Unlock transcriptomic insights with machine learning. From raw sequencing data to actionable biological discoveries — our AI-powered RNA-Seq analysis accelerates your research with unprecedented accuracy.
RNA-Seq has become the gold standard for transcriptome analysis. Our AI-powered platform combines cutting-edge machine learning algorithms with validated bioinformatics pipelines to deliver accurate, reproducible results.
Integrated analysis modules covering the complete transcriptomics workflow.
Machine learning-enhanced analysis for improved sensitivity and reduced false discovery rates.
Comprehensive pathway analysis using KEGG, Reactome, and GO databases.
De novo and reference-based assembly for novel species or improved annotation.
Comprehensive profiling with normalization, clustering, and visualization.
Tailored pipelines designed for your specific research questions.
Combine RNA-Seq with proteomics, metabolomics, or genomics for systems-level insights.
A streamlined, reproducible pipeline designed for accuracy and speed.
Upload FASTQ files via secure portal
FastQC, adapter trimming, filtering
STAR/HISAT2 or de novo assembly
Salmon/featureCounts with normalization
ML-enhanced differential expression
Pathway and GO analysis
Comprehensive report with figures
We recommend a minimum of 10 million reads per sample for standard differential expression analysis. For low-expression targets or splice isoform detection, we suggest 20-30 million reads. Total RNA, mRNA, or small RNA samples are all acceptable.
We support all organisms with available reference genomes. For model organisms (human, mouse, rat, Arabidopsis, yeast, E. coli), we use validated reference annotations. For non-model organisms, we offer de novo assembly or custom annotations.
Batch effect correction is standard in our pipeline. We use ComBat, limma's removeBatchEffect, and ML-based methods to identify and correct systematic biases while preserving true biological variation. We provide before/after visualizations.
Deliverables include raw count matrices, normalized expression tables (TPM, FPKM, CPM), differential expression results, pathway enrichment reports, publication-ready figures (PNG, SVG, PDF), and comprehensive analysis reports.
Yes, we offer expedited processing. Standard turnaround is 48-72 hours for analysis after QC passing. Rush processing (24 hours) is available with additional fees. Contact us to discuss timelines for your specific project.
Our methods are grounded in peer-reviewed research from leading journals.
Feng, S., Wang, Z., Jin, Y., & Xu, S. (2024). TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework. PLoS One, 19(7), e0305857.
Prada-Luengo, I., Schuster, V., Liang, Y., et al. (2023). N-of-one differential gene expression without control samples using a deep generative model. Genome Biology, 24, 198.
Xie, J., Chen, Y., Luo, S., et al. (2024). Tracing unknown tumor origins with a biological-pathway-based transformer model. Cell Reports Methods, 4(6), 100797.
Get a custom project proposal for your RNA-Seq analysis. Our team will design an analysis strategy tailored to your research goals.
RNA-Seq has become the gold standard for transcriptome analysis. Our AI-powered platform combines cutting-edge machine learning algorithms with validated bioinformatics pipelines to deliver accurate, reproducible results.
Integrated analysis modules covering the complete transcriptomics workflow.
Machine learning-enhanced analysis for improved sensitivity and reduced false discovery rates.
Comprehensive pathway analysis using KEGG, Reactome, and GO databases.
De novo and reference-based assembly for novel species or improved annotation.
Comprehensive profiling with normalization, clustering, and visualization.
Tailored pipelines designed for your specific research questions.
Combine RNA-Seq with proteomics, metabolomics, or genomics for systems-level insights.
A streamlined, reproducible pipeline designed for accuracy and speed.
Upload FASTQ files via secure portal
FastQC, adapter trimming, filtering
STAR/HISAT2 or de novo assembly
Salmon/featureCounts with normalization
ML-enhanced differential expression
Pathway and GO analysis
Comprehensive report with figures
We recommend a minimum of 10 million reads per sample for standard differential expression analysis. For low-expression targets or splice isoform detection, we suggest 20-30 million reads. Total RNA, mRNA, or small RNA samples are all acceptable.
We support all organisms with available reference genomes. For model organisms (human, mouse, rat, Arabidopsis, yeast, E. coli), we use validated reference annotations. For non-model organisms, we offer de novo assembly or custom annotations.
Batch effect correction is standard in our pipeline. We use ComBat, limma's removeBatchEffect, and ML-based methods to identify and correct systematic biases while preserving true biological variation. We provide before/after visualizations.
Deliverables include raw count matrices, normalized expression tables (TPM, FPKM, CPM), differential expression results, pathway enrichment reports, publication-ready figures (PNG, SVG, PDF), and comprehensive analysis reports.
Yes, we offer expedited processing. Standard turnaround is 48-72 hours for analysis after QC passing. Rush processing (24 hours) is available with additional fees. Contact us to discuss timelines for your specific project.
Our methods are grounded in peer-reviewed research from leading journals.
Feng, S., Wang, Z., Jin, Y., & Xu, S. (2024). TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework. PLoS One, 19(7), e0305857.
Prada-Luengo, I., Schuster, V., Liang, Y., et al. (2023). N-of-one differential gene expression without control samples using a deep generative model. Genome Biology, 24, 198.
Xie, J., Chen, Y., Luo, S., et al. (2024). Tracing unknown tumor origins with a biological-pathway-based transformer model. Cell Reports Methods, 4(6), 100797.
Get a custom project proposal for your RNA-Seq analysis. Our team will design an analysis strategy tailored to your research goals.
Tell us about your project and we'll get back within 24 hours.
