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Ron Shamir

Tel Aviv University, Computer Science, Faculty Member

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Ron Shamir received his PhD in Operations Research from UC Berkeley in 1984 and has been on the faculty of Computer Science at Tel Aviv University since 1987. He heads the Edmond J. Safra Center for Bioinformatics and holds the Raymond and Beverly Sackler Chair in Bioinformatics. He develops algorithmic and statistical methods and tools for understanding the genome and human disease. He published more than 250 scientific works, including 17 books and edited volumes, and has supervised more than 50 research students.

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Kansas State University

Jeffrey Townsend

Yale University

Viswanathan Chinnusamy

INDIAN AGRICULTURAL RESEARCH INSTITUTE, NEW DELHI, INDIA

Nicole Zacharia

University of Akron

Roshan Chitrakar

Nepal College of Information Technology

Graduate Center of the City University of New York

Dominic D'Agostino

University of South Florida

Ozden Cobanoglu

Uludag University

Chandra Mukhopadhyay

GADVASU

Félix Meutchieye

University of Dschang, Cameroon

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Papers by Ron Shamir

Single cell Hi-C identifies plastic chromosome conformations underlying the gastrulation enhancer landscape

Nature Communications, Jun 29, 2023

Embryonic development involves massive proliferation and differentiation of cell lineages. This m... more Embryonic development involves massive proliferation and differentiation of cell lineages. This must be supported by chromosome replication and epigenetic reprogramming, but how proliferation and cell fate acquisition are balanced in this process is not well understood. Here we use single cell Hi-C to map chromosomal conformations in post-gastrulation mouse embryo cells and study their distributions and correlations with matching embryonic transcriptional atlases. We find that embryonic chromosomes show a remarkably strong cell cycle signature. Despite that, replication timing, chromosome compartment structure, topological associated domains (TADs) and promoter-enhancer contacts are shown to be variable between distinct epigenetic states. About 10% of the nuclei are identified as primitive erythrocytes, showing exceptionally compact and organized compartment structure. The remaining cells are broadly associated with ectoderm and mesoderm identities, showing only mild differentiation of TADs and compartment structures, but more specific localized contacts in hundreds of ectoderm and mesoderm promoter-enhancer pairs. The data suggest that while fully committed embryonic lineages can rapidly acquire specific chromosomal conformations, most embryonic cells are showing plastic signatures driven by complex and intermixed enhancer landscapes.

CT-FOCS: a novel method for inferring cell type-specific enhancer-promoter maps

ABSTRACTSpatiotemporal gene expression patterns are governed to a large extent by the activity of... more ABSTRACTSpatiotemporal gene expression patterns are governed to a large extent by the activity of enhancer elements, which engage in physical contacts with their target genes. Identification of enhancer-promoter (EP) links that are functional only in a specific subset of cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS, a statistical inference method that uses linear mixed effect models to infer EP links that show marked activity only in a single or a small subset of cell types out of a large panel of probed cell types. Analyzing 808 samples from FANTOM5, covering 472 cell lines, primary cells, and tissues, CT-FOCS inferred such EP links more accurately than recent state-of-the-art methods. Furthermore, we show that strictly cell type-specific EP links are very uncommon in the human genome.

Genome Rearrangement Problems with Single and Multiple Gene Copies: A Review

Bioinformatics and Phylogenetics, 2019

Problems of genome rearrangement arise in both evolution and cancer research. Basic genome rearra... more Problems of genome rearrangement arise in both evolution and cancer research. Basic genome rearrangement models assume that the genome contains a single copy of each gene and the only changes in the genome are structural, i.e. reordering of segments. In contrast, numerical changes such as deletions and duplications, which change the number of copies of genes, have been observed in species evolution and prominently in tumorigenesis. Here, we review various computational models of evolution by rearrangements designed for the analysis of species or cancer genomes, focusing mainly on genomes with multiple gene copies. Models differ in the assumptions taken on the genome structure and in the type of rearrangements allowed during their evolution. Most problems regarding genomes with multiple gene copies are computationally hard, and practical methods for their analysis are reviewed. As more high resolution genomes become available, especially in cancer, better models and efficient algorithms will be needed. Prologue The computational study of genome rearrangements is a sub-area of computational biology born about 25 years ago. Over that period, it has flourished and developed into a fascinating research area, combining beautiful combinatorial models, elegant theory and applications. Models of the first generation, motivated by species evolution, were simple (though their analysis was sometimes quite sophisticated) and

