Biology subjects

Shamir, R.

Publications and source records attributed to Shamir, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Mapping chromothripsis across human cancer cell lines reveals genome evolution and therapeutic vulnerabilities

Chromothripsis is a catastrophic chromosomal rearrangement found in ~40% of primary tumors, yet its landscape in cancer cell lines (CCLs) remains uncharacterized. To address this gap, we developed a pipeline to detect chromothripsis-like events from copy-number data alone, and mapped their occurrence across 1,646 human CCLs. Chromothripsis patterns were broadly conserved between primary tumors and CCLs, but CCLs displayed some distinct features and a significantly higher prevalence of chromothripsis. Chromothripsis continued to emerge during culture propagation, likely contributing to its increased prevalence in CCLs. Integrating genomic and functional data revealed selective vulnerabilities: chromothriptic cells were resistant to DNA damage-inducing agents but highly sensitive to histone deacetylase inhibitors (HDACi). Using isogenic models, we validated that HDACi could synergistically sensitize chromothriptic cells to DNA damage-inducing agents. Our study establishes the first comprehensive resource of chromothripsis in CCLs and reveals functional consequences and therapeutic vulnerabilities of chromothripsis-positive cancers.

cancer biology↗

bioRxiv2026

PlasChain: an algorithm for improving long plasmid reconstruction from metagenome assemblies

BackgroundPlasmids play a critical role in horizontal gene transfer and the spread of antibiotic resistance. However, recovering complete plasmid sequences from metagenomic samples remains highly challenging due to extensive repeat content, structural heterogeneity, and large variation in plasmid size. Existing methods typically identify plasmids from metagenome assemblies by exploiting coverage differences or detecting minimum-weight cycles in assembly graphs. While effective for dominant plasmids, these approaches often fail to recover low-abundance and long plasmids. ResultsHere we present PlasChain, a novel algorithm designed to improve plasmid assembly and identification from complex metagenomic data. Building upon the cycle-peeling strategy of SCAPP, PlasChain incorporates contig path information and a cycle-merging procedure to prevent long plasmids from being fragmented into multiple shorter cycles. In addition, PlasChain jointly leverages paired-end read alignments, sequence composition patterns, and coverage variation to filter out assembly artifacts and reduce false positives. We evaluated PlasChain against state-of-the-art plasmid assemblers, including SCAPP and metaplasmidSPAdes, using a diverse set of simulated and real metagenomic datasets. Across nearly all benchmarks, PlasChain demonstrates superior performance in recovering long plasmids while maintaining competitive accuracy in assembling short plasmids. Furthermore, analysis of real metagenomic samples shows that PlasChain is capable of assembling previously uncharacterized plasmids, including putative megaplasmids that are typically underrepresented in current plasmid databases. ConclusionsPlasChain is a novel graph-based plasmid assembler that improves the recovery of long plasmids from short-read metagenomic data. Evaluation on diverse simulated and real metagenomic datasets demonstrates that PlasChain consistently outperforms existing plasmid assemblers, especially for long plasmid reconstruction. These results highlight the potential of PlasChain to facilitate comprehensive characterization of plasmid diversity, antimicrobial resistance, and horizontal gene transfer in complex microbial communities. The source code and testing data of PlasChain are freely available at https://github.com/SDU-ACG-Lab/PlasChain. Supplementary InformationThis manuscript is accompanied by a supplementary file.

bioinformatics↗

bioRxiv2026

Persistent stromal reprogramming defines incomplete mucosal healing and predicts therapeutic response in ulcerative colitis

Mucosal healing (MH) is the primary therapeutic endpoint in ulcerative colitis (UC), yet frequent relapses suggest it does not reflect complete tissue recovery. To define the basis of this vulnerability, we generated a multimodal atlas of UC integrating single-cell and bulk transcriptomics, Visium HD spatial profiling, and multiplexed imaging across 89 patients. We show that MH represents a distinct biological state marked by persistent stromal remodeling along three axes: emergence of inflammatory fibroblasts, sustained loss of OGN niche-supporting fibroblasts, and expansion of pericytes with matrix-remodeling features and reduced vascular association. Spatial analyses revealed persistent reorganization of mucosal tissue domains despite apparent clinical remission. Across independent cohorts, baseline inflammatory fibroblast and pericyte signatures robustly predicted non-response to anti-TNF therapy. These findings suggest that patients in MH remain in a biologically altered state linked to relapse risk and identify stromal reprogramming as a determinant of disease persistence and therapeutic response in UC.

