Bioinformatics,
end to end.
OMICs, AI, and scientific judgment — under one roof.
Excelra's bioinformatics group supports drug discovery across the full data lifecycle: from OMICs pipeline engineering and multi-OMICs analytics to biomarker discovery, target identification, and scientific consulting.
Bioinformatics is a broad field.
Most groups pick a corner. We don't.
A "powerhouse" isn't a slogan — it's what you call a group that can actually run an OMICs pipeline on Nextflow, curate a disease landscape, build a patient-stratification classifier, integrate a knowledge graph, and walk a steering committee through what the data means. All in the same engagement. All on the same team.
The full stack, under one roof
Computational biologists, domain experts, data scientists, and R&D-IT engineers — one team across curation, OMICs, AI/ML, consulting, and platform engineering.
AI/ML built into the workflow
Predictive analytics, drug-response classifiers, patient stratification, KG link prediction, trial benchmarking — part of how we work, not bolt-ons.
Every modality, every scale
Bulk and single-cell RNA-seq, spatial transcriptomics, proteomics, metabolomics, WGS/WES, HLA typing — production pipelines on Nextflow, Airflow, Databricks.
Real therapeutic depth
Oncology, Immunology, CNS, Nephrology, Rare disease — embedded MD/PhD scientists keep the analysis answering the biological question.
Insights, not handoffs
Target dossiers, biomarker hypotheses, MoA elucidation, Go/No-Go recommendations — the work ends where decisions begin.
What we do.
Six capability lanes across data engineering, OMICs, AI/ML, computational biology, and scientific consulting — combined as a program demands. Tap any lane to open it.
Multi-OMICs analysis & pipelines
Custom OMICs pipeline development on Nextflow, Airflow, or Databricks. Coverage across bulk RNA-seq, scRNAseq, spatial transcriptomics, proteomics, glycoproteomics, metabolomics, WGS/WES, and HLA typing.
Data curation & integration
Custom curation, ontology management, FAIRification, semantic enrichment, knowledge graphs, and ETL pipelines — turning unstructured data into analysis-ready assets.
Predictive analytics & AI/ML
Machine learning for biomarker discovery, drug-response prediction, patient stratification, indication prioritization, and trial benchmarking.
Scientific consulting
Target identification, prioritization, safety analysis, dossier building, disease-landscape assessments, repositioning and asset life-cycle management.
Computational biology
Pathway and network analysis, mechanism-of-action elucidation, systems-biology modeling, and integrative multi-OMICs analysis.
Applications & visualization
R-Shiny, Java, and cloud-native applications. Custom databases, BioVisualizer dashboards, and visualization via Spotfire, Tableau, R, and Python.
This is what the
data looks like.
A differential-expression heatmap — genes by samples, scaled by fold-change. Pink reads as upregulation, blue as downregulation. Hover any tile to read the value.
From data to insight.
A typical engagement flows through five tightly-coupled stages — with the same accountable team across the full lifecycle.
Extract & curate
Custom curation, literature mining, gold-standard datasets, and refresh pipelines.
Annotate & normalize
Ontology management, vocabulary control, semantic enrichment, FAIRification.
Integrate
ETL architecture, knowledge graphs, multi-OMICs integration, custom databases.
Analyze & model
ML modeling, statistical analysis, biological interpretation, hypothesis generation.
Interpret
Insight reports, interactive dashboards, recommendations, and dossiers.
Where we go deep.
Bioinformatics is most useful when the people running it understand the disease biology. Excelra embeds domain experts in every program.
Selected engagements.
Recent projects across oncology pipelines, RNA therapeutics, and AI-enabled cohort analysis. Drawn from Excelra's published case studies.
Production-ready bioinformatics pipelines for a Computational Oncology department
A U.S. oncology biotech needed customized pipelines for RNA-Seq, scRNA-Seq, WGS/WES, and HLA typing at scale.
A modular architecture on Nextflow and Docker, rolled out in phases with continuous QC and internalized Sarek workflows.
High-performance, reproducible workflows across four data modalities — supporting biomarker discovery at scale.
Refining off-target prediction for antisense oligonucleotide screening
A global pharma innovator needed to reduce false positives in ASO off-target prediction and lower validation burden.
Co-built a roadmap combining ASO–transcript alignment mining, interpretable ML for mismatch patterns, and RNA-seq validation.
A scalable, mismatch-tolerant pipeline that significantly expanded transcriptome alignment coverage.
AI-enabled cancer cohort analysis at population scale
A U.S. precision-medicine biotech needed scalable pipelines to integrate real-world evidence and somatic testing data.
Analytics workflows combining predictive modeling, AI-driven cohort stratification, and pathway-level enrichment.
Production analytics for precision-oncology decision support, with biological pathway context.