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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)).

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Journal ArticleDOI

Spatiotemporal regulation of enhancers during cardiogenesis

TL;DR: The identification of enhancers in the developing heart has revealed new insights into the transcriptional mechanisms of how tissue-specific gene expression patterns are established, maintained, and change dynamically during development and upon physiological stress.
Book ChapterDOI

Bioinformatics Principles for Deciphering Cardiovascular Diseases

TL;DR: The basic principles of bioinformatics and core algorithms and tools applicable to CVD are summarized by focusing on those relevant to the most commonly used omics data types including genomics, epigenomics, transcriptomics, metabolomics, proteomics, and microbiota.
Dissertation

Next generation sequencing data in bovine: quality control, imputation, and application

TL;DR: NSERC, DairyGen, Canadian Dairy Network, Canadian Cattle Genome Project, and Dairy Research Cluster 2 contribute to this report.
Posted ContentDOI

Multipopulational Transcriptome Analysis of Post-Weaned Beef Cattle at Arrival Further Validates Candidate Biomarkers for Predicting Clinical Bovine Respiratory Disease

TL;DR: Receiver operating characteristic (ROC) curves generated from expression data for six DEGs identified in current and previous studies demonstrated good-to-excellent predictability for classifying disease occurrence and severity.
Journal ArticleDOI

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine.

TL;DR: In this article, the authors focus on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and present a collection of appropriate state-of-the-art methodologies to study them.
References
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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.
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