https://thelovelab.r-universe.devRecent package updates in thelovelabR-universehttps://github.com/thelovelab.pnghttps://thelovelab.r-universe.devFri, 30 Oct 2026 14:15:37 GMTmichaelisaiahlove@gmail.com (Michael Love)Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.https://github.com/r-universe/bioc/actions/runs/41308195580Fri, 30 Oct 2026 14:15:37 GMTDESeq21.53.7successhttps://bioc.r-universe.devhttps://github.com/bioc/DESeq2DESeq2.RmdDESeq2.htmlAnalyzing RNA-seq data with DESeq22016-11-18 04:43:202026-10-30 14:15:37michaelisaiahlove@gmail.com (Michael Love)Transcript quantification import from Salmon and other quantifiers with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility.https://github.com/r-universe/bioc/actions/runs/41308919688Sun, 25 Oct 2026 20:24:46 GMTtximeta1.31.10successhttps://bioc.r-universe.devhttps://github.com/bioc/tximetatximeta.Rmdtximeta.htmltximeta: transcript quantification import with automatic metadata2018-07-12 07:53:352026-10-25 20:24:30michaelisaiahlove@gmail.com (Michael Love)Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files.https://github.com/r-universe/bioc/actions/runs/37926475849Wed, 30 Sep 2026 18:01:05 GMTfishpond2.19.2successhttps://bioc.r-universe.devhttps://github.com/bioc/fishpondswish.Rmdswish.htmlSwish: differential expression accounting for inferential uncertainty2019-08-03 19:09:552026-09-28 17:14:58allelic.Rmdallelic.htmlSEESAW - Allelic expression analysis with Salmon and Swish2022-02-06 12:21:062025-01-02 16:55:58michaelisaiahlove@gmail.com (Michael Love)Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.https://github.com/r-universe/bioc/actions/runs/41308471657Mon, 28 Sep 2026 14:43:52 GMTtximport1.41.1successhttps://bioc.r-universe.devhttps://github.com/bioc/tximporttximport.Rmdtximport.htmlImporting transcript abundance with tximport2015-12-01 15:29:592026-09-28 14:40:58beatrizcampillo29@gmail.com (Beatriz Campillo)Translate differential transcript usage results into discrete splice events.https://github.com/r-universe/bioc/actions/runs/37202043320Fri, 04 Sep 2026 19:31:58 GMTsplicelogic1.1.4successhttps://bioc.r-universe.devhttps://github.com/bioc/splicelogicsplicelogic.Rmdsplicelogic.htmlsplicelogic: transcript sets to splice events2026-02-03 19:55:382026-09-04 19:28:06michaelisaiahlove@gmail.com (Michael Love)RNA-seq workflow for differential transcript usage (DTU) following Salmon quantification. This workflow uses Bioconductor packages tximport, DRIMSeq, and DEXSeq to perform a DTU analysis on simulated data. It also shows how to use stageR to perform two-stage testing of DTU, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU.https://github.com/r-universe/bioc/actions/runs/41199388111Tue, 28 Apr 2026 12:43:26 GMTrnaseqDTU1.33.0successhttps://bioc.r-universe.devhttps://github.com/bioc/rnaseqDTUrnaseqDTU.RmdrnaseqDTU.htmlSwimming downstream: statistical analysis of differential transcript usage following Salmon quantification2018-06-05 16:47:122019-07-16 18:31:46michaelisaiahlove@gmail.com (Michael Love)Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.https://github.com/r-universe/bioc/actions/runs/41199453202Tue, 28 Apr 2026 12:43:10 GMTrnaseqGene1.37.0failurehttps://bioc.r-universe.devhttps://github.com/bioc/rnaseqGene