Molecular Systems Biology 3; Article number 104; doi:10.1038/msb4100144 Citation: Molecular Systems Biology 3:104

The biological interpretation of genetic interactions is a major challenge. Recently, Kelley and ... more The biological interpretation of genetic interactions is a major challenge. Recently, Kelley and Ideker proposed a method to analyze together genetic and physical networks, which explains many of the known genetic interactions as linking different pathways in the physical network. Here, we extend this method and devise novel analytic tools for interpreting genetic interactions in a physical context. Applying these tools on a large-scale Saccharomyces cerevisiae data set, our analysis reveals 140 between-pathway models that explain 3765 genetic interactions, roughly doubling those that were previously explained. Model genes tend to have short mRNA half-lives and many phosphorylation sites, suggesting that their stringent regulation is linked to pathway redundancy. We also identify 'pivot' proteins that have many physical interactions with both pathways in our models, and show that pivots tend to be essential and highly conserved. Our analysis of models and pivots sheds light on the organization of the cellular machinery as well as on the roles of individual proteins.

Molecular Systems Biology 4; Article number 209; doi:10.1038/msb.2008.42 Citation: Molecular Systems Biology 4:209

www.molecularsystemsbiology.com From E-MAPs to module maps: dissecting quantitative genetic inter... more

On reducing complexity of deep sequencing data analysis

The volume of deep sequencing data generated over the last decade has grown exponentially, as mac... more The volume of deep sequencing data generated over the last decade has grown exponentially, as machines that output many millions of short sequences in parallel replaced slower Sanger-sequencing machines that read individual DNA molecules. As a result of this change, throughput has exploded and the price per base has plummeted by five orders or magnitude. This data growth means that the time required to perform many standard processing tasks, such as alignment and assembly, also grows exponentially if algorithms that were designed for much smaller numbers (or lower volumes) of sequences are applied. In this thesis, we introduce new techniques designed to address these challenges. We use advanced data structures to compress and index sequence data, aiming to allow efficient querying with reduced storage space. In the context of sequence compression, we demonstrate a faster approach than extant methods to compress read sequences relative to a reference by representing sequences using B...

Network-Induced Classi cation Kernels for Gene Expression Pro le Analysis

Computational classi cation of gene expression pro les into distinct disease phenotypes has been ... more Computational classi cation of gene expression pro les into distinct disease phenotypes has been highly successful to date. Still, robustness, accuracy and biological interpretation of the results have been limited, and it was suggested that use of protein interaction information jointly with the expression pro les can improve the results. Here, we study three aspects of this problem. First, we show that interactions are indeed relevant by showing that co-expressed genes tend to be closer in the network of interactions. Second, we show that the improved performance of one extant method utilizing expression and interactions is not really due to the biological information in the network, while in another method this is not the case. Finally, we develop a new kernel method called NICK that integrates network and expression data for SVM classi cation, and demonstrate that overall it achieves better results than extant methods while running two orders of magnitude faster.

The EXPANDER for Transcriptom

Genome-wide analysis of cellular transcriptomes using RNA-seq or expression arrays is a major mai... more Genome-wide analysis of cellular transcriptomes using RNA-seq or expression arrays is a major mainstay of current biological and biomedical research. EXPANDER (EXPression ANalyzer and DisplayER) is a comprehensive software package for analysis of expression data, with built-in support for 18 different organisms. It is designed as a “one-stop shop” platform for transcriptomic analysis, allowing for execution of all analysis steps starting with gene expression data matrix. Analyses offered include low-level preprocessing and normalization, differential expression analysis, clustering, bi-clustering, supervised grouping, high-level functional and pathway enrichment tests, and networks and motif analyses. A variety of options is offered for each step, using established algorithms, including many developed and published by our laboratory. EXPANDER has been continuously developed since 2003, having to date over 18,000 downloads and 540 citations. One of the innovations in the recent versi...

Title : Constructing module maps for integrated analysis of heterogeneous biological networks Running title : From heterogeneous networks to module maps

Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed... more Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed. Here we take a network-based approach to this challenge. Given two networks, representing different types of gene interactions, we construct a map of linked modules, where modules are genes strongly connected in the first network and links represent strong inter-module connections in the second. We develop novel algorithms that considerably outperform prior art on simulated and real data from three distinct domains. First, by analyzing protein-protein interactions and negative genetic interactions in yeast we discover epistatic relations among protein complexes. Second, we analyze protein-protein interactions and DNA damage-specific positive genetic interactions in yeast, and reveal functional rewiring among protein complexes, suggesting novel mechanisms of DNA damage response. Finally, using transcriptomes of non-small cell lung cancer patients, we analyze networks of global co-expres...

