We deliver comprehensive whole genome sequencing analysis powered by advanced AI algorithms. Our platform processes raw sequencing data through state-of-the-art deep learning pipelines for accurate gene annotation, de novo genome assembly, and comprehensive variant detection across all organism types.
Whole genome sequencing generates massive amounts of data—approximately 100GB per human genome. Traditional analysis methods struggle with the scale and complexity. Our AI-driven platform transforms this challenge into opportunity, delivering results with unprecedented accuracy and speed.
We combine proven bioinformatics tools with cutting-edge AI to deliver accurate, comprehensive WGS analysis.
Our deep learning base callers achieve >99.9% accuracy by training convolutional neural networks on signal and image data from sequencing instruments. We implement rigorous quality control to identify and filter low-quality reads before downstream analysis.
We utilize GPU-accelerated alignment tools that dramatically reduce processing time while maintaining accuracy. Our pipeline supports both reference-based alignment and de novo assembly approaches.
Our AI-powered annotation pipeline combines evidence from multiple sources including ab initio predictions, homology searches, and RNA-seq evidence to produce comprehensive gene models.
From raw sequencing data to actionable insights, our WGS analysis pipeline covers every step.
Assemble new genomes without reference sequences. Our algorithms handle complex repetitive regions and deliver chromosome-level assemblies for any organism.
Map reads to reference genomes with high accuracy. We support GRCh38, T2T-CHM13, and custom pangenome references for improved variant detection.
Identify genes and functional elements using our AI models trained on thousands of verified genomes across all domains of life.
Detect SNPs, indels, and structural variants using ensemble methods that combine multiple algorithms for maximum sensitivity and precision.
Traditional WGS analysis pipelines are slow, resource-intensive, and often miss subtle patterns in complex genomic regions. Our AI-powered approach addresses these limitations.
GPU-accelerated pipelines reduce analysis time from days to hours. NVIDIA Parabricks integration delivers 80x speedup over CPU-based workflows.
Enginoma variant analysis achieves state-of-the-art accuracy by learning complex sequence patterns. Our ensemble approach further improves precision in difficult regions.
From highly repetitive centromeres to complex structural variations, our AI algorithms detect variants that traditional tools miss.
Optimized compute resources and intelligent sampling strategies reduce overall project costs while maintaining analytical depth.
Our WGS analysis platform serves a broad range of genomic research and clinical applications across life sciences.
Rare disease research programs rely on comprehensive WGS to identify pathogenic variants, characterize structural rearrangements, and support clinical interpretation in unsolved cases. Our pipeline covers all mutation types from single-nucleotide variants to large chromosomal events.
Track pathogen evolution, identify antimicrobial resistance determinants, and reconstruct transmission chains from outbreak samples. Our platform handles mixed infections, emerging variants, and cross-species horizontal gene transfer events with equal rigor.
Support crop improvement programs with reference-quality genome assemblies, trait-linked marker identification, and genomic selection pipelines. We process both model species and non-model organisms with draft or incomplete assemblies.
Build pan-genomes, reconstruct phylogenetic relationships, and characterize gene family expansions across species collections. Our analysis supports both closely related strains and deeply divergent taxa across the tree of life.
From raw sequencing data to validated results in four streamlined steps.
Upload your FASTQ/BAM files through our secure portal. We accept data from all major sequencing platforms.
Automated QC assessment with read filtering, coverage estimation, and contamination screening.
Deep learning models process data through alignment, assembly, annotation, and variant calling pipelines.
Results validated against quality benchmarks and delivered with comprehensive documentation.
Our methods are based on peer-reviewed research from leading genomics journals and institutions.
Poplin R, et al. A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology, 2018, 36(10): 983–987.
Cheng H, et al. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nature Methods, 2021, 18(2): 170–175.
Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 2018, 34(18): 3094–3100.
Nurk S, et al. The complete sequence of a human genome. Science, 2022, 376(6588): 44–53.
Chen S, et al. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta, 2023, 2(2): e107.
Upload your sequencing data and let our AI-powered platform deliver accurate, comprehensive results.
We support all major sequencing platforms including Illumina, Oxford Nanopore, PacBio, and BGI/MGI. Our pipelines are optimized for both short-read and long-read sequencing data.
Standard analysis takes 24-48 hours for human genomes. Complex projects like de novo assembly may take 3-7 days depending on genome size and coverage.
We have pre-built references and annotation models for all major model organisms including human, mouse, rat, zebrafish, fruit fly, C. elegans, yeast, E. coli, and Arabidopsis. We can also create custom references for any organism.
Our AI algorithms are specifically trained to handle challenging regions including repetitive sequences, segmental duplications, and GC-rich regions. We utilize graph-based references and ensemble methods for maximum accuracy.
Yes. Beyond raw variant calls and gene predictions, we offer functional annotation, pathway analysis, and clinical interpretation services to help you understand the biological significance of your results.
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