Bio Co-Scientist

Omics Analysis

Faster, Smarter Research

AI optimized for omics data automates complex analysis workflows, delivering fast, accurate results — no specialized analytics expertise required.

Compare and integrate multiple analysis results side by side, and uncover insights that individual analyses alone can't reveal.

Your AI Research Partner

Freely explore a wide range of analyses with no added cost, and track your research progress and key insights at a glance.

Once analysis is complete, reports, methods sections, and presentation materials are generated automatically — making it easier to prepare your publications and presentations.

AI optimized for omics data automates complex analysis workflows, delivering fast, accurate results
— no specialized analytics expertise required.

Compare and integrate multiple analysis results side by side, and uncover insights that individual analyses alone can't reveal.

Faster, Smarter Research

Freely explore a wide range of analyses with no added cost, and track your research progress and key insights at a glance.

Once analysis is complete, reports, methods sections, and presentation materials are generated automatically
— making it easier to prepare your publications and presentations.

Your AI Research Partner

Omics Analysis

Bio Co-Scientist

All core omics analyses.
One platform.

All core omics analyses.
One platform.

All core omics analyses.
One platform.

Genomics

  • Variant Interpretation & DB Matching

    • ClinVar · dbSNP · gnomAD · COSMIC · cBioPortal · DepMap

  • GWAS & Mendelian Randomization

    • plink2 · gcta · MendelianRandomization · Bayesian fine mapping (PyMC) — ≤ 10K samples × ≤ 1M variants

  • Small-scale reference alignment

    • bwa · bowtie2 · samtools — bacteria, exon panel, single chromosome

  • Public genomic database

    • Ensembl · UCSC · gget · Monarch · Mouse Phenome

Transcriptomics

Epigenomics

Proteomics

Metabolomics

Microbiomics

WGCNA Gene Network Analysis

This project utilized WGCNA to identify 14 co-expression modules within a mouse liver dataset. Among these, the blue module exhibited a significant positive correlation with cholesterol levels, suggesting a coordinated regulatory role.

FASTQ-to-Variant Calling: Hypermutator Emergence and Parallel pykF Adaptation in E. coli Ara-3

This project investigates whether short-read sequencing of three evolved E.
coli Ara-3 lineages can identify the genetic basis of divergent mutation rates.
From raw FASTQ reads, a reproducible pipeline (QC, alignment, haploid calling, filtering, annotation) compares mutation burden across lineages.
It tests whether one lineage has become a hypermutator, shown by a large, largely unique variant burden co-occurring with mismatch-repair mutations (mutS, mutL), and whether lineages share adaptive pykF mutations reflecting convergent selection.

Genomics

  • Variant Interpretation & DB Matching

    • ClinVar · dbSNP · gnomAD · COSMIC · cBioPortal · DepMap

  • GWAS & Mendelian Randomization

    • plink2 · gcta · MendelianRandomization · Bayesian fine mapping (PyMC) — ≤ 10K samples × ≤ 1M variants

  • Small-scale reference alignment

    • bwa · bowtie2 · samtools — bacteria, exon panel, single chromosome

  • Public genomic database

    • Ensembl · UCSC · gget · Monarch · Mouse Phenome

Transcriptomics

Epigenomics

Proteomics

Metabolomics

Microbiomics

WGCNA Gene Network Analysis

This project utilized WGCNA to identify 14 co-expression modules within a mouse liver dataset. Among these, the blue module exhibited a significant positive correlation with cholesterol levels, suggesting a coordinated regulatory role.

FASTQ-to-Variant Calling: Hypermutator Emergence and Parallel pykF Adaptation in E. coli Ara-3

This project investigates whether short-read sequencing of three evolved E.
coli Ara-3 lineages can identify the genetic basis of divergent mutation rates.
From raw FASTQ reads, a reproducible pipeline (QC, alignment, haploid calling, filtering, annotation) compares mutation burden across lineages.
It tests whether one lineage has become a hypermutator, shown by a large, largely unique variant burden co-occurring with mismatch-repair mutations (mutS, mutL), and whether lineages share adaptive pykF mutations reflecting convergent selection.

We’ve got the answers

We’ve got the answers

We’ve got the answers

Can I run analyses without any coding experience?

Yes — no coding required.

You don’t need to know Linux, R, or Python.

Simply upload your data through the web interface and request analysis strategies via AI chat.
From execution to interpretation, the entire process is handled automatically.

How is this different from ChatGPT or Gemini?

Will my data be used to train AI models?

Who owns the figures generated on this platform?

How is customer data protected?

Ready to revolutionize
Your drug discovery process?

Sign up with your email and start using HyperLab right now

HITS Inc.

CEO : Woo Youn Kim

Address : 8F, 28, Teheran-ro 4-gil, Gangnam-gu, Seoul, Republic of Korea

Tel : +82-2-6953-0317

Company registration number : 260-88-01818

© HITS Inc. All rights reserved.

Ready to revolutionize
Your drug discovery process?

Sign up with your email and start using HyperLab right now

HITS Inc.

CEO : Woo Youn Kim

Address : 8F, 28, Teheran-ro 4-gil, Gangnam-gu,
Seoul, Republic of Korea

Tel : +82-2-6953-0317

Company registration number : 260-88-01818

© HITS Inc. All rights reserved.

Ready to revolutionize
Your drug discovery process?

Sign up with your email and start using HyperLab right now

HITS Inc.

CEO : Woo Youn Kim

Address : 8F, 28, Teheran-ro 4-gil, Gangnam-gu, Seoul, Republic of Korea

Tel : +82-2-6953-0317

Company registration number : 260-88-01818

© HITS Inc. All rights reserved.