seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.

Over the past two decades, researchers have discovered a special form of alternative splicing that produces a circular form of RNA. Although these circular RNAs (circRNAs) have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of current studie...

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Main Authors: Mohamed Chaabane, Kalina Andreeva, Jae Yeon Hwang, Tae Lim Kook, Juw Won Park, Nigel G F Cooper
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-10-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1008338
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spelling doaj-9891f01e45f1491d8bbe8f503d5afcdd2021-04-21T15:44:54ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-10-011610e100833810.1371/journal.pcbi.1008338seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.Mohamed ChaabaneKalina AndreevaJae Yeon HwangTae Lim KookJuw Won ParkNigel G F CooperOver the past two decades, researchers have discovered a special form of alternative splicing that produces a circular form of RNA. Although these circular RNAs (circRNAs) have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of current studies has been on the tissue-specific circRNAs that exist only in one tissue but not in other tissues or on the disease-specific circRNAs that exist in certain disease conditions, such as cancer, but not under normal conditions. This approach was conducted in the relative absence of methods that analyze a group of common circRNAs that exist in both conditions, but are more abundant in one condition relative to another (differentially expressed). Studies of differentially expressed circRNAs (DECs) between two conditions would serve as a significant first step in filling this void. Here, we introduce a novel computational tool, seekCRIT (seek for differentially expressed CircRNAs In Transcriptome), that identifies the DECs between two conditions from high-throughput sequencing data. Using rat retina RNA-seq data from ischemic and normal conditions, we show that over 74% of identifiable circRNAs are expressed in both conditions and over 40 circRNAs are differentially expressed between two conditions. We also obtain a high qPCR validation rate of 90% for DECs with a FDR of < 5%. Our results demonstrate that seekCRIT is a novel and efficient approach to detect DECs using rRNA depleted RNA-seq data. seekCRIT is freely downloadable at https://github.com/UofLBioinformatics/seekCRIT. The source code is licensed under the MIT License. seekCRIT is developed and tested on Linux CentOS-7.https://doi.org/10.1371/journal.pcbi.1008338
collection DOAJ
language English
format Article
sources DOAJ
author Mohamed Chaabane
Kalina Andreeva
Jae Yeon Hwang
Tae Lim Kook
Juw Won Park
Nigel G F Cooper
spellingShingle Mohamed Chaabane
Kalina Andreeva
Jae Yeon Hwang
Tae Lim Kook
Juw Won Park
Nigel G F Cooper
seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.
PLoS Computational Biology
author_facet Mohamed Chaabane
Kalina Andreeva
Jae Yeon Hwang
Tae Lim Kook
Juw Won Park
Nigel G F Cooper
author_sort Mohamed Chaabane
title seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.
title_short seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.
title_full seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.
title_fullStr seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.
title_full_unstemmed seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.
title_sort seekcrit: detecting and characterizing differentially expressed circular rnas using high-throughput sequencing data.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2020-10-01
description Over the past two decades, researchers have discovered a special form of alternative splicing that produces a circular form of RNA. Although these circular RNAs (circRNAs) have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of current studies has been on the tissue-specific circRNAs that exist only in one tissue but not in other tissues or on the disease-specific circRNAs that exist in certain disease conditions, such as cancer, but not under normal conditions. This approach was conducted in the relative absence of methods that analyze a group of common circRNAs that exist in both conditions, but are more abundant in one condition relative to another (differentially expressed). Studies of differentially expressed circRNAs (DECs) between two conditions would serve as a significant first step in filling this void. Here, we introduce a novel computational tool, seekCRIT (seek for differentially expressed CircRNAs In Transcriptome), that identifies the DECs between two conditions from high-throughput sequencing data. Using rat retina RNA-seq data from ischemic and normal conditions, we show that over 74% of identifiable circRNAs are expressed in both conditions and over 40 circRNAs are differentially expressed between two conditions. We also obtain a high qPCR validation rate of 90% for DECs with a FDR of < 5%. Our results demonstrate that seekCRIT is a novel and efficient approach to detect DECs using rRNA depleted RNA-seq data. seekCRIT is freely downloadable at https://github.com/UofLBioinformatics/seekCRIT. The source code is licensed under the MIT License. seekCRIT is developed and tested on Linux CentOS-7.
url https://doi.org/10.1371/journal.pcbi.1008338
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