NDLI: A study on fast calling variants from next-generation sequencing data using decision tree

Content Provider	Springer Nature : BioMed Central
Author	Li, Zhentang Wang, Yi Wang, Fei
Abstract	Background The rapid development of next-generation sequencing (NGS) technology has continuously been refreshing the throughput of sequencing data. However, due to the lack of a smart tool that is both fast and accurate, the analysis task for NGS data, especially those with low-coverage, remains challenging. Results We proposed a decision-tree based variant calling algorithm. Experiments on a set of real data indicate that our algorithm achieves high accuracy and sensitivity for SNVs and indels and shows good adaptability on low-coverage data. In particular, our algorithm is obviously faster than 3 widely used tools in our experiments. Conclusions We implemented our algorithm in a software named Fuwa and applied it together with 4 well-known variant callers, i.e., Platypus, GATK-UnifiedGenotyper, GATK-HaplotypeCaller and SAMtools, to three sequencing data sets of a well-studied sample NA12878, which were produced by whole-genome, whole-exome and low-coverage whole-genome sequencing technology respectively. We also conducted additional experiments on the WGS data of 4 newly released samples that have not been used to populate dbSNP.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-018-2147-9.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-018-2147-9
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2018-04-19
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Next-generation sequencing Variant calling Decision tree Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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