Bioinformatics · Computational biology · Data science

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.

LIVE · SINGLE-CELL EMBEDDINGscRNA · 4 clusters
Cells profiled
0
Genes detected
0
UMAP-1 →hover to highlight
Population APopulation BReference
Why a powerhouse
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.

SCOPE
The full stack, under one roof

Computational biologists, domain experts, data scientists, and engineers working as one integrated team.

AI · ML
AI/ML built into the workflow

Predictive analytics, patient stratification, knowledge graphs, and cohort analytics embedded in every project.

OMICS
Every modality, every scale

RNA-seq, spatial omics, proteomics, metabolomics, genomics, and production-grade bioinformatics pipelines.

DOMAIN
Real therapeutic depth

MD and PhD scientists bringing expertise across oncology, immunology, CNS, nephrology, and rare diseases.

DECISIONS
Insights, not handoffs

From biomarker discovery to Go/No-Go decisions, we deliver actionable scientific outcomes.

capabilities
What we do.

Six capability lanes spanning data engineering, OMICs, AI/ML, computational biology, and scientific consulting — designed to be combined as a program demands.

OMICS

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.

RNA-seqscRNAseqSpatial TxProteomicsWGS/WESNextflow
DATA

Data curation & integration

+

Custom curation, ontology management, FAIRification, semantic enrichment, knowledge graphs, and ETL pipelines — turning unstructured data into analysis-ready assets.

CurationOntologyKnowledge GraphsFAIR
AI · ML

Predictive analytics & AI/ML

+

Machine learning for biomarker discovery, drug-response prediction, patient stratification, indication prioritization, and trial benchmarking.

BiomarkersClassifiersStratification
CONSULTING

Scientific consulting

+

Target identification, prioritization, safety analysis, dossier building, disease-landscape assessments, repositioning and asset life-cycle management.

Target IDDossiersRepositioningMoA
COMP BIO

Computational biology

+

Pathway and network analysis, mechanism-of-action elucidation, systems-biology modeling, and integrative multi-OMICs analysis.

PathwaysNetworksMoASystems Bio
PLATFORMS

Applications & visualization

+

R-Shiny, Java, and cloud-native applications. Custom databases, BioVisualizer dashboards, and visualization via Spotfire, Tableau, R, and Python.

R-ShinyJavaSpotfireTableau
Live · expression matrix

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.

SAMPLE 01differential expression · log₂FCSAMPLE 24
workflow
From data to insight.

A typical engagement flows through five tightly-coupled stages, with the same accountable team across the full lifecycle.

03 · Workflow

From data to insight.

A typical engagement flows through five tightly-coupled stages — with the same accountable team across the full lifecycle.

01
STAGE 01

Extract & curate

Custom curation, literature mining, gold-standard datasets, and refresh pipelines.

02
STAGE 02

Annotate & normalize

Ontology management, vocabulary control, semantic enrichment, FAIRification.

03
STAGE 03

Integrate

ETL architecture, knowledge graphs, multi-OMICs integration, custom databases.

04
STAGE 04

Analyze & model

ML modeling, statistical analysis, biological interpretation, hypothesis generation.

05
STAGE 05

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.

TA / Oncology
Oncology

Predictive, prognostic & resistance biomarkers Tumor-type screening & prioritization TME & intra-tumoral MoA scRNA-seq data management

TA / Immunology
Immunology

OMICs dataset curation & analysis Target dossiers & shallow dives Patient stratification Combination potential analysis

TA / CNS
CNS

Multi-OMICs target ID PGx for drug response Portfolio expansion via repositioning Pathway & GEO landscape analysis

TA / Nephrology
Nephrology

CKD · Diabetic nephropathy · IgAN Lupus nephritis · RCC · Renal fibrosis Cell-type-specific receptor analysis Clinical endpoint discovery

05 · Case studies

Selected engagements.

Recent projects across oncology pipelines, RNA therapeutics, and AI-enabled cohort analysis. Drawn from Excelra's published case studies.

01
Oncology · Pipeline engineering · Nextflow + Docker

Production-ready bioinformatics pipelines for a Computational Oncology department

→ Context

A U.S. oncology biotech needed customized pipelines for RNA-Seq, scRNA-Seq, WGS/WES, and HLA typing at scale.

→ Approach

A modular architecture on Nextflow and Docker, rolled out in phases with continuous QC and internalized Sarek workflows.

→ Outcome

High-performance, reproducible workflows across four data modalities — supporting biomarker discovery at scale.

NextflowDockerSarekRNA-SeqscRNA-SeqWGS/WES
02
RNA therapeutics · ASO off-target prediction · ML

Refining off-target prediction for antisense oligonucleotide screening

→ Context

A global pharma innovator needed to reduce false positives in ASO off-target prediction and lower validation burden.

→ Approach

Co-built a roadmap combining ASO–transcript alignment mining, interpretable ML for mismatch patterns, and RNA-seq validation.

→ Outcome

A scalable, mismatch-tolerant pipeline that significantly expanded transcriptome alignment coverage.

ASORNA-seqInterpretable MLFAIR
03
Precision oncology · AI/ML · Cohort analytics

AI-enabled cancer cohort analysis at population scale

→ Context

A U.S. precision-medicine biotech needed scalable pipelines to integrate real-world evidence and somatic testing data.

→ Approach

Analytics workflows combining predictive modeling, AI-driven cohort stratification, and pathway-level enrichment.

→ Outcome

Production analytics for precision-oncology decision support, with biological pathway context.

RWECohort StratificationPathway EnrichmentCompliance
Start Your Journey

Have a question in your pipeline?

We're happy to walk through scope, approach, and timelines for a target ID program, an OMICs pipeline build, a biomarker study, or anything else in the bioinformatics space.