Chromatin immunoprecipitation sequencing (ChIP-seq) and the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) have become essential technologies to effectively measure protein-DNA interactions and chromatin accessibility. However, there is a need for a scalable and reproducible pipeline that incorporates proper normalization between samples, correction of copy number variations, and integration of new downstream analysis tools. Here we present Containerized Bioinformatics workflow for Reproducible ChIP/ATAC-seq Analysis (CoBRA), a modularized computational workflow which quantifies ChIP-seq and ATAC-seq peak regions and performs unsupervised and supervised analyses. CoBRA provides a comprehensive state-of-the-art ChIP-seq and ATAC-seq analysis pipeline that can be used by scientists with limited computational experience. This enables researchers to gain rapid insight into protein-DNA interactions and chromatin accessibility through sample clustering, differential peak calling, motif enrichment, comparison of sites to a reference database, and pathway analysis. CoBRA is publicly available online at https://bitbucket.org/cfce/cobra.
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http://dx.doi.org/10.1016/j.gpb.2020.11.007 | DOI Listing |
Sci Rep
September 2024
Department of Bioinformatics-BiGCaT, NUTRIM/MHeNs, Maastricht University, Maastricht, The Netherlands.
Bioinform Adv
August 2024
Department of Microbiology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia.
Motivation: Visualization and comparison of genome maps of bacteriophages can be very effective, but none of the tools available on the market allow visualization of gene conservation between multiple sequences at a glance. In addition, most bioinformatic tools running locally are command line only, making them hard to setup, debug, and monitor.
Results: To address these motivations, we developed synphage, an easy-to-use and intuitive tool to generate synteny diagrams from GenBank files.
F1000Res
July 2024
Department of Microbiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Background: Determining the appropriate computational requirements and software performance is essential for efficient genomic surveillance. The lack of standardized benchmarking complicates software selection, especially with limited resources.
Methods: We developed a containerized benchmarking pipeline to evaluate seven long-read assemblers-Canu, GoldRush, MetaFlye, Strainline, HaploDMF, iGDA, and RVHaplo-for viral haplotype reconstruction, using both simulated and experimental Oxford Nanopore sequencing data of HIV-1 and other viruses.
Bioinformatics
July 2024
Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, United States.
bioRxiv
June 2024
Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany.
Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community.
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