Rapid Python used on Big Data to Discover Human Genetic Variation<\/p>\n
Presented by Deniz Kural<\/p>\n
Advances in genome sequencing has enabled large-scale projects such as\nthe 1000 Genomes Project to sequence genomes across diverse populations\naround the world, resulting in very large data sets. I use Python for\nrapid development of algorithms for processing & analyzing genomes and\ndiscovering thousands of new variants, including "Mobile Elements" that\ncopy&paste; themselves across the genome.<\/p>\n
Abstract<\/p>\n
Recent advances in high-throughput sequencing now enables accurate\nsequencing human genomes at a low cost & high speed. This technology is\nnow used to initiate projects involving large-scale sequencing of many\ngenomes. The 1000 Genomes project aims to sequence 2500 genomes across\n27 world populations, and has initially completed its Pilot phase. The\naim of the project is to discover & characterize novel variants. These\nvariants enable association studies that investigate the link between\ngenomic variation & phenotypes, including disease.<\/p>\n
A class of variants, known as "Structural Variants" represent a\nheterogenous class of larger variants, such as inversions, duplications,\ndeletions, and various kinds of insertions.<\/p>\n
I use Python to for rapid development of algorithms to process, analyze,\nand annotate very large data sets. In particular, I focus on Mobile\nElements, pieces of DNA that copy&paste; across the genome. These\nelements constitute roughly half of the genome, whereas protein-coding\ngenes account for roughly 1.5 % of the genome.<\/p>\n
I will discuss distributed computing, genomics, and big data within the\ncontext of Python.<\/p>\n","pubDate":"Fri, 11 Mar 2011 00:00:00 +0000","guid":"tag:pyvideo.org,2011-03-11:\/pycon-us-2011\/pycon-2011--rapid-python-used-on-big-data-to-disc.html","category":["PyCon US 2011","bigdata","casestudy","dna","genomes","pycon","pycon2011"]}}}