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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

Epigenetic and chromosomal features drive transposon insertion in Drosophila melanogaster

TL;DR: In this paper , the authors identify 43 316 de novo and 237 germline insertions from four long-terminal-repeat (LTR) transposons, one LINE transposon (I-element), and one DNA transposition (P-element).
Journal ArticleDOI

Transcriptome Analysis of Duck and Chicken Brains Infected with Aquatic Bird Bornavirus-1 (ABBV-1)

TL;DR: Overall, the transcriptomic analysis of the duck and chicken brains showed that the main biological responses to ABBV-1 infection were immune-associated and corresponded with the levels of inflammation in the CNS.
Journal ArticleDOI

In-depth analysis of large-scale screening of WRKY members based on genome-wide identification

TL;DR: In this paper , a total of 74 DoWRKY genes were identified from Dendrobium officinale Kimura et Migo genome, based on the genome-wide analysis, an in-depth analysis of gene structure and conserved motif was performed.
Posted ContentDOI

Evaluation on Efficient Detection of Structural Variants at Low Coverage by Long-Read Sequencing

TL;DR: In this article, the authors evaluated the performance of three SV calling algorithms (PBHoney-Tails, PBHoney Spots and Sniffles) under different PacBio coverages on two personal genomes, NA12878 and HX1.
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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