immunology↗

bioRxiv2026

Mapping Genetic Risk Associations to Cellular Contexts via Deep Learning and Biological Ontologies

Translating genome-wide association studies (GWAS) signals into trait-relevant cellular contexts remains challenging due to the complexity of the genomic regulatory code and linkage disequilibrium among associated variants. We present a novel computational framework that aggregates deep learning-based predictions of the functional effects of noncoding variants on transcriptional regulatory elements across GWAS loci and empirically evaluates their statistical significance. By organizing these aggregated signals within biological ontologies, our approach enables statistically calibrated interpretation of GWAS associations, highlighting relevant cell-type and tissue contexts across human traits.

bioinformatics↗

bioRxiv2026

Spatial Rewiring of Enterocyte Identity in Celiac Disease

Enterocytes in the human small intestine exhibit distinct functional states in different zones along the crypt-villus axis, a feature that is thought to convey optimal absorption. In celiac disease (CeD), autoimmune destruction of enterocytes leads to villus blunting, but how this altered tissue morphology affects enterocyte states is unclear. Using spatial and single-cell transcriptomics, we show that in patients with CeD, enterocytes acquire a novel identity characterized by co-expression of multiple zonal programs. This aberrant zonal co-expression results from reduced distances between BMP- and WNT-producing mesenchymal cells, leading to overlapping morphogen fields. In addition, we identify a subset of metaplastic cells that adopt gastric pit cell-like identities in discrete tissue patches. Our findings provide a detailed view of epithelial remodeling in CeD and establish a resource for understanding the cellular basis of malabsorption associated with villus blunting.

systems biology↗

bioRxiv2025

Human biliary atresia extrahepatic cholangiocyte organoids express increased ER and oxidative stress, altered drug metabolism and cell polarity changes

Background & AimsBiliary atresia (BA), the leading cause of liver transplantation in children, presents in neonates with jaundice and progressive extrahepatic bile duct obstruction, yet its etiology and pathogenesis remain unknown. Here, we aimed to investigate the molecular mechanisms underlying BA and the susceptibility of cholangiocytes in the extrahepatic biliary tree using patient-derived extrahepatic cholangiocyte organoids (EHCOs). MethodsEHCOs were derived from common bile ducts remnants of BA patients undergoing Kasai portoenterostomy and from non-BA controls at the time of liver transplantation. Transcriptomic profiling was performed via bulk RNA sequencing, and analyzed in two ways: differentially expressed pathways and perturbation analysis to predict aberrant functions. Key findings were validated through mechanistic assays, immunofluorescence staining, qPCR and transmission electron microscopy (TEM). ResultsTranscriptomic analysis predicted significant alteration in endoplasmic reticulum (ER) stress, dysregulations of drug metabolism, alongside pronounced alterations in cellular adhesion and polarity-related genes in BA-derived EHCOs. Cell-to-cell alterations were observed with various proteins including E-cadherin, RhoU, Sox17 and CFTR. BA EHCOs had an increased endoplasmic reticulum (ER) stress response, exemplified by elevated PERK, BiP, and ATF4 along with abnormal ER on TEM. Furthermore CHOP, ERO1A, WFS1, and SOD3 were decreased suggestive of abnormal ER stress response. BA EHCOs displayed increased toxicity to biliatresone-induced injury and inhibition of cytochrome P450 resulted in attenuation of the ER stress markers PERK, BiP and ATF4. Finally, liver hilum biopsies from BA patients undergoing Kasai portoenterostomy confirmed elevated PERK and PGR78(BiP) consistent with the EHCOs analysis. ConclusionsBA EHCOs exhibit disrupted polarity, ER stress, and increased susceptibility to drug toxicity. These findings highlight key pathogenic mechanisms in BA and suggest that targeting these pathways may help mitigate cholangiocyte injury in BA. Impact and implicationsThis study provides the first transcriptomic and functional analysis of human extrahepatic cholangiocyte organoids (EHCOs) derived from biliary atresia (BA) patients. By focusing on the extra-hepatic biliary tree, we identified key mechanisms of cholangiocyte injury, including persistent ER stress, impaired stress response pathways, altered drug metabolism and disrupted epithelial polarity. These findings highlight ER stress and metabolic vulnerability as potential therapeutic targets and establish EHCOs as a tractable model for investigating BA pathogenesis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/649927v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@150ab7org.highwire.dtl.DTLVardef@172953forg.highwire.dtl.DTLVardef@1a46e4eorg.highwire.dtl.DTLVardef@45c5c6_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIFirst transcriptomic profiling of extrahepatic cholangiocyte organoids (EHCOs) from BA patients, C_LIO_LIrevealing distinct molecular alterations compared to controls. C_LIO_LIBA EHCOs exhibit disrupted epithelial polarity, with downregulation of E-cadherin and Sox17 and upregulation of CFTR. C_LIO_LIER stress is a hallmark of BA cholangiocytes, with elevated PERK, BiP, and ATF4, and C_LIO_LIdysregulation of downstream effectors including CHOP, ERO1A, and SOD3. C_LIO_LIBA EHCOs are more susceptible to biliatresone-induced injury, with enhanced ER stress and structural damage. C_LIO_LIInhibition of cytochrome P450 activity (CYP4A) reduces ER stress markers. C_LI

