Transcriptomics

RNA-seq Analysis: From Raw FASTQ
to Differential Expression

BioMate runs the full nf-core/rnaseq pipeline on AWS Batch — alignment, quantification, and differential expression with DESeq2, edgeR, or limma — and returns Gold/Silver/Bronze QC-graded results with a downloadable methods report. No command line, no infrastructure.

Try free → See all genomics services
What BioMate does

A complete, validated RNA-seq pipeline

BioMate routes RNA-seq requests to the nf-core/rnaseq pipeline — a community-maintained, peer-reviewed Nextflow workflow used by genomics labs worldwide. Every step is containerized for reproducibility and runs on AWS Batch with automatic scaling.

Pipeline stage Tools Output
Quality control FastQC, Trim Galore, MultiQC QC report, adapter-trimmed reads
Alignment STAR, HISAT2 Sorted BAM files, alignment stats
Quantification Salmon, RSEM, featureCounts Count matrices, TPM/FPKM tables
Differential expression DESeq2, edgeR, limma-voom DEG tables, volcano plots, MA plots
Pathway enrichment clusterProfiler, GSEA, fgsea GO / KEGG enrichment plots, ranked gene lists
QC grading BioMate QC engine Gold / Silver / Bronze per metric with thresholds cited
Supported modalities

Common RNA-seq workflows

Describe your experiment in plain English and BioMate routes to the correct sub-pipeline automatically.

Bulk RNA-seq

Standard differential expression from poly-A or ribo-depleted libraries. DESeq2 or edgeR with shrinkage estimators, multiple-testing correction, and annotated DEG tables.

Single-cell RNA-seq

10x Genomics Chromium and Drop-seq support via nf-core/scrnaseq. Seurat and Scanpy for clustering, UMAP, marker identification, and cell-type annotation.

Spatial Transcriptomics

Visium and Slide-seq workflows with Seurat spatial modules. Gene expression mapped onto tissue histology with region-specific differential expression.

Long-read RNA-seq

PacBio IsoSeq and Oxford Nanopore direct RNA sequencing. Full-length isoform detection, novel splice junction discovery, and transcript quantification without assembly.

How it works

Three steps from FASTQ to findings

  1. Describe your experiment in plain English Tell BioMate what you have: sample count, species, contrast of interest, and any special requirements. No pipeline knowledge needed.
  2. BioMate routes to nf-core/rnaseq The AI selects the correct aligner, quantification method, and DE tool for your experiment type, pre-fills all parameters, and confirms before running on AWS Batch.
  3. Results with QC grade and methods report Receive DEG tables, volcano plots, GSEA enrichment, a Gold/Silver/Bronze QC score per metric, and a downloadable methods section ready for your manuscript.
Example QC output
Mapping rate GOLD  94.2%
Reads per sample GOLD  28M
Duplication rate SILVER  38%
Strandedness detected AUTO  RF
DESeq2 · 847 DEGs (padj <0.05, |LFC|>1) · Methods report ready
FAQ

Common questions about RNA-seq in BioMate

What tools does BioMate use for RNA-seq?

BioMate runs the nf-core/rnaseq pipeline with STAR or HISAT2 for alignment, Salmon or RSEM for quantification, and DESeq2, edgeR, or limma-voom for differential expression. FastQC and MultiQC handle quality control. All steps execute on AWS Batch with fully containerized, reproducible environments.

What input files do I need?

FASTQ files — paired-end or single-end. BioMate auto-detects strandedness and library format from the data itself, so you don't need to specify these manually. You can describe your experiment in plain English and BioMate will configure the pipeline appropriately.

How long does RNA-seq analysis take?

Typically 2–4 hours for a standard 6-sample experiment (3 treatment, 3 control) on AWS Batch. Larger experiments with 20+ samples or long-read data may take 6–8 hours. BioMate streams real-time step updates so you can monitor progress as each phase completes.

Can I customize DESeq2 parameters?

Yes. BioMate surfaces all key DESeq2 parameters through the parameter panel before running: adjusted p-value threshold, log2FC cutoff, normalization method (median-of-ratios or VST), and contrast specification. You can accept the defaults or edit any value before launching the analysis.

Get started

Run your first RNA-seq analysis today

No infrastructure to configure. No command line. Start with your FASTQ files and a plain-English description of your experiment.

Try free →
All genomics services → Platform features → Bioconductor methods in BioMate →