What is RNA-seq analysis?

RNA-seq (RNA sequencing) analysis is the process of turning raw sequencing reads into gene-level expression measurements you can compare across samples or conditions. A standard bulk RNA-seq analysis includes read QC, trimming, alignment to a reference genome, gene quantification, normalization, and differential expression, and usually ends with downstream interpretation like pathway or gene-set enrichment.

What is the best RNA-seq analysis software for genomics teams?

The best RNA-seq analysis software for most genomics teams combines validated pipelines (STAR, HISAT2, Salmon, DESeq2, edgeR), a point-and-click interface for bench scientists, and automatic logging of versions, parameters, and references so bioinformaticians can audit and re-run every result. Bridge Horizon packages all of that in one shared workspace.

What are the steps in an RNA-seq analysis pipeline?

A typical bulk RNA-seq pipeline runs: (1) FastQC for read quality, (2) adapter and quality trimming, (3) alignment with STAR or HISAT2 (or pseudo-alignment with Salmon/Kallisto), (4) gene-level quantification with featureCounts or Salmon, (5) normalization, (6) differential expression with DESeq2 or edgeR, and (7) downstream visualization (PCA, volcano plots, heatmaps). Bridge Horizon runs each step as a tracked, versioned workflow.

What is the best RNA-seq analysis tool or software?

There isn't a single 'best' tool, the right stack depends on your reference, sample size, and lab conventions. The most widely used combinations are STAR + featureCounts + DESeq2, HISAT2 + featureCounts + edgeR, and Salmon + tximport + DESeq2. Bridge Horizon ships validated templates for each, and also runs your own Nextflow or Snakemake pipelines unchanged so you don't have to switch toolchains.

How do I perform differential gene expression analysis?

Define your sample groups (e.g. treatment vs. control) in a sample sheet, include any covariates (batch, sex, timepoint), and run a differential expression tool like DESeq2 or edgeR on your count matrix. The output is a results table of log fold-changes, p-values, and adjusted p-values per gene, usually visualized as volcano and MA plots. On Horizon you set this up through a guided UI and every contrast is saved as a re-runnable workflow.

How do I analyze RNA-seq data from FASTQ files?

Upload your FASTQs (or point Horizon at a sequencing run), choose an RNA-seq pipeline template, pick your reference genome and parameters, and run it. Horizon handles QC, trimming, alignment, quantification, and differential expression end-to-end. Results land in a shared workspace your bioinformatician and PI can review without re-uploading files.

Can I run RNA-seq analysis without coding?

Yes. Bridge Horizon wraps validated RNA-seq pipelines in a point-and-click interface so bench scientists can run alignment, quantification, and differential expression on their own data. Your bioinformatician validates the pipeline once and anyone on the team can re-run it with their own samples and parameters.

How do I make RNA-seq analysis reproducible?

Reproducibility comes down to pinning four things on every run: the pipeline code (and version), the container/environment, the reference (genome + annotation), and the full parameter set including random seeds. Bridge Horizon captures all of this automatically, so re-running the same analysis six months later returns the same numbers, no 'which version of STAR did we use?' archaeology.

Can I reanalyze RNA-seq data from a count matrix or GEO dataset?

Yes. If you already have a count matrix from a previous run, a collaborator, or a public dataset (GEO, SRA, recount3), upload it and start from normalization or differential expression. Horizon logs the input matrix checksum, package versions, design matrix, and contrasts so the downstream analysis is just as reproducible as one started from FASTQs.

How long does an RNA-seq analysis take?

Wall-clock time depends on read count and reference size, but a typical bulk RNA-seq run on Horizon (alignment through differential expression for a few dozen samples) completes in a few hours. The bigger time saver is removing the handoff queue, bench scientists don't wait days for a bioinformatician to start the run.

Why use online RNA-seq analysis tools instead of running RNA-seq locally?

Running locally works for one person, but it breaks down when bench scientists, bioinformaticians, and PIs need to share data, reproduce results, or re-run an old analysis. A platform like Bridge Horizon keeps the same tools, adds controlled access, automatic provenance, and shared workspaces, so the team moves faster without losing reproducibility.

RNA-seq analysis software

RNA-seq analysis software your whole team can run, share, and reproduce

Run validated bulk RNA-seq pipelines on Bridge Horizon: QC, alignment, quantification, and differential expression in one shared workspace. Designed for US genomics teams who need reproducibility without the handoff queue.

