PEER (Probabilistic Estimation of Expression Residuals) is a Bayesian sparse factor analysis framework for inferring and removing hidden confounders from gene expression data before eQTL, sQTL, and pQ
Use with AI
Install the MCP server or CLI to instantly fetch PEER documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/peer
bedGraphToBigWig converts bedGraph coverage files to bigWig binary format for genome browsers (UCSC, IGV, WashU Epigenome Browser). Essential for ChIP-seq, ATAC-seq, RNA-seq, and WGBS coverage tracks.
1 shared topic • 1 shared operation
deepTools — suite of Python tools for efficient analysis and visualization of high-throughput sequencing data including ChIP-seq, ATAC-seq, MNase-seq, and RNA-seq. BAM-to-bigWig conversion with normal
1 shared topic • 1 shared operation
FusionCatcher — tool for detecting somatic fusion genes, translocations, and chimeric transcripts from RNA-seq data. Identifies known and novel gene fusions in tumor and normal samples using multiple
1 shared topic • 1 shared operation
PyDESeq2 — Python implementation of DESeq2 for differential gene expression analysis from bulk RNA-seq count data. Performs size factor normalization, genewise dispersion estimation, Wald tests with B
1 shared topic • 1 shared operation
Qualimap — platform-independent quality control tool for next-generation sequencing alignment data. Provides BAM QC (coverage, insert size, GC content, mapping quality), RNA-seq QC (gene body coverage
1 shared topic • 1 shared operation