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GeneTrail: A Framework for the Analysis of High-Throughput Profiles

Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are req...

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Autores principales: Gerstner, Nico, Kehl, Tim, Lenhof, Kerstin, Eckhart, Lea, Schneider, Lara, Stöckel, Daniel, Backes, Christina, Meese, Eckart, Keller, Andreas, Lenhof, Hans-Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481803/
https://www.ncbi.nlm.nih.gov/pubmed/34604304
http://dx.doi.org/10.3389/fmolb.2021.716544
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author Gerstner, Nico
Kehl, Tim
Lenhof, Kerstin
Eckhart, Lea
Schneider, Lara
Stöckel, Daniel
Backes, Christina
Meese, Eckart
Keller, Andreas
Lenhof, Hans-Peter
author_facet Gerstner, Nico
Kehl, Tim
Lenhof, Kerstin
Eckhart, Lea
Schneider, Lara
Stöckel, Daniel
Backes, Christina
Meese, Eckart
Keller, Andreas
Lenhof, Hans-Peter
author_sort Gerstner, Nico
collection PubMed
description Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms. GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de.
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spelling pubmed-84818032021-10-01 GeneTrail: A Framework for the Analysis of High-Throughput Profiles Gerstner, Nico Kehl, Tim Lenhof, Kerstin Eckhart, Lea Schneider, Lara Stöckel, Daniel Backes, Christina Meese, Eckart Keller, Andreas Lenhof, Hans-Peter Front Mol Biosci Molecular Biosciences Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms. GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de. Frontiers Media S.A. 2021-09-16 /pmc/articles/PMC8481803/ /pubmed/34604304 http://dx.doi.org/10.3389/fmolb.2021.716544 Text en Copyright © 2021 Gerstner, Kehl, Lenhof, Eckhart, Schneider, Stöckel, Backes, Meese, Keller and Lenhof. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Gerstner, Nico
Kehl, Tim
Lenhof, Kerstin
Eckhart, Lea
Schneider, Lara
Stöckel, Daniel
Backes, Christina
Meese, Eckart
Keller, Andreas
Lenhof, Hans-Peter
GeneTrail: A Framework for the Analysis of High-Throughput Profiles
title GeneTrail: A Framework for the Analysis of High-Throughput Profiles
title_full GeneTrail: A Framework for the Analysis of High-Throughput Profiles
title_fullStr GeneTrail: A Framework for the Analysis of High-Throughput Profiles
title_full_unstemmed GeneTrail: A Framework for the Analysis of High-Throughput Profiles
title_short GeneTrail: A Framework for the Analysis of High-Throughput Profiles
title_sort genetrail: a framework for the analysis of high-throughput profiles
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481803/
https://www.ncbi.nlm.nih.gov/pubmed/34604304
http://dx.doi.org/10.3389/fmolb.2021.716544
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