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From next-generation resequencing reads to a high-quality variant data set
被引:69
作者:
Pfeifer, S. P.
[1
,2
,3
]
机构:
[1] Ecole Polytech Fed Lausanne, Sch Life Sci, Lausanne, Switzerland
[2] Swiss Inst Bioinformat, Lausanne, Switzerland
[3] Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA
来源:
关键词:
ACCURATE ERROR-CORRECTION;
SEQUENCING DATA;
CALLING PIPELINES;
GENOMIC SEQUENCE;
ALIGNMENT;
DISCOVERY;
ADAPTER;
TOOL;
ALGORITHMS;
FRAMEWORK;
D O I:
10.1038/hdy.2016.102
中图分类号:
Q14 [生态学(生物生态学)];
学科分类号:
071012 ;
0713 ;
摘要:
Sequencing has revolutionized biology by permitting the analysis of genomic variation at an unprecedented resolution. High-throughput sequencing is fast and inexpensive, making it accessible for a wide range of research topics. However, the produced data contain subtle but complex types of errors, biases and uncertainties that impose several statistical and computational challenges to the reliable detection of variants. To tap the full potential of high-throughput sequencing, a thorough understanding of the data produced as well as the available methodologies is required. Here, I review several commonly used methods for generating and processing next-generation resequencing data, discuss the influence of errors and biases together with their resulting implications for downstream analyses and provide general guidelines and recommendations for producing high-quality single-nucleotide polymorphism data sets from raw reads by highlighting several sophisticated reference-based methods representing the current state of the art.
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页码:111 / 124
页数:14
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