3CAC: improving the classification of phages and plasmids in metagenomic assemblies using assembly graphs

MotivationBacteriophages and plasmids usually coexist with their host bacteria in microbial commu... more MotivationBacteriophages and plasmids usually coexist with their host bacteria in microbial communities and play important roles in microbial evolution. Accurately identifying sequence contigs as phages, plasmids, and bacterial chromosomes in mixed metagenomic assemblies is critical for further unravelling their functions. Many classification tools have been developed for identifying either phages or plasmids in metagenomic assemblies. However, only two classifiers, PPR-Meta and viralVerify, were proposed to simultaneously identify phages and plasmids in mixed metagenomic assemblies. Due to the very high fraction of chromosome contigs in the assemblies, both tools achieve high precision in the classification of chromosomes but perform poorly in classifying phages and plasmids. Short contigs in these assemblies are often wrongly classified or classified as uncertain.ResultsHere we present 3CAC, a new three-class classifier that improves the precision of phage and plasmid classificati...

Improving the efficiency of de Bruijn graph construction using compact universal hitting sets

High-throughput sequencing techniques generate large volumes of DNA sequencing data at ultra-fast... more High-throughput sequencing techniques generate large volumes of DNA sequencing data at ultra-fast speed and extremely low cost. As a consequence, sequencing techniques have become ubiquitous in biomedical research and are used in hundreds of genomic applications. Efficient data structures and algorithms have been developed to handle the large datasets produced by these techniques. The prevailing method to index DNA sequences in those data structures and algorithms is by k-mers (k-long substrings) known as minimizers. Minimizers are the smallest k-mers selected in every consecutive window of a fixed length in a sequence, where the smallest is determined according to a predefined order, e.g., lexicographic. Recently, a new k-mer order based on a universal hitting set (UHS) was suggested. While several studies have shown that orders based on a small UHS have improved properties, the utility of using a small UHS in high-throughput sequencing analysis tasks has not been demonstrated to d...

MONET: Multi-omic patient module detection by omic selection

Recent advances in experimental biology allow creation of datasets where several genome-wide data... more Recent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present Monet (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. Monet discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most extant multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures using Bayesian statistics. We tested Monet extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that Mo...

NEMO: cancer subtyping by integration of partial multi-omic data

Bioinformatics, 2019

Motivation Cancer subtypes were usually defined based on molecular characterization of single omi... more Motivation Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. Results We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the...

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

Nucleic Acids Research, 2018

Our paper included a benchmark comparing nine methods for multi-omics clustering, including PINS ... more Our paper included a benchmark comparing nine methods for multi-omics clustering, including PINS (1). We reported that PINS crashed when run on a breast cancer dataset. We performed all the benchmarks on a 64-bit computer, using the 32-bit version of R. In later tests we observed that PINS did not crash on 64-bit R, and it only crashed on 32-bit R due to insufficient memory. The clustering that PINS obtained on the breast cancer dataset had 4 enriched clinical parameters, and the P-value for the logrank test on that clustering was 0.05.

PRODIGY: personalized prioritization of driver genes

BackgroundEvolution of cancer is driven by few somatic mutations that disrupt cellular processes,... more BackgroundEvolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, while most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification, but patient-specific driver gene identification remains a challenge.MethodsWe developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize collecting Steiner tree model. Mutated genes...

Designing small universal k-mer hitting sets for improved analysis of high-throughput sequencing

PLoS computational biology, 2017

With the rapidly increasing volume of deep sequencing data, more efficient algorithms and data st... more With the rapidly increasing volume of deep sequencing data, more efficient algorithms and data structures are needed. Minimizers are a central recent paradigm that has improved various sequence analysis tasks, including hashing for faster read overlap detection, sparse suffix arrays for creating smaller indexes, and Bloom filters for speeding up sequence search. Here, we propose an alternative paradigm that can lead to substantial further improvement in these and other tasks. For integers k and L > k, we say that a set of k-mers is a universal hitting set (UHS) if every possible L-long sequence must contain a k-mer from the set. We develop a heuristic called DOCKS to find a compact UHS, which works in two phases: The first phase is solved optimally, and for the second we propose several efficient heuristics, trading set size for speed and memory. The use of heuristics is motivated by showing the NP-hardness of a closely related problem. We show that DOCKS works well in practice a...