molecular biology↗

bioRxiv2025

Integrating gene expression, mutation and copy number data to identify driver genes of recurrent chromosome-arm losses

Aneuploidy is a hallmark of cancer, yet the specific genes driving recurrent chromosome-arm losses remain largely unknown. Here, we present a systematic framework integrating gene expression, mutation, and copy number data to identify candidate driver genes of cancer type-specific recurrent chromosome-arm losses across 20 cancer types, using [~]7,500 tumors from The Cancer Genome Atlas. By analyzing focal deletions and point mutations that co-occur with, or are mutually exclusive with, chromosome-arm losses, we pinpoint 311 candidate drivers associated with 160 cancer type-specific recurrent events. Our approach identifies known aneuploidy drivers such as TP53 and PTEN, while revealing multiple novel candidates, including established tumor suppressor genes not previously linked to aneuploidy. Furthermore, we leverage gene expression changes associated with these chromosome-arm losses to propose pathway-level alterations that may drive cancer progression. Integrating these findings highlights key candidate drivers underlying the observed gene expression alterations, thereby reinforcing their biological relevance. This work provides the first comprehensive catalogue of candidate driver genes for recurrently lost chromosome-arms in human cancer.

cancer biology↗

bioRxiv2024

Race-dependent variability in the human tumor mycobiome

IntroductionRecently, Narunsky-Haziza et. al. showed that fungi species identified in a variety of cancer types may have prognostic and diagnostic signficane. We used that data in order to better understand the effects of demographic factors (age, sex, BMI, and race) on the intratumor mycobiome composition. Materials and MethodsWe first tested the data in view of recent critiques of microbiome data processing procedures, and concluded that the batch correction and transformation used on it may produce false signals. Instead, we explored 14 combinations of data transformation and batch correction methods on data of 224 fungal species across 13 cancer types. Propensity scores were utilized to adjust for potential confounders such as histological type and tumor stage. To minimize false outcomes, we identified as positive results only those fungi species that showed significant difference in abundance across a demographic factor within a particular cancer type, using data normalized according to all 14 combinations. Results and DiscussionWe observed significant differences in fungal species abundance within tumors for certain demographic characteristics. Most differences were among races in specific cancers. The findings indicate that there are intricate interactions among the mycobiome, cancer types, and patient demographics. Our study highlights the need for accounting for potential confounders in order to further understanding of the mycobiomes role in cancer, and underscores the importance of data processing techniques.

