Format

Send to

Choose Destination
Mar Genomics. 2016 Dec;30:3-13. doi: 10.1016/j.margen.2016.04.012. Epub 2016 May 13.

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

Author information

1
The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark. Electronic address: fonseca@binf.ku.dk.
2
The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
3
Section of Forensic Genetics, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark.
4
Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark.
5
Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark; CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Rua dos Bragas 177, 4050-123 Porto, Portugal.

Abstract

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.

KEYWORDS:

Comparative genomics; Genotype likelihoods; Population genomics; RADseq; RNAseq; Targeted sequencing

PMID:
27184710
DOI:
10.1016/j.margen.2016.04.012
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center