Reconstructing cancer karyotypes from short read data: the half full and half empty glass

Background: During cancer progression genomes undergo point mutations as well as larger segmental... more Background: During cancer progression genomes undergo point mutations as well as larger segmental changes. The latter include, among others, segmental deletions duplications, translocations and inversions. The result is a highly complex, patient-specific cancer karyotype. Using high-throughput technologies of deep sequencing and microarrays it is possible to interrogate a cancer genome and produce chromosomal copy number profiles and a list of breakpoints (“jumps”) relative to the normal genome. This information is very detailed but local, and does not give the overall picture of the cancer genome. One of the basic challenges in cancer genome research is to use such information to infer the cancer karyotype. We present here an algorithmic approach, based on graph theory and integer linear programming, that receives segmental copy number and breakpoint data as input and produces a cancer karyotype that is most concordant with them. We used simulations to evaluate the utility of our a...

Complexity and algorithms for copy-number evolution problems

Algorithms for Molecular Biology, 2017

Background: Cancer is an evolutionary process characterized by the accumulation of somatic mutati... more Background: Cancer is an evolutionary process characterized by the accumulation of somatic mutations in a population of cells that form a tumor. One frequent type of mutations is copy number aberrations, which alter the number of copies of genomic regions. The number of copies of each position along a chromosome constitutes the chromosome's copy-number profile. Understanding how such profiles evolve in cancer can assist in both diagnosis and prognosis. Results: We model the evolution of a tumor by segmental deletions and amplifications, and gauge distance from profile a to b by the minimum number of events needed to transform a into b. Given two profiles, our first problem aims to find a parental profile that minimizes the sum of distances to its children. Given k profiles, the second, more general problem, seeks a phylogenetic tree, whose k leaves are labeled by the k given profiles and whose internal vertices are labeled by ancestral profiles such that the sum of edge distances is minimum. Conclusions: For the former problem we give a pseudo-polynomial dynamic programming algorithm that is linear in the profile length, and an integer linear program formulation. For the latter problem we show it is NP-hard and give an integer linear program formulation that scales to practical problem instance sizes. We assess the efficiency and quality of our algorithms on simulated instances.

Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate

PLOS Computational Biology, 2017

In almost every field in genomics, large-scale biomedical datasets are used to report association... more In almost every field in genomics, large-scale biomedical datasets are used to report associations. Extracting associations that recur across multiple studies while controlling the false discovery rate is a fundamental challenge. Here, we consider an extension of Efron's single-study twogroups model to allow joint analysis of multiple studies. We assume that given a set of p-values obtained from each study, the researcher is interested in associations that recur in at least k > 1 studies. We propose new algorithms that differ in how the study dependencies are modeled. We compared our new methods and others using various simulated scenarios. The top performing algorithm, SCREEN (Scalable Cluster-based REplicability ENhancement), is our new algorithm that is based on three stages: (1) clustering an estimated correlation network of the studies, (2) learning replicability (e.g., of genes) within clusters, and (3) merging the results across the clusters using dynamic programming. We applied SCREEN to two real datasets and demonstrated that it greatly outperforms the results obtained via standard meta-analysis. First, on a collection of 29 case-control large-scale gene expression cancer studies, we detected a large up-regulated module of genes related to proliferation and cell cycle regulation. These genes are both consistently up-regulated across many cancer studies, and are well connected in known gene networks. Second, on a recent pan-cancer study that examined the expression profiles of patients with or without mutations in the HLA complex, we detected an active module of up-regulated genes that are related to immune responses. Thanks to our ability to quantify the false discovery rate, we detected thrice more genes as compared to the original study. Our module contains most of the genes reported in the original study, and many new ones. Interestingly, the newly discovered genes are needed to establish the connectivity of the module.

Utilizing somatic mutation data from numerous studies for cancer research: proof of concept and applications

Oncogene, 2017

Large cancer projects measure somatic mutations in thousands of samples, gradually assembling a c... more Large cancer projects measure somatic mutations in thousands of samples, gradually assembling a catalog of recurring mutations in cancer. Many methods analyze these data jointly with auxiliary information with the aim of identifying subtype-specific results. Here, we show that somatic gene mutations alone can reliably and specifically predict cancer subtypes. Interpretation of the classifiers provides useful insights for several biomedical applications. We analyze the COSMIC database, which collects somatic mutations from The Cancer Genome Atlas (TCGA) as well as from many smaller scale studies. We use multi-label classification techniques and the Disease Ontology hierarchy in order to identify cancer subtype-specific biomarkers. Cancer subtype classifiers based on TCGA and the smaller studies have comparable performance, and the smaller studies add a substantial value in terms of validation, coverage of additional subtypes, and improved classification. The gene sets of the classifiers are used for threefold contribution. First, we refine the associations of genes to cancer subtypes and identify novel compelling candidate driver genes. Second, using our classifiers we successfully predict the primary site of metastatic samples. Third, we provide novel hypotheses regarding detection of subtype-specific synthetic lethality interactions. From the cancer research community perspective, our results suggest that curation efforts, such as COSMIC, have great added and complementary value even in the era of large international cancer projects.

