Unlock evolutionary relationships and functional insights through machine learning-powered genome comparison. Our platform identifies conserved regions, species-specific adaptations, and orthologous gene families at unprecedented scale and accuracy.
Traditional comparative genomics relies on heuristic alignments that struggle with divergent sequences and large-scale genomic rearrangements. Our AI-powered platform combines deep learning models with proven bioinformatics algorithms to deliver superior accuracy across diverse evolutionary distances.
From multi-species genome alignment to pan-genome construction, our platform delivers actionable evolutionary insights.
Deep learning-enhanced multiple sequence alignment for accurate detection of conserved regions and structural variations.
Comprehensive phylogenetic analysis including tree reconstruction, divergence estimation, and molecular evolution modeling.
Whole-genome comparison to identify conserved syntenic blocks and evolutionary breakpoints.
Accurate identification and classification of orthologous gene groups using graph-based clustering and ML.
Genome-wide synteny mapping to trace evolutionary rearrangements and understand genomic context.
Characterize complete gene repertoire including conserved core genes and variable accessory genes.
Streamlined workflow from data submission to publication-ready results.
Upload genomes via secure portal with optional metadata
Automated QC for genome completeness and quality assessment
Deep learning-enhanced alignment and ortholog detection
Comprehensive reports, figures, and raw data output
We accept all standard formats including FASTA, GenBank (GBK), EMBL, GFF3, and raw sequencing reads (FASTQ). Our pipeline can also work directly with NCBI accession numbers for public genomes.
Our platform scales from pairwise comparisons to thousands of genomes. Standard analyses typically include 10-100 genomes, while large-scale pan-genome studies can encompass hundreds or thousands of strains.
We support all organisms from bacteria to eukaryotes. Our pre-built databases include major model organisms, and we can create custom references for any non-model organism.
Deliverables include aligned sequences, phylogenetic trees (Newick/NEXUS), synteny maps, gene presence/absence matrices, ortholog assignments, statistical summaries, and publication-ready figures.
Yes, we routinely work with draft genomes at various assembly levels. Our ML models are trained to handle fragmented assemblies and can provide quality assessments of genome completeness.
Our methods are grounded in peer-reviewed research from leading journals.
Dewar, A.E., Hao, C., Belcher, L.J., Ghoul, M., & West, S.A. (2024). Bacterial lifestyle shapes pangenomes. Proceedings of the National Academy of Sciences, 121(21), e2320170121.
Shao, Y., Zhou, L., Li, F., Zhao, L., Zhang, B.L., Shao, F., et al. (2023). Phylogenomic analyses provide insights into primate evolution. Science, 380(6648), 913-924.
Simakov, O., Bredeson, J., Bhockey, D.A., et al. (2022). Deeply conserved synteny and the evolution of metazoan chromosomes. Science Advances, 8(5), eabi5884.
Hibbins, M.S., Breithaupt, L.C., & Hahn, M.W. (2023). Phylogenomic comparative methods: Accurate evolutionary inferences in the presence of gene tree discordance. Proceedings of the National Academy of Sciences, 120(22), e2220389120.
Lupo, U., Sgarbossa, D., & Bitbol, A.F. (2022). Protein language models trained on multiple sequence alignments learn phylogenetic relationships. Nature Communications, 13, 6298.
Our comparative genomics experts can help you design the optimal analysis strategy for your research questions.
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