bioinformatics↗

bioRxiv2023

Natural and age-related variation in circulating human hematopoietic stem cells

Hematopoietic stem and progenitor cells (HSPCs) deliver life-long multi-lineage output. However, with aging, we exhibit certain characteristic blood count changes and accumulation of clonal disorders. Better understanding of inter-individual variation in HSPC behavior is needed to understand these age-related phenomena and the transition from health to chronic and acute hematological malignancies. Here we study 627K single circulating CD34+ HSPCs (cHSPCs) from 148 healthy individuals, along with their clinical information and clonal hematopoiesis (CH) profiles, to characterize population-wide and age-related hematopoietic variability. Individuals with CH were linked with reduced frequencies of lymphocyte progenitors and higher RDW. An age-related decrease in lymphoid progenitors was observed, predominantly in males. Inter-individual transcriptional variation in expression of a Lamin-A signature and stemness gene programs were linked with aging and presence of macrocytic anemia. Based on our model for healthy cHSPC variation we construct the normal reference for cHSPC subtype frequencies. We show how compositional and expression deviations from this normal reference can robustly identify myeloid malignancies and pre-malignant states. Together, our data and methodologies present a novel resource, shedding light on various age-related hematopoietic processes, and a comprehensive normal cHSPC reference, which can serve as a tool for diagnosing and characterizing hematological disorders.

molecular biology↗

bioRxiv2023

4CAC: 4-class classification of metagenome assembliesusing machine learning and assembly graphs

Microbial communities usually harbor a mix of bacteria, archaea, plasmids, viruses, and microeukaryotes. Within these communities, viruses, plasmids, and microeukaryotes coexist in relatively low abundance, yet they engage in intricate interactions with bacteria. Moreover, viruses and plasmids, as mobile genetic elements, play important roles in horizontal gene transfer and the development of antibiotic resistance within microbial populations. However, due to the difficulty of identifying viruses, plasmids, and microeukaryotes in microbial communities, our understanding of these minor classes lags behind that of bacteria and archaea. Recently, several classifiers have been developed to separate one or two minor classes from bacteria and archaea in metagenome assemblies, but none can classify all of the four classes simultaneously. Moreover, existing classifiers have low precision on minor classes. Here, we developed a classifier called 4CAC that is able to identify viruses, plasmids, microeukaryotes, and prokaryotes simultaneously from metagenome assemblies. 4CAC generates an initial four-way classification using several sequence length-adjusted XGBoost models and further improves the classification using the assembly graph. Evaluation on simulated and real metagenome datasets demonstrates that 4CAC substantially outperforms existing classifiers and combinations thereof on short reads. On long reads, it also shows an advantage unless the abundance of the minor classes is very low. 4CAC runs 1-2 orders of magnitude faster than the other classifiers. The 4CAC software is available at https://github.com/Shamir-Lab/4CAC.

bioinformatics↗

bioRxiv2022

Clustering multilayered cytoarchitectonic surface-based data in the brain

The laminar microstructure of the cerebral cortex is considered a unique anatomical mark of the development, function, connectivity, and even various pathologies of the brain. In recent years, multiple neuroimaging studies have utilized magnetic resonance imaging (MRI) relaxometry to visualize and explore this intricate microstructure. By successfully delineating the cortical laminar components, the applicability of T1 relaxometry has been expanded beyond solely a direct measure of myeline content. However, validating its applicability as a measure of cortical cytoarchitecture demands analyzing the complex resulting laminar datasets. In this study, we adapt and implement an algorithm for clustering cell omics profiles to cluster these complex microstructural cortical datasets. The resulting clusters correspond with an established atlas of cytoarchitectonic features, providing robust validation of T1 imaging as a tool for exploring cortical laminar composition. Lastly, we demonstrate the applicability of this framework in the exploration of the cytoarchitectonic features behind various unique skillsets.

