Bioinformatics Technician

Tufts University•Grafton, MA

About The Position

The Department of Clinical Sciences is responsible for providing didactic and clinical training of veterinary students, conducting research pertaining to diseases of large and small animals, and providing health care services for large and small animals. What You'll Do This is a Bioinformatics Technician position in a translational cancer genomics research laboratory that supports comparative clinical trials in pet dogs with naturally occurring cancers. The research laboratory performs a wide variety of computational analyses, including whole genome sequencing, RNA sequencing (bulk and single cell), whole methylome sequencing, custom NGS panels, liquid biopsy, and spatial transcriptomics. The primary focus of this position is to help optimize and complete research tasks related to the computational analysis of multiple datasets Essential functions and responsibilities as a Bioinformatics Tech will be: Perform computational experiments. Detailed digital data recording of all work. Assist with the organization of datasets, documentation of data analysis. Facilitate data transfer within and outside of Tufts using Globus. Maintain sample metadata and clinical observations gathered in REDCap databases. Perform all aspects of data analysis: raw data QC, alignment and pre-processing, primary and secondary data analysis. Learn new approaches and then apply these to undertake a variety of cancer-related analyses including data sets generated from whole genome sequencing, whole exome sequencing, custom mutation panels, whole methylome sequencing, methylation panels, bulk RNAseq, single cell/nuclei RNAseq (3’, 5’ and FLEX/FFPE), ATACseq, nCounter panels, spatial transcriptomics, ChIP-seq, cut-and-run and others Apply machine learning algorithms to clinical multi-omic datasets. Assist with collaborative and service projects Attend and actively participate in group lab meeting and collaborative project calls. Assist in training of new lab members to perform basic computational processes. Help run optimized analytic tools on clinical samples and generate preliminary reports for review by Dr. Gardner. Miscellaneous duties: Assist with manuscript, grant and presentation preparation. This may include figure generation, power calculations and drafting data-specific sections for review. This description is not intended to be all-inclusive. Employee may perform other duties as assigned to meet the ongoing needs of the organization.

Requirements

Knowledge and experience as typically acquired by Master's Degree in computer science or related field
Familiarity with computational analysis of non-human/non-murine datasets.
Previous experience in analytics of cancer genomic data.
Exceptional interpersonal and communication skills with the ability to interact effectively with co-workers, clinicians/investigators, students, and staff.
Enthusiastic, genuine, empathetic and self-motivated professional with a positive, upbeat attitude.

Nice To Haves

PhD in computer science or related field.
1-3 years of cancer computational experience (can be work experience or experience obtained through PhD/thesis)
Strong interpersonal skills
Attention to detail, and the ability to troubleshoot and develop custom pipelines for analysis of canine omics data.
Strong background in cancer bioinformatics and experience working with non-human/non-murine datasets is preferred.

Responsibilities

Perform computational experiments. Detailed digital data recording of all work.
Assist with the organization of datasets, documentation of data analysis.
Facilitate data transfer within and outside of Tufts using Globus.
Maintain sample metadata and clinical observations gathered in REDCap databases.
Perform all aspects of data analysis: raw data QC, alignment and pre-processing, primary and secondary data analysis.
Learn new approaches and then apply these to undertake a variety of cancer-related analyses including data sets generated from whole genome sequencing, whole exome sequencing, custom mutation panels, whole methylome sequencing, methylation panels, bulk RNAseq, single cell/nuclei RNAseq (3’, 5’ and FLEX/FFPE), ATACseq, nCounter panels, spatial transcriptomics, ChIP-seq, cut-and-run and others
Apply machine learning algorithms to clinical multi-omic datasets.
Assist with collaborative and service projects
Attend and actively participate in group lab meeting and collaborative project calls.
Assist in training of new lab members to perform basic computational processes.
Help run optimized analytic tools on clinical samples and generate preliminary reports for review by Dr. Gardner.
Assist with manuscript, grant and presentation preparation. This may include figure generation, power calculations and drafting data-specific sections for review.