FetchPA is an intuitive pipeline which allows users with virtually no scripting and version control experience to install and manage all software for end-to-end analyses of ATAC-Seq data and guides the user through the identification of differentially accessible regions.
Abstract
Local genome accessibility strongly correlates with activity of cis-regulatory elements, and Assay for Transposase-Accessible Chromatin coupled with next-generation sequencing (ATAC-Seq) has emerged as method of choice to profile chromatin accessibility in both healthy and pathogenic conditions. The introduction of streamlined protocols and manufacturer kits has made this technique accessible to labs of a variety of disciplines, background, and research interests. Many bioinformatics tools have been created for the quality control, mapping, and visualization of ATAC-seq data, however these tools require familiarity with shell scripting, version control, UNIX directory structure, Python and/or R. Several pipelines for the processing of ATAC-seq data have been developed, yet even with these tools, bioinformatic analyses represents a bottleneck between wet-lab protocol execution and graphical representation of differentially accessible regions. To address this problem, we assembled FetchPA, an intuitive pipeline which allows users with virtually no scripting and version control experience to install and manage all software for end-to-end analyses of ATAC-Seq data. FetchPA handles both local and public repository sources of sequencing data, executes standard QC benchmarks, and handles genome assembly and alignment using industry-standard PEPATAC pipeline. Further, it guides the user through the identification of differentially accessible regions and allows basic exploratory analyses via a dialogue interface. FetchPA operates in Windows Subsystem for Linux (WSL) and is installed via a single script that handles all individual tools, as well as their dependencies and updates, reference genome annotations and system resource allocation.
RNA-Seq, analyses of RNA abundance by next-generation sequencing, has become a near-universal tool in modern biology. Availability of streamlined protocols and kits, straightforward ability to multiplex hundreds of samples, low cost of short-read sequencing, and well-established analytical pipelines make RNA-Seq a meth...
Dustin R. Fetch, Alexey A. Soshnev· bioRxiv· 1 citation
Background/Objectives: ATAC-seq footprinting can infer transcription-factor (TF) occupancy across the genome at near-base-pair resolution. However, its broad adoption is limited by high computational demands, complex command-line workflows, and fragmented support for bulk data with biological replicates and single-cell...
A comprehensive ATAC-seq protocol that is utilized by laboratories, along with detailed troubleshooting steps at each point, is outlined in the hopes that it will aid other researchers in utilizing this tool to the fullest capability.
Kaitlyn Gura, T. Long, Motoki Takaku et al.· Methods in molecular biology· 0 citations
Tagmentation-based methods such as ATAC-seq and Cut&Tag have provided easy ways to profile the epigenome in low-input samples and even single cells. In this contribution, we discuss forms of bias (i.e. technical variations) in tagmentation-based data, in particular ATAC-seq, and introduce three R/bioconductor packages...
Graphical abstract Translation elongation relies on accurate codon-anticodon pairing. Here, we present tTEscanR, an R package designed to investigate this translational interface. By quantifying mRNA codon demand alongside tRNA anticodon availability, tTEscanR provides scalable estimates of translation rates directly f...
Ana Varas-Sánchez, Carlos J. Gallardo-Dodd, Qun Li et al.· bioRxiv· 0 citations
Abstract Motivation Despite the growing use of HiChIP to investigate protein-directed chromatin architecture, a comprehensive and reproducible pipeline for analysing these datasets-from raw reads to multiscale 3D genome features-remains lacking. Existing tools often focus on isolated components, such as loop calling or...
Abhishek Agarwal, Ziad Al Bkhetan, Dariusz Plewczynski· Bioinformatics· 0 citations
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