neuroscience↗

bioRxiv2022

Efficient minimizer orders for large values of k using minimum decycling sets

Minimizers are ubiquitously used in data structures and algorithms for efficient searching, mapping, and indexing of high-throughput DNA sequencing data. Minimizer schemes select a minimum k-mer in every L-long sub-sequence of the target sequence, where minimality is with respect to a predefined k-mer order. Commonly used minimizer orders select more k-mers than necessary and therefore provide limited improvement in runtime and memory usage of downstream analysis tasks. The recently introduced universal k-mer hitting sets produce minimizer orders with fewer selected k-mers. Unfortunately, generating compact universal k-mer hitting sets is currently infeasible for k > 13, and thus cannot help in the many applications that require minimizer orders for larger k. Here, we close the gap of efficient minimizer orders for large values of k by introducing decycling-set-based minimizer orders, new orders based on minimum decycling sets. We show that in practice these new minimizer orders select a number of k-mers comparable to that of minimizer orders based on universal k-mer hitting sets, and can also scale up to larger k. Furthermore, we developed a method that computes the minimizers in a sequence on the fly without keeping the k-mers of a decycling set in memory. This enables the use of these minimizer orders for any value of k. We expect the new orders to improve the runtime and memory usage of algorithms and data structures in high-throughput DNA sequencing analysis.

bioinformatics↗

bioRxiv2022

Integration of Gene Expression and DNA Methylation Data Across Different Experiments

Integrative analysis of multi-omic datasets has proven to be extremely valuable in cancer research and precision medicine. However, obtaining multimodal data from the same samples is often difficult. Integrating multiple datasets of different omics remains a challenge, with only a few available algorithms developed to solve it. Here, we present INTEND (IntegratioN of Transcriptomic and EpigeNomic Data), a novel algorithm for integrating gene expression and DNA methylation datasets covering disjoint sets of samples. To enable integration, INTEND learns a predictive model between the two omics by training on multi-omic data measured on the same set of samples. In comprehensive testing on eleven TCGA cancer datasets spanning 4329 patients, INTEND achieves significantly superior results compared to four state-of-the-art integration algorithms. We also demonstrate INTENDs ability to uncover connections between DNA methylation and the regulation of gene expression in the joint analysis of two lung adenocarcinoma single-omic datasets from different sources. INTENDs data-driven approach makes it a valuable multi-omic data integration tool. The code for INTEND is available at https://github.com/Shamir-Lab/INTEND.

bioinformatics↗

bioRxiv2022

Periductal bile acid exposure causes cholangiocyte injury and fibrosis

IntroductionBile duct integrity is essential for the maintenance of the structure and function of the biliary tree. We previously showed that cholangiocyte injury in a toxic model of biliary atresia leads to increased monolayer permeability. Increased epithelial permeability was also shown in other cholangiopathies. We hypothesized that after initial cholangiocyte injury, leakage of bile acids into the duct submucosa propagates cholangiocyte damage and fibrosis. We thus aimed to determine the impact of bile acid exposure on cholangiocytes and the potential therapeutic effect of a non-toxic bile acid. Materials and methodsExtrahepatic bile duct explants were isolated from adult and neonatal BALB/c mice. Explants were cultured with or without glycochenodeoxycholic acid and ursodeoxycholic acid. They were then fixed and stained. ResultsExplants treated with glycochenodeoxycholic acid demonstrated cholangiocyte injury with monolayer disruption and partial lumen obstruction compared to control ducts. Massons trichrome stains revealed increased collagen fibers. Myofibroblast marker -SMA stains were significantly elevated in the periductal region. The addition of ursodeoxycholic acid resulted in decreased cholangiocyte injury and reduced fibrosis. ConclusionsBile acid leakage into the submucosa after initial cholangiocyte injury may serve as a possible mechanism of disease propagation and progressive fibrosis in cholangiopathies.

