scispace - formally typeset
Journal ArticleDOI

Sequencing depth and coverage: key considerations in genomic analyses

TLDR
The issue of sequencing depth in the design of next-generation sequencing experiments is discussed and current guidelines and precedents on the issue of coverage are reviewed for four major study designs, including de novo genome sequencing, genome resequencing, transcriptome sequencing and genomic location analyses.
Abstract
Sequencing technologies have placed a wide range of genomic analyses within the capabilities of many laboratories. However, sequencing costs often set limits to the amount of sequences that can be generated and, consequently, the biological outcomes that can be achieved from an experimental design. In this Review, we discuss the issue of sequencing depth in the design of next-generation sequencing experiments. We review current guidelines and precedents on the issue of coverage, as well as their underlying considerations, for four major study designs, which include de novo genome sequencing, genome resequencing, transcriptome sequencing and genomic location analyses (for example, chromatin immunoprecipitation followed by sequencing (ChIP-seq) and chromosome conformation capture (3C)).

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Diversity within diversity: Parasite species richness in poison frogs assessed by transcriptomics.

TL;DR: It is proposed that endoparasites may escape poison frogs' chemical defenses by colonizing tissues with fewer alkaloids than the frog's skin, where most toxins are stored.
Posted ContentDOI

Extensive Copy Number Variation in Fermentation-Related Genes among Saccharomyces cerevisiae Wine Strains

TL;DR: The results suggest that CN variation is a substantial contributor to the genomic diversity of wine yeast strains and identify several candidate loci whose levels of CN variation may affect the adaptation and performance ofwine yeast strains during fermentation.
Book ChapterDOI

Targeted DNA Region Re-sequencing

TL;DR: This chapter describes various re-sequencing methods with a focus on target enrichment, and lays out experimental design considerations, bioinformatics pipelines, and proper reporting of results for target enrichment.
Journal ArticleDOI

Helping decision making for reliable and cost‐effective 2b‐RAD sequencing and genotyping analyses in non‐model species

TL;DR: It is demonstrated that selective‐base ligation does not affect genomic differentiation between individuals, indicating that this technique can be used in species with large genome sizes to adjust the number of loci to the study scope and to maintain suitable sequencing depth for a reliable genotyping without compromising the results.
References
More filters
Journal ArticleDOI

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Journal ArticleDOI

Initial sequencing and analysis of the human genome.

Eric S. Lander, +248 more
- 15 Feb 2001 - 
TL;DR: The results of an international collaboration to produce and make freely available a draft sequence of the human genome are reported and an initial analysis is presented, describing some of the insights that can be gleaned from the sequence.
Journal ArticleDOI

Differential expression analysis for sequence count data.

Simon Anders, +1 more
- 27 Oct 2010 - 
TL;DR: A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.
Journal ArticleDOI

RNA-Seq: a revolutionary tool for transcriptomics

TL;DR: The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
Journal ArticleDOI

TopHat: discovering splice junctions with RNA-Seq

TL;DR: The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer.
Related Papers (5)