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🗂️ Feature Table

This table provides an at-a-glance view of what Mithrl supports today, what’s in beta, and what’s on the roadmap. Features span the full discovery and development pipeline: from RNA-seq through multi-omics, and into downstream clinical programs. We iterate fast. If there’s a feature your team needs sooner, reach out to your Customer Success Manager.

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✅ Legend

SymbolStatus
✅Fully supported
🧪Beta (available on request)
🔜On roadmap (not yet released)

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🔎 Feature Overview

FeatureStatusData TypesDescription
Exploratory Data Analysis✅Bulk, scRNA-seqSample QC, normalization, outlier detection
Differential Expression✅Bulk, scRNA-seqIdentify genes altered by treatment
Functional Enrichment✅Bulk, scRNA-seqKEGG/Reactome/GO pathway analysis of DEGs
Target Discovery✅Bulk, scRNA-seq, PPI networks, Curated knowledge basesRank and prioritize genes using networks, enrichment, and literature
Protein-Protein Interaction✅Bulk, scRNA-seqVisualize gene interaction networks and identify hubs or regulators
Clustering✅Bulk, scRNA-seqGroup similar samples or cells by expression profile
Dimensionality Reduction✅Bulk, scRNA-seqVisualize structure using PCA, UMAP, or t-SNE
Cell Type Identification✅scRNA-seqMatch cell clusters to reference annotations from public atlases
Molecular Subtyping🧪BulkClassify tumor/cell type based on canonical subtype models (e.g., PAM50)
Discovery Engine🧪AllAuto-surface known associations, drug links, and co-expression via LLMs
WGS/WES Analysis🔜GenomicDetect mutations and structural variants; link to transcriptomic impact
DNA Methylation (Bulk + sc)🔜EpigenomicIdentify differentially methylated regions and correlate with gene activity
Proteomics Integration🔜ProteomicConnect transcript expression to protein-level data and changes
Metabolomics Integration🔜MetabolomicMap transcriptomic shifts to metabolic pathways and readouts
Multi-Omics Cohort Analysis🔜Multi-omicsMerge multiple omics (e.g., RNA, methylation, protein) into unified analysis
Lead Optimization AI🔜DownstreamSuggest lead molecules based on gene signature, pathway response, and SAR overlays
High-Throughput Screening AI🔜DownstreamInterpret large HTS datasets using AI-guided dimensionality reduction, target filtering, and phenotypic enrichment
IND Support AI🔜PreclinicalIdentify toxicogenomic signatures, validate biomarkers across species, and generate IND-ready transcriptomic justifications
Clinical Trial Analysis AI🔜ClinicalStratify trial arms, match molecular subtypes to response groups, identify responder biomarkers, and generate trial decision support summaries

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🔄 Feature Availability by Workflow Stage

FeatureStatusNotes
Exploratory Data Analysis✅Includes normalization, QC plots, sample filtering
Cell Type Identification🧪Uses marker gene scoring and optional references
Dimensionality Reduction✅Supports PCA, UMAP, t-SNE, PACMAP
FeatureStatusNotes
Differential Expression✅DESeq2 (bulk), Wilcoxon (scRNA)
Clustering✅Leiden/Louvain supported
Molecular Subtyping🧪PAM50, with more to come
FeatureStatusNotes
Functional Enrichment✅KEGG-based analysis with overlays
Protein-Protein Interaction✅Includes hub identification
Target Discovery✅Integrates expression, networks, and literature
Discovery Engine🧪RAG-powered literature summarization
FeatureStatusNotes
Clinical Validation Tools🔜Scoring gene safety, selectivity, and expression specificity
Combination Therapy Modeling🔜Detect synergy from co-perturbed pathways
Proprietary Knowledge Graph🔜Unifies internal + public annotations and relationships

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📌 Notes

  • Beta features may change rapidly and could return incomplete results under certain conditions.
  • Roadmap features are under development and subject to change based on customer input.
  • You can request access to beta tools via your Customer Success channel.