Abstract: Emerging Linked-Read technologies (aka Read-Cloud or barcoded short-reads) have revived interest in short-read technology as a viable way to understand large-scale structure in genomes and metagenomes. Linked-Read technologies, such as the 10x Chromium system, use a microfluidic system and a specialized set of 3′ barcodes (aka UIDs) to tag short DNA reads sourced from the same long fragment of DNA; subsequently, the tagged reads are sequenced on standard short read platforms. This approach results in interesting compromises. Each long fragment of DNA is only sparsely covered by reads, no information about the ordering of reads from the same fragment is preserved, and 3′ barcodes match reads from roughly 2-20 long fragments of DNA. However, compared to long read technologies the cost per base to sequence is far lower, far less input DNA is required, and their base error rate is that of Illumina short-reads. In this talk, we discuss novel algorithms and some of the advantages of Linked-Reads over standard short read sequencing technologies with applications to whole genome re-sequencing and metagenomics.
More about Iman: Iman Hajirasouliha is Assistant Professor of Computational Genomics at the Institute for Computational Biomedicine at Weill Cornell Medicine of Cornell University and a member of the Englander Institute for Precision Medicine and the Meyer Cancer Center, New York, USA. He completed a Postdoctoral Scholarship at the Computer Science Department, Stanford University, and a Simons Research Fellowship at the University of California, Berkeley. His research focuses on computational genomics and metagenomics, computational digital pathology, large-scale sequence analysis, and characterizing somatic variations and intra-tumor heterogeneity in cancer.
Professor of Biology
Dr. Fairbrother majored in Chemistry at Oberlin College (Oberlin, OH) and received his PhD from Columbia University in 2000. Dr. Fairbrother was a PhRMA Post-doctoral Fellow in Informatics at Massachusetts Institute of Technology (MIT) under mentorship of Christopher Burge and Nobel Laureate Phillip Sharp. Dr Fairbrother is currently a tenured, associate professor in the MCB Department and the Director of Graduate Studies for the Center for Computational Molecular Biology at Brown. His research has focused on precision medicine and RNA genomics. the Fairbrother lab is using high-throughput biochemical screens and computational methods to understand the specificity of RNA processing. Results from Dr. Fairbrother’s lab suggest 1/3 of all hereditary disease mutations affect the processing of genes. More recently, Dr. Fairbrother and his laboratory have become interested in developing methods for analyzing clinical sequencing experiments (e.g., whole-genome and whole-exome sequencing data). To this end, he is active with the Mendelian Genetics Research Group at Harvard.
Ralph S. O’Connor Associate Professor of Biology
Associate Professor of Computer Science
Our lab’s research is in genome informatics, the use of computational and statistical approaches to understand genomes. Our ultimate goal is to achieve a complete understanding of the structure and function of genomes. Specifically, how information is encoded in genomes and how this encoding allows for precise reproducible biological processes and developmental programs, yet is harnessed by evolution to generate remarkable diversity. We work toward this goal both through the study of genome function and evolution, and through the development of tools that support the broader genomics community. For more information, please visit: http://bio.jhu.edu/directory/james-taylor/
About James: James Taylor is the Ralph S. O’Connor Associate Professor of Biology and associate professor of computer science at Johns Hopkins University. Until 2014, he was an associate professor in the departments of biology and mathematics and computer science at Emory University. He is one of the original developers of the Galaxy platform for data analysis, and his group continues to work on extending the Galaxy platform. His group also works on understanding genomic and epigenomic regulation of gene transcription through integrated analysis of functional genomic data. James received a Ph.D. in computer science from Penn State University, where he was involved in several vertebrate genome projects and the ENCODE project.
Associate Professor of Computer Science and Biochemistry
Algorithm and method development for high-resolution structure determination of protein structure using single-particle cryo-electron microscopy, cryo-electron tomography and sub-volume averaging.
Recent advances in direct electron detector technology combined with effective strategies for image analysis have enabled the routine use of single particle cryo-Electron Microscopy (EM) to determine the structure of a variety of protein complexes at near-atomic resolution. The increased availability and access to cryo-EM resources within the structural biology community, has highlighted the need for robust and automated workflows for data analysis that can effectively and rapidly convert raw data into 3D structures. Although many components of the data processing pipeline can already run in an unsupervised manner, there are still several steps where user involvement is required in order to produce meaningful structures. The identification of these bottlenecks is the first step towards achieving the ambitious goal of fully automating the structure determination process by cryo-EM. In this talk, I’ll identify some of the roadblocks that stand in the way of full automation in single particle image analysis and discuss strategies for streamlining and establishing robust workflows for high resolution structure determination by cryo-EM.
Associate Professor of Computer Science and Engineering
Associate Professor of Biochemistry and Molecular Biology
My research falls at the interface of biology and theoretical computer science, specifically in problems where rigorous algorithms and analysis can have a demonstrated impact in the biological sciences. My main focus has been on genome assembly and variation detection, though I am interested in a variety of areas such as phylogenetics, graph theory, computational complexity, on-line algorithms, and networking.