PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments.
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ABSTRACT:
SUBMITTER: Smith JP
PROVIDER: S-EPMC8632735 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Summary:ATAC-seq is rapidly emerging as one of the major experimental approaches to probe chromatin accessibility genome-wide. Here, we present 'esATAC', a highly integrated easy-to-use R/Bioconductor package, for systematic ATAC-seq data analysis. It covers essential steps for full analyzing procedure, including raw data processing, quality control and downstream statistical analysis such as peak calling, enrichment analysis and transcription factor footprinting. esATAC supports one command line execution for preset pipelines and provides flexible interfaces for building customized pipelines. Availability and implementation:esATAC package is open source under the GPL-3.0 license. It is implemented in R and C++. Source code and binaries for Linux, MAC OS X and Windows are available through Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/esATAC.html). Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6061683 | biostudies-literature
Project description:Assay for Transposase Accessible Chromatin (ATAC-seq) is an open chromatin profiling assay that is adapted to interrogate chromatin accessibility from small cell numbers. ATAC-seq surmounted a major technical barrier and enabled epigenome profiling of clinical samples. With this advancement in technology, we are now accumulating ATAC-seq samples from clinical samples at an unprecedented rate. These epigenomic profiles hold the key to uncovering how transcriptional programs are established in diverse human cells and are disrupted by genetic or environmental factors. Thus, the barrier to deriving important clinical insights from clinical epigenomic samples is no longer one of data generation but of data analysis. Specifically, we are still missing easy-to-use software tools that will enable non-computational scientists to analyze their own ATAC-seq samples. To facilitate systematic pre-processing and management of ATAC-seq samples, we developed an interactive, cross-platform, user-friendly and customized desktop application: interactive-ATAC (I-ATAC). I-ATAC integrates command-line data processing tools (FASTQC, Trimmomatic, BWA, Picard, ATAC_BAM_shiftrt_gappedAlign.pl, Bedtools and Macs2) into an easy-to-use platform with user interface to automatically pre-process ATAC-seq samples with parallelized and customizable pipelines. Its performance has been tested using public ATAC-seq datasets in GM12878 and CD4+T cells and a feature-based comparison is performed with some available interactive LIMS (Galaxy, SMITH, SeqBench, Wasp, NG6, openBIS). I-ATAC is designed to empower non-computational scientists to process their own datasets and to break to exclusivity of data analyses to computational scientists. Additionally, I-ATAC is capable of processing WGS and ChIP-seq samples, and can be customized by the user for one-independent or multiple-sequential operations.
| S-EPMC5702251 | biostudies-literature
Project description:The success rate of molecular replacement (MR) falls considerably when search models share less than 35% sequence identity with their templates, but can be improved significantly by using fold-recognition methods combined with exhaustive MR searches. Models based on alignments calculated with fold-recognition algorithms are more accurate than models based on conventional alignment methods such as FASTA or BLAST, which are still widely used for MR. In addition, by designing MR pipelines that integrate phasing and automated refinement and allow parallel processing of such calculations, one can effectively increase the success rate of MR. Here, updated results from the JCSG MR pipeline are presented, which to date has solved 33 MR structures with less than 35% sequence identity to the closest homologue of known structure. By using difficult MR problems as examples, it is demonstrated that successful MR phasing is possible even in cases where the similarity between the model and the template can only be detected with fold-recognition algorithms. In the first step, several search models are built based on all homologues found in the PDB by fold-recognition algorithms. The models resulting from this process are used in parallel MR searches with different combinations of input parameters of the MR phasing algorithm. The putative solutions are subjected to rigid-body and restrained crystallographic refinement and ranked based on the final values of free R factor, figure of merit and deviations from ideal geometry. Finally, crystal packing and electron-density maps are checked to identify the correct solution. If this procedure does not yield a solution with interpretable electron-density maps, then even more alternative models are prepared. The structurally variable regions of a protein family are identified based on alignments of sequences and known structures from that family and appropriate trimmings of the models are proposed. All combinations of these trimmings are applied to the search models and the resulting set of models is used in the MR pipeline. It is estimated that with the improvements in model building and exhaustive parallel searches with existing phasing algorithms, MR can be successful for more than 50% of recognizable homologues of known structures below the threshold of 35% sequence identity. This implies that about one-third of the proteins in a typical bacterial proteome are potential MR targets.
