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...

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Main Authors: Dongliang Yu, Min Xu, Hidetaka Ito, Weishan Shao, Xiaoxia Ma, Huizhong Wang, Yijun Meng
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-11-01
Series:Frontiers in Genetics
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fgene.2018.00546/full
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spelling doaj-10ee6d7de1104d618c9a63457728a2302020-11-25T02:50:07ZengFrontiers Media S.A.Frontiers in Genetics1664-80212018-11-01910.3389/fgene.2018.00546413243Tracking microRNA Processing Signals by Degradome Sequencing Data AnalysisDongliang Yu0Min Xu1Hidetaka Ito2Weishan Shao3Xiaoxia Ma4Huizhong Wang5Yijun Meng6College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, ChinaCollege of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, ChinaFaculty of Science, Hokkaido University, Sapporo, JapanCollege of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, ChinaCollege of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, ChinaCollege of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, ChinaCollege of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, ChinaDegradome 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.https://www.frontiersin.org/article/10.3389/fgene.2018.00546/fullmicroRNA annotationdegradometissue-/cell line-specific“loop-to-base” processingsingle-stranded cropping
collection DOAJ
language English
format Article
sources DOAJ
author Dongliang Yu
Min Xu
Hidetaka Ito
Weishan Shao
Xiaoxia Ma
Huizhong Wang
Yijun Meng
spellingShingle Dongliang Yu
Min Xu
Hidetaka Ito
Weishan Shao
Xiaoxia Ma
Huizhong Wang
Yijun Meng
Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis
Frontiers in Genetics
microRNA annotation
degradome
tissue-/cell line-specific
“loop-to-base” processing
single-stranded cropping
author_facet Dongliang Yu
Min Xu
Hidetaka Ito
Weishan Shao
Xiaoxia Ma
Huizhong Wang
Yijun Meng
author_sort Dongliang Yu
title Tracking microRNA Processing Signals by Degradome Sequencing Data Analysis
title_short 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_sort tracking microrna processing signals by degradome sequencing data analysis
publisher Frontiers Media S.A.
series Frontiers in Genetics
issn 1664-8021
publishDate 2018-11-01
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.
topic microRNA annotation
degradome
tissue-/cell line-specific
“loop-to-base” processing
single-stranded cropping
url https://www.frontiersin.org/article/10.3389/fgene.2018.00546/full
work_keys_str_mv AT dongliangyu trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
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AT hidetakaito trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT weishanshao trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT xiaoxiama trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT huizhongwang trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
AT yijunmeng trackingmicrornaprocessingsignalsbydegradomesequencingdataanalysis
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