Accurate promoter and enhancer identification in 127 ENCODE and Roadmap Epigenomics cell types and tissues by GenoSTAN

Zacher, Benedikt; Michel, Margaux; Schwalb, Björn; Cramer, Patrick; Tresch, Achim; Gagneur, Julien

doi:10.1101/041020

Cited by 34 publications

(57 citation statements)

References 63 publications

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“…We annotated candidate regions using protein coding genes from Ensembl (release 82), promoters and enhancers mapped by GenoSTAN (Zacher et al 2016), a measure of background selection (B-scores) (McVicker et al 2009). Candidate regions were also overlapped with regions previously suggested to have experienced recurrent selective sweeps in apes on the X Chromosome Nam et al 2015), regions of Neandertal ancestry Vernot and Akey 2014), and long regions devoid of Neandertal and Denisova ancestry (Vernot et al 2016).…”

Section: Annotationsmentioning

confidence: 99%

Detecting ancient positive selection in humans using extended lineage sorting

et al. 2017

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Natural selection that affected modern humans early in their evolution has likely shaped some of the traits that set presentday humans apart from their closest extinct and living relatives. The ability to detect ancient natural selection in the human genome could provide insights into the molecular basis for these human-specific traits. Here, we introduce a method for detecting ancient selective sweeps by scanning for extended genomic regions where our closest extinct relatives, Neandertals and Denisovans, fall outside of the present-day human variation. Regions that are unusually long indicate the presence of lineages that reached fixation in the human population faster than expected under neutral evolution. Using simulations, we show that the method is able to detect ancient events of positive selection and that it can differentiate those from background selection. Applying our method to the 1000 Genomes data set, we find evidence for ancient selective sweeps favoring regulatory changes and present a list of genomic regions that are predicted to underlie positively selected human specific traits.[Supplemental material is available for this article.]Modern humans differ from their closest extinct relatives, Neandertals, in several aspects, including skeletal and skull morphology (Weaver 2009), and may also differ in other traits that are not preserved in the archeological record (Varki et al. 2008;Laland et al. 2010). Natural selection may have played a role in fixing these traits on the modern human lineage. However, the selection events driving the fixation would have been restricted to a specific timeframe, extending from the split between archaic and modern humans ca. 650,000 yr ago to the split of modern human populations from each other around 100,000 yr ago . While methods exist that can be used to scan the genome for the remnants of past or ongoing positive selection (Nielsen et al. 2007; Pybus and Shapiro 2009), current methods have limited power to detect positive selection on the human lineage that acted during this older timeframe (see Sabeti et al. 2006 for a review on detection methods and their timeframes): an unusually high ratio of functional changes to nonfunctional changes, such as the d N /d S test, requires millions of years and often multiple events of selection to generate detectable signals (Kryazhimskiy and Plotkin 2008), while unusual patterns of genetic diversity between individuals and populations (e.g., extended homozygosity, Tajima's D, F ST ) are most powerful during the selective sweep or shortly after (Sabeti et al. 2006;Oleksyk et al. 2010). A favorable substitution is not expected to leave a mark on linked neutral variation beyond 250,000 yr in humans (Przeworski 2002(Przeworski , 2003.The genome sequencing of archaic humans (Neandertals and Denisovans) to high coverage (Meyer et al. 2012;Prüfer et al. 2014) has spawned new methods to investigate the genetic basis of modern human traits that are not shared by the archaics (Pääbo 2014). One method, called 3P-CLR, models allele ...

