Cargando…

Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis

Degradome sequencing (degradome-seq) was widely used for cleavage site mapping on the microRNA (miRNA) targets. Here, the application value of degradome-seq data in tracking the miRNA processing intermediates was reported. By adopting the parameter “signal/noise” ratio, prominent degradome signals o...

Descripción completa

Detalles Bibliográficos
Autores principales: Yu, Dongliang, Xu, Min, Ito, Hidetaka, Shao, Weishan, Ma, Xiaoxia, Wang, Huizhong, Meng, Yijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6246748/
https://www.ncbi.nlm.nih.gov/pubmed/30487815
http://dx.doi.org/10.3389/fgene.2018.00546
_version_ 1783372385114652672
author Yu, Dongliang
Xu, Min
Ito, Hidetaka
Shao, Weishan
Ma, Xiaoxia
Wang, Huizhong
Meng, Yijun
author_facet Yu, Dongliang
Xu, Min
Ito, Hidetaka
Shao, Weishan
Ma, Xiaoxia
Wang, Huizhong
Meng, Yijun
author_sort Yu, Dongliang
collection PubMed
description Degradome sequencing (degradome-seq) was widely used for cleavage site mapping on the microRNA (miRNA) targets. Here, the application value of degradome-seq data in tracking the miRNA processing intermediates was reported. By adopting the parameter “signal/noise” ratio, prominent degradome signals on the miRNA precursors were extracted. For the 15 species analyzed, the processing of many miRNA precursors were supported by the degradome-seq data. We found that the supporting ratio of the “high-confidence” miRNAs annotated in miRBase was much higher than that of the “low-confidence.” For a specific species, the percentage of the miRNAs with degradome-supported processing signals was elevated by the increment of degradome sampling diversity. More interestingly, the tissue- or cell line-specific processing patterns of the miRNA precursors partially contributed to the accumulation patterns of the mature miRNAs. In this study, we also provided examples to show the value of the degradome-seq data in miRNA annotation. Based on the distribution of the processing signals, a renewed model was proposed that the stems of the miRNA precursors were diced through a “single-stranded cropping” mode, and “loop-to-base” processing was much more prevalent than previously thought. Together, our results revealed the remarkable capacity of degradome-seq in tracking miRNA processing signals.
format Online
Article
Text
id pubmed-6246748
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-62467482018-11-28 Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis Yu, Dongliang Xu, Min Ito, Hidetaka Shao, Weishan Ma, Xiaoxia Wang, Huizhong Meng, Yijun Front Genet Genetics Degradome sequencing (degradome-seq) was widely used for cleavage site mapping on the microRNA (miRNA) targets. Here, the application value of degradome-seq data in tracking the miRNA processing intermediates was reported. By adopting the parameter “signal/noise” ratio, prominent degradome signals on the miRNA precursors were extracted. For the 15 species analyzed, the processing of many miRNA precursors were supported by the degradome-seq data. We found that the supporting ratio of the “high-confidence” miRNAs annotated in miRBase was much higher than that of the “low-confidence.” For a specific species, the percentage of the miRNAs with degradome-supported processing signals was elevated by the increment of degradome sampling diversity. More interestingly, the tissue- or cell line-specific processing patterns of the miRNA precursors partially contributed to the accumulation patterns of the mature miRNAs. In this study, we also provided examples to show the value of the degradome-seq data in miRNA annotation. Based on the distribution of the processing signals, a renewed model was proposed that the stems of the miRNA precursors were diced through a “single-stranded cropping” mode, and “loop-to-base” processing was much more prevalent than previously thought. Together, our results revealed the remarkable capacity of degradome-seq in tracking miRNA processing signals. Frontiers Media S.A. 2018-11-14 /pmc/articles/PMC6246748/ /pubmed/30487815 http://dx.doi.org/10.3389/fgene.2018.00546 Text en Copyright © 2018 Yu, Xu, Ito, Shao, Ma, Wang and Meng. http://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
Yu, Dongliang
Xu, Min
Ito, Hidetaka
Shao, Weishan
Ma, Xiaoxia
Wang, Huizhong
Meng, Yijun
Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis
title Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis
title_full Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis
title_fullStr Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis
title_full_unstemmed Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis
title_short Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis
title_sort tracking microrna processing signals by degradome sequencing data analysis
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6246748/
https://www.ncbi.nlm.nih.gov/pubmed/30487815
http://dx.doi.org/10.3389/fgene.2018.00546
work_keys_str_mv AT yudongliang trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT xumin trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT itohidetaka trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT shaoweishan trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT maxiaoxia trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT wanghuizhong trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT mengyijun trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis