SnakePipes: facilitating flexible, scalable and integrative epigenomic analysis.
Ontology highlight
ABSTRACT: Summary
Due to the rapidly increasing scale and diversity of epigenomic data, modular and scalable analysis workflows are of wide interest. Here we present snakePipes, a workflow package for processing and downstream analysis of data from common epigenomic assays: ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq. snakePipes enables users to assemble variants of each workflow and to easily install and upgrade the underlying tools, via its simple command-line wrappers and yaml files.Availability and implementation
snakePipes can be installed via conda: `conda install -c mpi-ie -c bioconda -c conda-forge snakePipes'. Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Bhardwaj V
PROVIDER: S-EPMC6853707 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Epigenomic and genomic changes affect gene expression and contribute to tumor development. The histone modifications trimethylated histone H3 lysine 4 (H3K4me3) and lysine 27 (H3K27me3) are epigenetic regulators associated to active and silenced genes, respectively and alterations of these modifications have been observed in cancer. Furthermore, genomic aberrations such as DNA copy number changes are common events in tumors. Pheochromocytoma is a rare endocrine tumor of the adrenal gland that mostly occurs sporadic with unknown epigenetic/genetic cause. The majority of cases are benign. Here we aimed to combine the genome-wide profiling of H3K4me3 and H3K27me3, obtained by the ChIP-chip methodology, and DNA copy number data with global gene expression examination in a malignant pheochromocytoma sample. The integrated analysis of the tumor expression levels, in relation to normal adrenal medulla, indicated that either histone modifications or chromosomal alterations, or both, have great impact on the expression of a substantial fraction of the genes in the investigated sample. Candidate tumor suppressor genes identified with decreased expression, a H3K27me3 mark and/or in regions of deletion were for instance TGIF1, DSC3, TNFRSF10B, RASSF2, HOXA9, PTPRE and CDH11. More genes were found with increased expression, a H3K4me3 mark, and/or in regions of gain. Potential oncogenes detected among those were GNAS, INSM1, DOK5, ETV1, RET, NTRK1, IGF2, and the H3K27 trimethylase gene EZH2. Our approach to associate histone methylations and DNA copy number changes to gene expression revealed apparent impact on global gene transcription, and enabled the identification of candidate tumor genes for further exploration.
| S-EPMC2912476 | biostudies-literature
Project description:Circular RNAs (circRNAs) are evolutionarily conserved and abundant non-coding RNAs whose functions and regulatory mechanisms remain largely unknown. Here, we identify and characterize an epigenomically distinct group of circRNAs (TAH-circRNAs), which are transcribed to a higher level than their host genes. By integrative analysis of cistromic and transcriptomic data, we find that compared with other circRNAs, TAH-circRNAs are expressed more abundantly and have more transcription factors (TFs) binding sites and lower DNA methylation levels. Concordantly, TAH-circRNAs are enriched in open and active chromatin regions. Importantly, ChIA-PET results showed that 23-52% of transcription start sites (TSSs) of TAH-circRNAs have direct interactions with cis-regulatory regions, strongly suggesting their independent transcriptional regulation from host genes. In addition, we characterize molecular features of super-enhancer-driven circRNAs in cancer biology. Together, this study comprehensively analyzes epigenomic characteristics of circRNAs and identifies a distinct group of TAH-circRNAs that are independently transcribed via enhancers and super-enhancers by TFs. These findings substantially advance our understanding of the regulatory mechanism of circRNAs and may have important implications for future investigations of this class of non-coding RNAs.
