Ting-You Wang1, Rendong Yang1,2. 1. The Hormel Institute, University of Minnesota, 801 16th Ave NE, Austin, MN 55912, USA. 2. Masonic Cancer Center, University of Minnesota, 425 E. River Pkwy, Minneapolis, MN 55455, USA.
Abstract
BACKGROUND: Internal tandem duplications (ITDs) are tandem duplications within coding exons and are important prognostic markers and drug targets for acute myeloid leukemia (AML). Next-generation sequencing has enabled the discovery of ITD at single-nucleotide resolution. ITD allele frequency is used in the risk stratification of patients with AML; higher ITD allele frequency is associated with poorer clinical outcomes. However, the ITD allele frequency data are often unavailable to treating physicians and the detection of ITDs with accurate variant allele frequency (VAF) estimation remains challenging for short-read sequencing. RESULTS: Here we present the ScanITD approach, which performs a stepwise seed-and-realignment procedure for ITD detection with accurate VAF prediction. The evaluations on simulated and real data demonstrate that ScanITD outperforms 3 state-of-the-art ITD detectors, especially for VAF estimation. Importantly, ScanITD yields better accuracy than general-purpose structural variation callers for predicting ITD size range duplications. CONCLUSIONS: ScanITD enables the accurate identification of ITDs with robust VAF estimation. ScanITD is written in Python and is open-source software that is freely accessible at https://github.com/ylab-hi/ScanITD.
BACKGROUND: Internal tandem duplications (ITDs) are tandem duplications within coding exons and are important prognostic markers and drug targets for acute myeloid leukemia (AML). Next-generation sequencing has enabled the discovery of ITD at single-nucleotide resolution. ITD allele frequency is used in the risk stratification of patients with AML; higher ITD allele frequency is associated with poorer clinical outcomes. However, the ITD allele frequency data are often unavailable to treating physicians and the detection of ITDs with accurate variant allele frequency (VAF) estimation remains challenging for short-read sequencing. RESULTS: Here we present the ScanITD approach, which performs a stepwise seed-and-realignment procedure for ITD detection with accurate VAF prediction. The evaluations on simulated and real data demonstrate that ScanITD outperforms 3 state-of-the-art ITD detectors, especially for VAF estimation. Importantly, ScanITD yields better accuracy than general-purpose structural variation callers for predicting ITD size range duplications. CONCLUSIONS: ScanITD enables the accurate identification of ITDs with robust VAF estimation. ScanITD is written in Python and is open-source software that is freely accessible at https://github.com/ylab-hi/ScanITD.
Authors: David H Spencer; Haley J Abel; Christina M Lockwood; Jacqueline E Payton; Philippe Szankasi; Todd W Kelley; Shashikant Kulkarni; John D Pfeifer; Eric J Duncavage Journal: J Mol Diagn Date: 2012-11-14 Impact factor: 5.568
Authors: Xiaoyu Chen; Ole Schulz-Trieglaff; Richard Shaw; Bret Barnes; Felix Schlesinger; Morten Källberg; Anthony J Cox; Semyon Kruglyak; Christopher T Saunders Journal: Bioinformatics Date: 2015-12-08 Impact factor: 6.937
Authors: Timothy J Ley; Christopher Miller; Li Ding; Benjamin J Raphael; Andrew J Mungall; A Gordon Robertson; Katherine Hoadley; Timothy J Triche; Peter W Laird; Jack D Baty; Lucinda L Fulton; Robert Fulton; Sharon E Heath; Joelle Kalicki-Veizer; Cyriac Kandoth; Jeffery M Klco; Daniel C Koboldt; Krishna-Latha Kanchi; Shashikant Kulkarni; Tamara L Lamprecht; David E Larson; Ling Lin; Charles Lu; Michael D McLellan; Joshua F McMichael; Jacqueline Payton; Heather Schmidt; David H Spencer; Michael H Tomasson; John W Wallis; Lukas D Wartman; Mark A Watson; John Welch; Michael C Wendl; Adrian Ally; Miruna Balasundaram; Inanc Birol; Yaron Butterfield; Readman Chiu; Andy Chu; Eric Chuah; Hye-Jung Chun; Richard Corbett; Noreen Dhalla; Ranabir Guin; An He; Carrie Hirst; Martin Hirst; Robert A Holt; Steven Jones; Aly Karsan; Darlene Lee; Haiyan I Li; Marco A Marra; Michael Mayo; Richard A Moore; Karen Mungall; Jeremy Parker; Erin Pleasance; Patrick Plettner; Jacquie Schein; Dominik Stoll; Lucas Swanson; Angela Tam; Nina Thiessen; Richard Varhol; Natasja Wye; Yongjun Zhao; Stacey Gabriel; Gad Getz; Carrie Sougnez; Lihua Zou; Mark D M Leiserson; Fabio Vandin; Hsin-Ta Wu; Frederick Applebaum; Stephen B Baylin; Rehan Akbani; Bradley M Broom; Ken Chen; Thomas C Motter; Khanh Nguyen; John N Weinstein; Nianziang Zhang; Martin L Ferguson; Christopher Adams; Aaron Black; Jay Bowen; Julie Gastier-Foster; Thomas Grossman; Tara Lichtenberg; Lisa Wise; Tanja Davidsen; John A Demchok; Kenna R Mills Shaw; Margi Sheth; Heidi J Sofia; Liming Yang; James R Downing; Greg Eley Journal: N Engl J Med Date: 2013-05-01 Impact factor: 91.245
Authors: Zev N Kronenberg; Edward J Osborne; Kelsey R Cone; Brett J Kennedy; Eric T Domyan; Michael D Shapiro; Nels C Elde; Mark Yandell Journal: PLoS Comput Biol Date: 2015-12-01 Impact factor: 4.475
Authors: Michael A Eberle; Epameinondas Fritzilas; Peter Krusche; Morten Källberg; Benjamin L Moore; Mitchell A Bekritsky; Zamin Iqbal; Han-Yu Chuang; Sean J Humphray; Aaron L Halpern; Semyon Kruglyak; Elliott H Margulies; Gil McVean; David R Bentley Journal: Genome Res Date: 2016-11-30 Impact factor: 9.043
Authors: Navin Rustagi; Oliver A Hampton; Jie Li; Liu Xi; Richard A Gibbs; Sharon E Plon; Marek Kimmel; David A Wheeler Journal: BMC Bioinformatics Date: 2016-04-27 Impact factor: 3.169
Authors: Jian Yuan Goh; Chik Hong Kuick; Masahiro Sugiura; Sze Jet Aw; Manli Zhao; Hongfeng Tang; Sandini Gunaratne; Fucun Zhu; Lin Cai; Bin Tean Teh; Paul S Thorner; Kenneth Tou En Chang Journal: J Pathol Clin Res Date: 2022-07-14