Single cell Hi-C identifies plastic chromosome conformations underlying the gastrulation enhancer landscape

Nature Communications, Jun 29, 2023

Embryonic development involves massive proliferation and differentiation of cell lineages. This m... more Embryonic development involves massive proliferation and differentiation of cell lineages. This must be supported by chromosome replication and epigenetic reprogramming, but how proliferation and cell fate acquisition are balanced in this process is not well understood. Here we use single cell Hi-C to map chromosomal conformations in post-gastrulation mouse embryo cells and study their distributions and correlations with matching embryonic transcriptional atlases. We find that embryonic chromosomes show a remarkably strong cell cycle signature. Despite that, replication timing, chromosome compartment structure, topological associated domains (TADs) and promoter-enhancer contacts are shown to be variable between distinct epigenetic states. About 10% of the nuclei are identified as primitive erythrocytes, showing exceptionally compact and organized compartment structure. The remaining cells are broadly associated with ectoderm and mesoderm identities, showing only mild differentiation of TADs and compartment structures, but more specific localized contacts in hundreds of ectoderm and mesoderm promoter-enhancer pairs. The data suggest that while fully committed embryonic lineages can rapidly acquire specific chromosomal conformations, most embryonic cells are showing plastic signatures driven by complex and intermixed enhancer landscapes.

CT-FOCS: a novel method for inferring cell type-specific enhancer-promoter maps

ABSTRACTSpatiotemporal gene expression patterns are governed to a large extent by the activity of... more ABSTRACTSpatiotemporal gene expression patterns are governed to a large extent by the activity of enhancer elements, which engage in physical contacts with their target genes. Identification of enhancer-promoter (EP) links that are functional only in a specific subset of cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS, a statistical inference method that uses linear mixed effect models to infer EP links that show marked activity only in a single or a small subset of cell types out of a large panel of probed cell types. Analyzing 808 samples from FANTOM5, covering 472 cell lines, primary cells, and tissues, CT-FOCS inferred such EP links more accurately than recent state-of-the-art methods. Furthermore, we show that strictly cell type-specific EP links are very uncommon in the human genome.

Genome Rearrangement Problems with Single and Multiple Gene Copies: A Review

Bioinformatics and Phylogenetics, 2019

Problems of genome rearrangement arise in both evolution and cancer research. Basic genome rearra... more Problems of genome rearrangement arise in both evolution and cancer research. Basic genome rearrangement models assume that the genome contains a single copy of each gene and the only changes in the genome are structural, i.e. reordering of segments. In contrast, numerical changes such as deletions and duplications, which change the number of copies of genes, have been observed in species evolution and prominently in tumorigenesis. Here, we review various computational models of evolution by rearrangements designed for the analysis of species or cancer genomes, focusing mainly on genomes with multiple gene copies. Models differ in the assumptions taken on the genome structure and in the type of rearrangements allowed during their evolution. Most problems regarding genomes with multiple gene copies are computationally hard, and practical methods for their analysis are reviewed. As more high resolution genomes become available, especially in cancer, better models and efficient algorithms will be needed. Prologue The computational study of genome rearrangements is a sub-area of computational biology born about 25 years ago. Over that period, it has flourished and developed into a fascinating research area, combining beautiful combinatorial models, elegant theory and applications. Models of the first generation, motivated by species evolution, were simple (though their analysis was sometimes quite sophisticated) and

Molecular Systems Biology 3; Article number 104; doi:10.1038/msb4100144 Citation: Molecular Systems Biology 3:104

The biological interpretation of genetic interactions is a major challenge. Recently, Kelley and ... more The biological interpretation of genetic interactions is a major challenge. Recently, Kelley and Ideker proposed a method to analyze together genetic and physical networks, which explains many of the known genetic interactions as linking different pathways in the physical network. Here, we extend this method and devise novel analytic tools for interpreting genetic interactions in a physical context. Applying these tools on a large-scale Saccharomyces cerevisiae data set, our analysis reveals 140 between-pathway models that explain 3765 genetic interactions, roughly doubling those that were previously explained. Model genes tend to have short mRNA half-lives and many phosphorylation sites, suggesting that their stringent regulation is linked to pathway redundancy. We also identify 'pivot' proteins that have many physical interactions with both pathways in our models, and show that pivots tend to be essential and highly conserved. Our analysis of models and pivots sheds light on the organization of the cellular machinery as well as on the roles of individual proteins.

