Biology subjects

Shamir, R.

Publications and source records attributed to Shamir, R..

32 records · Page 2Linked to original sources

bioRxiv2020

DOMINO: a novel network-based module detection algorithm with reduced rate offalse calls

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes activity scores as input and report sub-networks that show significant over-representation of accrued activity signal ( active modules), thus representing biological processes that presumably play key roles in the analyzed biological conditions. Although such methods exist for almost two decades, only a handful of studies attempted to compare the biological signals captured by different methods. Here, we systematically evaluated six popular AMI methods on gene expression (GE) and GWAS data. Notably, we observed that GO terms enriched in modules detected by these methods on the real data were often also enriched on modules found on randomly permuted input data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation-based method that evaluates the empirical significance of GO terms reported as enriched in modules. We used the method to fashion five novel performance criteria for evaluating AMI methods. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir-Lab.

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bioRxiv2020

MONET: Multi-omic patient module detection by omic selection

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 Monet compares favorably with other multi-omic clustering methods. We demonstrate Monets biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that Monet is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that Monet is a valuable tool that can provide complementary results to those provided by extant algorithms for multi-omic analysis.

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bioRxiv2020

SCAPP: An algorithm for improved plasmid assembly in metagenomes

BackgroundMetagenomic sequencing has led to the identification and assembly of many new bacterial genome sequences. These bacteria often contain plasmids: usually small, circular double-stranded DNA molecules that may transfer across bacterial species and confer antibiotic resistance. These plasmids are generally less studied and understood than their bacterial hosts. Part of the reason for this is insufficient computational tools enabling the analysis of plasmids in metagenomic samples. ResultsWe developed SCAPP (Sequence Contents-Aware Plasmid Peeler) - an algorithm and tool to assemble plasmid sequences from metagenomic sequencing. SCAPP builds on some key ideas from the Recycler algorithm while improving plasmid assemblies by integrating biological knowledge about plasmids. We compared the performance of SCAPP to Recycler and metaplasmidSPAdes on simulated metagenomes, real human gut microbiome samples, and a human gut plasmidome dataset that we generated. We also created plasmidome and metagenome data from the same cow rumen sample and used the parallel sequencing data to create a novel assessment procedure. Overall, SCAPP outperformed Recycler and metaplasmidSPAdes across this wide range of datasets. ConclusionsSCAPP is an easy to use Python package that enables the assembly of full plasmid sequences from metagenomic samples. It outperformed existing metagenomic plasmid assemblers in most cases, and assembled novel and clinically relevant plasmids in samples we generated such as a human gut plasmidome. SCAPP is open-source software available from: https://github.com/Shamir-Lab/SCAPP.

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bioRxiv2019

PlasClass improves plasmid sequence classification

BackgroundMany bacteria contain plasmids, but separating between contigs that originate on the plasmid and those that are part of the bacterial genome can be difficult. This is especially true in metagenomic assembly, which yields many contigs of unknown origin. Existing tools for classifying sequences of plasmid origin give less reliable results for shorter sequences, are trained using a fraction of the known plasmids, and can be difficult to use in practice. ResultsWe present PlasClass, a new plasmid classifier. It uses a set of standard classifiers trained on the most current set of known plasmid sequences for different sequence lengths. PlasClass outperforms the state-of-the-art plasmid classification tool on shorter sequences, which constitute the majority of assembly contigs, while using less time and memory. ConclusionsPlasClass can be used to easily classify plasmid and bacterial genome sequences in metagenomic or isolate assemblies. It is available from: https://github.com/Shamir-Lab/PlasClass

bioinformatics↗

bioRxiv2019

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

Spatiotemporal 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.

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bioRxiv2019

PROMO: An interactive tool for analyzing clinically-labeled multi-omic cancer datasets

BackgroundAnalysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies ( omics) and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis.\n\nResultsWe developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and for stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMOs main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA.\n\nConclusionsPROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.

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bioRxiv2018

PRODIGY: personalized prioritization of driver genes

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 are ranked by their aggregated impact on all deregulated pathways. ResultsIn testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further towards personalized medicine and treatment. AvailabilityThe Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY.

