Your Dedicated Analytical Partner for Fractional Bioinformatic Leadership & Specialized Multi-omic Solutions.
Get full-stack bioinformatics support—from custom pipeline engineering and multi-omic analyses to dedicated fractional expertise embedded directly within your R&D team.
Two Flexible Engagement Tracks
Fractional Retainer: Dedicated monthly computational hours (10–40 hrs/mo) acting as your ongoing bioinformatic lead with zero full-time overhead.
Project-Based (SOW): Tailored execution for custom workflows, tertiary analyses, target validation, and publication/FDA-ready reports.
What We Offer
Full End-to-End Multi-Omic Analysis & Research
From raw FASTQ reads to final peer-reviewed publication—we execute every phase of computational R&D with publication-grade rigor.
Specialized Solutions
1. Clinico-Omic Analysis
End-to-end analytical execution across the entire spectrum of modern omics and clinical metadata. We process, harmonize, and extract actionable insights from both single-modality and multi-modal study cohorts.
2. Advanced Bioinformatics
Go beyond off-the-shelf bioinformatic tools. We deploy literature-curated workflows, custom algorithmic engineering, multi-omic fusion engines, and interpretable machine learning tailored to complex, non-linear biological data.
Overview: Uncover deep, non-linear biological relationships buried inside your datasets. Bainom deploys specialized tertiary analysis pipelines and machine learning frameworks meticulously curated from top-tier scientific literature, bringing advanced pattern-recognition models directly into your execution workspace.
Sample Capabilities & Modal Implementations:
Bulk RNA-Seq & Epigenomics: Alternative splicing analysis, immune cell deconvolution, non-coding RNA mapping, and weighted gene co-expression network analysis (WGCNA).
Single-Cell & Spatial Omics: Dynamic cell-cell signaling networks (CellChat, NicheNet), spatial pattern architectures, RNA velocity/trajectory inference, and automated high-fidelity cell-type identification.
Overview: Bypass the rigid constraints of static software tools. Bainom programmatically converts abstract biological hypotheses, custom mathematical equations, or third-party GitHub libraries into stable, production-grade computational assets tailored to your study design.
Sample Capabilities & Architectural Implementations:
Abstract Biological Rule Execution: Materialize custom spatial/genomic rules on demand (e.g., automatically identifying and quantifying signal peaks located exactly 50–100 nucleotides downstream from stop codons).
Bespoke Kinetic Analytics: Execute multi-step, time-course data fitting loops (e.g., iCLIP read-count fitting to calculate precise transcript-wide per-site occupancy rates on non-standard experimental setups).
Equation-to-Pipeline Translation: Directly program non-linear differential equations and custom kinetic models into active processing layers.
Overview: Gain a multi-layered molecular picture by breaking down data silos. Bainom processes your study conditions through two distinct integration pathways depending on your data availability.
Key Integration Pathways:
Multi-Modal Clinico-Omic Fusion: Structurally fuse and harmonize disparate datasets (e.g., scRNA-Seq + scATAC-Seq, RNA-Seq + Proteomics) directly with complex patient clinical phenotypes using state-of-the-art predictive integration models.
Disease Matrix Expansion (Single-Track Expansion): If you only possess a single-omic track (e.g., standalone bulk RNA-Seq), Bainom cross-references your inputs with our pre-compiled Multi-omic Disease Matrix. By integrating structural-functional DNA/RNA attributes from global baseline repositories, the platform infers promoter states, 3’UTR sequence motif enrichments, and predicted protein expression levels to reveal exact mechanisms driving disease states versus controls.
Overview: Extract actionable therapeutic targets and biomarker panels using production-grade machine learning built for high-dimensional ($p \gg n$) biological data.
Key Capabilities & ML Infrastructure:
Interpretability-First Architectures: Leverage SHAP values, attention weights, and feature attribution methods so every ML prediction links directly back to targetable biological pathways and mechanisms.
Ultra-Sparse Feature Selection: Isolate minimal, clinically viable biomarker panels from massive multi-omic matrices without overfitting.
Cross-Cohort Model Benchmarking: Rigorously validate models across independent public repositories (TCGA, GEO, UK Biobank) to ensure generalizability and publication readiness.
3. Clinical Intelligence & Translation
Bridge the gap between raw multi-omic features and actionable clinical translation. We map, score, and prioritize molecular candidates against global disease repositories and regulatory frameworks.
Target Prioritization & Feature Ranking
Ranking candidate biomarkers and drug targets using custom multi-factor scoring engines that integrate differential expression, network centrality, and druggability metrics.
Clinical Knowledge Base Matching
Cross-referencing prioritized genes/pathways against global clinical trial registries, FDA drug-target databases, TCGA/GEO patient cohorts, and variant effect predictors.
Translational Mechanism Reporting
Translating high-dimensional computational outputs into clear, executive-ready translational dossiers designed for internal leadership, investors, and FDA pre-submission context.
4. Infrastructure, Grant Writing & Research Article Prep
Enterprise-grade computational infrastructure designed for reproducibility, compliance, and non-dilutive funding—backed by publication-ready scientific communication.
Manuscript Drafting & Methodology: Co-authoring and drafting bioinformatic methodology, results, and discussion sections tailored to high-impact journals (e.g., Nature, Cell, Nucleic Acids Research).
Publication-Grade Visualization: Generating vector graphics (
.svg/.pdf), high-resolution multi-panel main figures, and supplementary data tables adhering to journal submission guidelines.Peer Review & Revision Support: Addressing editor and reviewer comments, running supplementary validation analyses, and drafting response letters during peer review
SBIR/STTR & NIH Proposals: Direct support for NIH, NSF, and foundation grant applications—drafting bioinformatic execution plans, Data Management & Sharing (DMS) plans, and preliminary data figures to maximize funding success.
Fractional Key Personnel: Serving as a co-investigator, senior bioinformatic lead, or computational key personnel on grant submissions to satisfy technical leadership requirements.
Containerized Pipelines: Modular, containerized execution (Docker, Nextflow, Snakemake) on scalable cloud environments (AWS/GCP), ensuring 100% reproducible, version-controlled workflows.
Data Governance & FAIR Principles: Structuring data architecture to align with FAIR principles and clinical compliance protocols for seamless data sharing and IP protection.