Molecular Systems Biology 4; Article number 209; doi:10.1038/msb.2008.42 Citation: Molecular Systems Biology 4:209

www.molecularsystemsbiology.com From E-MAPs to module maps: dissecting quantitative genetic inter... more

On reducing complexity of deep sequencing data analysis

The volume of deep sequencing data generated over the last decade has grown exponentially, as mac... more The volume of deep sequencing data generated over the last decade has grown exponentially, as machines that output many millions of short sequences in parallel replaced slower Sanger-sequencing machines that read individual DNA molecules. As a result of this change, throughput has exploded and the price per base has plummeted by five orders or magnitude. This data growth means that the time required to perform many standard processing tasks, such as alignment and assembly, also grows exponentially if algorithms that were designed for much smaller numbers (or lower volumes) of sequences are applied. In this thesis, we introduce new techniques designed to address these challenges. We use advanced data structures to compress and index sequence data, aiming to allow efficient querying with reduced storage space. In the context of sequence compression, we demonstrate a faster approach than extant methods to compress read sequences relative to a reference by representing sequences using B...

Network-Induced Classi cation Kernels for Gene Expression Pro le Analysis

Computational classi cation of gene expression pro les into distinct disease phenotypes has been ... more Computational classi cation of gene expression pro les into distinct disease phenotypes has been highly successful to date. Still, robustness, accuracy and biological interpretation of the results have been limited, and it was suggested that use of protein interaction information jointly with the expression pro les can improve the results. Here, we study three aspects of this problem. First, we show that interactions are indeed relevant by showing that co-expressed genes tend to be closer in the network of interactions. Second, we show that the improved performance of one extant method utilizing expression and interactions is not really due to the biological information in the network, while in another method this is not the case. Finally, we develop a new kernel method called NICK that integrates network and expression data for SVM classi cation, and demonstrate that overall it achieves better results than extant methods while running two orders of magnitude faster.

The EXPANDER for Transcriptom

Genome-wide analysis of cellular transcriptomes using RNA-seq or expression arrays is a major mai... more Genome-wide analysis of cellular transcriptomes using RNA-seq or expression arrays is a major mainstay of current biological and biomedical research. EXPANDER (EXPression ANalyzer and DisplayER) is a comprehensive software package for analysis of expression data, with built-in support for 18 different organisms. It is designed as a “one-stop shop” platform for transcriptomic analysis, allowing for execution of all analysis steps starting with gene expression data matrix. Analyses offered include low-level preprocessing and normalization, differential expression analysis, clustering, bi-clustering, supervised grouping, high-level functional and pathway enrichment tests, and networks and motif analyses. A variety of options is offered for each step, using established algorithms, including many developed and published by our laboratory. EXPANDER has been continuously developed since 2003, having to date over 18,000 downloads and 540 citations. One of the innovations in the recent versi...

Title : Constructing module maps for integrated analysis of heterogeneous biological networks Running title : From heterogeneous networks to module maps

Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed... more Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed. Here we take a network-based approach to this challenge. Given two networks, representing different types of gene interactions, we construct a map of linked modules, where modules are genes strongly connected in the first network and links represent strong inter-module connections in the second. We develop novel algorithms that considerably outperform prior art on simulated and real data from three distinct domains. First, by analyzing protein-protein interactions and negative genetic interactions in yeast we discover epistatic relations among protein complexes. Second, we analyze protein-protein interactions and DNA damage-specific positive genetic interactions in yeast, and reveal functional rewiring among protein complexes, suggesting novel mechanisms of DNA damage response. Finally, using transcriptomes of non-small cell lung cancer patients, we analyze networks of global co-expres...

