Ron Shamir A

This study maps chromothripsis in 1,646 human cancer cell lines, showing its conservation with tumors, higher prevalence, and therapeutic vulnerabilities to HDAC inhibitors.

PlasChain improves long plasmid reconstruction from metagenome assemblies by addressing fragmentation and false positives, outperforming existing methods in recovering long plasmids.

This paper maps GWAS risk variants to relevant cellular contexts using deep learning and biological ontologies to interpret genetic associations.

The paper identifies 311 candidate driver genes for recurrent chromosome-arm losses in 20 cancer types using integrated gene expression, mutation, and copy number data from ~7,500 tumors.

The paper introduces 4CAC, a classifier that identifies four microbial classes from metagenome assemblies, outperforming existing methods on both short and long reads.

The paper examines how demographic factors affect the intratumor mycobiome composition across cancer types, finding race-dependent variability in fungal species abundance.

This study evaluates how imputation panels affect polygenic risk score accuracy across different ethnic populations, finding only marginal improvements when panels match the target population.

The paper characterizes age-related and inter-individual variation in circulating hematopoietic stem cells and establishes a normal reference to identify hematological disorders.

The paper introduces new minimizer orders for large k-values using decycling sets, improving efficiency in DNA sequencing analysis.

INTEND integrates gene expression and DNA methylation data across disjoint samples, outperforming existing methods in cancer datasets.

The paper clusters cortical microstructural datasets using an algorithm adapted from cell omics profiling, validating T1 relaxometry as a tool for exploring cortical laminar composition.

This paper introduces parameterized syncmer schemes for long-read mapping, showing they reduce unmapped and incorrectly mapped reads compared to existing methods.

3CAC improves phage and plasmid classification in metagenomic assemblies by leveraging assembly graph proximity, outperforming existing tools in precision and recall.

The paper introduces a dataset-adaptive minimizer order method to reduce memory usage in k-mer counting with minimal runtime increase.

The paper introduces a web-server for the DOMINO algorithm, which identifies active modules in gene networks and provides tools for analysis and interpretation.

CT-FOCS infers cell type-specific enhancer-promoter links using linear mixed effect models and outperforms existing methods on FANTOM5 data.

The paper demonstrates that using universal hitting sets improves de Bruijn graph construction efficiency by reducing runtime and memory usage in genome assembly.

SCAPP improves plasmid assembly in metagenomes by integrating biological knowledge, outperforming existing tools on simulated and real datasets.

The paper introduces DOMINO, a novel algorithm for identifying active modules in omics data, which outperforms existing methods by reducing false calls through permutation-based significance evaluation.

MONET is a multi-omic clustering method that identifies sample modules with subset-specific clustering structures, showing robustness to missing data and relevance in cancer analysis.

PlasClass improves plasmid sequence classification by outperforming existing tools on shorter sequences in metagenomic assemblies.

PROMO is a tool for analyzing multi-omic cancer datasets with clinical labels, enabling subtype discovery and biomarker identification without coding.

PRODIGY identifies patient-specific driver genes by analyzing mutation and expression data with pathway and interaction information, outperforming existing methods in multiple cancer cohorts.

NEMO is a multi-omics clustering algorithm that works with partial datasets, outperforming existing methods on full and imputed data, and is applied to AML patient data.

This paper reviews multi-omic and multi-view clustering algorithms and benchmarks them on cancer data from TCGA across ten cancer types.

The paper performs a meta-analysis of Hi-C, ChIP-seq, and RNA-seq data to show how 3D chromatin organization influences gene expression in different cell types under basal and stress conditions.

The paper introduces FOCS, a method to infer enhancer-promoter links using activity patterns, and presents an extensive E-P map covering over 300,000 links.

The paper presents an algorithm to reconstruct cancer karyotypes from short read data using graph theory and integer linear programming, showing it can correctly infer a large portion of karyotypes even with noisy data.

Faucet constructs assembly graphs incrementally using a streaming algorithm, reducing disk usage and improving contiguity in metagenome assemblies.

The paper improves minimizers and winnowing schemes by analyzing k-mer ordering and introducing universal hitting sets to enhance performance.