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SNVHMM: predicting single nucleotide variants from next generation sequencing

Jiawen Bian12, Chenglin Liu2, Hongyan Wang2, Jing Xing23, Priyanka Kachroo2 and Xiaobo Zhou4*

Author Affiliations

1 School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China

2 Department of Radiology, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston 77030 TX, USA

3 Department of Statistics, Hubei University of Economics, Wuhan, 430025, China

4 Department of Diagnostic Radiology, Center for Bioinformatics & Systems Biology, Wake Forest University - School of Medicine, Winston-Salem 27103, NC, USA

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BMC Bioinformatics 2013, 14:225  doi:10.1186/1471-2105-14-225

Published: 15 July 2013

Additional files

Additional file 1: Table S1:

Statistical performance of SNVHMM for different minimum and valid coverage (d), as well as for different MQ and BQ value when the sequencing depth of lobular breast cancer data is 10X. Table S2: Statistical performance of SNVHMM for different minimum and valid coverage (d), as well as for different MQ and BQ value when the sequencing depth of lobular breast cancer data is 40X. Table S3: 23 reported mutated genes in Bejar,R. et al. (2011) and Thol,F. et al. (2012) are checked by SNVHMM. 4 new genes that are found in 5 MDS RNA-Seq sample and 2 MDS whole exome samples are found by SNVHMM and validated by our lab. Table S4: description of 4 MDS-related mutated genes found by SNVHMM and validated by our lab in 5 RNA-Seq and 2 whole exome samples.

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