Evaluations and Computational Considerations of Transcriptome Assemblies
Bioinformatics Scientist
Texas A&M AgriLife Research
Genomics and Bioinformatics Service
Location: Koldus Building - Room 110
Time: May 01, 2017 - 2:00-3:00pm
Abstract
Reconstructing the genome and transcriptome of non-model species are essential steps in expanding our understanding of the organism and developing therapeutic approaches to fight disease. The advancement of sequencing technologies had paved the way to accomplish reference genome generations. There are numerous approaches that has been proposed for the task in the recent years, however, there is still a need for closer investigations of the factors that influence the quality of the results. To that end, we designed a multi-step pipeline that includes variety of pre-processing and quality control steps. We utilized the human RNA-Seq data from the Sequencing Quality Control Consortium as a well-characterized and comprehensive resource with available well- studied human reference genome. The XSEDE allocations, sponsored by NSF, enabled us to achieve the computational demands of the project. The high memory Blacklight, Greenfield, and Bridges systems at the Pittsburgh Supercomputing Center were essential to accomplish multiple steps of this research project. This seminar presents our results and the computational aspects of our comprehensive transcriptome assembly and validation study.
Speaker's Bio
Dr. Noushin Ghaffari received her M.S. degree in Computer Information Systems from University of Houston-Clear Lake in 2006 and her Ph.D. degree in Electrical and Computer Engineering from Texas A&M University in 2012. She is currently a bioinformatics scientist at AgriLife Genomics and Bioinformatics, where she is involved in designing next generation sequencing experiments and analyzing high throughput genomic data. Furthermore, she provides training on next generation sequencing methods, bioinformatics analysis pipelines, statistical analysis and modeling for Texas A&M University faculty members, researchers, and students. She also serves on graduate student thesis and dissertation committees. Dr. Ghaffari has an extensive network modeling expertise, and has developed a novel mathematical-based inference approach to derive gene regulatory networks (GRN), designed stationary control policies for altering the long run dynamics of the networks toward more desirable states, and developed reduction methods to eliminate unnecessary genes from a GRN. Throughout her career, she has utilized High Performance Computing in her research projects. She is currently the principal investigator of computational grants sponsored by National Science Foundation (NSF) via the Extreme Science and Engineering Discovery Environment (XSEDE), which has facilitated her high resource demanding research.