Predicting the molecular complexity of sequencing libraries

Daley, Timothy; Smith, Andrew D.

doi:10.1038/nmeth.2375

Cited by 284 publications

(316 citation statements)

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“…1a). As we are comparing libraries with different sequencing depths, we used the PreSeq package 15 to extrapolate and compare the potential complexity of our libraries (Fig. 1b).…”

Section: Resultsmentioning

confidence: 99%

“…For mined data sets using short, single-end reads, reads were extended to 300 bp before generating RPKM values. Potential library complexity was determined using the extrapolate function of the PreSeq package 15 . For expression analysis, normalization of RNA-seq read enrichment was calculated as RPKM at exonic regions only (RefSeq transcripts).…”

Section: Methodsmentioning

confidence: 99%

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An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations

et al. 2015

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Combined chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) has enabled genome-wide epigenetic profiling of numerous cell lines and tissue types. A major limitation of ChIP-seq, however, is the large number of cells required to generate high-quality data sets, precluding the study of rare cell populations. Here, we present an ultra-low-input micrococcal nuclease-based native ChIP (ULI-NChIP) and sequencing method to generate genome-wide histone mark profiles with high resolution from as few as 10 3 cells. We demonstrate that ULI-NChIP-seq generates high-quality maps of covalent histone marks from 10 3 to 10 6 embryonic stem cells. Subsequently, we show that ULI-NChIP-seq H3K27me3 profiles generated from E13.5 primordial germ cells isolated from single male and female embryos show high similarity to recent data sets generated using 50-180 Â more material. Finally, we identify sexually dimorphic H3K27me3 enrichment at specific genic promoters, thereby illustrating the utility of this method for generating high-quality and -complexity libraries from rare cell populations.

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“…1a). As we are comparing libraries with different sequencing depths, we used the PreSeq package 15 to extrapolate and compare the potential complexity of our libraries (Fig. 1b).…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations

et al. 2015

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“…A higher value of the overdispersion parameter r (40.05 based on our experience) can be used as an indicator of poor library complexity. Another method for characterizing the molecular complexity of sequencing library is an empirical Bayesian method implemented in preseq software (Daley and Smith, 2013).…”

Section: Estimation Of the Dae Ratio At Informative Snp Positions Andmentioning

confidence: 99%

Using next-generation RNA sequencing to identify imprinted genes

2014

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Genomic imprinting is manifested as differential allelic expression (DAE) depending on the parent-of-origin. The most direct way to identify imprinted genes is to directly score the DAE in a context where one can identify which parent transmitted each allele. Because many genes display DAE, simply scoring DAE in an individual is not sufficient to identify imprinted genes. In this paper, we outline many technical aspects of a scheme for identification of imprinted genes that makes use of RNA sequencing (RNA-seq) from tissues isolated from F1 offspring derived from the pair of reciprocal crosses. Ideally, the parental lines are from two inbred strains that are not closely related to each other. Aspects of tissue purity, RNA extraction, library preparation and bioinformatic inference of imprinting are all covered. These methods have already been applied in a number of organisms, and one of the most striking results is the evolutionary fluidity with which novel imprinted genes are gained and lost within genomes. The general methodology is also applicable to a wide range of other biological problems that require quantification of allele-specific expression using RNA-seq, such as cis-regulation of gene expression, X chromosome inactivation and random monoallelic expression.

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“…This method relies on the observation that the curve of rarefied counts of any feature (for example, operational taxonomic units, named species, predicted genes, functional categories or even short motifs) should plateau if the sample is close to saturation. Use of rarefaction curves in microbial community studies was popularized by tools such as mothur (Schloss et al, 2009) and recently extended to include accurate projections at higher sequencing efforts by preseq (Daley and Smith, 2013), which allows the estimation of coverage across features (arithmetic mean). However, this technique and others like it typically rely on a high-quality assembly, comprehensive reference data sets or both, which are often unavailable for complex or poorly characterized communities (with the probable exception of ribosomal RNA (rRNA) genes).…”

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confidence: 99%