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{ "category": "PyCon US 2011", "language": "English", "slug": "pycon-2011--rapid-python-used-on-big-data-to-disc", "speakers": [ "Deniz Kural" ], "tags": [ "bigdata", "casestudy", "dna", "genomes", "pycon", "pycon2011" ], "id": 414, "state": 1, "title": "Rapid Python used on Big Data to Discover Human Genetic Variation", "summary": "", "description": "Rapid Python used on Big Data to Discover Human Genetic Variation\n\nPresented by Deniz Kural\n\nAdvances in genome sequencing has enabled large-scale projects such as the\n1000 Genomes Project to sequence genomes across diverse populations around the\nworld, resulting in very large data sets. I use Python for rapid development\nof algorithms for processing & analyzing genomes and discovering thousands of\nnew variants, including \"Mobile Elements\" that copy&paste; themselves across\nthe genome.\n\nAbstract\n\nRecent advances in high-throughput sequencing now enables accurate sequencing\nhuman genomes at a low cost & high speed. This technology is now used to\ninitiate projects involving large-scale sequencing of many genomes. The 1000\nGenomes project aims to sequence 2500 genomes across 27 world populations, and\nhas initially completed its Pilot phase. The aim of the project is to discover\n& characterize novel variants. These variants enable association studies that\ninvestigate the link between genomic variation & phenotypes, including\ndisease.\n\nA class of variants, known as \"Structural Variants\" represent a heterogenous\nclass of larger variants, such as inversions, duplications, deletions, and\nvarious kinds of insertions.\n\nI use Python to for rapid development of algorithms to process, analyze, and\nannotate very large data sets. In particular, I focus on Mobile Elements,\npieces of DNA that copy&paste; across the genome. These elements constitute\nroughly half of the genome, whereas protein-coding genes account for roughly\n1.5 % of the genome.\n\nI will discuss distributed computing, genomics, and big data within the\ncontext of Python.\n\n", "quality_notes": "", "copyright_text": "Creative Commons Attribution-NonCommercial-ShareAlike 3.0", "embed": "", "thumbnail_url": "http://a.images.blip.tv/Pycon-PyCon2011RapidPythonUsedOnBigDataToDiscoverHumanGenet382.png", "duration": null, "video_ogv_length": 110103548, "video_ogv_url": null, "video_ogv_download_only": false, "video_mp4_length": null, "video_mp4_url": "http://05d2db1380b6504cc981-8cbed8cf7e3a131cd8f1c3e383d10041.r93.cf2.rackcdn.com/pycon-us-2011/414_rapid-python-used-on-big-data-to-discover-human-genetic-variation.mp4", "video_mp4_download_only": false, "video_webm_length": null, "video_webm_url": null, "video_webm_download_only": false, "video_flv_length": null, "video_flv_url": null, "video_flv_download_only": false, "source_url": "", "whiteboard": "", "recorded": "2011-03-11", "added": "2012-02-23T04:20:00", "updated": "2014-04-08T20:28:28.023" }