Computational Scientist - Dr. Sachet Ashok Shukla's Laboratory

SUMMARY

The Shukla lab is a highly translational group focused on antigen discovery and development of personalized immunotherapies. We develop computational approaches and deploy state-of-the-art technologies and experimental assays to elucidate mechanisms underlying immunogenicity, immune escape and clinical outcomes to cancer immunotherapy. We are seeking highly motivated and skilled computational individuals who will work within an interdisciplinary team comprising computational scientists, software engineers, oncologists, and immunologists on tumor immunity projects across cancer types.

The Computational Scientist will lead the analysis and interpretation of data generated from long-read and single-cell sequencing. A primary responsibility will be to develop pipelines and novel methods for analysis of data from Pacific Biosciences and Oxford Nanopore Technologies platforms. The candidate will also analyze data from single-cell sequencing experiments including scRNA-seq, CITE-seq, ATAC-seq and TCR-seq to answer key scientific questions and provide bioinformatic support to ongoing research efforts.

CORE VALUES

Caring: By our words and actions we demonstrate caring toward everyone.

• We are sensitive to the concerns of our patients, their loved ones and our colleagues.
• We are respectful and courteous to each other and practice cultural humility.
• We promote and reward teamwork and inclusion.

Integrity: We work together with professionalism to merit the trust of our colleagues and those we serve in all that we do.

• We hold ourselves, and each other, accountable for our work - decisions and data - and for practicing our values and ethics.
• We advocate for diversity and equity for our workforce, for those we serve and for our community.
• We communicate frequently, honestly, openly and responsibly.

Discovery: We embrace creativity and seek new knowledge from diverse perspectives.

• We encourage continuous learning, seeking out information and new ideas.
• We team with each other to identify and resolve problems.
• We seek personal growth and enable others to do so.

Safety: We provide a safe environment - physically and psychologically - for our patients, for our colleagues and for our community.

• We create a sense of security and empowerment and are committed to keeping one another free from harm.
• We embrace a framework and best practices for the highest quality of care and service.
• We inspire trust by modeling excellence in our work and acceptance of each person's contributions.

Stewardship: We protect and preserve our institutional reputation and the precious resources - people, time, financial and environmental - entrusted to us.

• We prioritize the health and well-being of each other.
• We act responsibly to safeguard the institution's finances.
• We ensure the proper care and use of time, data, materials, equipment and property afforded to us.

Key Functions

Analysis of long-read sequencing data

• Develop pipelines and novel methods for genome and RNA data analysis from PacBio Sequel IIe and Oxford Nanopore MinION instruments
• Deploy best-in-class de novo genome assembly, variant detection, isoform and aberrant expression characterization (alternate splicing, gene fusion, structural rearrangements) using long-read sequencing data at bulk and single-cell levels
• Perform comparative analysis of PacBio and Oxford Nanopore platforms

Single-cell sequencing analysis

• Perform temporal analysis of single-cell RNA-seq, ATAC-seq, CITE-seq, Ribo-seq and TCR/BCR-seq data to infer clonal dynamics and evolutionary trajectories of tumor and immune cells
• Create novel algorithms for integrating short and long-read single-cell sequencing results in order to attain comprehensive variant detection, isoform identification and quantification
• Develop robust normalization, integration and batch correction workflows for analysis of single-cell data

Leadership role in ongoing research projects

• Work closely with immunologists and computational scientists in designing and interpreting experimental results
• Keep up-to-date with current pertinent literature and present innovative ideas to the group
• Provide advanced bioinformatic, statistical and visualization support to ongoing research projects in the lab
• Organize data and document and code developed for each project

Bachelor's degree in Biomedical Engineering, Electrical Engineering, Physics, Applied Mathematics or related field. Preferred Education: Master's degree (M.S.) or PhD in Computational Biology, Computer Science, Engineering, Applied Mathematics, Biostatistics or a related discipline with BS/MS in Immunology, Cancer Biology or a related discipline. Five years experience in scientific software development/analysis. With preferred degree three years experience required. Preferred Experience: The ideal candidate will have formal training and experience in analysis of data from at least one long-read sequencing platform and a wide range of single-cell sequencing profiling data. The candidate will have experience in the creation or use of genome assembly, transcriptional isoform annotation, gene modeling, RNA-seq based fusion detection and/or spliced alignments algorithms. Prior knowledge of cancer biology or immunology is a plus. Proficiency in scripting, statistical computing and use of high-performance computing environments is expected. Software development experience is desirable but not required. Must thrive in an academic/professional atmosphere, where interdisciplinary teams are central to project success. It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html.