Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling

Samb, Rawane; Khadraoui, Khader; Belleau, Pascal; Deschênes, Astrid; Lakhal‐Chaieb, Lajmi; Droit, Arnaud

doi:10.1515/sagmb-2014-0098

Cited by 13 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Peaks in genomic blacklisted regions defined by ENCODE (ENCFF356LFX) were excluded and only peaks that were enriched at least ten-fold over background were kept for further analysis. Qualified peaks were merged using consensusSeekeR (v.1.16) in which at least two samples had one peak in the same region ( 34 ). Only samples with more than two epigenetic marks available were included in this analysis.…”

Section: Methodsmentioning

confidence: 99%

The 5-Hydroxymethylcytosine Landscape of Prostate Cancer

et al. 2022

View full text Add to dashboard Cite

Analysis of DNA methylation is a valuable tool to understand disease progression and is increasingly being used to create diagnostic and prognostic clinical biomarkers. While conversion of cytosine to 5-methylcytosine (5mC) commonly results in transcriptional repression, further conversion to 5-hydroxymethylcytosine (5hmC) is associated with transcriptional activation. Here we perform the first study integrating whole-genome 5hmC with DNA, 5mC, and transcriptome sequencing in clinical samples of benign, localized, and advanced prostate cancer. 5hmC is shown to mark activation of cancer drivers and downstream targets. Furthermore, 5hmC sequencing revealed profoundly altered cell states throughout the disease course, characterized by increased proliferation, oncogenic signaling, dedifferentiation, and lineage plasticity to neuroendocrine and gastrointestinal lineages. Finally, 5hmC sequencing of cell-free DNA from patients with metastatic disease proved useful as a prognostic biomarker able to identify an aggressive subtype of prostate cancer using the genes TOP2A and EZH2, previously only detectable by transcriptomic analysis of solid tumor biopsies. Overall, these findings reveal that 5hmC marks epigenomic activation in prostate cancer and identify hallmarks of prostate cancer progression with potential as biomarkers of aggressive disease. Significance: In prostate cancer, 5-hydroxymethylcytosine delineates oncogene activation and stage-specific cell states and can be analyzed in liquid biopsies to detect cancer phenotypes. See related article by Wu and Attard, p. 3880

show abstract

Section: Methodsmentioning

confidence: 99%

The 5-Hydroxymethylcytosine Landscape of Prostate Cancer

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Usually, a consensus peak file is used for this purpose, which comprises the set of peaks that are shared between samples, and in which the start and end location of overlapping peaks are adjusted (through the so-called merging of peaks) to thus yield one consensus peak. The ENCODE pipeline provides a workflow with merge and filter steps for constructing a consensus peak file 156 , although other tools can serve the same purpose (for example, consen-susSeekeR 180 ). Alternatively, a predefined set of regions or a binned genome can be used as features in downstream analyses 55,64 .…”

Section: Peak Callingmentioning

confidence: 99%

Chromatin accessibility profiling methods

Minnoye

Marinov

Krausgruber

et al. 2021

Nat Rev Methods Primers

122

View full text Add to dashboard Cite

Chromatin accessibility refers to the level of physical compaction of chromatin, a complex formed by DNA and associated proteins consisting mainly of histones, transcription factors (TFs), chromatin-modifying enzymes and chromatin-remodelling complexes 1-3. Although eukaryotic genomes are generally packed into nucleosomes, which comprise ~147 bp of DNA wrapped around an octamer of histones 4,5 , nucleosome occupancy is not uniform in the genome, and varies across tissues and cell types. Nucleosomes are typically depleted at genomic locations that represent cis-regulatory elementsenhancers and promoters, among others-that interact with transcriptional regulators (for example, TFs), resulting in accessible chromatin 6-10. Profiling chromatin accessibility on a genome-wide scale is an excellent tool to map putative regulatory elements in a cell type or cell state. Post-translational chemical modifications of chromatin, including DNA methylation (in vertebrates) and histone methylation and acetylation, are dynamic and change between different cell states, similar to nucleosome positioning. These post-translational modifications are often correlated with chromatin accessibility and can reflect specific functionalities of genomic regions related to the regulation of gene expression 11,12. Changes in these post-translational modifications, such as increased or decreased histone methylation and acetylation, are affected by a large set of chromatin-modifying enzymes that can be recruited to chromatin regions by TFs. These modifications alter the physico-chemical properties of the chromatin, which in turn can influence the formation of transcriptional condensates 13,14. In addition, active chromatin remodelling impacts nucleosome occupancy; for example, the SWI/SNF complexes use ATP hydrolysis to alter histone-DNA contacts, thereby repositioning or removing nucleosomes 15. Dynamic changes in the chromatin structure, chemical modifications and nucleosome positioning form a crucial interplay with the TFs that drive differentiation of cells during development 16,17. Initial changes in chromatin accessibility are caused by the binding of TFs, which outcompete histones and recruit cofactors, including ATP-dependent chromatin remodellers 18,19 , or by TFs that preferentially bind to their recognition sequence in nucleosomal DNA 20,21. The binding of these initial TFs, known as pioneer factors, can recruit other TFs to co-bind and further stabilize the nucleosome-depleted

show abstract

“…To account for overdispersion, the multinomial can be extended to the Dirichlet-multinomial (DM) distribution 6 . Because of its flexibility, the DM distribution has found applications in forensic genetics 7 , microbiome data analysis 8 , the analysis of single-cell data 9 and for identifying nucleosome positions 10 . Another extension of the multinomial is the Dirichlet negative multinomial distribution 11 , which allows modeling of correlated count data and was applied in the analysis of clinical trial recruitment 12 .…”

Section: Introductionmentioning

confidence: 99%

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

2016

View full text Add to dashboard Cite

There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.

show abstract

Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling

Cited by 13 publications

References 10 publications

The 5-Hydroxymethylcytosine Landscape of Prostate Cancer

The 5-Hydroxymethylcytosine Landscape of Prostate Cancer

Chromatin accessibility profiling methods

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

Contact Info

Product

Resources

About