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Semi-supervised peak calling with SPAN and JBR genome browser

 : The widespread application of ChIP-seq led to a growing need for consistent analysis of multiple epigenetics profiles, for instance, in human studies where multiple replicates are a common element of design. Such multi-samples experimental designs introduced analytical and computational challenge...

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Autores principales: Shpynov, Oleg, Dievskii, Aleksei, Chernyatchik, Roman, Tsurinov, Petr, Artyomov, Maxim N
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502234/
https://www.ncbi.nlm.nih.gov/pubmed/34019098
http://dx.doi.org/10.1093/bioinformatics/btab376
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author Shpynov, Oleg
Dievskii, Aleksei
Chernyatchik, Roman
Tsurinov, Petr
Artyomov, Maxim N
author_facet Shpynov, Oleg
Dievskii, Aleksei
Chernyatchik, Roman
Tsurinov, Petr
Artyomov, Maxim N
author_sort Shpynov, Oleg
collection PubMed
description  : The widespread application of ChIP-seq led to a growing need for consistent analysis of multiple epigenetics profiles, for instance, in human studies where multiple replicates are a common element of design. Such multi-samples experimental designs introduced analytical and computational challenges. For example, when peak calling is done independently for each sample, small differences in signal strength/quality lead to a very different number of peaks for individual samples, making group-level analysis difficult. On the other side, when samples are pooled together for joint analysis, individual-level statistical differences are averaged out. Recently, we have demonstrated that a semi-supervised peak calling approach (SPAN) allows for robust analysis of multiple epigenetic profiles while preserving individual sample statistics. Here, we present this approach’s implementation, centered around the JBR genome browser, a stand-alone tool that allows for accessible and streamlined annotation, analysis and visualization. Specifically, JBR supports graphical interactive manual region selection and annotation, thereby addressing supervised learning’s key procedural challenge. Furthermore, JBR includes the capability for peak optimization, i.e. calibration of sample-specific peak calling parameters by leveraging manual annotation. This procedure can be applied to a broad range of ChIP-seq datasets of different quality and chromatin accessibility ATAC-seq, including single-cell experiments. JBR was designed for efficient data processing, resulting in fast viewing and analysis of multiple replicates, up to thousands of tracks. Accelerated execution and integrated semi-supervised peak calling make JBR and SPAN next-generation visualization and analysis tools for multi-sample epigenetic data. AVAILABILITY AND IMPLEMENTATION: SPAN and JBR run on Linux, Mac OS and Windows, and is freely available at https://research.jetbrains.org/groups/biolabs/tools/span-peak-analyzer and https://research.jetbrains.org/groups/biolabs/tools/jbr-genome-browser. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-95022342022-09-26 Semi-supervised peak calling with SPAN and JBR genome browser Shpynov, Oleg Dievskii, Aleksei Chernyatchik, Roman Tsurinov, Petr Artyomov, Maxim N Bioinformatics Applications Notes  : The widespread application of ChIP-seq led to a growing need for consistent analysis of multiple epigenetics profiles, for instance, in human studies where multiple replicates are a common element of design. Such multi-samples experimental designs introduced analytical and computational challenges. For example, when peak calling is done independently for each sample, small differences in signal strength/quality lead to a very different number of peaks for individual samples, making group-level analysis difficult. On the other side, when samples are pooled together for joint analysis, individual-level statistical differences are averaged out. Recently, we have demonstrated that a semi-supervised peak calling approach (SPAN) allows for robust analysis of multiple epigenetic profiles while preserving individual sample statistics. Here, we present this approach’s implementation, centered around the JBR genome browser, a stand-alone tool that allows for accessible and streamlined annotation, analysis and visualization. Specifically, JBR supports graphical interactive manual region selection and annotation, thereby addressing supervised learning’s key procedural challenge. Furthermore, JBR includes the capability for peak optimization, i.e. calibration of sample-specific peak calling parameters by leveraging manual annotation. This procedure can be applied to a broad range of ChIP-seq datasets of different quality and chromatin accessibility ATAC-seq, including single-cell experiments. JBR was designed for efficient data processing, resulting in fast viewing and analysis of multiple replicates, up to thousands of tracks. Accelerated execution and integrated semi-supervised peak calling make JBR and SPAN next-generation visualization and analysis tools for multi-sample epigenetic data. AVAILABILITY AND IMPLEMENTATION: SPAN and JBR run on Linux, Mac OS and Windows, and is freely available at https://research.jetbrains.org/groups/biolabs/tools/span-peak-analyzer and https://research.jetbrains.org/groups/biolabs/tools/jbr-genome-browser. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-05-21 /pmc/articles/PMC9502234/ /pubmed/34019098 http://dx.doi.org/10.1093/bioinformatics/btab376 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Shpynov, Oleg
Dievskii, Aleksei
Chernyatchik, Roman
Tsurinov, Petr
Artyomov, Maxim N
Semi-supervised peak calling with SPAN and JBR genome browser
title Semi-supervised peak calling with SPAN and JBR genome browser
title_full Semi-supervised peak calling with SPAN and JBR genome browser
title_fullStr Semi-supervised peak calling with SPAN and JBR genome browser
title_full_unstemmed Semi-supervised peak calling with SPAN and JBR genome browser
title_short Semi-supervised peak calling with SPAN and JBR genome browser
title_sort semi-supervised peak calling with span and jbr genome browser
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502234/
https://www.ncbi.nlm.nih.gov/pubmed/34019098
http://dx.doi.org/10.1093/bioinformatics/btab376
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