What RNA-seq analysis looks like on Horizon

Bulk RNA-seq turns raw sequencing reads into gene-level expression measurements you can compare across samples. The pipeline typically runs read QC, trimming, alignment to a reference genome, gene-level quantification, and differential expression between conditions.

On Bridge Horizon, that whole workflow lives in one shared workspace. Bench scientists run the pipeline themselves through a guided interface; bioinformaticians see the same data, runs, and parameters and can dig in when something needs a closer look.

Every run is logged automatically with its pipeline version, parameters, reference, and inputs, so any teammate can re-run it identically months later. No copy-pasting parameter sheets, no 'which version did we use?' threads.

How Bridge Horizon runs it

From raw data to results, in one workspace

Upload your sequencing data

Bring FASTQs in directly or connect a sequencing run. Horizon handles QC and trimming as part of the pipeline.

Pick the RNA-seq pipeline

Choose a validated template (STAR + DESeq2, HISAT2 + edgeR, Salmon, or your own Nextflow/Snakemake pipeline).

Set your contrasts

Define your sample groups, conditions, and covariates in a guided sample sheet. No YAML files to hand-edit.

Run and review

Horizon runs alignment, quantification, and differential expression. Results land as a tracked run with every parameter logged.

Share with your team

Your bioinformatician and PI see the same run, same plots, same numbers. Comment, re-run, or hand off without exporting files.

Re-run anytime

Need a different threshold or contrast? Click re-run, change what you want, and Horizon keeps the original run alongside the new one for side-by-side comparison.

Reproducible bioinformatics workflows

Every run is reproducible by default

Reproducibility shouldn't depend on whoever ran the analysis still being on the team. Bridge Horizon captures everything that went into a result, the pipeline version, parameters, reference, container image, and inputs, so any teammate can re-run it identically months or years later.

Versioned pipelines

Pipelines are pinned to a specific version on every run, so an old analysis doesn't silently change when the pipeline does.

Full parameter logs

Every parameter, input file, and reference is recorded automatically. No lab notebook entries to maintain by hand.

Audit-ready provenance

When a reviewer, client, or auditor asks how a figure was made, you can show them the full chain in one click.

Built for the whole genomics team

For research scientists

Run validated pipelines on your own sequencing data without waiting in the bioinformatics queue.

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For NGS core facilities

Deliver traceable sequencing results to clients faster, with built-in access controls and expiring links.

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FAQ

Common questions

RNA-seq analysis tools

The best RNA-seq analysis tools share one workspace

Most teams stitch together Galaxy, custom scripts, Jupyter notebooks, and general-purpose LLMs. Bridge Horizon unifies the best RNA-seq analysis workflows in one controlled, traceable environment. Sort any column to compare how each option handles a capability.

Fully supported Partial / manual setup Not supported

RNA-seq analysis tools capability comparison, sortable by capability or by tool.

Automatic version, parameter, and reference loggingEvery run records tool versions, parameters, reference build, and inputs without anyone writing it down manually.Provenance for NGS cores	Fully supported	Not supported	Not supported	Not supported
Bench scientist and bioinformatician share one workspaceBoth roles see the same data, runs, and results with role-scoped permissions instead of trading files over email.Built for bench scientists	Fully supported	Partial or manual setup required	Not supported	Not supported
Human support for US genomics teamsNamed support contacts who understand genomics workflows, not community forums or generic ticket queues.Talk to the team	Fully supported	Not supported	Not supported	Not supported
Re-runnable months later with identical resultsA past run can be re-executed from its recorded state and produce byte-comparable outputs.Reproducible single-cell runs	Fully supported	Partial or manual setup required	Not supported	Not supported
Run bulk RNA-seq without the command lineA bench scientist can launch a full alignment-to-counts run from a browser, with no shell, scheduler, or container setup.See the RNA-seq workflow	Fully supported	Partial or manual setup required	Not supported	Not supported
Validated STAR / HISAT2 / Salmon templatesPipelines ship pre-configured with pinned tool versions and reference genomes, tested against known-good datasets.Which tools are included?	Fully supported	Partial or manual setup required	Not supported	Not supported

Single-cell RNA-seq analysis

Run Seurat pipelines for clustering, integration, and annotation in the same shared workspace.

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Bioinformatics tools FAQ

Common questions about RNA-seq pipelines, reproducibility, and how Bridge Horizon compares to other tools.

Read the FAQ

See how Horizon fits your team.

Built on production genomics experience since 2020, Horizon makes every dataset, pipeline run, and parameter shared and traceable across your entire team.