Itsik Pe’er

The paper analyzes transcriptomic changes in metastatic pancreatic cancer using a patient's autopsy data to identify site-specific gene programs and clonal phylogeny.

This paper introduces copangraph, a framework for comparative metagenomic analysis using sequence graphs, showing improved accuracy and computational efficiency in capturing genomic variation across microbial communities.

The paper identifies sample processing errors in microbiome studies using host genetic profiles from metagenomic sequencing and experimental metadata.

DEBIAS-M corrects processing bias in microbiome data, improving cross-study generalization of prediction models by learning interpretable bias-correction factors.

The paper investigates coverage irregularities in 16S amplicon data to infer microbial growth dynamics, finding no correlation with WGS-based methods.

SEACells identifies metacells from single-cell data, improving gene-peak associations and revealing chromatin dynamics in hematopoiesis and COVID-19.

The paper introduces CoPTR, a tool for computing peak-to-trough ratios from metagenomic data, showing it is more accurate and provides more estimates than existing methods, and demonstrates its application in a case-control study of irritable bowel disease.

This paper introduces a spatial Gaussian Mixture Model to recover global microbial spatial arrangements from MaPS-seq data, showing improved clustering and capturing known and novel spatial behaviors in the mouse gut microbiome.

MiSDEED generates synthetic longitudinal multi-omics data for microbiome studies to aid in power analysis and design.

The paper identifies ultra-rare exonic variants in an Ashkenazi Jewish founder population, implicating cadherins in schizophrenia through recurrent mutations and altered purifying selection.

The paper improves expression quantitative trait loci associations by simultaneously analyzing reference panels and GWAS cohorts using a new method called EMBER.

SICaRiO improves indel detection by using a gradient boosting classifier trained on GIAB data, enhancing variant calling pipelines and identifying genomic features responsible for indel calling errors.

LUMINATE infers microbial trajectories from longitudinal count data, offering faster and more accurate results than existing methods while distinguishing biological and technical zeros.

This paper introduces CLiP, a method to detect heterogeneity in GWAS cohorts by analyzing correlations between polygenic risk score predictors, and applies it to schizophrenia data showing significant heterogeneity.

arcasHLA is a tool for high-resolution HLA typing from RNA sequencing data, achieving high accuracy and speed compared to existing methods.

This study investigates the ability to re-identify individuals through long-range familial searches in consumer genomics databases, finding that half of European-descent searches could yield close matches enabling re-identification.

The paper sequenced a population-specific reference panel at high depth to improve imputation accuracy and variant filtering in the Ashkenazi Jewish population.

Dystruct improves population structure inference by modeling ancient DNA with a time-series approach, outperforming existing methods in ancient-contemporary datasets.

The paper investigates replication failure in GWAS by correcting the Winner’s Curse and shows that accurate reporting of sample sizes and ancestry improves replication rates.

The paper introduces a 2-way k-means model for clustering microbiome samples that can handle mixed compositions between clusters, applied to 16S rDNA sequencing data from the Human Vaginal Microbiome Project.