Christopher Benway

About Christopher Benway

I am a computational biologist with a deep background in molecular biology and statistical genetics, currently advancing cancer biomarker discovery at Exosome Diagnostics (Bio-Techne). My work bridges state-of-the-art experimental data, bioinformatics, and machine learning to support research and development programs for novel diagnostic signatures, with a focus on exosomal biomarkers, RNA-seq, and multiomic integration.Previously, at Boston Children’s Hospital, I led computational efforts in the Sampson Lab to uncover the genetic basis of kidney disease, integrating GWAS, eQTL mapping, single-cell transcriptomics, and causal inference. My PhD research at Tufts focused on miRNA:mRNA targeting and nephrotoxicity, laying a strong foundation in high-throughput functional genomics.At Exosome Diagnostics (Bio-Techne), I focus on making sense of complex RNA-seq and multi-omic datasets to support biomarker discovery. I’ve built and maintained scalable Nextflow pipelines, developed cross-validated ML models for omics classification, and created internal tools for QC, feature selection, and result reporting. I’m passionate about making robust, reproducible bioinformatics workflows that deliver real-world biological and clinical impact.

Experience

1. Senior Bioinformatics Scientist

   Exosome Diagnostics, A Subsidiary Of Mdxhealth

   Aug 2023 — Present · Waltham, MA, US

   As a senior member of the computational team, leveraged extensive experience across biology, sequencing, bioinformatics, and human clinical research to develop innovative exosomal multiomics platform and biomarker discovery research initiatives. Specialized in oncology biomarker discovery with primary focus on prostate and colorectal cancer, developing liquid biopsy assays and companion diagnostics to advance precision cancer medicine and improve patient outcomes. Developed comprehensive QC and cross-validation frameworks for machine learning biomarker discovery and multiomic data integration, supporting RNA-seq, small-RNA-seq, proteomics, and cell-free DNA (cfDNA) modalities in clinical research Optimized and implemented batch effect correction methods and tissue deconvolutionprofiling for human liquid biopsy studies Led multiple companion diagnostic (CDx) projects utilizing exosomal RNA-seq and small-RNA-seq for novel biomarker discovery, delivering high-quality results and meeting accelerated timelines to support clinical trials for pharmaceutical clients. Developed and maintained small RNA-seq NGS pipeline in Nextflow, achieving EV-miRNA profiles exceeding literature standards for detection, sensitivity, and reproducibility. Ensured reproducibility of bioinformatics workflows using GitHub for version control, Nextflow for orchestration, and Docker/Conda for environment management Utilized large-scale public datasets such as ENCODE, GTEx, Roadmap Epigenomics, and TCGA to support multiomic data integration and biological discovery Co-led development of data normalization pipeline for deep learning models in cancer classification tasks, contributing to AI Innovation Stream Led initiatives across next generation sequencing, bioinformatics, and project management teams, presenting results to external partners and internal stakeholders

Education

• Tufts University

  Doctor of Philosophy - PhD, Genetics

  2011 — 2017

• Harvard University

  Bachelor of Arts - BA, Biology

  2004 — 2008

Skills

• Statistics
• Genetics
• Gene Expression
• Elisa
• Data Analysis
• Functional Genomics
• Cell Culture
• Bioinformatics
• Polymerase Chain Reaction (Pcr)
• Protein Purification
• Western Blotting
• Lifesciences
• Computational Biology
• Immunology
• Laboratory
• Flow Cytometry
• Microscopy
• Mirna
• Qpcr
• Sequence Analysis
• Gwas
• Real-Time Polymerase Chain Reaction (Qpcr)
• Research
• Molecular Biology
• Fluorescence Microscopy
• Laboratory Skills
• Qualitative Research
• Pcr
• Sequencing
• Rnaseq
• Genomics
• Transfection
• R
• Molecular Cloning
• Powerpoint
• Microsoft Excel
• Science
• Biochemistry
• Tissue Culture
• Microsoft Office