Novaflow

Novaflow is a bioinformatics data analysis platform designed for life science researchers who need to process and interpret experimental data without relying on dedicated computational staff. The platform accepts raw data files — including CSVs, FASTQs, and BAMs — and returns publication-ready plots, analysis summaries, and exportable Jupyter notebooks, typically in minutes rather than weeks or months.

Novaflow serves academic labs working in genomics, transcriptomics, and proteomics; biotech teams looking to reduce headcount while maintaining analytical throughput; and clinical or diagnostic groups running high-throughput assays. It is positioned for experimentalists who have biological domain knowledge but limited coding expertise, as well as teams that lack dedicated bioinformatics analysts.

How It Works

• Users upload experimental data and describe their experiment in plain English (e.g., "scRNA-seq of treated vs. control organoids").
• Novaflow infers the appropriate workflow — such as QC, normalization, and UMAP clustering for single-cell RNA-seq, differential expression for bulk RNA-seq, or peak calling and motif analysis for ATAC-seq.
• The platform provisions compute, runs the pipeline, and returns plots, summaries, and a reproducible notebook the researcher can retain, rerun, or extend.

Core Capabilities

• Natural-language querying: Researchers can ask questions about their data in plain English, such as "Which genes are most differentially expressed?"
• LLM-powered pipeline orchestration: The platform plans, generates, and executes bioinformatics workflows automatically based on the uploaded data and experiment description.
• Publication-ready outputs: Results are returned as plots suitable for direct use in scientific publications.
• Exportable notebooks and code: Every analysis produces a Jupyter notebook with reproducible Python code, supporting auditability and downstream customisation.
• Versioned runs and provenance tracking: Analyses are versioned so researchers can rerun, compare, and share results with confidence.

Problem Context

• Life science labs face high analyst costs — small labs commonly spend $100,000 or more per year per analyst, with larger labs spending significantly more.
• Analyst-to-experimentalist ratios of roughly 1:5 mean researchers often wait weeks to months for results.
• Useful bioinformatics analysis requires both strong coding ability and deep biological context, a combination most individual researchers do not have.
• Many labs operate with fragmented tooling, custom scripts, and limited scalable compute infrastructure.

Novaflow was founded by Aman, a former computational biologist, and Amulya, a software engineer previously at Zoom. The product was developed in response to firsthand observations of analysis bottlenecks in research lab settings.