Computational Biologist

Job Title: Computational Biologist
Location: Mississauga, CAD(Remote)

Job Type:Contract role

Description:

Project Role

In-depth analysis of snRNAseq datafrom clinical trial specimens collected pre- and post-treatment to identifyboth tumor intrinsic & extrinsic mechanisms of resistance or sensitivity totargeted therapies. The project involves applying robust methods included butnot limited to differential abundance (MILO), cell-cell communication(CellChat), DESeq2, pseudotime inference, topic modeling, and others, to inferassociations between gene expression, cell states, signaling pathways, andtreatment outcomes.

This project will evolve around theanalysis of data collected from clinical trial specimens with the goal ofidentifying novel response and resistance mechanisms in the context ofimmunotherapy treatment. This will include analyzing pre-treatment single celland bulk RNAseq of these specimens. Additionally, data from digital pathologyand mutation profiling will be analyzed in an integrative manner. Analyses willinclude R packages such as Seurat and Limma as well as some more advancedpipelines, to infer associations between treatment outcomes and geneexpression, cell states or signaling pathways.

Must Have:

• Proficiency in R AND python

• Experience analyzing single-cellRNA-seq datasets

• Experience with working on HighPerformance Computing clusters

• Experience with the followingscRNAseq analysis tools: Cell-Chat, Seurat, scVI, scanpy.

Responsibilities

• Perform computational analyses to:

o Identify cell types, cell states,genes, and pathways associated with clinical outcomes using single-nuc RNAseqdata.

o Infer pseudo-time basedtrajectories in pre- and post-treatment specimens and determine theirassociation with clinical outcomes.

o Construct gene signaturesassociated with particular cell states pre and post-treatment

• Collaborate closely with othercomputational and wet lab scientists

• Contribute intellectually to theongoing research projects

• Present at team & departmentmeetings

Education

• Education level: PhD (preferred),M.S.

• Preferred majors/disciplines:Bioinformatics, Computational Biology, or related field

Qualifications

• Background in computationalbiology, bioinformatics, or related field

• Proficiency in R, Python, Unix,and Git

• Experience with performingstatistical analysis

• Experience with analyzing bulk andsingle-cell RNA-seq

• Experience with single-cellanalysis tools including Seurat, scVI & scanpy

Nice-to-Have Expertise

• Familiarity with MILO &Hotspot for differential abundance and topic modeling