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Shengchang Gu

Bio: Shengchang Gu is an academic researcher. The author has contributed to research in topics: Phylogenomics & RNA-Seq. The author has an hindex of 5, co-authored 6 publications receiving 2966 citations.

Papers
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Journal ArticleDOI
Bernhard Misof, Shanlin Liu, Karen Meusemann1, Ralph S. Peters, Alexander Donath, Christoph Mayer, Paul B. Frandsen2, Jessica L. Ware2, Tomas Flouri3, Rolf G. Beutel4, Oliver Niehuis, Malte Petersen, Fernando Izquierdo-Carrasco3, Torsten Wappler5, Jes Rust5, Andre J. Aberer3, Ulrike Aspöck6, Ulrike Aspöck7, Horst Aspöck7, Daniela Bartel7, Alexander Blanke8, Simon Berger3, Alexander Böhm7, Thomas R. Buckley9, Brett Calcott10, Junqing Chen, Frank Friedrich11, Makiko Fukui12, Mari Fujita8, Carola Greve, Peter Grobe, Shengchang Gu, Ying Huang, Lars S. Jermiin1, Akito Y. Kawahara13, Lars Krogmann14, Martin Kubiak11, Robert Lanfear15, Robert Lanfear16, Robert Lanfear17, Harald Letsch7, Yiyuan Li, Zhenyu Li, Jiguang Li, Haorong Lu, Ryuichiro Machida8, Yuta Mashimo8, Pashalia Kapli3, Pashalia Kapli18, Duane D. McKenna19, Guanliang Meng, Yasutaka Nakagaki8, José Luis Navarrete-Heredia20, Michael Ott21, Yanxiang Ou, Günther Pass7, Lars Podsiadlowski5, Hans Pohl4, Björn M. von Reumont22, Kai Schütte11, Kaoru Sekiya8, Shota Shimizu8, Adam Slipinski1, Alexandros Stamatakis3, Alexandros Stamatakis23, Wenhui Song, Xu Su, Nikolaus U. Szucsich7, Meihua Tan, Xuemei Tan, Min Tang, Jingbo Tang, Gerald Timelthaler7, Shigekazu Tomizuka8, Michelle D. Trautwein24, Xiaoli Tong25, Toshiki Uchifune8, Manfred Walzl7, Brian M. Wiegmann26, Jeanne Wilbrandt, Benjamin Wipfler4, Thomas K. F. Wong1, Qiong Wu, Gengxiong Wu, Yinlong Xie, Shenzhou Yang, Qing Yang, David K. Yeates1, Kazunori Yoshizawa27, 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. Kjer2, Xin Zhou 
07 Nov 2014-Science
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.
Abstract: Insects are the most speciose group of animals, but the phylogenetic relationships of many major lineages remain unresolved. We inferred the phylogeny of insects from 1478 protein-coding genes. Phylogenomic analyses of nucleotide and amino acid sequences, with site-specific nucleotide or domain-specific amino acid substitution models, produced statistically robust and congruent results resolving previously controversial phylogenetic relations hips. We dated the origin of insects to the Early Ordovician [~479 million years ago (Ma)], of insect flight to the Early Devonian (~406 Ma), of major extant lineages to the Mississippian (~345 Ma), and the major diversification of holometabolous insects to the Early Cretaceous. Our phylogenomic study provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.

1,998 citations

Journal ArticleDOI
TL;DR: The conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution, compared with two other popular transcriptome assemblers.
Abstract: Motivation: Transcriptome sequencing has long been the favored method for quickly and inexpensively obtaining a large number of gene sequences from an organism with no reference genome. Owing to the rapid increase in throughputs and decrease in costs of next- generation sequencing, RNA-Seq in particular has become the method of choice. However, the very short reads (e.g. 2 � 90 bp paired ends) from next generation sequencing makes de novo assembly to recover complete or full-length transcript sequences an algorithmic challenge. Results: Here, we present SOAPdenovo-Trans, a de novo transcriptome assembler designed specifically for RNA-Seq. We evaluated its performance on transcriptome datasets from rice and mouse. Using as our benchmarks the known transcripts from these wellannotated genomes (sequenced a decade ago), we assessed how SOAPdenovo-Trans and two other popular transcriptome assemblers handled such practical issues as alternative splicing and variable expression levels. Our conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution. Availability and implementation: Source code and user manual are available at http://sourceforge.net/projects/soapdenovotrans/. Contact: xieyl@genomics.cn or bgi-soap@googlegroups.com Supplementary information: Supplementary data are available at Bioinformatics online.

