scholarly journals Ultra-Fast Next Generation Human Genome Sequencing Data Processing Using DRAGEN<sup>TM</sup> Bio-IT Processor for Precision Medicine

2017 ◽  
Vol 07 (01) ◽  
pp. 9-19 ◽  
Author(s):  
Amit Goyal ◽  
Hyuk Jung Kwon ◽  
Kichan Lee ◽  
Reena Garg ◽  
Seon Young Yun ◽  
...  
Keyword(s):  
Data Processing ◽  
Precision Medicine ◽  
Human Genome ◽  
Genome Sequencing ◽  
Next Generation ◽  
Sequencing Data ◽  
Human Genome Sequencing

scholarly journals Overview of Genomic Tools for Circular Visualization in next-Generation Genomic Sequencing Era

2018 ◽  
Author(s):  
Alisha Parveen ◽  
Sukank Khurana ◽  
Abhishek Kumar
Keyword(s):  
Human Genome ◽  
Genome Sequencing ◽  
Genomic Sequencing ◽  
Next Generation ◽  
Sequencing Data ◽  
The Third ◽  
Genomic Tools ◽  
Rapid Changes ◽  
Human Genome Sequencing ◽  
Visualization Tools

After human genome sequencing and rapid changes in genome sequencing methods, we have entered in the era of rapidly accumulating genome-sequencing data. This has poses development of several types of methods for representing results of genome sequencing data. Circular genome visualizations tools are also critical in this area as they provide rapid interpretation and simple visualization of overall data. In the last 15 years, we have seen rapid changes in circular visualization tools after the development of the circos tool with 1&ndash;2 tools published per year. Herein we have summarized and revisited all these tools until the third quarter of 2018.&nbsp;

scholarly journals Practical guide for managing large-scale human genome data in research

2020 ◽  
Vol 66 (1) ◽  
pp. 39-52
Author(s):  
Tomoya Tanjo ◽  
Yosuke Kawai ◽  
Katsushi Tokunaga ◽  
Osamu Ogasawara ◽  
Masao Nagasaki
Keyword(s):  
Data Processing ◽  
Whole Genome Sequencing ◽  
Human Genome ◽  
Genome Sequencing ◽  
Large Scale ◽  
Genomic Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Genome Data ◽  
Human Genome Data

AbstractStudies in human genetics deal with a plethora of human genome sequencing data that are generated from specimens as well as available on public domains. With the development of various bioinformatics applications, maintaining the productivity of research, managing human genome data, and analyzing downstream data is essential. This review aims to guide struggling researchers to process and analyze these large-scale genomic data to extract relevant information for improved downstream analyses. Here, we discuss worldwide human genome projects that could be integrated into any data for improved analysis. Obtaining human whole-genome sequencing data from both data stores and processes is costly; therefore, we focus on the development of data format and software that manipulate whole-genome sequencing. Once the sequencing is complete and its format and data processing tools are selected, a computational platform is required. For the platform, we describe a multi-cloud strategy that balances between cost, performance, and customizability. A good quality published research relies on data reproducibility to ensure quality results, reusability for applications to other datasets, as well as scalability for the future increase of datasets. To solve these, we describe several key technologies developed in computer science, including workflow engine. We also discuss the ethical guidelines inevitable for human genomic data analysis that differ from model organisms. Finally, the future ideal perspective of data processing and analysis is summarized.

scholarly journals Overview of Genomic Tools for Circular Visualization in the Nextgeneration Genomic Sequencing Era

2019 ◽  
Vol 20 (2) ◽  
pp. 90-99 ◽  
Author(s):  
Alisha Parveen ◽  
Sukant Khurana ◽  
Abhishek Kumar
Keyword(s):  
Human Genome ◽  
Genome Sequencing ◽  
Genomic Sequencing ◽  
Sequencing Data ◽  
The Third ◽  
Genomic Tools ◽  
Genome Visualization ◽  
Rapid Changes ◽  
Human Genome Sequencing ◽  
Visualization Tools

After human genome sequencing and rapid changes in genome sequencing methods, we have entered into the era of rapidly accumulating genome-sequencing data. This has derived the development of several types of methods for representing results of genome sequencing data. Circular genome visualization tools are also critical in this area as they provide rapid interpretation and simple visualization of overall data. In the last 15 years, we have seen rapid changes in circular visualization tools after the development of the circos tool with 1-2 tools published per year. Herein we have summarized and revisited all these tools until the third quarter of 2018.

scholarly journals Correction: A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health

PLoS Medicine ◽  
2018 ◽  
Vol 15 (8) ◽  
pp. e1002650
Author(s):  
Muin J. Khoury ◽  
W. Gregory Feero ◽  
David A. Chambers ◽  
Lawrence C. Brody ◽  
Nazneen Aziz ◽  
...  
Keyword(s):  
Translational Research ◽  
Human Genome ◽  
Genome Sequencing ◽  
Appropriate Use ◽  
Research Framework ◽  
Human Genome Sequencing ◽  
Improve Health

Pressing ahead with human genome sequencing

1995 ◽  
Vol 11 (2) ◽  
pp. 121-125 ◽  
Author(s):  
Richard A. Gibbs
Keyword(s):  
Human Genome ◽  
Genome Sequencing ◽  
Human Genome Sequencing

Variant calling and quality control of large-scale human genome sequencing data

2019 ◽  
Vol 3 (4) ◽  
pp. 399-409 ◽  
Author(s):  
Brandon Jew ◽  
Jae Hoon Sul
Keyword(s):  
Quality Control ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Large Scale ◽  
Variant Calling ◽  
Sequencing Data ◽  
Computational Approaches ◽  
Sequencing Errors ◽  
Human Genome Sequencing ◽  
Number Of Individuals

Abstract Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.

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