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bioRxiv2018

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

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.\n\nResults: We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multiomics 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 outperformed nine state-of-the-art multi-omics clustering algorithms on full data and on imputed partial data. On some of the partial data tests, PVC, a multiview algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization.\n\nAvailability: Code for NEMO and for reproducing all NEMO results in this paper is in github.\n\nContact: rshamir@tau.ac.il\n\nSupplementary information: Supplementary data are available online.

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bioRxiv2018

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

High throughput experimental methods developed in recent years have been used to collect large biomedical omics datasets. Clustering of such datasets has proven invaluable for biological and medical research, and helped reveal structure in data from several domains. Such analysis is often based on investigation of a single omic. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data. Clustering multi-omic data has the potential to reveal further systems-level insights, but raises computational and biological challenges. Here we review algorithms for multi-omics clustering, and discuss key issues in applying these algorithms. Our review covers methods developed specifically for multi-omic data as well as generic multi-view methods developed in the machine learning community for joint clustering of multiple data types.\n\nIn addition, using cancer data from TCGA, we perform an extensive benchmark spanning ten different cancer types, providing the first systematic benchmark comparison of leading multi-omics and multiview clustering algorithms. The results highlight several key questions regarding the use of single-vs. multi-omics, the choice of clustering strategy, the power of generic multi-view methods and the use of approximated p-values for gauging solution quality. Due to the rapidly increasing use of multi-omics data, these issues may be important for future progress in the field.

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bioRxiv2018

Genomic meta-analysis of the interplay between 3D chromatin organization and gene expression programs under basal and stress conditions

BackgroundOur appreciation of the critical role of the 3D organization of the genome in gene regulation is steadily increasing. Recent 3C-based deep sequencing techniques elucidated a hierarchy of structures that underlie the spatial organization of the genome in the nucleus. At the top of this hierarchical organization are chromosomal territories and the megabase-scale A/B compartments that correlate with transcriptional activity within cells. Below them are the relatively cell-type invariant topologically associated domains (TADs), characterized by high frequency of physical contacts between loci within the same TAD and are assumed to function as regulatory units. Within TADs, chromatin loops bring enhancers and target promoters to close spatial proximity. Yet, we still have only rudimentary understanding how differences in chromatin organization between different cell types affect cell-type specific gene expression programs that are executed under basal and challenged conditions.\n\nResultsHere, we carried out a large-scale meta-analysis that integrated Hi-C data from thirteen different cell lines and dozens of ChIP-seq and RNA-seq datasets measured on these cells, either under basal conditions or after treatment. Pairwise comparisons between cell lines demonstrated the strong association between modulation of A/B compartmentalization, differential gene expression and transcription factor (TF) binding events. Furthermore, integrating the analysis of transcriptomes of different cell lines in response to various challenges, we show that 3D organization of cells under basal conditions constrains not only gene expression programs and TF binding profiles that are active under the basal condition but also those induced in response to treatment.\n\nConclusionsOur results further elucidate the role of dynamic genome organization in regulation of differential gene expression between different cell types, and indicate the impact of intra-TAD enhancer-promoter interactions that are established under basal conditions on both the basal and treatment-induced gene expression programs.

genomics↗

bioRxiv2017

An extensive enhancer-promoter map generated by genome-scale analysis of enhancer and gene activity patterns

Massive efforts have documented hundreds of thousands of putative enhancers in the human genome. A pressing genomic challenge is to identify which of these enhancers are functional and map them to the genes they regulate. We developed a novel method for inferring enhancer-promoter (E-P) links based on correlated activity patterns across many samples. Our method, called FOCS, uses rigorous statistical validation tailored for zero-inflated data, identifying the most important E-P links in each gene model. We applied FOCS to the wide epigenomic and transcriptomic datasets recorded by the ENCODE, Roadmap Epigenomics and FANTOM5 projects, together covering 2,630 samples of human primary cells, tissues and cell lines. In addition, building on expression of enhancer RNAs (eRNAs) as an exquisite mark of enhancer activity and on the robust detection of eRNAs by the GRO-seq technique, we compiled a compendium of eRNA and gene expression profiles based on public GRO-seq data from 245 samples and 23 human cell types. Applying FOCS to this compendium further expanded the coverage of our inferred E-P map. Benchmarking against gold standard E-P links from ChIA-PET and eQTL data, we demonstrate that FOCS prediction of E-P links outperforms extant methods. Collectively, we inferred >300,000 cross-validated E-P links spanning ~16K known genes. Our study presents an improved method for inferring regulatory links between enhancers and promoters, and provides an extensive resource of E-P maps that could greatly assist the functional interpretation of the noncoding regulatory genome. FOCS and our predicted E-P map are publicly available at http://acgt.cs.tau.ac.il/focs.

