Packs
1 packResults for “rna-seq”
7 skills
k-dense-ai deeptools
Process and analyze high-throughput sequencing data with deepTools for quality control, normalization, comparison, and publication-quality visualizations of ChIP-seq, RNA-seq, and ATAC-seq experiments.
30.2k · bundle
k-dense-ai bulk-rnaseq
Orchestrates a complete bulk RNA-seq differential-expression study from raw FASTQ reads through QC, alignment, quantification, differential expression, pathway enrichment, and publication figures.
30.2k · bundle
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k-dense-ai pydeseq2
Perform differential gene expression analysis for bulk RNA-seq data using PyDESeq2, supporting formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
30.2k · bundle
gabrielmoreira dnasp
Reimplements DnaSP 6 for population genetics analysis of aligned DNA sequences, including nucleotide diversity, haplotype statistics, neutrality tests, linkage disequilibrium, recombination, mismatch distribution, InDel polymorphism, between-population divergence, outgroup-based tests, HKA test, McDonald-Kreitman.
17 · bundle
vimalinx asn2ref
Use when converting `Seq-entry` ASN.1/XML-like citation content into compact `CITATION` XML blocks for EDirect-style matching workflows.
0 · bundle
k-dense-ai scikit-bio
Analyze biological sequences, alignments, phylogenetic trees, and diversity metrics (alpha/beta, UniFrac) with ordination (PCoA) and PERMANOVA for microbiome and community ecology data.
30.2k · bundle
gabrielmoreira de-summary
Takes pre-computed differential expression results from DESeq2, edgeR, limma, or PyDESeq2 and produces a structured, publication-ready summary with ranked gene lists, biological themes, and key observations.
17