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Section: Annotationsmentioning

confidence: 99%

Detecting ancient positive selection in humans using extended lineage sorting

et al. 2017

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“…The ENCODE 67 , NIH Roadmap Epigenomics 68 and related 35 studies characterized the presence and variable states of key regulatory elements throughout the genomes of >100 different somatic cell types on the basis of multiparametric integrative analysis methodology 63 , but did not examine any germ cell types. One previous study provided an initial characterization of histone modifications in fetal mouse germ cell types, but did not examine postnatal germ cells, and therefore did not characterize epigenetic programming distinguishing SSCs from progenitors.…”

Section: Discussionmentioning

confidence: 99%

Unique Epigenetic Programming Distinguishes Regenerative Spermatogonial Stem Cells in the Developing Mouse Testis

Cheng

Chen

Hale

et al. 2019

Preprint

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Spermatogonial stem cells (SSCs) both self-renew and give rise to progenitor spermatogonia that enter steady-state spermatogenesis in the mammalian testis. However, questions remain regarding the extent to which SSCs and progenitors represent stably distinct spermatogonial subtypes. Here we provide the first multiparametric integrative analysis of mammalian germ cell epigenomes comparable to that done by the ENCODE Project for >100 somatic cell types.Differentially expressed genes distinguishing SSCs and progenitors showed distinct histone modification patterns as well as differences in distal intergenic low-methylated regions. Motifenrichment analysis predicted transcription factors that regulate this spermatogonial subtypespecific epigenetic programming, and gene-specific chromatin immunoprecipitation analyses confirmed subtype-specific differences in binding of a subset of these factors to target genes.Collectively, these results suggest that SSCs and progenitors are stably distinct spermatogonial subtypes differentially programmed to either self-renew and maintain regenerative capacity as SSCs, or lose regenerative capacity and initiate lineage commitment as progenitors.

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“…Different distributional assumptions about the next-generation sequencing data including ChIP-seq data have been made, such as Poisson [85], and negative binomial [86,87], the log-concave Poisson approach [48], and Poisson log-normal [88]. The most common distributional assumption for the ChIP-seq coverage is the Poisson distribution.…”

Section: Overall Approachmentioning

confidence: 99%

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

Osmala

Lähdesmäki

2019

Preprint

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Background: The human genome is far from completely annotated. Specifically, the locations ofgenedistal regulatory enhancers are difficult to locate. Enhancers are binding sites of transcription factors and occupied by nucleosomes with modified histones. The binding sites of transcription factors (TFs) and the localization of histone modifications can be quantified by the chromatin immunoprecipitation assay coupled with next generation sequencing (ChIP-seq). The resulting data has been successfully adopted for genomewide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type, and they do not utilize the pattern of the ChIP-seq coverage profiles efficiently. It is also difficult to estimate the accuracy and specificity of the genome-wide enhancer predictions. Results:We developed PREPRINT, a PRobabilistic Enhancer PRedictIoN Tool. We considered the pattern of, for example, the ChIP-seq coverage profile around the enhancers. The data at the positive and negative examples of enhancers was utilized to probabilistically model the enhancer coverage pattern and to train a kernel-based classifier. We demonstrated the performance of the method using ENCODE data from two cell lines. The predicted enhancers were computationally validated based on the TFs and co-regulatory factor binding sites. We compared our enhancer predictions to the ones obtained by other methods. The effects of different parameter choices during training, testing and validation were studied, and finally, the approach to validate the genome-wide predictions was investigated. Conclusion: PREPRINT performed comparably to the state-of-the-art methods and provided probabilistic interpretation (i.e. uncertainty) for the predictions. PREPRINT generalized to data from cell type not utilized for training, and often the performance of PREPRINT was superior to RFECS. We observed that the choice of training data and the choice of parameter values at different steps of the enhancer prediction and validation influenced on the final set of predictions. PREPRINT identifed biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.

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Accurate promoter and enhancer identification in 127 ENCODE and Roadmap Epigenomics cell types and tissues by GenoSTAN

Cited by 34 publications

References 63 publications

Detecting ancient positive selection in humans using extended lineage sorting

Detecting ancient positive selection in humans using extended lineage sorting

Unique Epigenetic Programming Distinguishes Regenerative Spermatogonial Stem Cells in the Developing Mouse Testis

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

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