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bioRxiv2017

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

BackgroundDuring 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.\n\nWe 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 approach, and applied it to real data.\n\nResultsBy using a simulation model, we were able to estimate the correctness and robustness of the algorithm in a spectrum of scenarios. Under our base scenario, designed according to observations in real data, the algorithm correctly inferred 69% of the karyotypes. However, when using less stringent correctness metrics that account for incomplete and noisy data, 87% of the reconstructed karyotypes were correct. Furthermore, in scenarios where the data were very clean and complete, accuracy rose to 90%-100%. Some examples of analysis of real data, and the karyotypes reconstructed by our algorithm, are also presented.\n\nConclusionWhile reconstruction of complete, perfect karyotype based on short read data is very hard, a large portion of the reconstruction will still be correct and can provide useful information.

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bioRxiv2017

Faucet: streaming de novo assembly graph construction

MotivationWe present Faucet, a 2-pass streaming algorithm for assembly graph construction. Faucet builds an assembly graph incrementally as each read is processed. Thus, reads need not be stored locally, as they can be processed while downloading data and then discarded. We demonstrate this functionality by performing streaming graph assembly of publicly available data, and observe that the ratio of disk use to raw data size decreases as coverage is increased.\n\nResultsFaucet pairs the de Bruijn graph obtained from the reads with additional meta-data derived from them. We show these metadata - coverage counts collected at junction k-mers and connections bridging between junction pairs - contain most salient information needed for assembly, and demonstrate they enable cleaning of metagenome assembly graphs, greatly improving contiguity while maintaining accuracy. We compared Faucets resource use and assembly quality to state of the art metagenome assemblers, as well as leading resource-efficient genome assemblers. Faucet used orders of magnitude less time and disk space than the specialized metagenome assemblers MetaSPAdes and Megahit, while also improving on their memory use; this broadly matched performance of other assemblers optimizing resource efficiency - namely, Minia and LightAssembler. However, on metagenomes tested, Faucets outputs had 14-110% higher mean NGA50 lengths compared to Minia, and 2-11-fold higher mean NGA50 lengths compared to LightAssembler, the only other streaming assembler available.\n\nAvailabilityFaucet is available at https://github.com/Shamir-Lab/Faucet\n\nContactrshamir@tau.ac.il,eranhalperin@gmail.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2017

Improving the performance of minimizers andwinnowing schemes

The minimizers scheme is a method for selecting k-mers from sequences. It is used in many bioinformatics software tools to bin comparable sequences or to sample a sequence in a deterministic fashion at approximately regular intervals, in order to reduce memory consumption and processing time. Although very useful, the minimizers selection procedure has undesirable behaviors (e.g., too many k-mers are selected when processing certain sequences). Some of these problems were already known to the authors of the minimizers technique, and the natural lexicographic ordering of k-mers used by minimizers was recognized as their origin. Many software tools using minimizers employ ad hoc variations of the lexicographic order to alleviate those issues.\n\nWe provide an in-depth analysis of the effect of k-mer ordering on the performance of the minimizers technique. By using small universal hitting sets (a recently defined concept), we show how to significantly improve the performance of minimizers and avoid some of its worse behaviors. Based on these results, we encourage bioinformatics software developers to use an ordering based on a universal hitting set or, if not possible, a randomized ordering, rather than the lexicographic order. This analysis also settles negatively a conjecture (by Schleimer et al.) on the expected density of minimizers in a random sequence.\n\nThe software used for this analysis is available on GitHub: https://github.com/gmarcais/minimizers.git.\n\nContact: gmarcais@cs.cmu.edu

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