| S-EPMC7868561 | biostudies-literature
Project description:BACKGROUND: Urothelial carcinoma of the bladder (UC) is a common malignancy. Although extensive transcriptome analysis has provided insights into the gene expression patterns of this tumor type, the mechanistic underpinnings of differential methylation remain poorly understood. Multi-level genomic data may be used to profile the regulatory potential and landscape of differential methylation in cancer and gain understanding of the processes underlying epigenetic and phenotypic characteristics of tumors. METHODS: We perform genome-wide DNA methylation profiling of 98 gene-expression subtyped tumors to identify between-tumor differentially methylated regions (DMRs). We integrate multi-level publically available genomic data generated by the ENCODE consortium to characterize the regulatory potential of UC DMRs. RESULTS: We identify 5,453 between-tumor DMRs and derive four DNA methylation subgroups of UC with distinct associations to clinicopathological features and gene expression subtypes. We characterize three distinct patterns of differential methylation and use ENCODE data to show that tumor subgroup-defining DMRs display differential chromatin state, and regulatory factor binding preferences. Finally, we characterize an epigenetic switch involving the HOXA-genes with associations to tumor differentiation states and patient prognosis. CONCLUSIONS: Genome-wide DMR methylation patterns are reflected in the gene expression subtypes of UC. UC DMRs display three distinct methylation patterns, each associated with intrinsic features of the genome and differential regulatory factor binding preferences. Epigenetic inactivation of HOX-genes correlates with tumor differentiation states and may present an actionable epigenetic alteration in UC.
| S-EPMC4373102 | biostudies-literature
Project description:Clozapine (CLZ) is the only antipsychotic drug that has been proven to be effective in patients with refractory psychosis, but it has also been proposed as an effective mood stabilizer; however, the complex mechanisms of action of CLZ are not yet fully known. To find predictors of CLZ-associated phenotypes (i.e., the metabolic ratio, dosage, and response), we explore the genomic and epigenomic characteristics of 44 patients with refractory psychosis who receive CLZ treatment based on the integration of polygenic risk score (PRS) analyses in simultaneous methylome profiles. Surprisingly, the PRS for bipolar disorder (BD-PRS) was associated with the CLZ metabolic ratio (pseudo-R2 = 0.2080, adjusted p-value = 0.0189). To better explain our findings in a biological context, we assess the protein-protein interactions between gene products with high impact variants in the top enriched pathways and those exhibiting differentially methylated sites. The GABAergic synapse pathway was found to be enriched in BD-PRS and was associated with the CLZ metabolic ratio. Such interplay supports the use of CLZ as a mood stabilizer and not just as an antipsychotic. Future studies with larger sample sizes should be pursued to confirm the findings of this study.
| S-EPMC7913835 | biostudies-literature
Project description:Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.
| S-EPMC10928517 | biostudies-literature
Project description:ChIP-Seq, a technique that allows for quantification of DNA sequences bound by transcription factors or histones, has been widely used to characterize genome-wide DNA-protein binding at baseline and induced by specific exposures. Integrating results of multiple ChIP-Seq datasets is a convenient approach to identify robust DNA- protein binding sites and determine their cell-type specificity. We developed brocade, a computational pipeline for reproducible analysis of publicly available ChIP-Seq data that creates R markdown reports containing information on datasets downloaded, quality control metrics, and differential binding results. Glucocorticoids are commonly used anti-inflammatory drugs with tissue-specific effects that are not fully understood. We demonstrate the utility of brocade via the analysis of five ChIP-Seq datasets involving glucocorticoid receptor (GR), a transcription factor that mediates glucocorticoid response, to identify cell type-specific and shared GR binding sites across the five cell types. Our results show that brocade facilitates analysis of individual ChIP-Seq datasets and comparative studies involving multiple datasets.
