›› 2017, Vol. 37 ›› Issue (11): 1575-.doi: 10.3969/j.issn.1674-8115.2017.11.022

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Analysis method based on the gene-panel sequencing data

LI Jian-feng1, YAN Tian-qi2, CUI Bo-wen1, KONG Jie3, WANG Shu1, CHEN Bing1, HUANG Jin-yan1   

  1. 1. State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;  2. Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China;  3. the Institute of Health Sciences, Shanghai Institutes for Biological Sciences of the Chinese Academy of Sciences / Shanghai Jiao Tong University School of Medicine, Shanghai 200031, China
  • Online:2017-11-28 Published:2018-01-10
  • Supported by:
    National Natural Science Foundation of China, 81570122; Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support, 20161303

Abstract: Objective · To establish an integrative method for the gene-panel sequencing data to automatically complete quality control, detection of gene mutation and visualization.  Methods · Integrate several methods, e.g. FastQC, preprocessing and information of sequences (Prinseq) to develop an R package that can be used to visualize and control the quality of the raw sequencing reads and final mutations result. The sequencing reads mapped against to the reference genome using Burrows-Wheeler Alignment Tool (BWA)/Torrent Mapping Alignment Program (TMAP). Lofreq, Varscan2, the Genome Analysis Toolkit (GATK) and Torrent Variant Caller (TVC) were used to detect gene mutation and get the variant call format (VCF) format file. Annotate the gene mutation sites using Annovar.  Results · Thirty-six cases of acute myeloid leukemia sequencing from Ion Torrent Personal Genome Machine (PGM) platform were passed by this analysis tool. Ten mutation sites of 2 demo data were found in DNMT3A, TET2, JAK2, PHF6, ASXL1, NPM1 and CEBPA which were validated by sanger sequencing.  Conclusion · The analysis method that integrated and developed several tools for gene-panel sequencing data analysis can accomplish the gene-panel sequencing data analysis effectively. Besides, it can reduce the false positive ratio and improve the sensitivity of gene mutation detection that provides support for the analysis of gene-panel sequencing data.

Key words:  next-generation sequencing, gene-panel sequencing, quality control, detection of mutations, visualization

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