Bioinformatician I - Anczukow Lab

The Jackson Laboratory•Farmington, CT

About The Position

The Bioinformatician will develop algorithms and computational resources to analyze how dysregulated RNA splicing contributes to human disease. Working with the principal investigator and experimental collaborators, the role translates biomedical questions into algorithmic approaches, implements them as tested and reproducible software, and applies them to bulk, single-cell, and spatial transcriptomic data. The bioinformatician takes scientific ownership of method development, advancing novel approaches through benchmarking, peer-reviewed publication, and open release. The work includes mechanistic interpretation of splicing changes, connecting them with regulators and functional consequences, and proposing and prioritizing experimental validation. The position presents findings internally and externally, works independently with limited supervision, collaborates with lab members, and helps update and maintain widely used pipelines and tools. The role develops and applies methods to patient-derived, model-system, and public datasets; integrates splicing data with genomic variation and other molecular modalities; and builds reproducible, openly released Nextflow pipelines. It also contributes to manuscripts and grant applications, stays current with relevant literature, and mentors junior staff and trainees as needed.

Requirements

Bachelor's degree in Bioinformatics or a relevant biological sciences field plus five years of professional bioinformatics work experience, or a master's degree in Bioinformatics or a relevant biological sciences field.
Minimum education: bachelor's degree.
Minimum experience: 2 years.
Deep knowledge of RNA biology and genomics, including splicing mechanisms, cis-regulatory elements, and splicing factors.
Demonstrated experience analyzing splicing in transcriptomic data, including understanding tool limitations and failure modes.
Proficiency in Python, R, and Bash, and experience with workflow management using Nextflow.
Experience developing algorithms in machine learning, statistics, or genomics, evidenced by novel methods or substantial extensions of existing methods.
Software engineering practices including version control, automated testing, containerization, and documentation.
Ability to read, critique, and apply primary literature related to current projects.
Ability to lead projects and work independently with limited supervision.
Clear written and oral communication of methods and results to scientific audiences.
First-author publication in a peer-reviewed journal.

Nice To Haves

PhD degree in bioinformatics, biology, or a related field.
Experience with single-cell and/or spatial transcriptomics.
Experience with HPC or cloud compute environments at scale.
Experience mentoring students or junior colleagues.
Experience with human cohort or controlled-access data (TCGA, GTEx, dbGaP) and splicing QTL analysis.
Experience with deep-learning models of sequence-to-splicing relationships.
Experience with long-read RNA sequencing and isoform-level analysis.
Prior projects on splicing in a disease context, including identification of disease-associated splicing events or splicing-factor dysregulation.

Responsibilities

Design, implement, test, and benchmark novel algorithms and statistical or machine-learning models for splicing analysis, including event detection and quantification, differential splicing, isoform-level analysis, and prediction of variant effects on splicing.
Lead method development through to manuscript-ready stage and serve as a co-author on resulting publications.
Apply methods to patient-derived, model-system, and public datasets, including bulk and single-cell data, to identify disease-associated splicing alterations, upstream drivers, and downstream consequences.
Integrate splicing data with genomic variation, long-read transcriptomics, epigenomic, and proteomic data to support mechanistic interpretation.
Build version-controlled, containerized, documented Nextflow pipelines and release tools openly to support reproducible analyses.
Work with the PI and experimental collaborators to define questions, design validation experiments, and interpret results.
Present pipelines, methods, and results at internal meetings and external conferences, and contribute to manuscripts and grant applications.
Keep current with splicing, RNA biology, and computational genomics literature.
Mentor junior staff and trainees as needed.
Assist and collaborate with lab members to advance their research and help update and maintain widely used pipelines and tools.