| S-EPMC7153109 | biostudies-literature
Project description:While recent integrative analyses of copy number and gene expression data in breast cancer have revealed a complex molecular landscape with multiple subtypes and many oncogenic/tumour suppressor driver events, much less is known about the role of DNA methylation in shaping breast cancer taxonomy and defining driver events.Here, we applied a powerful integrative network algorithm to matched DNA methylation and RNA-Seq data for 724 estrogen receptor (ER)-positive (ER+) breast cancers and 111 normal adjacent tissue specimens from The Cancer Genome Atlas (TCGA) project, in order to identify putative epigenetic driver events and to explore the resulting molecular taxonomy. This revealed the existence of nine functionally deregulated epigenetic hotspots encompassing a total of 146 genes, which we were able to validate in independent data sets encompassing over 1000 ER+ breast cancers. Integrative clustering of the matched messenger RNA (mRNA) and DNA methylation data over these genes resulted in only two clusters, which correlated very strongly with the luminal-A and luminal B subtypes. Overall, luminal-A and luminal-B breast cancers shared the same epigenetically deregulated hotspots but with luminal-B cancers exhibiting increased aberrant DNA methylation patterns relative to normal tissue. We show that increased levels of DNA methylation and mRNA expression deviation from the normal state define a marker of poor prognosis. Our data further implicates epigenetic silencing of WNT signalling antagonists and bone morphogenetic proteins (BMP) as key events underlying both luminal subtypes but specially of luminal-B breast cancer. Finally, we show that DNA methylation changes within the identified epigenetic interactome hotspots do not exhibit mutually exclusive patterns within the same cancer sample, instead exhibiting coordinated changes within the sample.Our results indicate that the integrative DNA methylation and transcriptomic landscape of ER+ breast cancer is surprisingly homogeneous, defining two main subtypes which strongly correlate with luminal-A/B subtype status. In particular, we identify WNT and BMP signalling as key epigenetically deregulated tumour suppressor pathways in luminal ER+ breast cancer, with increased deregulation seen in luminal-B breast cancer.
| S-EPMC4673726 | biostudies-other
Project description:Hepatocellular carcinoma harbors numerous genomic and epigenomic aberrations of DNA copy numbers and DNA methylation. Transcriptomic deregulation by these aberrations plays key driver roles in heterogeneous progression of cancers. Here, we profile DNA copy numbers, DNA methylation, and messenger RNA expression levels from 64 cases of hepatocellular carcinoma specimens. We find that the frequencies of the aberrancies of the DNA copy-number-correlated (CNVcor) expression genes and the methylation-correlated expression (METcor) genes are co-regulated significantly. Multi-omics integration of the CNVcor and METcor genes reveal three prognostic subtypes of hepatocellular carcinoma, which can be validated by an independent data. The most aggressive subtype expressing stemness genes has frequent BAP1 mutations, implying its pivotal role in the aggressive tumor progression. In conclusion, our integrative analysis of genomic and epigenomic regulation provides new insights on the multi-layered pathobiology of hepatocellular carcinoma, which might be helpful in developing precision management for hepatocellular carcinoma patients.Hepatocellular carcinoma is known to harbour numerous genomic and epigenomic aberrations, driving transcriptomic deregulation. Here, the authors integrate genomic, epigenomic, and expression data to reveal three prognostic subtypes, providing insight to the pathobiology of hepatocellular carcinoma.
| S-EPMC5635060 | biostudies-literature
Project description:MotivationGene-environment interaction (GEI) studies are a general framework that can be used to identify genetic variants that modify the effects of environmental, physiological, lifestyle or treatment effects on complex traits. Moreover, accounting for GEIs can enhance our understanding of the genetic architecture of complex diseases and traits. However, commonly used statistical software programs for GEI studies are either not applicable to testing certain types of GEI hypotheses or have not been optimized for use in large samples.ResultsHere, we develop a new software program, GEM (Gene-Environment interaction analysis in Millions of samples), which supports the inclusion of multiple GEI terms, adjustment for GEI covariates and robust inference, while allowing multi-threading to reduce computation time. GEM can conduct GEI tests as well as joint tests of genetic main and interaction effects for both continuous and binary phenotypes. Through simulations, we demonstrate that GEM scales to millions of samples while addressing limitations of existing software programs. We additionally conduct a gene-sex interaction analysis on waist-hip ratio in 352 768 unrelated individuals from the UK Biobank, identifying 24 novel loci in the joint test that have not previously been reported in combined or sex-specific analyses. Our results demonstrate that GEM can facilitate the next generation of large-scale GEI studies and help advance our understanding of the genetic architecture of complex diseases and traits.Availability and implementationGEM is freely available as an open source project at https://github.com/large-scale-gxe-methods/GEM.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8545347 | biostudies-literature
Project description:The number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here we present a method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear running times with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle.
| S-EPMC6882857 | biostudies-literature