txome.ai, an AI-driven transcriptome analysis and biomarker discovery platform

The platform is self-serve, fully configured, cloud-based and easy to use.

Sign up, upload your files, and access an interactive report with quality control metrics, differentially expressed transcripts and genes,  structural variants, pathways, gene set enrichment, machine-learning based classifier assessments and more.

Transcriptome analysis

Sign Up

Registering is easy.  Click the link below to find the right plan.


Upload Files

Upload your FASTQ files and metadata, or link to your S3 with our simple interface.


Click “RUN”

txome.ai is fully configured with simple options and extracts rich transcriptomic features for biomarker discovery.


View Report

Access machine learning assessments, structural variants, differential expression, quality, and more.


All-In-One platform with features to empower discovery and translational research

Rigorous quality control

txome.ai trims reads to increase the Q30 rate, corrects for biases, detects unwanted contamination, and provides detailed insights on the bias distribution. 

Ultrafast and highly accurate mapping

View accurate quantification fast and detailed mapping statistics like the number of dovetail, concordant, and compact fragments. 

Multiple differential analysis

Access differential gene expression, differential transcript expression, and differential transcript usage. 

Functional profiling

Get meaningful interpretation of differential gene expression using gene set enrichment analysis on GO databases and pathway analysis.

Cutting-edge structural variant (TSV) detection

Understand their translational impact, TSV type, informative visualizations, and the location of breakpoints. LEARN MORE >

Novel transcripts detection

Detect transcripts that are not present in the reference. Mapping information is provided to the new reference along with expression information with the new transcripts.   

Alternative splicing and percent spliced in

txome.ai detects possible alternative splicing events and calculates percent sliced in for each of the events.   

Multiple model machine learning

Multiple machine learning models are run to predict the labels of interest. A variety of performance measures are provided for prediction and cross-validation.

Detailed expression-based analysis

Understand expression clustering, principal component analysis (PCA), and high variance gene identification. 

Technical support is always included is always included in your plan

If you need additional help in designing or interpreting your experiments, or training, Ocean can also provide these services with our expert computational biology team.

Please fill out the form and we’ll be in touch to discuss how we can address your unique needs.

Samsung Medical Center uses txome.ai for gastric cancer study

The abstract, “Novel target discovery in immunotherapy-resistant gastric cancer using a comprehensive RNA-seq analysis pipeline” was shared at ASCO’s annual meeting. Read more about the methods, results, and conclusions of the study.