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minute: A MINUTE-ChIP data analysis workflow

View ORCID ProfileCarmen Navarro, View ORCID ProfileMarcel Martin, View ORCID ProfileSimon Elsässer
doi: https://doi.org/10.1101/2022.03.14.484318
Carmen Navarro
1Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Tomtebodavägen 23, 17165 Stockholm, Sweden
2Ming Wai Lau Centre for Reparative Medicine, Stockholm node, Karolinska Institutet, Solnavägen 9, 17165 Stockholm, Sweden
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Marcel Martin
3Dept of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden
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Simon Elsässer
1Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Tomtebodavägen 23, 17165 Stockholm, Sweden
2Ming Wai Lau Centre for Reparative Medicine, Stockholm node, Karolinska Institutet, Solnavägen 9, 17165 Stockholm, Sweden
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  • For correspondence: simon.elsasser@scilifelab.se
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Abstract

Quantitative ChIP-seq methods are essential for accurately characterizing and comparing genome-wide DNA-protein interactions across samples. Procedures that enable such quantitative comparisons involve addition of spike-in chromatin or recombinant nucleosome material, or a multiplexed process using barcoding of chromatin fragments. ChIP-seq analysis workflows typically require a number of computational steps involving multiple tools in order to reach interpretable results, and quantitative analyses require additional steps that ensure scaling of the processed output according to the quantitative measurements. Crucially, the different quantitative approaches have unique analysis requirements reflecting the disparate experimental workflows, hence no universal analysis pipeline exists for quantitative ChIP-seq. Here, we developed minute, a user-friendly computational workflow to easily process multiplexed ChIP data that handles the specific needs of quantitative ChIP. minute enables transformation of raw, multiplexed FASTQ files into a set of normalized, scaled bigWig files that can serve as a basis for a quantitative, comparative downstream analysis. minute is implemented in Python and Snakemake and paired with a Conda environment, to facilitate usability and reproducibility in different platforms.

Source code of minute is available on GitHub: https://github.com/NBISweden/minute

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 17, 2022.
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minute: A MINUTE-ChIP data analysis workflow
Carmen Navarro, Marcel Martin, Simon Elsässer
bioRxiv 2022.03.14.484318; doi: https://doi.org/10.1101/2022.03.14.484318
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minute: A MINUTE-ChIP data analysis workflow
Carmen Navarro, Marcel Martin, Simon Elsässer
bioRxiv 2022.03.14.484318; doi: https://doi.org/10.1101/2022.03.14.484318

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