extend the standard NMF to binary matrix factorization (BMF for short): given a binary matrix X, we want to factorize X into two binary matrices W, H (thus conserving the most important integer property of the objective matrix X) satisfying X ap WH. Two algorithms are studied and compared. These methods rely on a fundamental boundedness property of NMF which we propose and prove. This new property also provides a natural normalization scheme that eliminates the bias of factor matrices. Experiments on both synthetic and real world datasets are conducted to show the competency and effectiveness of BMF.

MOTIVATION: With more and more data on molecular networks (e.g. protein interaction networks, gene regulatory networks and metabolic networks) available, the discovery of conserved patterns or signaling pathways by comparing various kinds of networks among different species or within a species becomes an increasingly important problem. However, most of the conventional approaches either restrict comparative analysis to special structures, such as pathways, or adopt heuristic algorithms due to computational burden. RESULTS: In this article, to find the conserved substructures, we develop an efficient algorithm for aligning molecular networks based on both molecule similarity and architecture similarity, by using integer quadratic programming (IQP). Such an IQP can be relaxed into the corresponding quadratic programming (QP) which almost always ensures an integer solution, thereby making molecular network alignment tractable without any approximation. The proposed framework is very flexible and can be applied to many kinds of molecular networks including weighted and unweighted, directed and undirected networks with or without loops. AVAILABILITY: Matlab code and data are available from http://zhangroup.aporc.org/bioinfo/MNAligner or http://intelligent.eic.osaka-sandai.ac.jp/chenen/software/MNAligner, or upon request from authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Inferring the relationships between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various post-translational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, e.g. TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes. RESULTS: In this article, we propose a novel method for inferring transcriptional regulatory networks (TRN) from gene expression data based on protein transcription complexes and mass action law. With gene expression data and TFAs estimated from transcription complex information, the inference of TRN is formulated as a linear programming (LP) problem which has a globally optimal solution in terms of L(1) norm error. The proposed method not only can easily incorporate ChIP-Chip data as prior knowledge, but also can integrate multiple gene expression datasets from different experiments simultaneously. A unique feature of our method is to take into account protein cooperation in transcription process. We tested our method by using both synthetic data and several experimental datasets in yeast. The extensive results illustrate the effectiveness of the proposed method for predicting transcription regulatory relationships between TFs with co-regulators and target genes.

BACKGROUND: Annotation of protein functions is an important task in the post-genomic era. Most early approaches for this task exploit only the sequence or global structure information. However, protein surfaces are believed to be crucial to protein functions because they are the main interfaces to facilitate biological interactions. Recently, several databases related to structural surfaces, such as pockets and cavities, have been constructed with a comprehensive library of identified surface structures. For example, CASTp provides identification and measurements of surface accessible pockets as well as interior inaccessible cavities. RESULTS: A novel method was proposed to predict the Gene Ontology (GO) functions of proteins from the pocket similarity network, which is constructed according to the structure similarities of pockets. The statistics of the networks were presented to explore the relationship between the similar pockets and GO functions of proteins. Cross-validation experiments were conducted to evaluate the performance of the proposed method. Results and codes are available at: http://zhangroup.aporc.org/bioinfo/PSN/. CONCLUSION: The computational results demonstrate that the proposed method based on the pocket similarity network is effective and efficient for predicting GO functions of proteins in terms of both computational complexity and prediction accuracy. The proposed method revealed strong relationship between small surface patterns (or pockets) and GO functions, which can be further used to identify active sites or functional motifs. The high quality performance of the prediction method together with the statistics also indicates that pockets play essential roles in biological interactions or the GO functions. Moreover, in addition to pockets, the proposed network framework can also be used for adopting other protein spatial surface patterns to predict the protein functions.

BACKGROUND: Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechanism of protein-protein interactions at the domain level and be further exploited to improve the accuracy of protein interaction prediction. RESULTS: In this paper, we aim to identify cooperative domains for protein interactions by extending two-domain interactions to multi-domain interactions. Based on the high-throughput experimental data from multiple organisms with different reliabilities, the interactions of domains were inferred by a Linear Programming algorithm with Multi-domain pairs (LPM) and an Association Probabilistic Method with Multi-domain pairs (APMM). Experimental results demonstrate that our approach not only can find cooperative domains effectively but also has a higher accuracy for predicting protein interaction than the existing methods. Cooperative domains, including strongly cooperative domains and superdomains, were detected from major interaction databases MIPS and DIP, and many of them were verified by physical interactions from the crystal structures of protein complexes in PDB which provide intuitive evidences for such cooperation. Comparison experiments in terms of protein/domain interaction prediction justified the benefit of considering multi-domain cooperation. CONCLUSION: From the computational viewpoint, this paper gives a general framework to predict protein interactions in a more accurate manner by considering the information of both multi-domains and multiple organisms, which can also be applied to identify cooperative domains, to reconstruct large complexes and further to annotate functions of domains. Supplementary information and software are provided in http://intelligent.eic.osaka-sandai.ac.jp/chenen/MDCinfer.htm and http://zhangroup.aporc.org/bioinfo/MDCinfer.

