FASTQ to Gene Counts for SNT Hashing

Problem Context

Researchers using Illumina’s Single Cell 3’ RNA Prep kit with SNT cell hashing need to process FASTQ files into gene counts. Illumina’s official tools don’t support this workflow, forcing manual workarounds that waste time and risk errors. Without a dedicated pipeline, experiments stall, delaying insights and potentially revenue-generating discoveries.

Pain Points

Users struggle with fragmented tools—Illumina’s support directs them to unsupported pipelines, and open-source tools like hashsolo require pre-processed SNT counts. They waste hours scripting custom solutions or hiring consultants, only to face compatibility issues. Failed attempts leave them stuck with raw FASTQs they can’t analyze, blocking critical research deadlines.

Impact

Every hour spent troubleshooting costs labs thousands in lost productivity. Delayed analyses can push back drug discovery timelines or grant deadlines, risking funding. Frustration leads to avoidable errors in data interpretation, compromising the integrity of high-stakes experiments. Without a reliable pipeline, teams resort to inefficient workarounds that scale poorly with project size.

Urgency

This problem is urgent because scRNAseq experiments are time-sensitive—cells degrade, and delays can invalidate results. Researchers can’t afford to wait for Illumina support or reinvent the wheel. The lack of a turnkey solution directly impacts their ability to publish, secure funding, or advance therapies. Ignoring it means wasted resources and missed opportunities.

Target Audience

Bioinformatics analysts, single-cell genomics researchers, and lab technicians in pharma, academia, and biotech. Teams using Illumina’s SNT hashing for multi-omics studies or large-scale screens also face this. Contract research organizations (CROs) processing client data need this to avoid delays. Anyone relying on Illumina’s scRNAseq workflows but stuck at the hashing step is a potential user.

Solution Approach

CellHash Pipeline is a dedicated, cloud-based (or self-hosted) tool that processes Illumina SNT FASTQ files into hashed gene counts in minutes. Users upload their FASTQs, select parameters, and receive ready-to-analyze counts compatible with hashsolo, scanpy, or other downstream tools. The pipeline handles Illumina’s SNT-specific formatting automatically, eliminating manual scripting. Pricing starts at $50/month for individual researchers, with tiered options for labs.

Key Features

1. Illumina SNT Optimization: The pipeline is pre-configured for Illumina’s SNT kit, ensuring compatibility with their latest formats.
2. Integration Ready: Outputs are directly compatible with hashsolo, scanpy, or Seurat, so users can plug results into their existing workflows.
3. Cloud or Self-Hosted: Choose between a managed cloud service or a Docker-based self-hosted version for data-sensitive labs.

User Experience

Users start by dragging FASTQ files into the web app or running a CLI command. The pipeline processes the data in the background, notifying them via email when counts are ready. They download the results and import them into their preferred analysis tool—no coding required. For labs, admins can set up team accounts with usage tracking. Researchers save hours per experiment and avoid compatibility headaches.

Differentiation

Unlike generic bioinformatics tools, CellHash Pipeline is purpose-built for Illumina’s SNT hashing workflow. It eliminates the need to stitch together open-source tools or rely on unsupported vendor pipelines. The solution is faster, more reliable, and requires no manual scripting. Competitors either don’t exist (for this exact problem) or are fragmented open-source projects lacking Illumina-specific optimizations.

Scalability

The product scales from individual researchers to large labs. Cloud users pay per project or seat, while self-hosted labs can expand usage internally. Future features include support for multi-omics data (e.g., CITE-seq) and integration with lab information management systems (LIMS). Add-ons like priority processing or custom pipeline tuning can increase revenue per user over time.

Expected Impact

Users regain control of their workflows, reducing experiment delays by 80%+. Labs save thousands in consultant fees and avoid costly errors from manual workarounds. Researchers can focus on analysis instead of troubleshooting, accelerating discoveries. For pharma, faster turnaround times mean quicker drug candidate validation. The tool becomes a mission-critical part of their single-cell genomics pipeline.