Literature DB >> 28163155

Next-generation sequencing: big data meets high performance computing.

Bertil Schmidt1, Andreas Hildebrandt2.   

Abstract

The progress of next-generation sequencing has a major impact on medical and genomic research. This high-throughput technology can now produce billions of short DNA or RNA fragments in excess of a few terabytes of data in a single run. This leads to massive datasets used by a wide range of applications including personalized cancer treatment and precision medicine. In addition to the hugely increased throughput, the cost of using high-throughput technologies has been dramatically decreasing. A low sequencing cost of around US$1000 per genome has now rendered large population-scale projects feasible. However, to make effective use of the produced data, the design of big data algorithms and their efficient implementation on modern high performance computing systems is required.
Copyright © 2017 Elsevier Ltd. All rights reserved.

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Year:  2017        PMID: 28163155     DOI: 10.1016/j.drudis.2017.01.014

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  20 in total

1.  Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop.

Authors:  Kathleen M Jagodnik; Simon Koplev; Sherry L Jenkins; Lucila Ohno-Machado; Benedict Paten; Stephan C Schurer; Michel Dumontier; Ruben Verborgh; Alex Bui; Peipei Ping; Neil J McKenna; Ravi Madduri; Ajay Pillai; Avi Ma'ayan
Journal:  J Biomed Inform       Date:  2017-05-10       Impact factor: 6.317

2.  A study on fast calling variants from next-generation sequencing data using decision tree.

Authors:  Zhentang Li; Yi Wang; Fei Wang
Journal:  BMC Bioinformatics       Date:  2018-04-19       Impact factor: 3.169

3.  Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application.

Authors:  Gaye Lightbody; Valeriia Haberland; Fiona Browne; Laura Taggart; Huiru Zheng; Eileen Parkes; Jaine K Blayney
Journal:  Brief Bioinform       Date:  2019-09-27       Impact factor: 11.622

Review 4.  Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.

Authors:  Antonio Jesús Banegas-Luna; Jorge Peña-García; Adrian Iftene; Fiorella Guadagni; Patrizia Ferroni; Noemi Scarpato; Fabio Massimo Zanzotto; Andrés Bueno-Crespo; Horacio Pérez-Sánchez
Journal:  Int J Mol Sci       Date:  2021-04-22       Impact factor: 5.923

5.  Houston Methodist Variant Viewer: An Application to Support Clinical Laboratory Interpretation of Next-generation Sequencing Data for Cancer.

Authors:  Paul A Christensen; Yunyun Ni; Feifei Bao; Heather L Hendrickson; Michael Greenwood; Jessica S Thomas; S Wesley Long; Randall J Olsen
Journal:  J Pathol Inform       Date:  2017-11-23

6.  The High-Throughput Analyses Era: Are We Ready for the Data Struggle?

Authors:  Valeria D'Argenio
Journal:  High Throughput       Date:  2018-03-02

7.  HSRA: Hadoop-based spliced read aligner for RNA sequencing data.

Authors:  Roberto R Expósito; Jorge González-Domínguez; Juan Touriño
Journal:  PLoS One       Date:  2018-07-31       Impact factor: 3.240

Review 8.  No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects.

Authors:  Antoine Danchin; Christos Ouzounis; Taku Tokuyasu; Jean-Daniel Zucker
Journal:  Microb Biotechnol       Date:  2018-05-28       Impact factor: 5.813

9.  KAUST Metagenomic Analysis Platform (KMAP), enabling access to massive analytics of re-annotated metagenomic data.

Authors:  Intikhab Alam; Allan Anthony Kamau; David Kamanda Ngugi; Takashi Gojobori; Carlos M Duarte; Vladimir B Bajic
Journal:  Sci Rep       Date:  2021-06-01       Impact factor: 4.379

10.  SISTEMA: A large and standardized collection of transcriptome data sets for human pluripotent stem cell research.

Authors:  Margot Jarrige; Hélène Polvèche; Alexandre Carteron; Stéphane Janczarski; Marc Peschanski; Didier Auboeuf; Cécile Martinat
Journal:  iScience       Date:  2021-06-24
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