Next-generation biology: Sequencing and data analysis approaches for non-model organisms

被引:122
作者
da Fonseca, Rute R. [1 ]
Albrechtsen, Anders [1 ]
Themudo, Goncalo Espregueira [3 ]
Ramos-Madrigal, Jazmin [2 ]
Sibbesen, Jonas Andreas [1 ]
Maretty, Lasse [1 ]
Zepeda-Mendoza, M. Lisandra [2 ]
Campos, Paula F. [2 ,4 ]
Heller, Rasmus [1 ]
Pereira, Ricardo J. [2 ]
机构
[1] Univ Copenhagen, Dept Biol, Bioinformat Ctr, Copenhagen, Denmark
[2] Univ Copenhagen, Ctr GeoGenet, Nat Hist Museum Denmark, Copenhagen, Denmark
[3] Univ Copenhagen, Sect Forens Genet, Dept Forens Med, Copenhagen, Denmark
[4] Centro Univ Porto, CIMAR CIIMAR, Ctr Interdisciplinar Marinha & Ambiental, Rua Bragas 177, P-4050123 Oporto, Portugal
关键词
RADseq; RNAseq; Targeted sequencing; Genotype likelihoods; Comparative genomics; Population genomics; SHORT-READ; ULTRACONSERVED ELEMENTS; POPULATION GENOMICS; UNIVERSAL TOOL; ION TORRENT; ALIGNMENT; TRANSCRIPTOME; DNA; GENES; ENRICHMENT;
D O I
10.1016/j.margen.2016.04.012
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
As sequencing technologies become more affordable, it is now realistic to propose studying the evolutionary history of virtually any organism on a genomic scale. However, when dealing with non-model organisms it is not always easy to choose the best approach given a specific biological question, a limited budget, and challenging sample material. Furthermore, although recent advances in technology offer unprecedented opportunities for research in non-model organisms, they also demand unprecedented awareness from the researcher regarding the assumptions and limitations of each method. In this review we present an overview of the current sequencing technologies and the methods used in typical high-throughput data analysis pipelines. Subsequently, we contextualize high-throughput DNA sequencing technologies within their applications in non-model organism biology. We include tips regarding managing unconventional sample material, comparative and population genetic approaches that do not require fully assembled genomes, and advice on how to deal with low depth sequencing data. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
引用
收藏
页码:3 / 13
页数:11
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