730 citations

Posted Content
TL;DR: SOAPdenovo-Trans as mentioned in this paper is a de novo transcriptome assembler designed specifically for RNA-Seq that provides higher contiguity, lower redundancy, and faster execution.
Abstract: Motivation: Transcriptome sequencing has long been the favored method for quickly and inexpensively obtaining the sequences for a large number of genes from an organism with no reference genome. With the rapidly increasing throughputs and decreasing costs of next generation sequencing, RNA-Seq has gained in popularity; but given the typically short reads (e.g. 2 x 90 bp paired ends) of this technol- ogy, de novo assembly to recover complete or full-length transcript sequences remains an algorithmic challenge. Results: We present SOAPdenovo-Trans, a de novo transcriptome assembler designed specifically for RNA-Seq. Its performance was evaluated on transcriptome datasets from rice and mouse. Using the known transcripts from these well-annotated genomes (sequenced a decade ago) as our benchmark, we assessed how SOAPdenovo- Trans and two other popular software handle the practical issues of alternative splicing and variable expression levels. Our conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy, and faster execution. Availability and Implementation: Source code and user manual are at this http URL Contact: xieyl@genomics.cn or bgi-soap@googlegroups.com

615 citations

Journal ArticleDOI
TL;DR: This unit shows how to use the SOAPaligner package to align short reads to reference, and includes a protocol for SNP calling from SOAP2 alignment with SOAPsnp.
Abstract: This unit shows how to use the SOAPaligner package to align short reads to reference. The use of the two most common versions of SOAPaligner, SOAP2 and SOAP3-dp, will be described in detail. The unit also includes a protocol for SNP calling from SOAP2 alignment with SOAPsnp.

59 citations

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, 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, Harald Letsch, Yiyuan Li, Zhenyu Li, Jiguang Li, Haorong Lu, Ryuichiro Machida, Yuta Mashimo, 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, 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 
01 Jan 2014
TL;DR: A phylogenetic analysis of protein-coding genes from all major insect orders and close relatives was performed by Misof et al. as discussed by the authors, who used this resolved phylogenetic tree together with fossil analysis to date the origin of insects to ~479 million years ago and to resolve longcontroversial subjects in insect phylogeny.
Abstract: Toward an insect evolution resolution Insects are the most diverse group of animals, with the largest number of species. However, many of the evolutionary relationships between insect species have been controversial and difficult to resolve. Misof et al. performed a phylogenomic analysis of protein-coding genes from all major insect orders and close relatives, resolving the placement of taxa. The authors used this resolved phylogenetic tree together with fossil analysis to date the origin of insects to ~479 million years ago and to resolve long-controversial subjects in insect phylogeny. Science, this issue p. 763 The phylogeny of all major insect lineages reveals how and when insects diversified. Insects are the most speciose group of animals, but the phylogenetic relationships of many major lineages remain unresolved. We inferred the phylogeny of insects from 1478 protein-coding genes. Phylogenomic analyses of nucleotide and amino acid sequences, with site-specific nucleotide or domain-specific amino acid substitution models, produced statistically robust and congruent results resolving previously controversial phylogenetic relations hips. We dated the origin of insects to the Early Ordovician [~479 million years ago (Ma)], of insect flight to the Early Devonian (~406 Ma), of major extant lineages to the Mississippian (~345 Ma), and the major diversification of holometabolous insects to the Early Cretaceous. Our phylogenomic study provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.

52 citations


Cited by
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Journal ArticleDOI
TL;DR: This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts.
Abstract: High-throughput sequencing of mRNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate and flexible software to reduce the raw read data to comprehensible results. HISAT (hierarchical indexing for spliced alignment of transcripts), StringTie and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol's execution time depends on the computing resources, but it typically takes under 45 min of computer time. HISAT, StringTie and Ballgown are available from http://ccb.jhu.edu/software.shtml.