3CAC: improving the classification of phages and plasmids in metagenomic assemblies using assembly graphs

MotivationBacteriophages and plasmids usually coexist with their host bacteria in microbial commu... more MotivationBacteriophages and plasmids usually coexist with their host bacteria in microbial communities and play important roles in microbial evolution. Accurately identifying sequence contigs as phages, plasmids, and bacterial chromosomes in mixed metagenomic assemblies is critical for further unravelling their functions. Many classification tools have been developed for identifying either phages or plasmids in metagenomic assemblies. However, only two classifiers, PPR-Meta and viralVerify, were proposed to simultaneously identify phages and plasmids in mixed metagenomic assemblies. Due to the very high fraction of chromosome contigs in the assemblies, both tools achieve high precision in the classification of chromosomes but perform poorly in classifying phages and plasmids. Short contigs in these assemblies are often wrongly classified or classified as uncertain.ResultsHere we present 3CAC, a new three-class classifier that improves the precision of phage and plasmid classificati...

Improving the efficiency of de Bruijn graph construction using compact universal hitting sets

High-throughput sequencing techniques generate large volumes of DNA sequencing data at ultra-fast... more High-throughput sequencing techniques generate large volumes of DNA sequencing data at ultra-fast speed and extremely low cost. As a consequence, sequencing techniques have become ubiquitous in biomedical research and are used in hundreds of genomic applications. Efficient data structures and algorithms have been developed to handle the large datasets produced by these techniques. The prevailing method to index DNA sequences in those data structures and algorithms is by k-mers (k-long substrings) known as minimizers. Minimizers are the smallest k-mers selected in every consecutive window of a fixed length in a sequence, where the smallest is determined according to a predefined order, e.g., lexicographic. Recently, a new k-mer order based on a universal hitting set (UHS) was suggested. While several studies have shown that orders based on a small UHS have improved properties, the utility of using a small UHS in high-throughput sequencing analysis tasks has not been demonstrated to d...

MONET: Multi-omic patient module detection by omic selection

Recent advances in experimental biology allow creation of datasets where several genome-wide data... more Recent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present Monet (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. Monet discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most extant multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures using Bayesian statistics. We tested Monet extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that Mo...

NEMO: cancer subtyping by integration of partial multi-omic data

Bioinformatics, 2019

Motivation Cancer subtypes were usually defined based on molecular characterization of single omi... more Motivation Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. Results We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the...

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

Nucleic Acids Research, 2018

Our paper included a benchmark comparing nine methods for multi-omics clustering, including PINS ... more Our paper included a benchmark comparing nine methods for multi-omics clustering, including PINS (1). We reported that PINS crashed when run on a breast cancer dataset. We performed all the benchmarks on a 64-bit computer, using the 32-bit version of R. In later tests we observed that PINS did not crash on 64-bit R, and it only crashed on 32-bit R due to insufficient memory. The clustering that PINS obtained on the breast cancer dataset had 4 enriched clinical parameters, and the P-value for the logrank test on that clustering was 0.05.

PRODIGY: personalized prioritization of driver genes

BackgroundEvolution of cancer is driven by few somatic mutations that disrupt cellular processes,... more BackgroundEvolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, while most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification, but patient-specific driver gene identification remains a challenge.MethodsWe developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize collecting Steiner tree model. Mutated genes...

Designing small universal k-mer hitting sets for improved analysis of high-throughput sequencing

PLoS computational biology, 2017

With the rapidly increasing volume of deep sequencing data, more efficient algorithms and data st... more With the rapidly increasing volume of deep sequencing data, more efficient algorithms and data structures are needed. Minimizers are a central recent paradigm that has improved various sequence analysis tasks, including hashing for faster read overlap detection, sparse suffix arrays for creating smaller indexes, and Bloom filters for speeding up sequence search. Here, we propose an alternative paradigm that can lead to substantial further improvement in these and other tasks. For integers k and L > k, we say that a set of k-mers is a universal hitting set (UHS) if every possible L-long sequence must contain a k-mer from the set. We develop a heuristic called DOCKS to find a compact UHS, which works in two phases: The first phase is solved optimally, and for the second we propose several efficient heuristics, trading set size for speed and memory. The use of heuristics is motivated by showing the NP-hardness of a closely related problem. We show that DOCKS works well in practice a...

Reconstructing cancer karyotypes from short read data: the half full and half empty glass

Background: During cancer progression genomes undergo point mutations as well as larger segmental... more Background: During cancer progression genomes undergo point mutations as well as larger segmental changes. The latter include, among others, segmental deletions duplications, translocations and inversions. The result is a highly complex, patient-specific cancer karyotype. Using high-throughput technologies of deep sequencing and microarrays it is possible to interrogate a cancer genome and produce chromosomal copy number profiles and a list of breakpoints (“jumps”) relative to the normal genome. This information is very detailed but local, and does not give the overall picture of the cancer genome. One of the basic challenges in cancer genome research is to use such information to infer the cancer karyotype. We present here an algorithmic approach, based on graph theory and integer linear programming, that receives segmental copy number and breakpoint data as input and produces a cancer karyotype that is most concordant with them. We used simulations to evaluate the utility of our a...

