Machine Learning-Based Method for Obesity Risk Evaluation Using Single-Nucleotide Polymorphisms Derived from Next-Generation Sequencing

2018 ◽  
Vol 25 (12) ◽  
pp. 1347-1360 ◽  
Author(s):  
Hsin-Yao Wang ◽  
Shih-Cheng Chang ◽  
Wan-Ying Lin ◽  
Chun-Hsien Chen ◽  
Szu-Hsien Chiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Next Generation Sequencing ◽  
Single Nucleotide Polymorphisms ◽  
Risk Evaluation ◽  
Nucleotide Polymorphisms ◽  
Next Generation ◽  
Obesity Risk ◽  
Single Nucleotide ◽  
Generation Sequencing

Status and future perspectives of single nucleotide polymorphisms (SNPs) markers in farmed fishes: Way ahead using next generation sequencing

Gene Reports ◽  
2017 ◽  
Vol 6 ◽  
pp. 81-86 ◽  
Author(s):  
Kiran Dashrath Rasal ◽  
Vemlawada Chakrapani ◽  
Amrendra Kumar Pandey ◽  
Avinash Rambhau Rasal ◽  
Jitendra K. Sundaray ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Single Nucleotide Polymorphisms ◽  
Nucleotide Polymorphisms ◽  
Next Generation ◽  
Future Perspectives ◽  
Single Nucleotide ◽  
Generation Sequencing

scholarly journals Next-generation Sequence-analysis Toolkit (NeST): A standardized bioinformatics framework for analyzing Single Nucleotide Polymorphisms in next-generation sequencing data

2018 ◽  
Author(s):  
Shashidhar Ravishankar ◽  
Sarah E. Schmedes ◽  
Dhruviben S. Patel ◽  
Mateusz Plucinski ◽  
Venkatachalam Udhayakumar ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Single Nucleotide Polymorphisms ◽  
Variant Calling ◽  
Next Generation Sequencing Data ◽  
Nucleotide Polymorphisms ◽  
Next Generation ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Bioinformatics Tools ◽  
Generation Sequencing

AbstractRapid advancements in next-generation sequencing (NGS) technologies have led to the development of numerous bioinformatics tools and pipelines. As these tools vary in their output function and complexity and some are not well-standardized, it is harder to choose a suitable pipeline to identify variants in NGS data. Here, we present NeST (NGS-analysis Toolkit), a modular consensus-based variant calling framework. NeST uses a combination of variant callers to overcome potential biases of an individual method used alone. NeST consists of four modules, that integrate open-source bioinformatics tools, a custom Variant Calling Format (VCF) parser and a summarization utility, that generate high-quality consensus variant calls. NeST was validated using targeted-amplicon deep sequencing data from 245 Plasmodium falciparum isolates to identify single-nucleotide polymorphisms conferring drug resistance. The results were verified using Sanger sequencing data for the same dataset in a supporting publication [28]. NeST offers a user-friendly pipeline for variant calling with standardized outputs and minimal computational demands for easy deployment for use with various organisms and applications.

scholarly journals Repli-seq: genome-wide analysis of replication timing by next-generation sequencing

2017 ◽  
Author(s):  
Claire Marchal ◽  
Takayo Sasaki ◽  
Daniel Vera ◽  
Korey Wilson ◽  
Jiao Sima ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Replication Timing ◽  
Nucleotide Polymorphisms ◽  
Robust Methods ◽  
Next Generation ◽  
Single Nucleotide ◽  
Cellular Processes ◽  
Genome Wide ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

ABSTRACTCycling cells duplicate their DNA content during S phase, following a defined program called replication timing (RT). Early and late replicating regions differ in terms of mutation rates, transcriptional activity, chromatin marks and sub-nuclear position. Moreover, RT is regulated during development and is altered in disease. Exploring mechanisms linking RT to other cellular processes in normal and diseased cells will be facilitated by rapid and robust methods with which to measure RT genome wide. Here, we describe a rapid, robust and relatively inexpensive protocol to analyze genome-wide RT by next-generation sequencing (NGS). This protocol yields highly reproducible results across laboratories and platforms. We also provide computational pipelines for analysis, parsing phased genomes using single nucleotide polymorphisms (SNP) for analyzing RT allelic asynchrony, and for direct comparison to Repli-chip data obtained by analyzing nascent DNA by microarrays.

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