title: Copy number variation analysis based on AluScan sequences.
abstract: BACKGROUND
  AluScan combines inter-Alu PCR using multiple Alu-based primers with opposite orientations and next-generation sequencing to capture a huge number of Alu-proximal genomic sequences for investigation. Its requirement of only sub-microgram quantities of DNA facilitates the examination of large numbers of samples. However, the special features of AluScan data rendered difficult the calling of copy number variation (CNV) directly using the calling algorithms designed for whole genome sequencing (WGS) or exome sequencing.
  
  
  RESULTS
  In this study, an AluScanCNV package has been assembled for efficient CNV calling from AluScan sequencing data employing a Geary-Hinkley transformation (GHT) of read-depth ratios between either paired test-control samples, or between test samples and a reference template constructed from reference samples, to call the localized CNVs, followed by use of a GISTIC-like algorithm to identify recurrent CNVs and circular binary segmentation (CBS) to reveal large extended CNVs. To evaluate the utility of CNVs called from AluScan data, the AluScans from 23 non-cancer and 38 cancer genomes were analyzed in this study. The glioma samples analyzed yielded the familiar extended copy-number losses on chromosomes 1p and 9. Also, the recurrent somatic CNVs identified from liver cancer samples were similar to those reported for liver cancer WGS with respect to a striking enrichment of copy-number gains in chromosomes 1q and 8q. When localized or recurrent CNV-features capable of distinguishing between liver and non-liver cancer samples were selected by correlation-based machine learning, a highly accurate separation of the liver and non-liver cancer classes was attained.
  
  
  CONCLUSIONS
  The results obtained from non-cancer and cancerous tissues indicated that the AluScanCNV package can be employed to call localized, recurrent and extended CNVs from AluScan sequences. Moreover, both the localized and recurrent CNVs identified by this method could be subjected to machine-learning selection to yield distinguishing CNV-features that were capable of separating between liver cancers and other types of cancers. Since the method is applicable to any human DNA sample with or without the availability of a paired control, it can also be employed to analyze the constitutional CNVs of individuals.
authors.affiliation: Division of Life Science and Applied Genomics Centre, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
authors.forename: Jian-Feng
authors.initials: JF
authors.lastname: Yang
authors.affiliation: Division of Life Science and Applied Genomics Centre, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
authors.forename: Xiao-Fan
authors.initials: XF
authors.lastname: Ding
authors.affiliation: National Center for Liver Cancer Research and Eastern Hepatobiliary Surgery Hospital, 225 Changhai Road, Shanghai, 200438 China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: Division of Life Science and Applied Genomics Centre, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
authors.forename: Wai-Kin
authors.initials: WK
authors.lastname: Mat
authors.affiliation: Department of Oncology, Nanjing First Hospital, No. 68 Changle Road, Nanjing, 210006 China.
authors.forename: Michelle Zhi
authors.initials: MZ
authors.lastname: Xu
authors.affiliation: Department of Oncology, Nanjing First Hospital, No. 68 Changle Road, Nanjing, 210006 China.
authors.forename: Jin-Fei
authors.initials: JF
authors.lastname: Chen
authors.affiliation: Department of Hematology, Changhai Hospital, Second Military Medical University, 174 Changhai Road, Shanghai, 200433 China.
authors.forename: Jian-Min
authors.initials: JM
authors.lastname: Wang
authors.affiliation: Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Baiziting 42, Nanjing, 210009 China.
authors.forename: Lin
authors.initials: L
authors.lastname: Xu
authors.affiliation: Division of Neurosurgery, Department of Surgery, Prince of Wales Hospital, Chinese University of Hong Kong, 30-32 Ngan Shing Street, Sha Tin, Hong Kong, China.
authors.forename: Wai-Sang
authors.initials: WS
authors.lastname: Poon
authors.affiliation: Division of Neurosurgery, Department of Surgery, Li Ka Shing Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong, China.
authors.forename: Ava
authors.initials: A
authors.lastname: Kwong
authors.affiliation: Division of Neurosurgery, Department of Surgery, Li Ka Shing Faculty of Medicine, University of Hong Kong, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong, China.
authors.forename: Gilberto Ka-Kit
authors.initials: GK
authors.lastname: Leung
authors.affiliation: Department of Neurosurgery, Queen Elizabeth Hospital, 30 Gascoigne Road, Kowloon, Hong Kong, China.
authors.forename: Tze-Ching
authors.initials: TC
authors.lastname: Tan
authors.affiliation: Department of Neurosurgery, Queen Elizabeth Hospital, 30 Gascoigne Road, Kowloon, Hong Kong, China.
authors.forename: Chi-Hung
authors.initials: CH
authors.lastname: Yu
authors.affiliation: Shenzhen Center for Disease Control and Prevention, No 8 Longyuan Road, Nanshan district, Shenzhen City, 518055 China.
authors.forename: Yue-Bin
authors.initials: YB
authors.lastname: Ke
authors.affiliation: Shenzhen Center for Disease Control and Prevention, No 8 Longyuan Road, Nanshan district, Shenzhen City, 518055 China.
authors.forename: Xin-Yun
authors.initials: XY
authors.lastname: Xu
authors.affiliation: Nanjing Brain Hospital and Nanjing Institute of Neuropsychiatry, Nanjing Medical University, Nanjing, 210029 China.
authors.forename: Xiao-Yan
authors.initials: XY
authors.lastname: Ke
authors.affiliation: Department of Medicine and Therapeutics, 9th floor, Clinical Sciences Building, The Prince of Wales Hospital, Shatin, Hong Kong.
authors.forename: Ronald Cw
authors.initials: RC
authors.lastname: Ma
authors.affiliation: Department of Medicine and Therapeutics, 9th floor, Clinical Sciences Building, The Prince of Wales Hospital, Shatin, Hong Kong.
authors.forename: Juliana Cn
authors.initials: JC
authors.lastname: Chan
authors.affiliation: Department of Neurosurgery, Beijing Tiantan Hospital, 6 Tiantan Xili, Dongcheng District, Capital Medical University, Beijing, 100050 China.
authors.forename: Wei-Qing
authors.initials: WQ
authors.lastname: Wan
authors.affiliation: Department of Neurosurgery, Beijing Tiantan Hospital, 6 Tiantan Xili, Dongcheng District, Capital Medical University, Beijing, 100050 China.
authors.forename: Li-Wei
authors.initials: LW
authors.lastname: Zhang
authors.affiliation: Division of Life Science and Applied Genomics Centre, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
authors.forename: Yogesh
authors.initials: Y
authors.lastname: Kumar
authors.affiliation: Division of Life Science and Applied Genomics Centre, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
authors.forename: Shui-Ying
authors.initials: SY
authors.lastname: Tsang
authors.affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084 China.
authors.forename: Shao
authors.initials: S
authors.lastname: Li
authors.affiliation: National Center for Liver Cancer Research and Eastern Hepatobiliary Surgery Hospital, 225 Changhai Road, Shanghai, 200438 China.|International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, 225 Changhai Road, Shanghai, 200438 China.
authors.forename: Hong-Yang
authors.initials: HY
authors.lastname: Wang
authors.affiliation: Division of Life Science and Applied Genomics Centre, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Xue
keywords: AluScan sequencing; CNV calling; Cancer classification; Machine learning