Complexity and algorithms for copy-number evolution problems

Algorithms for Molecular Biology, 2017

Background: Cancer is an evolutionary process characterized by the accumulation of somatic mutati... more Background: Cancer is an evolutionary process characterized by the accumulation of somatic mutations in a population of cells that form a tumor. One frequent type of mutations is copy number aberrations, which alter the number of copies of genomic regions. The number of copies of each position along a chromosome constitutes the chromosome's copy-number profile. Understanding how such profiles evolve in cancer can assist in both diagnosis and prognosis. Results: We model the evolution of a tumor by segmental deletions and amplifications, and gauge distance from profile a to b by the minimum number of events needed to transform a into b. Given two profiles, our first problem aims to find a parental profile that minimizes the sum of distances to its children. Given k profiles, the second, more general problem, seeks a phylogenetic tree, whose k leaves are labeled by the k given profiles and whose internal vertices are labeled by ancestral profiles such that the sum of edge distances is minimum. Conclusions: For the former problem we give a pseudo-polynomial dynamic programming algorithm that is linear in the profile length, and an integer linear program formulation. For the latter problem we show it is NP-hard and give an integer linear program formulation that scales to practical problem instance sizes. We assess the efficiency and quality of our algorithms on simulated instances.

Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate

PLOS Computational Biology, 2017

In almost every field in genomics, large-scale biomedical datasets are used to report association... more In almost every field in genomics, large-scale biomedical datasets are used to report associations. Extracting associations that recur across multiple studies while controlling the false discovery rate is a fundamental challenge. Here, we consider an extension of Efron's single-study twogroups model to allow joint analysis of multiple studies. We assume that given a set of p-values obtained from each study, the researcher is interested in associations that recur in at least k > 1 studies. We propose new algorithms that differ in how the study dependencies are modeled. We compared our new methods and others using various simulated scenarios. The top performing algorithm, SCREEN (Scalable Cluster-based REplicability ENhancement), is our new algorithm that is based on three stages: (1) clustering an estimated correlation network of the studies, (2) learning replicability (e.g., of genes) within clusters, and (3) merging the results across the clusters using dynamic programming. We applied SCREEN to two real datasets and demonstrated that it greatly outperforms the results obtained via standard meta-analysis. First, on a collection of 29 case-control large-scale gene expression cancer studies, we detected a large up-regulated module of genes related to proliferation and cell cycle regulation. These genes are both consistently up-regulated across many cancer studies, and are well connected in known gene networks. Second, on a recent pan-cancer study that examined the expression profiles of patients with or without mutations in the HLA complex, we detected an active module of up-regulated genes that are related to immune responses. Thanks to our ability to quantify the false discovery rate, we detected thrice more genes as compared to the original study. Our module contains most of the genes reported in the original study, and many new ones. Interestingly, the newly discovered genes are needed to establish the connectivity of the module.

Utilizing somatic mutation data from numerous studies for cancer research: proof of concept and applications

Oncogene, 2017

Large cancer projects measure somatic mutations in thousands of samples, gradually assembling a c... more Large cancer projects measure somatic mutations in thousands of samples, gradually assembling a catalog of recurring mutations in cancer. Many methods analyze these data jointly with auxiliary information with the aim of identifying subtype-specific results. Here, we show that somatic gene mutations alone can reliably and specifically predict cancer subtypes. Interpretation of the classifiers provides useful insights for several biomedical applications. We analyze the COSMIC database, which collects somatic mutations from The Cancer Genome Atlas (TCGA) as well as from many smaller scale studies. We use multi-label classification techniques and the Disease Ontology hierarchy in order to identify cancer subtype-specific biomarkers. Cancer subtype classifiers based on TCGA and the smaller studies have comparable performance, and the smaller studies add a substantial value in terms of validation, coverage of additional subtypes, and improved classification. The gene sets of the classifiers are used for threefold contribution. First, we refine the associations of genes to cancer subtypes and identify novel compelling candidate driver genes. Second, using our classifiers we successfully predict the primary site of metastatic samples. Third, we provide novel hypotheses regarding detection of subtype-specific synthetic lethality interactions. From the cancer research community perspective, our results suggest that curation efforts, such as COSMIC, have great added and complementary value even in the era of large international cancer projects.