Using generative adversarial networks for genome variant calling from low depth ONT sequencing data.
Sci Rep
; 12(1): 8725, 2022 05 30.
Article
in English
| MEDLINE | ID: covidwho-1947436
ABSTRACT
Genome variant calling is a challenging yet critical task for subsequent studies. Existing methods almost rely on high depth DNA sequencing data. Performance on low depth data drops a lot. Using public Oxford Nanopore (ONT) data of human being from the Genome in a Bottle (GIAB) Consortium, we trained a generative adversarial network for low depth variant calling. Our method, noted as LDV-Caller, can project high depth sequencing information from low depth data. It achieves 94.25% F1 score on low depth data, while the F1 score of the state-of-the-art method on two times higher depth data is 94.49%. By doing so, the price of genome-wide sequencing examination can reduce deeply. In addition, we validated the trained LDV-Caller model on 157 public Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) samples. The mean sequencing depth of these samples is 2982. The LDV-Caller yields 92.77% F1 score using only 22x sequencing depth, which demonstrates our method has potential to analyze different species with only low depth sequencing data.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Polymorphism, Single Nucleotide
/
COVID-19
Type of study:
Prognostic study
Topics:
Variants
Limits:
Humans
Language:
English
Journal:
Sci Rep
Year:
2022
Document Type:
Article
Affiliation country:
S41598-022-12346-7
Similar
MEDLINE
...
LILACS
LIS