AviCella does not treat RNA-seq as a fixed script. It reads every quality metric, count matrix, PCA, DEG table, and pathway result as evidence to interpret, challenge, and use for the next decision.
The pipeline can finish and still be wrong. Adapter remnants, poly-G artifacts, weak mapping, wrong strandedness, annotation mismatches, or an unmodeled lane effect can travel all the way into a beautiful volcano plot.
The expert work is deciding whether each intermediate result is technically reliable, biologically plausible, and consistent with the experiment before the next branch is allowed to run.
We are not a pipeline wrapper. AviCella is built for Interpretation: it examines each RNA-seq output, explains what it means, decides whether it is safe to trust, and chooses what should be checked next.
AviCella is a multi-AI agent platform for RNA-seq Intelligence. It reads each run like an expert pair: one side checks metrics, models, covariates, and tool assumptions; the other asks whether the result makes biological sense.
FASTQ quality, STAR mapping, assignment rates, PCA structure, dispersion, p-value histograms, and pathway enrichments are treated as Interpretation gates, not decoration.
A DEG table is not the answer. AviCella applies RNA-seq Intelligence by cross-checking leading genes, pathways, sample structure, and literature support before recommending the next analysis or validation step.
At each step the multi-AI agent platform asks what came in, what was run, which tool should be preferred, what came out, what must be monitored, what needs Interpretation biologically, and which downstream branches are now justified.
Check the biological question, sample sheet, layout, lanes, metadata, and whether the experiment can support the contrast being asked.
FastQC, fastp, MultiQC, read balance, GC, adapter content, duplication, and poly-G artifacts decide how trimming and filtering should proceed.
STAR logs, splice junctions, IGV checks, strandedness, assignment rate, and Salmon/featureCounts agreement are checked before trusting a matrix.
PCA, batch covariates, DESeq2 diagnostics, p-value histograms, pathways, TF activity, and literature support shape the next branch and final report.
A fixed order of commands cannot know when a GC shift, mapping drop, or PCA split has changed the meaning of the run.
A count matrix, DEG table, and enrichment plot each need technical and biological justification before becoming a conclusion.
The system actively looks for artifacts, confounding, outlier-driven genes, weak replicate structure, and literature contradictions.
The final deliverable carries confidence, limitations, validation ideas, and a clear recommendation for the next analysis or wet-lab check.