star-rna-seq-aligner

Solid

Splice-aware RNA-seq aligner producing sorted BAM and splice junction tables. Builds genome index, runs two-pass alignment for better junctions. Outputs sorted BAM, junctions (SJ.out.tab), stats (Log.final.out), optional gene counts. Use Salmon for fast pseudoalignment; STAR when a BAM is needed for variant calling, IGV, or ENCODE pipelines.

AI & Automation 362 stars 36 forks Updated 1 weeks ago NOASSERTION

Install

View on GitHub

Quality Score: 81/100

Stars 20%
85
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# STAR — Spliced RNA-seq Aligner ## Overview STAR (Spliced Transcripts Alignment to a Reference) aligns RNA-seq reads to a genome in a splice-aware manner, identifying novel and annotated splice junctions in a single pass. It generates coordinate-sorted BAM files compatible with samtools, IGV, deeptools, and GATK. STAR's 2-pass mode re-aligns reads using junctions discovered in the first pass, improving sensitivity for novel splice sites. With `--quantMode GeneCounts`, STAR simultaneously produces gene-level read count tables without requiring a separate featureCounts or HTSeq step. ## When to Use - Aligning bulk RNA-seq reads to a reference genome when downstream tools require a BAM file (variant calling, visualization, deeptools) - Running ENCODE-compliant RNA-seq pipelines that mandate genome alignment - Discovering novel splice junctions and alternative splicing events in the dataset - Generating gene count tables alongside BAM alignment in a single step with `--quantMode GeneCounts` - Processing long reads or reads with high mismatch rates by tuning `--outFilterMismatchNmax` - Use **Salmon** instead when you only need transcript/gene quantification and do not need a BAM file — Salmon is 20-50× faster ## Prerequisites - **Software**: STAR ≥ 2.7.0 (conda or compiled binary) - **Reference files**: genome FASTA + GTF annotation (same assembly) - **RAM**: 30–32 GB for human/mouse genome index; 8–16 GB for smaller genomes - **Disk**: ~25 GB for human genome index, ~5–10 ...

Details

Author
jaechang-hits
Repository
jaechang-hits/SciAgent-Skills
Created
6 months ago
Last Updated
1 weeks ago
Language
Python
License
NOASSERTION

Bundled in these plugins

Similar Skills

Semantically similar based on skill content — not just same category

AI & Automation Solid

bwa-mem2-dna-aligner

Fast short-read DNA aligner for WGS/WES/ChIP-seq. 2× faster BWA-MEM successor; outputs SAM/BAM with read group headers for GATK. Primary plus supplementary records for chimeric reads. Use STAR for RNA-seq splice-aware alignment; Bowtie2 is a comparable alternative.

362 Updated 1 weeks ago
jaechang-hits
AI & Automation Solid

samtools-bam-processing

CLI toolkit for SAM/BAM/CRAM: sort, index, convert, filter, QC alignments. Core commands: view, sort, index, flagstat, stats, depth, markdup, merge. Required between alignment and variant/peak calling. Use pysam for Python-native BAM access; deeptools for normalized coverage tracks.

362 Updated 1 weeks ago
jaechang-hits
AI & Automation Featured

alterlab-rnaseq-quant

Quantifies bulk RNA-seq transcript abundance with salmon (v1.11.4 selective alignment) and kallisto (v0.52.0, kb-python workflow), builds a decoy-aware gentrome index, runs quant with --validateMappings --gcBias -l A, then imports estimates via tximport/tximeta with a tx2gene map and hands differential expression to alterlab-pydeseq2. Warns that salmon's index format changed to SSHash (rebuild pre-v1.11.2 indices) and that 'salmon alevin' was REMOVED (single-cell now uses piscem + alevin-fry). Use when quantifying RNA-seq transcript abundance, running salmon or kallisto, building a decoy-aware index, or wiring tximport to DESeq2; for differential expression use alterlab-pydeseq2, for FASTQ-to-VCF variant calling use alterlab-nf-core-sarek. Part of the AlterLab Academic Skills suite.

66 Updated 1 weeks ago
AlterLab-IEU