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    1. Bridge Horizon
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    5. Bulk RNA-seq
    In HorizonGene and isoform expression analysis

    Bulk RNA-seq

    Quantify gene and isoform expression from paired-end bulk reads with exon-aware alignment and quasi-alignment, ending in count tables and one quality report.

    Input

    Raw paired-end FASTQ reads, with the organism for each sample set in the samplesheet.

    Output

    Deduplicated BAM alignments, gene count tables with gene names and biotypes, Salmon transcript quantification files, and a MultiQC report.

    New to this method?

    Read the RNA-seq analysis guide

    What bulk RNA-seq measures, how alignment, quantification, and differential expression fit together, and which tools teams use.

    Read the guide

    At a glance

    From reads to expression

    Eight steps turn paired-end reads into gene counts, transcript-level estimates, and a report for review.

    PreprocessingProcessingPost-processing
    InputPaired-end FASTQOrganism set per sample in the samplesheet
    01 / PreprocessingTrimmomaticTrim the reads
    02 / PreprocessingFastQCCheck raw and trimmed reads
    03 / ProcessingSTARAlign to the genome
    04 / ProcessingSamtoolsSort and index
    05 / ProcessingPicardMark duplicates
    06 / Post-processingfeatureCountsCount reads per gene
    07 / Post-processingSalmonQuantify transcripts
    08 / Post-processingMultiQCBring results together
    OutputExpression resultsCount tables, Salmon quants & MultiQC
    InputPaired-end FASTQOrganism set per sample in the samplesheet
    01 / PreprocessingTrimmomaticTrim the reads
    02 / PreprocessingFastQCCheck raw and trimmed reads
    03 / ProcessingSTARAlign to the genome
    04 / ProcessingSamtoolsSort and index
    05 / ProcessingPicardMark duplicates
    06 / Post-processingfeatureCountsCount reads per gene
    07 / Post-processingSalmonQuantify transcripts
    08 / Post-processingMultiQCBring results together
    OutputExpression resultsCount tables, Salmon quants & MultiQC
    Salmon runs in parallel with the alignment branch, so gene and isoform results arrive together.

    Inside the pipeline

    Why each step is here

    01 / preprocessing

    Trimmomatic

    Trim the reads

    What it does
    Trims adapters and low-quality bases from the paired-end reads.
    Why it is included
    Adapter sequence and poor-quality bases can mislead alignment and quantification.
    What it produces
    Trimmed paired-end reads ready for alignment.
    Why it matters
    Cleaner reads going in means more trustworthy counts coming out.
    02 / preprocessing

    FastQC

    Check raw and trimmed reads

    What it does
    Runs quality checks on both the raw and the trimmed reads.
    Why it is included
    Comparing reads before and after trimming shows whether trimming helped and whether quality concerns remain.
    What it produces
    Per-sample quality results that feed the final MultiQC report.
    Why it matters
    Spot read-quality concerns before relying on expression results.
    03 / processing

    STAR

    Align to the genome

    What it does
    Aligns the trimmed reads to the reference genome for each sample's organism, which is set per sample in the samplesheet.
    Why it is included
    Exon-aware alignment places reads across splice junctions, which gene-level quantitation depends on.
    What it produces
    Per-sample genome alignments.
    Why it matters
    Mixed-organism batches are handled in one run, with each sample aligned to its own reference.
    04 / processing

    Samtools

    Sort and index

    What it does
    Sorts and indexes the alignments.
    Why it is included
    Sorted, indexed alignments are what downstream tools and genome browsers expect.
    What it produces
    Sorted, indexed BAM files.
    Why it matters
    Alignments open directly in a genome browser without extra preparation.
    05 / processing

    Picard

    Mark duplicates

    What it does
    Adds read groups and marks PCR duplicates.
    Why it is included
    Reads copied during PCR amplification can inflate expression counts if they are not flagged.
    What it produces
    Deduplicated, read-group-annotated BAM files.
    Why it matters
    Counts reflect distinct molecules rather than amplification artifacts.
    06 / post-processing

    featureCounts

    Count reads per gene

    What it does
    Counts reads per gene on the deduplicated alignments, and gene names and biotypes are added from the GTF annotation file.
    Why it is included
    Gene-level counts are the starting point for differential expression analysis.
    What it produces
    Gene count tables annotated with gene names and biotypes.
    Why it matters
    Tables arrive analysis-ready, with readable gene names already attached.
    07 / post-processing

    Salmon

    Quantify transcripts

    What it does
    In parallel, quantifies transcript-level expression directly from the trimmed reads using quasi-alignment.
    Why it is included
    Isoform-level signal is invisible in gene counts alone.
    What it produces
    Salmon quantification files with transcript-level estimates.
    Why it matters
    Gene and isoform views of the same samples, from one run.
    08 / post-processing

    MultiQC

    Bring results together

    What it does
    Pulls all the QC metrics from trimming, alignment, and quantification into a Bridge Horizon-branded report.
    Why it is included
    Quality metrics are most useful when they can be reviewed together.
    What it produces
    A consolidated MultiQC report.
    Why it matters
    Share results, and context, as a link, not a transfer: one readable overview researchers, cores and collaborators can open directly.

    Your deliverables

    What comes back

    • Deduplicated, indexed BAM alignments for every sample
    • Gene count tables annotated with gene names and biotypes
    • Salmon transcript-level quantification files
    • Bridge Horizon-branded MultiQC report

    Common questions

    Bulk RNA-seq FAQ

    What the pipeline accepts, what comes back, how to read it, and how the results lead into differential expression and pathway analysis.

    Still deciding what your data needs? Email the team.

    Take the next step

    Run it in Horizon

    Bulk RNA-seq is available in Horizon. Sign in to work with your data and launch a run.

    Open Horizon

    Need a tailored pipeline?

    Talk with our team about building or adapting a workflow for your research, including deployment in your AWS environment.

    Contact the team

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