Transform genomic data into actionable insights. Our deep learning platform delivers precise variant detection, comprehensive annotation, and clinical-grade interpretation for research and precision medicine applications.
Our AI-driven variant calling and annotation platform combines state-of-the-art deep learning models with comprehensive genomic databases to deliver accurate, reliable variant detection and interpretation across the entire variant spectrum.
From rare disease research to cancer genomics and pharmacogenomics.
State-of-the-art neural network architectures for SNP, InDel, structural variant, and CNV detection.
Rich functional annotation using 50+ databases for gene models, pathways, and population frequencies.
ACMG/AMP guideline-based classification with evidence aggregation from clinical databases.
Specialized pipelines for tumor-normal matched samples with ultra-sensitive mutation detection.
Identify disease-causing variants through trio analysis, pedigree segregation, and association studies.
Predict drug response by analyzing pharmacogenomic variants in CYP450 genes and drug targets.
Streamlined workflow from data submission to clinical-ready reports.
Secure upload via HIPAA-compliant portal
Automated QC and contamination screening
AI-powered variant calling with confidence scores
Multi-database annotation and clinical reports
We support all major sequencing formats including BAM, CRAM, FASTQ (single and paired-end), and unified genomic formats. Our platform processes both short-read (Illumina) and long-read (PacBio, Oxford Nanopore) sequencing data.
Our platform detects SNPs, insertions, deletions (InDels), structural variants, copy number variations (CNVs), and can analyze both germline and somatic variants from various sequencing technologies.
Our deep learning models achieve 99%+ sensitivity for germline variants at 5% allele frequency and 95%+ sensitivity for somatic variants down to 1% VAF in high-depth samples.
Clinical variants are classified according to ACMG/AMP guidelines using computational predictions (SIFT, PolyPhen, CADD), population frequency data (gnomAD), clinical database matches (ClinVar), segregation analysis, and AI-mined literature evidence.
We support all major reference genomes including GRCh37/hg19, GRCh38/hg38, T2T-CHM13, and multiple organism-specific references (mouse, rat, rice, maize). Custom references can be accommodated for non-model organisms.
Our methods are grounded in peer-reviewed research from leading journals.
Chen, N.C., Kolesnikov, A., Goel, S., Yun, T., Chang, P.C., & Carroll, A. (2023). Improving variant calling using population data and deep learning. BMC Bioinformatics, 24, 197.
Khazeeva, G., Sablauskas, K., van der Sanden, B., Steyaert, W., Kwint, M., Rots, D., et al. (2022). DeNovoCNN: A deep learning approach to de novo variant calling in next generation sequencing data. Nucleic Acids Research, 50(9), e52.
Ramachandran, A., Lumetta, S.S., Klee, E.W., & Chen, D. (2021). HELLO: Improved neural network architectures and methodologies for small variant calling. BMC Bioinformatics, 22, 404.
Friedman, S., Gauthier, L., Farjoun, Y., & Banks, E. (2020). Lean and deep models for more accurate filtering of SNP and INDEL variant calls. Bioinformatics, 36(7), 2060-2067.
van den Belt, S., Zhao, H., & Alachiotis, N. (2024). Scalable CNN-based classification of selective sweeps using derived allele frequencies. Bioinformatics, 40(9), btae385.
Contact our team for a free consultation and project quote. We'll help you design the optimal variant analysis strategy.
Our AI-driven variant calling and annotation platform combines state-of-the-art deep learning models with comprehensive genomic databases to deliver accurate, reliable variant detection and interpretation across the entire variant spectrum.
From rare disease research to cancer genomics and pharmacogenomics.
State-of-the-art neural network architectures for SNP, InDel, structural variant, and CNV detection.
Rich functional annotation using 50+ databases for gene models, pathways, and population frequencies.
ACMG/AMP guideline-based classification with evidence aggregation from clinical databases.
Specialized pipelines for tumor-normal matched samples with ultra-sensitive mutation detection.
Identify disease-causing variants through trio analysis, pedigree segregation, and association studies.
Predict drug response by analyzing pharmacogenomic variants in CYP450 genes and drug targets.
Streamlined workflow from data submission to clinical-ready reports.
Secure upload via HIPAA-compliant portal
Automated QC and contamination screening
AI-powered variant calling with confidence scores
Multi-database annotation and clinical reports
We support all major sequencing formats including BAM, CRAM, FASTQ (single and paired-end), and unified genomic formats. Our platform processes both short-read (Illumina) and long-read (PacBio, Oxford Nanopore) sequencing data.
Our platform detects SNPs, insertions, deletions (InDels), structural variants, copy number variations (CNVs), and can analyze both germline and somatic variants from various sequencing technologies.
Our deep learning models achieve 99%+ sensitivity for germline variants at 5% allele frequency and 95%+ sensitivity for somatic variants down to 1% VAF in high-depth samples.
Clinical variants are classified according to ACMG/AMP guidelines using computational predictions (SIFT, PolyPhen, CADD), population frequency data (gnomAD), clinical database matches (ClinVar), segregation analysis, and AI-mined literature evidence.
We support all major reference genomes including GRCh37/hg19, GRCh38/hg38, T2T-CHM13, and multiple organism-specific references (mouse, rat, rice, maize). Custom references can be accommodated for non-model organisms.
Our methods are grounded in peer-reviewed research from leading journals.
Chen, N.C., Kolesnikov, A., Goel, S., Yun, T., Chang, P.C., & Carroll, A. (2023). Improving variant calling using population data and deep learning. BMC Bioinformatics, 24, 197.
Khazeeva, G., Sablauskas, K., van der Sanden, B., Steyaert, W., Kwint, M., Rots, D., et al. (2022). DeNovoCNN: A deep learning approach to de novo variant calling in next generation sequencing data. Nucleic Acids Research, 50(9), e52.
Ramachandran, A., Lumetta, S.S., Klee, E.W., & Chen, D. (2021). HELLO: Improved neural network architectures and methodologies for small variant calling. BMC Bioinformatics, 22, 404.
Friedman, S., Gauthier, L., Farjoun, Y., & Banks, E. (2020). Lean and deep models for more accurate filtering of SNP and INDEL variant calls. Bioinformatics, 36(7), 2060-2067.
van den Belt, S., Zhao, H., & Alachiotis, N. (2024). Scalable CNN-based classification of selective sweeps using derived allele frequencies. Bioinformatics, 40(9), btae385.
Contact our team for a free consultation and project quote. We'll help you design the optimal variant analysis strategy.
Tell us about your project and we'll get back within 24 hours.
