The Pipeline

From raw reads to publication-ready results.

Your FASTQ files pass through five configurable phases, each producing detailed reports, interactive visualizations, and downloadable outputs you can use directly in your research papers.

Step 01

Quality Control

fastp

Raw FASTQ files are assessed and trimmed with fastp. Low-quality bases, adapter sequences, and poly-G/X tails are removed. You can tune quality thresholds, minimum read length, and sliding-window parameters.

Outputs

  • Per-base quality scores
  • Read length distribution
  • Adapter content report
  • Passed/failed filter stats
  • Trimmed FASTQ files

Configurable parameters

  • Quality phred threshold
  • Minimum read length
  • Sliding window trimming
  • Poly-G / Poly-X trimming
  • Custom adapter sequences

Step 02

Preprocessing

fastp + custom filters

Cleaned reads are prepared for quantification. Paired-end files are validated, orphaned reads are handled, and files are organized into the structure downstream tools expect. This step ensures data integrity before alignment.

Outputs

  • Validated read pairs
  • Clean file manifests
  • Read count summaries

Configurable parameters

  • Pair validation mode
  • Orphan handling strategy

Step 03

Quantification

Salmon / STAR

Trimmed reads are quasi-mapped against a reference transcriptome using Salmon, or aligned with STAR for genome-level quantification. Gene-level expression counts are produced for every sample in your project.

Outputs

  • Gene-level counts (quant.sf)
  • Mapping rate per sample
  • Number of genes detected
  • Alignment statistics

Configurable parameters

  • Reference transcriptome
  • Quantification tool (Salmon / STAR)
  • Library type
  • Index build parameters

Step 04

Differential Expression

DESeq2

Expression counts from all samples are aggregated, normalized, and tested for differential expression using DESeq2. You assign samples to experimental groups (e.g. Control vs Treatment), and DESeq2 identifies genes with statistically significant expression changes.

Outputs

  • Results table (log2FC, p-adj, baseMean)
  • Volcano plot
  • MA plot
  • Heatmap of top DE genes
  • VST-normalized counts matrix
  • PCA plot

Configurable parameters

  • Reference group (Control)
  • Significance threshold (p-adj)
  • Log2 fold-change cutoff
  • Shrinkage estimator

Step 05

Pathway Analysis

GSEA / GO Enrichment

Differentially expressed gene lists are tested against curated pathway databases (MSigDB, Gene Ontology) using GSEA. The result is a map of which biological pathways and processes are activated or suppressed in your experiment.

Outputs

  • Enrichment score plots
  • GO enrichment table
  • Activation matrix heatmap
  • Leading edge gene lists
  • Downloadable results CSV

Configurable parameters

  • Gene set database
  • Permutation type
  • Minimum / maximum set size
  • Scoring metric

What you get at the end

After running the full pipeline, your project contains a complete, versioned record of every step, from the raw QC report through to pathway enrichment maps. Here's what you can view, customize, and download:

Volcano plots
MA plots
Heatmaps
PCA plots
Enrichment score curves
GO enrichment tables
Activation matrices
VST count matrices
Results CSVs
QC summary reports
Mapping statistics
Leading edge gene lists

Built for real research workflows

Beyond the core pipeline, NotchBio gives you the tools to manage, compare, and share your work.

Versioned pipeline runs

Every run records its exact parameters. Re-run with tweaked settings and compare results side by side without spreadsheet tracking.

Interactive visualizations

Volcano plots, MA plots, heatmaps, PCA, and enrichment maps are fully interactive. Zoom, hover, filter, and export figures directly from the browser.

Publication-ready exports

Download results as CSV, TSV, or high-resolution images. Every table and plot is formatted for direct inclusion in papers and supplementary materials.

Cell line & group management

Organize samples into experimental groups, assign cell lines, and track metadata. All downstream analysis respects your grouping automatically.

Re-run variations

Try different quality thresholds, quantification tools, or DE parameters. Each variation is stored as a separate version and nothing is overwritten.

SRA / GEO imports

Import public datasets from NCBI SRA, SRP, or GSE accession IDs with one click. Samples are downloaded and ready for analysis in minutes.

Start analyzing in minutes

Upload your FASTQ files or import from SRA, configure your parameters, and launch the pipeline. No server setup, no command line.

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