RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts

The physiology and behavior of living organisms are featured by time-related variations driven by molecular clockworks that arose during evolution stochastically and heterogeneously. Over the years, several high-throughput experiments were performed to evaluate time-dependent gene expression in diff...

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Autores principales: Castellana, Stefano, Biagini, Tommaso, Petrizzelli, Francesco, Cabibbo, Andrea, Mazzoccoli, Gianluigi, Mazza, Tommaso
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237250/
https://www.ncbi.nlm.nih.gov/pubmed/35774515
http://dx.doi.org/10.3389/fgene.2022.882044
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author Castellana, Stefano
Biagini, Tommaso
Petrizzelli, Francesco
Cabibbo, Andrea
Mazzoccoli, Gianluigi
Mazza, Tommaso
author_facet Castellana, Stefano
Biagini, Tommaso
Petrizzelli, Francesco
Cabibbo, Andrea
Mazzoccoli, Gianluigi
Mazza, Tommaso
author_sort Castellana, Stefano
collection PubMed
description The physiology and behavior of living organisms are featured by time-related variations driven by molecular clockworks that arose during evolution stochastically and heterogeneously. Over the years, several high-throughput experiments were performed to evaluate time-dependent gene expression in different cell types across several species and experimental conditions. Here, these were retrieved, manually curated, and analyzed by two software packages, BioCycle and MetaCycle, to infer circadian or ultradian transcripts across different species. These transcripts were stored in RhythmicDB and made publically available.
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spelling pubmed-92372502022-06-29 RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts Castellana, Stefano Biagini, Tommaso Petrizzelli, Francesco Cabibbo, Andrea Mazzoccoli, Gianluigi Mazza, Tommaso Front Genet Genetics The physiology and behavior of living organisms are featured by time-related variations driven by molecular clockworks that arose during evolution stochastically and heterogeneously. Over the years, several high-throughput experiments were performed to evaluate time-dependent gene expression in different cell types across several species and experimental conditions. Here, these were retrieved, manually curated, and analyzed by two software packages, BioCycle and MetaCycle, to infer circadian or ultradian transcripts across different species. These transcripts were stored in RhythmicDB and made publically available. Frontiers Media S.A. 2022-06-14 /pmc/articles/PMC9237250/ /pubmed/35774515 http://dx.doi.org/10.3389/fgene.2022.882044 Text en Copyright © 2022 Castellana, Biagini, Petrizzelli, Cabibbo, Mazzoccoli and Mazza. 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 Genetics
Castellana, Stefano
Biagini, Tommaso
Petrizzelli, Francesco
Cabibbo, Andrea
Mazzoccoli, Gianluigi
Mazza, Tommaso
RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts
title RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts
title_full RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts
title_fullStr RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts
title_full_unstemmed RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts
title_short RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts
title_sort rhythmicdb: a database of predicted multi-frequency rhythmic transcripts
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237250/
https://www.ncbi.nlm.nih.gov/pubmed/35774515
http://dx.doi.org/10.3389/fgene.2022.882044
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