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Chuyu Ye

dblp:191/0082 · also Chu-Yu Ye · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0003-0903-0356ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › non-coding RNA identification
non-coding RNA prediction
0.212016
PcircRNA_finder: a software for circRNA prediction in plants · Bioinform. 2016
Bioinformatics and computational biology › genomics
plant genomics
0.212016
PcircRNA_finder: a software for circRNA prediction in plants · Bioinform. 2016

Methods — techniques the papers use, named apart from their topics

RNA-seq analysis · 0.2
YearPublicationVenuePosition
2020 Characteristics of plant circular RNAs
abstract
Circular RNA (circRNA) is a kind of covalently closed single-stranded RNA molecules that have been proved to play important roles in transcriptional regulation of genes in diverse species. With the rapid development of bioinformatics tools, a huge number (95143) of circRNAs have been identified from different plant species, providing an opportunity for uncovering the overall characteristics of plant circRNAs. Here, based on publicly available circRNAs, we comprehensively analyzed characteristics of plant circRNAs with the help of various bioinformatics tools as well as in-house scripts and workflows, including the percentage of coding genes generating circRNAs, the frequency of alternative splicing events of circRNAs, the non-canonical splicing signals of circRNAs and the networks involving circRNAs, miRNAs and mRNAs. All this information has been integrated into an upgraded online database, PlantcircBase 3.0 (http://ibi.zju.edu.cn/plantcircbase/). In this database, we provided browse, search and visualization tools as well as a web-based blast tool, BLASTcirc, for prediction of circRNAs from query sequences based on searching against plant genomes and transcriptomes.
Qinjie Chu, Panpan Bai, Xintian Zhu, Lingfeng Mao 0002, Qian-Hao Zhu, Longjiang Fan, Chuyu Ye
Briefings Bioinform.8
2016 PcircRNA_finder: a software for circRNA prediction in plants
abstract
MOTIVATION: Recent studies reveal an important role of non-coding circular RNA (circRNA) in the control of cellular processes. Because of differences in the organization of plant and mammal genomes, the sensitivity and accuracy of circRNA prediction programs using algorithms developed for animals and humans perform poorly for plants. RESULTS: A circRNA prediction software for plants (termed PcircRNA_finder) was developed that is more sensitive in detecting circRNAs than other frequently used programs (such as find_circ and CIRCexplorer), Based on analysis of simulated and real rRNA-/RNAase R RNA-Seq data from Arabidopsis thaliana and rice PcircRNA_finder provides a more comprehensive sensitive, precise prediction method for plants circRNAs. AVAILABILITY AND IMPLEMENTATION: http://ibi.zju.edu.cn/bioinplant/tools/manual.htm CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.
Yongyi Yu, Chuyu Ye, Longjiang Fan
Bioinform.5