AI-driven data analysis pipelines to accelerate genomics research, from raw sequencing data to actionable biological insights.
Modern sequencing generates massive datasets. We help research teams extract meaning faster and more accurately.
A single whole-genome sequencing run generates over 100 GB of data. Traditional analysis methods cannot keep pace with the volume and complexity of multi-sample, multi-omic studies.
Machine learning models improve variant calling, reduce false positives, and identify subtle patterns in gene expression that manual curation would miss.
Automated pipelines with intelligent quality control reduce analysis turnaround from weeks to hours, letting researchers focus on discovery instead of data wrangling.
From raw sequencing data to actionable biological insights.
End-to-end analysis services designed for genomics research teams.
Alignment, variant calling (SNVs, indels, structural variants), annotation, and filtering pipelines built on GATK, DeepVariant, and custom ML models for improved sensitivity.
Bulk and single-cell RNA-seq analysis including transcript quantification, differential expression, pathway enrichment, and gene set analysis with publication-ready visualizations.
Cell type clustering, trajectory analysis, spatial deconvolution, and integration across experiments using frameworks like Scanpy, Seurat, and custom deep learning models.
ATAC-seq, ChIP-seq, and bisulfite sequencing analysis for profiling chromatin accessibility, histone modifications, and DNA methylation landscapes.
Combine genomic, transcriptomic, proteomic, and metabolomic datasets to uncover cross-layer regulatory mechanisms and build comprehensive biological models.
Custom Nextflow and Snakemake workflows, containerized environments, and cloud-native infrastructure for reproducible, scalable genomics analysis.
A structured approach to turning sequencing data into research outcomes.
We assess your raw sequencing data, run quality metrics (FastQC, MultiQC), and establish analysis parameters tailored to your experimental design.
Our pipelines handle alignment, quantification, variant calling, or expression analysis depending on your assay type, with built-in checkpoints and reproducibility.
We apply pathway analysis, gene ontology enrichment, network modeling, and literature-informed annotation to contextualize findings within your research question.
You receive comprehensive reports, interactive visualizations, reproducible code, and raw results. We support you through manuscript preparation and peer review.
Our genomics analysis capabilities support a wide range of research areas.
Tumor-normal variant analysis, mutational signature profiling, clonal evolution modeling, and neoantigen prediction.
GWAS analysis, ancestry estimation, admixture modeling, and polygenic risk score computation for large cohort studies.
Candidate gene prioritization, variant pathogenicity scoring, phenotype-genotype correlation, and clinical report generation.
Lineage tracing, pseudotime analysis, and regulatory network inference from single-cell and spatial transcriptomics data.
16S/ITS amplicon and shotgun metagenomic analysis, taxonomic profiling, functional annotation, and community diversity assessment.
Drug response variant identification, biomarker discovery, and integration with clinical trial data for precision medicine applications.
Let's discuss how our data analysis capabilities can support your next study.
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