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SEAseq: a portable and cloud-based chromatin occupancy analysis suite.
- Source :
-
BMC bioinformatics [BMC Bioinformatics] 2022 Feb 23; Vol. 23 (1), pp. 77. Date of Electronic Publication: 2022 Feb 23. - Publication Year :
- 2022
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Abstract
- Background: Genome-wide protein-DNA binding is popularly assessed using specific antibody pulldown in Chromatin Immunoprecipitation Sequencing (ChIP-Seq) or Cleavage Under Targets and Release Using Nuclease (CUT&RUN) sequencing experiments. These technologies generate high-throughput sequencing data that necessitate the use of multiple sophisticated, computationally intensive genomic tools to make discoveries, but these genomic tools often have a high barrier to use because of computational resource constraints.<br />Results: We present a comprehensive, infrastructure-independent, computational pipeline called SEAseq, which leverages field-standard, open-source tools for processing and analyzing ChIP-Seq/CUT&RUN data. SEAseq performs extensive analyses from the raw output of the experiment, including alignment, peak calling, motif analysis, promoters and metagene coverage profiling, peak annotation distribution, clustered/stitched peaks (e.g. super-enhancer) identification, and multiple relevant quality assessment metrics, as well as automatic interfacing with data in GEO/SRA. SEAseq enables rapid and cost-effective resource for analysis of both new and publicly available datasets as demonstrated in our comparative case studies.<br />Conclusions: The easy-to-use and versatile design of SEAseq makes it a reliable and efficient resource for ensuring high quality analysis. Its cloud implementation enables a broad suite of analyses in environments with constrained computational resources. SEAseq is platform-independent and is aimed to be usable by everyone with or without programming skills. It is available on the cloud at https://platform.stjude.cloud/workflows/seaseq and can be locally installed from the repository at https://github.com/stjude/seaseq .<br /> (© 2022. The Author(s).)
Details
- Language :
- English
- ISSN :
- 1471-2105
- Volume :
- 23
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- BMC bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 35193506
- Full Text :
- https://doi.org/10.1186/s12859-022-04588-z