Jeffrey Leek, Johns Hopkins University, Dissecting variation in RNA-seq measurements
RNA-sequencing is the most common tool for measuring gene expression. The reasons for its popularity include dramatically reduced costs over the last 10 yeas and increased flexibility over previous technologies such as microarrays. The price for this flexibility is a much larger quantity of raw data and greater computational cost associated with quantification of expression. Dealing with this data poses for statisticians, computer scientists, and consumers of sequencing data. In this talk I will discuss computational experiments we have performed to dissect variation in sequencing experiments due to biological, technological, and developmental variation. I will also discuss variation in RNA-seq due to often overlooked sources of variability such as annotation, assembly, and bioinformatic variability. I will introduce tools my group has been developing for modeling variation at single base resolution (https://github.com/lcolladotor/derfinder) and the transcript level https://github.com/alyssafrazee/ballgown) in a range of experimental conditions. This is joint work with Alyssa Frazee, Leonardo Collado Torres, Geo Pertea, Andrew Jaffe, Ben Langmead, Rafael Irizarry, and Steven Salzberg.
Jeff Leek is an Associate Professor of Biostatistics and Oncology and Biostatistics Department Career Development Chair at the Johns Hopkins Bloomberg School of Public Health. His data analyses have focused on the molecular profiles of brain development, breast cancer, stem cell self-renewal, and the immune response to major blunt force trauma. He is the co-editor of the Simply Statistics Blog (http://simplystatistics.org/) and co-director of the Johns Hopkins Specialization in Data Science (https://www.coursera.org/specialization/jhudatascience/1). His Data Analysis course on Coursera has enrolled more than 180,000 students.
Location: Carl Icahn Lab 101
Date/Time: 04/07/14 at 4:15 pm - 04/07/14 at 5:15 pm
Category: Quantitative & Computational Biology
Department: Lewis-Sigler Institute