No Account Required — Just Open and Go
RNAseek removes every barrier between you and your analysis. There are no usernames, no passwords, and no registration forms. The moment you open the platform in your browser, a secure, private session is created for you automatically.
Behind the scenes, RNAseek assigns your browser a cryptographically random session ID (a UUID) stored as a secure, HttpOnly cookie. This ID links your uploads, pipeline runs, and results to your browser session — and only your browser session. No one else can access your data.
Your 14-Day Session Window
Your session and all data associated with it remains active for 14 days from the moment it is created. During that window you can:
- Upload new datasets and launch analyses at any time.
- Return to the platform and pick up exactly where you left off.
- Download any results, reports, or visualizations you have generated.
What You Need Before You Begin
| Item | Required? | Details |
|---|---|---|
| Raw sequencing reads | .fq.gz or .fastq.gz files. At least two experimental groups
(e.g., 3 Control + 3 Treated). |
|
| Reference genome | Select from 11 pre-indexed genomes or upload your own FASTA + GTF/GFF. | |
| Condition mapping | A table or CSV assigning every sample to a biological group (e.g., Control vs. Treated). | |
| Batch IDs | Optional | Include a Batch column to trigger automatic ComBat-seq batch-effect correction. |
| Timepoints | Optional | Include a Timepoint column to switch DESeq2 to the Likelihood Ratio Test (time-series). |
Supported Analysis Types (Assay Tracks)
RNAseek supports four core assay types. Choose the one that matches your experiment during the Setup Wizard:
| Assay | Best For | Aligner | Key Tools |
|---|---|---|---|
| Standard RNA-Seq (Poly-A) | Gene expression profiling | HISAT2 (splice-aware) | featureCounts, DESeq2 |
| Small RNA / miRNA | Regulatory RNA quantification | Bowtie (miRBase) | samtools idxstats, DESeq2 |
| ChIP-seq | Histone / TF binding sites | BWA MEM | MACS2 peak calling, featureCounts |
| DNA Methylation | Bisulfite sequencing | Bismark | methylKit differential methylation |
Quick-Start Walkthrough
- Navigate to RNAseek in your browser — your session starts automatically.
- Create a new submission from the Active Workspace page.
- Upload your compressed FASTQ files (the uploader handles large files seamlessly).
- Map your conditions using the interactive table or a CSV upload.
- Select a reference genome from the dropdown (or upload a custom genome).
- Launch the core pipeline and watch progress in real time via live progress bars.
- Explore results in the Core Hub — interactive plots, downloadable tables, and 12 advanced modules.
The Chunked Uploader — Built for Large Files
Genomics files are large. A single paired-end RNA-seq experiment can easily produce tens of gigabytes of compressed FASTQ data. RNAseek’s uploader is specifically engineered for this reality.
Your browser automatically splits each file into 5 MB binary chunks before transmission. Each chunk is sent individually over HTTPS and reassembled on the server. If a network interruption occurs mid-upload, only the affected chunk needs to be retransmitted — you do not lose the entire file. Upload files of any size, even over slower connections.
Accepted File Formats
| Input Type | Accepted Formats | Notes |
|---|---|---|
| Raw reads | .fq.gz, .fastq.gz |
Must be gzip-compressed. |
| Aligned reads | .bam, .cram |
Skips alignment; proceeds to quantification. |
| Count matrix | .csv, .tsv |
Rows = genes, columns = samples. Non-negative integers only. |
| Metadata | .csv |
Condition mapping, batch IDs, timepoints. |
| Custom genome | .fa / .fasta + .gtf / .gff |
Triggers on-demand HISAT2 index build. Not available for Small RNA track. |
Paired-End Read Detection
If your experiment uses paired-end sequencing, name your files with the standard
_R1 / _R2 convention:
SampleB_R1.fq.gz SampleB_R2.fq.gz
RNAseek auto-detects paired reads from filenames. You can also manually toggle between Single-End and Paired-End mode in the Setup Wizard if your naming convention differs.
Mapping Experimental Conditions (Metadata)
After your files finish uploading, the Setup Wizard asks you to define the experimental design. You have two options:
Option A — Interactive Table
Best for small experiments. The wizard pre-populates a table with your uploaded filenames — just pick a condition from the dropdown for each sample.