physiology↗

bioRxiv2022

Using syncmers improves long-read mapping

MotivationSequencing long reads presents novel challenges to mapping. One such challenge is low sequence similarity between the reads and the reference, due to high sequencing error and mutation rates. This occurs, e.g., in a cancer tumor, or due to differences between strains of viruses or bacteria. A key idea in mapping algorithms is to sketch sequences with their minimizers. Recently, syncmers were introduced as an alternative sketching method that is more robust to mutations and sequencing errors. ResultsWe introduce parameterized syncmer schemes, a generalization of syncmers, and provide a theoretical analysis for multi-parameter schemes. By combining these schemes with downsampling or minimizers we can achieve any desired compression and window guarantee. We implemented the use of parameterized syncmer schemes in the popular minimap2 and Winnowmap2 mappers. In tests on simulated and real long read data from a variety of genomes, the syncmer-based algorithms, with scheme parameters selected on the basis of the theoretical analysis, reduced unmapped reads by 20-60% at high compression while usually using less memory. The advantage was more pronounced at low sequence identity. At sequence identity of 75% and medium compression, syncmer-minimap had only 37% as many unmapped reads, and 8% fewer of the reads that did map were incorrectly mapped. Even at lower compression and error rates, parameterized syncmer based mapping mapped more reads than the original minimizer-based mappers as well as mappers using the original syncmer schemes. We conclude that using parameterized syncmer schemes can improve mapping of long reads in a wide range of settings. Availabilityhttps://github.com/Shamir-Lab/syncmer_mapping Supplementary informationSupplementary data are available at https://github.com/Shamir-Lab/syncmer_mapping. Author summaryPopular long read mappers use minimizers, the minimal hashed k-mers from overlapping windows, as alignment seeds. Recent work showed that syncmers, which select a fixed set of k-mers as seeds, are more likely to be conserved under errors or mutations than minimizers, making them potentially useful for mapping error-prone long reads. We introduce a framework for creating syncmers, that we call parameterized syncmer schemes, which generalize those introduced so far, and provide a theoretical analysis of their properties. We implemented parameterized syncmer schemes in the minimap2 and Winnowmap2 long read mappers. Using parameters selected on the basis of our theoretical analysis we demonstrate improved mapping performance, with fewer unmapped and incorrectly mapped reads on a variety of simulated and real datasets. The improvements are consistent across a broad range of compression rates and sequence identities, with the most significant improvements for lower sequence identity (high error or mutation rates) and high compression.

bioinformatics↗

bioRxiv2021

Dataset-adaptive minimizer order reduces memory usage in k-mer counting

The rapid, continuous growth of deep sequencing experiments requires development and improvement of many bioinformatics applications for analysis of large sequencing datasets, including k-mer counting and assembly. Several applications reduce RAM usage by binning sequences. Binning is done by employing minimizer schemes, which rely on a specific order of the minimizers. It has been demonstrated that the choice of the order has a major impact on the performance of the applications. Here we introduce a method for tailoring the order to the dataset. Our method repeatedly samples the dataset and modifies the order so as to flatten the k-mer load distribution across minimizers. We integrated our method into Gerbil, a state-of-the-art memory efficient k-mer counter, and were able to reduce its memory footprint by 30% - 50% for large k, with only minor increase in runtime. Our tests also showed that the orders produced by our method produced superior results when transferred across datasets from the same species, with little or no order change. This enables memory reduction with essentially no increase in runtime.

bioinformatics↗

bioRxiv2021

The DOMINO web-server for active module identification analysis

Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal ("active modules"). Such modules can point out key molecular processes in the analyzed biological conditions. ResultsWe recently introduced a novel AMI algorithm called DOMINO, and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations, and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms. Availability and implementationThe server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.

bioinformatics↗

bioRxiv2021

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

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 classification. 3CAC starts with an initial three-class classification generated by existing classifiers and improves the classification of short contigs and contigs with low confidence classification by using proximity in the assembly graph. Evaluation on simulated metagenomes and on real human gut microbiome samples showed that 3CAC outperformed PPR-Meta and viralVerify in both precision and recall, and increased F1-score by 10-60 percentage points. AvailabilityThe 3CAC software is available on https://github.com/Shamir-Lab/3CAC. Contactrshamir@tau.ac.il Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