T
Thomas K. F. Wong
Researcher at Australian National University
Publications - 52
Citations - 10550
Thomas K. F. Wong is an academic researcher from Australian National University. The author has contributed to research in topics: Structural alignment & Pseudoknot. The author has an hindex of 12, co-authored 50 publications receiving 6417 citations. Previous affiliations of Thomas K. F. Wong include Commonwealth Scientific and Industrial Research Organisation & Queensland University of Technology.
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Journal ArticleDOI
ModelFinder: fast model selection for accurate phylogenetic estimates
Subha Kalyaanamoorthy,Subha Kalyaanamoorthy,Bui Quang Minh,Thomas K. F. Wong,Thomas K. F. Wong,Arndt von Haeseler,Arndt von Haeseler,Lars S. Jermiin,Lars S. Jermiin +8 more
TL;DR: ModelFinder is presented, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates by incorporating a model of rate heterogeneity across sites not previously considered in this context and by allowing concurrent searches of model space and tree space.
Journal ArticleDOI
Phylogenomics resolves the timing and pattern of insect evolution
Bernhard Misof,Shanlin Liu,Karen Meusemann,Ralph S. Peters,Alexander Donath,Christoph Mayer,Paul B. Frandsen,Jessica L. Ware,Tomas Flouri,Rolf G. Beutel,Oliver Niehuis,Malte Petersen,Fernando Izquierdo-Carrasco,Torsten Wappler,Jes Rust,Andre J. Aberer,Ulrike Aspöck,Ulrike Aspöck,Horst Aspöck,Daniela Bartel,Alexander Blanke,Simon Berger,Alexander Böhm,Thomas R. Buckley,Brett Calcott,Junqing Chen,Frank Friedrich,Makiko Fukui,Mari Fujita,Carola Greve,Peter Grobe,Shengchang Gu,Ying Huang,Lars S. Jermiin,Akito Y. Kawahara,Lars Krogmann,Martin Kubiak,Robert Lanfear,Robert Lanfear,Robert Lanfear,Harald Letsch,Yiyuan Li,Zhenyu Li,Jiguang Li,Haorong Lu,Ryuichiro Machida,Yuta Mashimo,Pashalia Kapli,Pashalia Kapli,Duane D. McKenna,Guanliang Meng,Yasutaka Nakagaki,José Luis Navarrete-Heredia,Michael Ott,Yanxiang Ou,Günther Pass,Lars Podsiadlowski,Hans Pohl,Björn M. von Reumont,Kai Schütte,Kaoru Sekiya,Shota Shimizu,Adam Slipinski,Alexandros Stamatakis,Alexandros Stamatakis,Wenhui Song,Xu Su,Nikolaus U. Szucsich,Meihua Tan,Xuemei Tan,Min Tang,Jingbo Tang,Gerald Timelthaler,Shigekazu Tomizuka,Michelle D. Trautwein,Xiaoli Tong,Toshiki Uchifune,Manfred Walzl,Brian M. Wiegmann,Jeanne Wilbrandt,Benjamin Wipfler,Thomas K. F. Wong,Qiong Wu,Gengxiong Wu,Yinlong Xie,Shenzhou Yang,Qing Yang,David K. Yeates,Kazunori Yoshizawa,Qing Zhang,Rui Zhang,Wenwei Zhang,Yunhui Zhang,Jing Zhao,Chengran Zhou,Lili Zhou,Tanja Ziesmann,Shijie Zou,Yingrui Li,Xun Xu,Yong Zhang,Huanming Yang,Jian Wang,Jun Wang,Karl M. Kjer,Xin Zhou +105 more
TL;DR: The phylogeny of all major insect lineages reveals how and when insects diversified and provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.
Journal ArticleDOI
SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner
Ruibang Luo,Thomas K. F. Wong,Jianqiao Zhu,Jianqiao Zhu,Chi-Man Liu,Xiaoqian Zhu,Ed X. Wu,Lap-Kei Lee,Haoxiang Lin,Wenjuan Zhu,David W. Cheung,Hing-Fung Ting,Siu-Ming Yiu,Shaoliang Peng,Chang Yu,Yingrui Li,Ruiqiang Li,Tak-Wah Lam +17 more
TL;DR: Compared with widely adopted aligners including BWA, Bowtie2, SeqAlto, CUSHAW2, GEM and GPU-based aligners, SOAP3-dp was found to be two to tens of times faster, while maintaining the highest sensitivity and lowest false discovery rate (FDR) on Illumina reads with different lengths.
Journal ArticleDOI
SOAP3: ultra-fast GPU-based parallel alignment tool for short reads.
Chi-Man Liu,Thomas K. F. Wong,Ed X. Wu,Ruibang Luo,Siu-Ming Yiu,Yingrui Li,Bingqiang Wang,Chang Yu,Xiaowen Chu,Kaiyong Zhao,Ruiqiang Li,Tak-Wah Lam +11 more
TL;DR: SOAP3 is the first short read alignment tool that leverages the multi-processors in a graphic processing unit (GPU) to achieve a drastic improvement in speed and aligns slightly more reads than BWA and Bowtie.
Journal ArticleDOI
Mixture models of nucleotide sequence evolution that account for heterogeneity in the substitution process across sites and across lineages
TL;DR: This work introduces a family of mixture models that approximate HAS without the assumption of an underlying predefined statistical distribution and presents two algorithms for searching model space and identifying an optimal model of evolution that is less likely to over- or underparameterize the data.