Pavel Pevzner ( ppevzner/) is Professor of Computer Science and Engineering at University of California San Diego (UCSD), where he holds the Ronald R. Taylor Chair and has taught a Bioinformatics Algorithms course for the last 12 years. In 2006, he was named a Howard Hughes Medical Institute Professor. In 2011, he founded the Algorithmic Biology Laboratory in St. Petersburg, Russia, which develops online bioinformatics platform Rosalind ( ). His research concerns the creation of bioinformatics algorithms for analyzing genome rearrangements, DNA sequencing, and computational proteomics. He authored Computational Molecular Biology (The MIT Press, 2000), co-authored (jointly with Neil Jones) An Introduction to Bioinformatics Algorithms (The MIT Press, 2004), and Bioinformatics Algorithms: An Active Learning Approach (Active Learning Publishers, 2014). For his research, he has been named a Fellow of both the Association for Computing Machinery (ACM) and the International Society for Computational Biology (ISCB).
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Abstract:Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders.Keywords: bioinformatics; clinical genetics; high throughput data; NGS pipeline; NGS platforms 2ff7e9595c
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