Option B — CSV Upload
Best for large experiments. Prepare a .csv with Filename and
Condition columns (required), plus optional Batch and
Timepoint.
Example metadata CSV:
| Filename | Condition | Batch (optional) | Timepoint (optional) |
|---|---|---|---|
WT_rep1_R1.fq.gz |
Control | Batch_1 | Day 0 |
WT_rep2_R1.fq.gz |
Control | Batch_1 | Day 0 |
WT_rep3_R1.fq.gz |
Control | Batch_2 | Day 0 |
KO_rep1_R1.fq.gz |
Treated | Batch_2 | Day 7 |
KO_rep2_R1.fq.gz |
Treated | Batch_1 | Day 7 |
KO_rep3_R1.fq.gz |
Treated | Batch_1 | Day 7 |
Batch – Providing batch IDs automatically triggers ComBat-seq batch correction during normalization, removing technical sequencing noise between batches.
Timepoint – Providing timepoints switches the statistical model from the Wald test to the Likelihood Ratio Test (LRT), which is more appropriate for time-series experimental designs.
Alternative Entry Points
Not starting from raw reads? RNAseek supports two shortcut entry points:
Pre-Aligned Reads (BAM/CRAM)
Upload aligned BAM or CRAM files. RNAseek skips QC & alignment and proceeds directly to gene quantification (featureCounts) → Stage 2 normalization & DEG testing.
Count Matrix (CSV/TSV)
Upload a gene-level count matrix. Bypasses Stage 1 entirely and jumps straight to filtering, normalization, DESeq2, and visualization. Requires: rows = genes, columns = samples, non-negative integers.
Selecting a Reference Genome
Choose the reference genome that matches your organism. RNAseek ships with 11 pre-indexed genomes:
| Organism | Assembly | Source |
|---|---|---|
| Homo sapiens | GRCh38 (hg38) | Ensembl / UCSC |
| Mus musculus | GRCm39 (mm39) | Ensembl / UCSC |
| Mus musculus | GRCm38 (mm10) | Ensembl / UCSC |
| Rattus norvegicus | mRatBN7.2 (rn7) | Ensembl |
| Danio rerio | GRCz11 (danRer11) | Ensembl |
| Gallus gallus | GRCg6a (galGal6) | Ensembl |
| Sus scrofa | Sscrofa11.1 (susScr11) | Ensembl |
| Drosophila melanogaster | BDGP6 (dm6) | Ensembl |
| C. elegans | WBcel235 | Ensembl |
| S. cerevisiae (Yeast) | sacCer3 (R64-1-1) | Ensembl |
| A. thaliana | TAIR10 | Ensembl |
.fa / .fasta) and annotation (.gtf / .gff).
RNAseek builds a HISAT2 index on-demand before alignment begins. Available for the Standard RNA-Seq
and ChIP-seq tracks. The Small RNA track requires species-specific miRBase indices and does not
support custom genomes.
Launching the Pipeline
Once your files are uploaded, metadata is mapped, and a genome is selected:
- Review your configuration in the Setup Wizard summary panel.
- Click Launch Pipeline.
- You will be redirected to the Processing page with real-time progress bars tracking every step.
Real-Time Progress Tracking
After launching the pipeline, the Processing page connects to the server via a live WebSocket connection. You will see a step-by-step progress bar updating in real time:
The Core Hub — Your Results Dashboard
When the pipeline finishes, you are taken to the Core Hub, a three-tab dashboard:
Overview
Summary statistics, QC report, downloadable data files.
Modules
12 advanced analytical micro-pipelines, unlocked instantly.
Single-Cell
Deconvolution gateway and spatial analysis spokes.
Stage 1 Results — Alignment & QC
The Overview tab provides the foundational outputs of your analysis:
| File | Format | Description |
|---|---|---|
| Compressed Alignments | .cram |
Deeply compressed files showing where every read mapped to the genome. |
| Raw Count Matrix | .csv |
Genes × Samples matrix of raw read counts. |
| QC Report | .html |
Interactive MultiQC report — Phred scores, GC content, adapter metrics, trimming stats. |
Stage 2 Results — Normalization, DEG Testing & Visualizations
This is where RNAseek transforms your raw data into biological insight — fully automatically:
- Low-count filtering removes genes with fewer than 10 total reads across all samples.
- Batch correction (if Batch IDs provided) applies ComBat-seq to remove technical noise.
