Algorithm Architecture & Decision Matrices
Detailed comparison cards highlighting algorithmic trade-offs, strengths, weaknesses, and recommended production setups.
Primary Software & Algorithms in Bulk RNA-Seq Transcriptomics
Direct command-line recipes, Conda environment specifications, memory footprints, and license metadata.
Gold-standard package for differential gene expression analysis of RNA-seq read counts using negative binomial generalized linear models and empirical Bayes dispersion shrinkage.
R -e 'BiocManager::install("DESeq2")'
Differential expression analysis for RNA-seq, ChIP-seq, and CITE-seq count data using overdispersed Poisson and negative binomial generalized linear models with quasi-likelihood F-tests.
R -e 'BiocManager::install("edgeR")'
Transforms RNA-seq count data into log2-CPM with precision weights based on the observed mean-variance relationship, enabling standard linear modeling and complex multi-factor designs.
R -e 'BiocManager::install("limma")'
Highly efficient transcript assembler and abundance estimator for RNA-seq. Uses maximum flow network algorithms to assemble complex spliced isoforms simultaneously.
conda install -c bioconda stringtie
Statistical detection of differential alternative splicing events: skipped exons (SE), alternative 5'/3' splice sites (A5SS/A3SS), mutually exclusive exons (MXE), and retained introns (RI).
conda install -c bioconda rmats
Statistical model for differential analysis of RNA-Seq that directly incorporates inferential variance (bootstrap uncertainty) calculated during kallisto pseudoalignment.
R -e 'install.packages("sleuth")'
Blazing-fast read summarization program for counting mapped RNA-seq or genomic DNA reads to genomic features (exons, genes, promoters). Substantially faster than HTSeq-count.
conda install -c bioconda subread
Proceed to Stage 6: Single-Cell & Spatial Transcriptomics
Sequencing pipelines proceed sequentially. Move forward to the next analytical domain to explore downstream algorithms and execution profiles.