| S-EPMC2394805 | biostudies-literature
Project description:ATAC-seq is widely used to measure chromatin accessibility and identify open chromatin regions (OCRs). OCRs usually indicate active regulatory elements in the genome and are directly associated with the gene regulatory network. The identification of differential accessibility regions (DARs) between different biological conditions is critical in determining the differential activity of regulatory elements. Differential analysis of ATAC-seq shares many similarities with differential expression analysis of RNA-seq data. However, the distribution of ATAC-seq signal intensity is different from that of RNA-seq data, and higher sensitivity is required for DARs identification. Many different tools can be used to perform differential analysis of ATAC-seq data, but a comprehensive comparison and benchmarking of these methods is still lacking. Here, we used simulated datasets to systematically measure the sensitivity and specificity of six different methods. We further discussed the statistical and signal density cut-offs in the differential analysis of ATAC-seq by applying them to real data. Batch effects are very common in high-throughput sequencing experiments. We illustrated that batch-effect correction can dramatically improve sensitivity in the differential analysis of ATAC-seq data. Finally, we developed a user-friendly package, BeCorrect, to perform batch effect correction and visualization of corrected ATAC-seq signals in a genome browser.
| S-EPMC7311460 | biostudies-literature
Project description:Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.
| S-EPMC7910485 | biostudies-literature
Project description:Next-generation sequencing is empowering genetic disease research. However, it also brings significant challenges for efficient and effective sequencing data analysis. We built a pipeline, called DNAp, for analyzing whole exome sequencing (WES) and whole genome sequencing (WGS) data, to detect mutations from disease samples. The pipeline is containerized, convenient to use and can run under any system, since it is a fully automatic process in Docker container form. It is also open, and can be easily customized with user intervention points, such as for updating reference files and different software or versions. The pipeline has been tested with both human and mouse sequencing datasets, and it has generated mutations results, comparable to published results from these datasets, and reproducible across heterogeneous hardware platforms. The pipeline DNAp, funded by the US Food and Drug Administration (FDA), was developed for analyzing DNA sequencing data of FDA. Here we make DNAp an open source, with the software and documentation available to the public at http://bioinformatics.astate.edu/dna-pipeline/ .
| S-EPMC5931599 | biostudies-literature
Project description:BACKGROUND:Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data. RESULTS:We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads. CONCLUSIONS:We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling.
| S-EPMC6069842 | biostudies-literature
Project description:Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) is widely used in studying chromatin biology, but a comprehensive review of the analysis tools has not been completed yet. Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). We also review the reconstruction of transcriptional regulatory networks with multiomics data and highlight the current challenges of each step. Finally, we describe the potential of single-cell ATAC-seq and highlight the necessity of developing ATAC-seq specific analysis tools to obtain biologically meaningful insights.
| S-EPMC6996192 | biostudies-literature
Project description:BackgroundAssay for Transposase-Accessible Chromatin (ATAC)-cap-seq is a high-throughput sequencing method that combines ATAC-seq with targeted nucleic acid enrichment of precipitated DNA fragments. There are increased analytical difficulties arising from working with a set of regions of interest that may be small in number and biologically dependent. Common statistical pipelines for RNA sequencing might be assumed to apply but can give misleading results on ATAC-cap-seq data. A tool is needed to allow a nonspecialist user to quickly and easily summarize data and apply sensible and effective normalization and analysis.ResultsWe developed atacR to allow a user to easily analyze their ATAC enrichment experiment. It provides comprehensive summary functions and diagnostic plots for studying enriched tag abundance. Application of between-sample normalization is made straightforward. Functions for normalizing based on user-defined control regions, whole library size, and regions selected from the least variable regions in a dataset are provided. Three methods for detecting differential abundance of tags from enriched methods are provided, including bootstrap t, Bayes factor, and a wrapped version of the standard exact test in the edgeR package. We compared the precision, recall, and F-score of each detection method on resampled datasets at varying replicate, significance threshold, and genes changed and found that the Bayes factor method had the greatest overall detection power, though edgeR was slightly stronger in simulations with lower numbers of genes changed.ConclusionsOur package allows a nonspecialist user to easily and effectively apply methods appropriate to the analysis of ATAC-cap-seq in a reproducible manner. The package is implemented in pure R and is fully interoperable with common workflows in Bioconductor.
| S-EPMC6047409 | biostudies-literature
Project description:UnlabelledHigh-throughput sequencing currently generates a wealth of small RNA (sRNA) data, making data mining a topical issue. Processing of these large data sets is inherently multidimensional as length, abundance, sequence composition, and genomic location all hold clues to sRNA function. Analysis can be challenging because the formulation and testing of complex hypotheses requires combined use of visualization, annotation and abundance profiling. To allow flexible generation and querying of these disparate types of information, we have developed the shortran pipeline for analysis of plant or animal short RNA sequencing data. It comprises nine modules and produces both graphical and MySQL format output.Availabilityshortran is freely available and can be downloaded from http://users-mb.au.dk/pmgrp/shortran/.
| S-EPMC3467745 | biostudies-literature