- DESeq2 normalization adjusts for library size and performs differential gene expression testing with FDR-corrected p-values.
- Outlier detection uses PCA-based Mahalanobis distance to flag suspect samples.
- Gene annotation queries the MyGene.info API to append human-readable gene descriptions and disease associations to every gene.
Downloadable files:
| File | Format | Description |
|---|---|---|
| Normalized Count Matrix | .csv |
Library-size-normalized (and batch-corrected, if applicable) expression values. Ready for external tools or ML. |
| Differential Expression Table | .csv |
Log2 Fold Change, p-value, adjusted p-value (FDR), gene descriptions, and disease associations. |
Interactive Plotly Visualizations:
Every plot is rendered directly in your browser — zoom, pan, hover for gene names, and export as PNG or SVG.
PCA Plot (2D/3D)
Sample clustering via Principal Component Analysis. Variance explained on each axis.
UMAP Plot
Non-linear dimensionality reduction revealing structure PCA might miss.
Volcano Plot
Log2FC vs. significance. Red = upregulated, blue = downregulated. Hover for gene names.
MA Plot
Mean expression vs. Log2FC for every gene, highlighting significant DEGs.
Heatmap
Top 50 DEGs with z-score normalization and color-coded group annotations.
Advanced Modules (Tier 2)
After the core pipeline completes, the Modules tab unlocks 12 specialized analytical micro-pipelines. These modules reuse your existing results — no re-uploading required. The tab uses a master-detail layout: browse the list on the left, configure and view results on the right.
Key modules:
WGCNA — Weighted Gene Co-expression Network Analysis
Identify clusters (modules) of co-expressed genes and correlate them with clinical traits. Upload a traits CSV or build one interactively. Outputs include module-trait correlation heatmaps, hub gene lists, and Enrichr pathway enrichment results.
Pathway & Gene Set Enrichment
Map your differentially expressed genes onto biological pathways and curated gene sets. RNAseek integrates multiple databases:
All available modules:
| Module | What It Does |
|---|---|
| WGCNA | Co-expression network analysis correlating gene modules to clinical traits. |
| Pathway Enrichment | GSEA/ORA with PathBank, KEGG, Reactome, GO, and BASys2 microbial pathways. |
| Alternative Splicing | Detects skipped exons and predicts protein domain changes (IsoformSwitchAnalyzeR). |
| RNA Editing / SNPs | Identifies A-to-I editing events and high-confidence variants (REDItools2). |
| Time Series | Models gene expression dynamics over time (ImpulseDE2). |
| Causal Networks | Infers gene regulatory networks from expression data (GRNBoost2). |
| Literature NLP | Mines published literature for known gene interactions (INDRA Bio). |
| Survival Analysis | Correlates gene expression with clinical survival outcomes (lifelines). |
| TCGA Comparison | Compares your data against public TCGA cancer cohorts. |
| Biomarker Discovery | Cross-references DEGs with the MarkerDB clinical biomarker database. |
| MOFA | Multi-omics factor analysis for integrating multiple data layers. |
| DIABLO | Supervised multi-omics integration with discriminant analysis (mixOmics). |
Downloading Your Data
Every downloadable file in the Core Hub has a clearly marked download button. You can download:
- Individual result files (click the download icon next to any file).
- The complete differential expression table with gene annotations.
- Raw and normalized count matrices for use in R, Python, or Excel.
- The interactive MultiQC HTML report for sharing with collaborators.
Single-Cell & Spatial Analysis (Advanced)
The Single-Cell tab provides access to predictive deconvolution and spatial analysis tools:
Deconvolution Gateway
Select a tissue-specific single-cell reference atlas and run computational deconvolution to
estimate cell-type fractions. Toggle between a quick summary and high-resolution
.h5ad pseudo-cell matrix generation.
Trajectory Inference
Trace developmental or disease trajectories through predicted cell states using pseudotime analysis (scanpy / PAGA).
Spatial Mapping
Project predicted cell types onto a tissue image (generic template or your own H&E slide) to visualize where cells physically reside (Tangram).
Spatial Autocorrelation
Search for specific genes and visualize their spatial expression patterns as heatmaps overlaid on the tissue image (Moran’s I / Squidpy).
.fq.gz / .fastq.gz (reads), .bam /
.cram (alignments), .csv / .tsv (count matrices).