Tweet
Post-Doctoral opportunity - RNA-seq meta-analysis
Post-Doctoral Associate position now open in bioinformatics and transcriptional network analysis
Oklahoma Medical Research Foundation, Arthritis & Clinical Immunology Department
Job Summary/Basic Function:
Almost one third of human genes are still uncharacterized, and very little is known about the ~40,000 non-coding RNAs across the genome. To address this problem, our lab is developing approaches to predict gene/transcript function, phenotype and disease relevance using an analysis of transcriptional networks, which has been highly accurate in experimental tests so far.
We’re seeking a Post-Doctoral Associate to help us with to compile and analyze RNA-sequencing data from a variety of sources, including public repositories, to better understand several biological phenomenon such as transcriptional modularity, factors that influence changes in transcriptional states and/or regulation, conditions which increase transcriptional variability and determine alternative splicing. This work has the potential to contribute to the discovery and characterization of novel genes and ncRNAs, particularly those that can potentially affect human disease.
The candidate should be familiar with methods of quality checking and processing RNA-sequencing data (e.g., the TUXEDO suite) and will work on developing methods to analyze RNA-seq data in bulk. The goal of the project is to identify factors that contribute to and/or correlate with changes from healthy to disease states, and to rapidly advance our understanding of non-coding RNA biology in general via functional prediction methods. We have many collaborative projects and anticipate many opportunities for co-authorship for the candidate. The candidate should have solid programming skills and demonstrable programming experience (e.g., publications where they contributed to algorithm development). Familiarity with software and methods for processing next-generation sequencing data is greatly preferred. We have access to a strong array of analytical computing resources.
Minimum Qualifications:
- Ph.D. in a quantitative discipline such as Computer Science, Bioinformatics, or in Physics/Biology with a strong quantitative background.
- Strong experience in writing computer programs in at least one widely used language (e.g., Python, C++, R, Visual Basic) is required.
Preferred Qualifications:
- Experience analyzing/processing next-generation sequencing data preferred.
- Machine learning and/or large scale data mining experience preferred.
- Familiarity with statistics is a plus.
- Must demonstrate strong personal initiative and the ability to work effectively as part of a team.
Please send CV to or Contact:
Jonathan D. Wren, Ph.D.
Oklahoma Medical Research Foundation
825 N.E. 13th Street, Rm MC103
Oklahoma City, Oklahoma 73104-5005
[email protected]
Oklahoma Medical Research Foundation, Arthritis & Clinical Immunology Department
Job Summary/Basic Function:
Almost one third of human genes are still uncharacterized, and very little is known about the ~40,000 non-coding RNAs across the genome. To address this problem, our lab is developing approaches to predict gene/transcript function, phenotype and disease relevance using an analysis of transcriptional networks, which has been highly accurate in experimental tests so far.
We’re seeking a Post-Doctoral Associate to help us with to compile and analyze RNA-sequencing data from a variety of sources, including public repositories, to better understand several biological phenomenon such as transcriptional modularity, factors that influence changes in transcriptional states and/or regulation, conditions which increase transcriptional variability and determine alternative splicing. This work has the potential to contribute to the discovery and characterization of novel genes and ncRNAs, particularly those that can potentially affect human disease.
The candidate should be familiar with methods of quality checking and processing RNA-sequencing data (e.g., the TUXEDO suite) and will work on developing methods to analyze RNA-seq data in bulk. The goal of the project is to identify factors that contribute to and/or correlate with changes from healthy to disease states, and to rapidly advance our understanding of non-coding RNA biology in general via functional prediction methods. We have many collaborative projects and anticipate many opportunities for co-authorship for the candidate. The candidate should have solid programming skills and demonstrable programming experience (e.g., publications where they contributed to algorithm development). Familiarity with software and methods for processing next-generation sequencing data is greatly preferred. We have access to a strong array of analytical computing resources.
Minimum Qualifications:
- Ph.D. in a quantitative discipline such as Computer Science, Bioinformatics, or in Physics/Biology with a strong quantitative background.
- Strong experience in writing computer programs in at least one widely used language (e.g., Python, C++, R, Visual Basic) is required.
Preferred Qualifications:
- Experience analyzing/processing next-generation sequencing data preferred.
- Machine learning and/or large scale data mining experience preferred.
- Familiarity with statistics is a plus.
- Must demonstrate strong personal initiative and the ability to work effectively as part of a team.
Please send CV to or Contact:
Jonathan D. Wren, Ph.D.
Oklahoma Medical Research Foundation
825 N.E. 13th Street, Rm MC103
Oklahoma City, Oklahoma 73104-5005
[email protected]