MOTIVATION: Microarray gene expression data has increasingly become the common data source that can provide insights into biological processes at a system-wide level. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to a large number of genes, which makes the problem of inferring gene regulatory network an ill-posed one. On the other hand, gene expression data generated by different groups worldwide are increasingly accumulated on many species and can be accessed from public databases or individual websites, although each experiment has only a limited number of time-points. RESULTS: This paper proposes a novel method to combine multiple time-course microarray datasets from different conditions for inferring gene regulatory networks. The proposed method is called GNR (Gene Network Reconstruction tool) which is based on linear programming and a decomposition procedure. The method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the prediction reliability. We tested GNR using both simulated data and experimental data in yeast and Arabidopsis. The result demonstrates the effectiveness of GNR in terms of predicting new gene regulatory relationship in yeast and Arabidopsis. AVAILABILITY: The software is available from http://zhangorup.aporc.org/bioinfo/grninfer/, http://digbio.missouri.edu/grninfer/ and http://intelligent.eic.osaka-sandai.ac.jp or upon request from the authors.

MOTIVATION: Haplotype information has become increasingly important in analyzing fine-scale molecular genetics data, such as disease genes mapping and drug design. Parsimony haplotyping is one of haplotyping problems belonging to NP-hard class. RESULTS: In this paper, we aim to develop a novel algorithm for the haplotype inference problem with the parsimony criterion, based on a parsimonious tree-grow method (PTG). PTG is a heuristic algorithm that can find the minimum number of distinct haplotypes based on the criterion of keeping all genotypes resolved during tree-grow process. In addition, a block-partitioning method is also proposed to improve the computational efficiency. We show that the proposed approach is not only effective with a high accuracy, but also very efficient with the computational complexity in the order of O(m2n) time for n single nucleotide polymorphism sites in m individual genotypes. AVAILABILITY: The software is available upon request from the authors, or from http://zhangroup.aporc.org/bioinfo/ptg/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supporting materials is available from http://zhangroup.aporc.org/bioinfo/ptg/bti572supplementary.pdf

MOTIVATION: Haplotype reconstruction based on aligned single nucleotide polymorphism (SNP) fragments is to infer a pair of haplotypes from localized polymorphism data gathered through short genome fragment assembly. An important computational model of this problem is the minimum error correction (MEC) model, which has been mentioned in several literatures. The model retrieves a pair of haplotypes by correcting minimum number of SNPs in given genome fragments coming from an individual's DNA. RESULTS: In the first part of this paper, an exact algorithm for the MEC model is presented. Owing to the NP-hardness of the MEC model, we also design a genetic algorithm (GA). The designed GA is intended to solve large size problems and has very good performance. The strength and weakness of the MEC model are shown using experimental results on real data and simulation data. In the second part of this paper, to improve the MEC model for haplotype reconstruction, a new computational model is proposed, which simultaneously employs genotype information of an individual in the process of SNP correction, and is called MEC with genotype information (shortly, MEC/GI). Computational results on extensive datasets show that the new model has much higher accuracy in haplotype reconstruction than the pure MEC model.

MOTIVATION: It is widely recognized that the hybridization process is prone to errors and that the future of DNA sequencing by hybridization is predicated on the ability to successfully cope with such errors. However, the occurrence of hybridization errors results in the computational difficulty of the reconstruction of DNA sequencing by hybridization. The reconstruction problem of DNA sequencing by hybridization with errors is a strongly NP-hard problem. So far the problem has not been solved well. RESULTS: In this paper, a new approach is presented to solve the reconstruction problem of DNA sequencing by hybridization, which realizes the computational part of the SBH experiment. The proposed algorithm accepts both the negative and positive errors. The computational experiments show that the algorithm behaves satisfactorily, especially for the case with k-tuple repetitions and positive errors.