3,755 citations

Journal ArticleDOI
TL;DR: PartitionFinder 2 is a program for automatically selecting best-fit partitioning schemes and models of evolution for phylogenetic analyses that includes the ability to analyze morphological datasets, new methods to analyze genome-scale datasets, and new output formats to facilitate interoperability with downstream software.
Abstract: PartitionFinder 2 is a program for automatically selecting best-fit partitioning schemes and models of evolution for phylogenetic analyses. PartitionFinder 2 is substantially faster and more efficient than version 1, and incorporates many new methods and features. These include the ability to analyze morphological datasets, new methods to analyze genome-scale datasets, new output formats to facilitate interoperability with downstream software, and many new models of molecular evolution. PartitionFinder 2 is freely available under an open source license and works on Windows, OSX, and Linux operating systems. It can be downloaded from www.robertlanfear.com/partitionfinder. The source code is available at https://github.com/brettc/partitionfinder.

3,445 citations

Journal ArticleDOI
Bernhard Misof, Shanlin Liu, Karen Meusemann1, Ralph S. Peters, Alexander Donath, Christoph Mayer, Paul B. Frandsen2, Jessica L. Ware2, Tomas Flouri3, Rolf G. Beutel4, Oliver Niehuis, Malte Petersen, Fernando Izquierdo-Carrasco3, Torsten Wappler5, Jes Rust5, Andre J. Aberer3, Ulrike Aspöck6, Ulrike Aspöck7, Horst Aspöck6, Daniela Bartel6, Alexander Blanke8, Simon Berger3, Alexander Böhm6, Thomas R. Buckley9, Brett Calcott10, Junqing Chen, Frank Friedrich11, Makiko Fukui12, Mari Fujita8, Carola Greve, Peter Grobe, Shengchang Gu, Ying Huang, Lars S. Jermiin1, Akito Y. Kawahara13, Lars Krogmann14, Martin Kubiak11, Robert Lanfear15, Robert Lanfear16, Robert Lanfear17, Harald Letsch6, Yiyuan Li, Zhenyu Li, Jiguang Li, Haorong Lu, Ryuichiro Machida8, Yuta Mashimo8, Pashalia Kapli18, Pashalia Kapli3, Duane D. McKenna19, Guanliang Meng, Yasutaka Nakagaki8, José Luis Navarrete-Heredia20, Michael Ott21, Yanxiang Ou, Günther Pass6, Lars Podsiadlowski5, Hans Pohl4, Björn M. von Reumont22, Kai Schütte11, Kaoru Sekiya8, Shota Shimizu8, Adam Slipinski1, Alexandros Stamatakis23, Alexandros Stamatakis3, Wenhui Song, Xu Su, Nikolaus U. Szucsich6, Meihua Tan, Xuemei Tan, Min Tang, Jingbo Tang, Gerald Timelthaler6, Shigekazu Tomizuka8, Michelle D. Trautwein24, Xiaoli Tong25, Toshiki Uchifune8, Manfred Walzl6, Brian M. Wiegmann26, Jeanne Wilbrandt, Benjamin Wipfler4, Thomas K. F. Wong1, Qiong Wu, Gengxiong Wu, Yinlong Xie, Shenzhou Yang, Qing Yang, David K. Yeates1, Kazunori Yoshizawa27, 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. Kjer2, Xin Zhou 
07 Nov 2014-Science
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.
Abstract: Insects are the most speciose group of animals, but the phylogenetic relationships of many major lineages remain unresolved. We inferred the phylogeny of insects from 1478 protein-coding genes. Phylogenomic analyses of nucleotide and amino acid sequences, with site-specific nucleotide or domain-specific amino acid substitution models, produced statistically robust and congruent results resolving previously controversial phylogenetic relations hips. We dated the origin of insects to the Early Ordovician [~479 million years ago (Ma)], of insect flight to the Early Devonian (~406 Ma), of major extant lineages to the Mississippian (~345 Ma), and the major diversification of holometabolous insects to the Early Cretaceous. Our phylogenomic study provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.

1,998 citations

Journal ArticleDOI
TL;DR: All of the major steps in RNA-seq data analysis are reviewed, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.
Abstract: RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

1,963 citations

Journal ArticleDOI
TL;DR: RAxML-NG is presented, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML, which offers improved accuracy, flexibility, speed, scalability, and usability compared with RAx ML/ exaML.
Abstract: MOTIVATION Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets. RESULTS We present RAxML-NG, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability compared with RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and the recently introduced transfer bootstrap support metric. AVAILABILITY AND IMPLEMENTATION The code is available under GNU GPL at https://github.com/amkozlov/raxml-ng. RAxML-NG web service (maintained by Vital-IT) is available at https://raxml-ng.vital-it.ch/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

1,765 citations