PMID- 34917425
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220429
IS  - 2167-8359 (Print)
IS  - 2167-8359 (Electronic)
IS  - 2167-8359 (Linking)
VI  - 9
DP  - 2021
TI  - CNV-P: a machine-learning framework for predicting high confident copy number 
      variations.
PG  - e12564
LID - 10.7717/peerj.12564 [doi]
LID - e12564
AB  - BACKGROUND: Copy-number variants (CNVs) have been recognized as one of the major 
      causes of genetic disorders. Reliable detection of CNVs from genome sequencing 
      data has been a strong demand for disease research. However, current software for 
      detecting CNVs has high false-positive rates, which needs further improvement. 
      METHODS: Here, we proposed a novel and post-processing approach for CNVs 
      prediction (CNV-P), a machine-learning framework that could efficiently remove 
      false-positive fragments from results of CNVs detecting tools. A series of CNVs 
      signals such as read depth (RD), split reads (SR) and read pair (RP) around the 
      putative CNV fragments were defined as features to train a classifier. RESULTS: 
      The prediction results on several real biological datasets showed that our models 
      could accurately classify the CNVs at over 90% precision rate and 85% recall 
      rate, which greatly improves the performance of state-of-the-art algorithms. 
      Furthermore, our results indicate that CNV-P is robust to different sizes of CNVs 
      and the platforms of sequencing. CONCLUSIONS: Our framework for classifying 
      high-confident CNVs could improve both basic research and clinical diagnosis of 
      genetic diseases.
CI  - (c) 2021 Wang et al.
FAU - Wang, Taifu
AU  - Wang T
AUID- ORCID: 0000-0003-4674-4454
AD  - BGI-Shenzhen, Shenzhen, China.
FAU - Sun, Jinghua
AU  - Sun J
AD  - BGI-Shenzhen, Shenzhen, China.
AD  - College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 
      China.
FAU - Zhang, Xiuqing
AU  - Zhang X
AD  - BGI-Shenzhen, Shenzhen, China.
AD  - College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 
      China.
AD  - Guangdong Enterprise Key Laboratory of Human Disease Genomics, Beishan Industrial 
      Zone, Shenzhen, China.
FAU - Wang, Wen-Jing
AU  - Wang WJ
AUID- ORCID: 0000-0002-4527-1168
AD  - BGI-Shenzhen, Shenzhen, China.
FAU - Zhou, Qing
AU  - Zhou Q
AD  - BGI-Shenzhen, Shenzhen, China.
LA  - eng
PT  - Journal Article
DEP - 20211202
PL  - United States
TA  - PeerJ
JT  - PeerJ
JID - 101603425
PMC - PMC8645205
OTO - NOTNLM
OT  - Copy number variant
OT  - Genome sequencing
OT  - Machine learning
COIS- The authors declare that they have no competing interests.
EDAT- 2021/12/18 06:00
MHDA- 2021/12/18 06:01
PMCR- 2021/12/02
CRDT- 2021/12/17 06:57
PHST- 2021/08/04 00:00 [received]
PHST- 2021/11/08 00:00 [accepted]
PHST- 2021/12/17 06:57 [entrez]
PHST- 2021/12/18 06:00 [pubmed]
PHST- 2021/12/18 06:01 [medline]
PHST- 2021/12/02 00:00 [pmc-release]
AID - 12564 [pii]
AID - 10.7717/peerj.12564 [doi]
PST - epublish
SO  - PeerJ. 2021 Dec 2;9:e12564. doi: 10.7717/peerj.12564. eCollection 2021.