scholarly journals EuMicrobedbLite: A lightweight genomic resource and analytic platform for draft oomycete genomes

2016 ◽  
Author(s):  
Arijit Panda ◽  
Diya Sen ◽  
Arup Ghosh ◽  
Akash Gupta ◽  
Mathu Malar C ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Genome Annotation ◽  
Gene Prediction ◽  
Trna Genes ◽  
Light Weight ◽  
Orthologous Genes ◽  
Emboss Package ◽  
Key Features ◽  
Genomic Resource ◽  
Analysis Platform

We have developed EuMicrobedbLite A light weight comprehensive genome resource and sequence analysis platform for oomycete organisms. EuMicrobedbLite is a successor of the VBI Microbial Database (VMD) that was built using the Genome Unified Schema (GUS). In this version, the GUS schema has been greatly simplified with removal of many obsolete modules and redesign of others to incorporate contemporary data. Several dependencies such as perl object layers used for data loading in VMD have been replaced with independent light weight scripts. EumicrobedbLite now runs on a powerful annotation engine developed at our lab called Genome Annotator Lite. Currently this database has 26 publicly available genomes and 10 EST datasets of oomycete organisms. The browser page has dynamic tracks presenting comparative genomics analyses, coding and non-coding data, tRNA genes, repeats and EST alignments. In addition, we have defined 44,777 core conserved proteins from twelve oomycete organisms that form 2974 clusters. Synteny viewing is enabled by incorporation of the Genome Synteny Viewer (GSV) tool. The user interface has undergone major changes for ease of browsing. Queryable comparative genomics information, conserved orthologous genes and pathways are among the new key features updated in this database. The browser has been upgraded to enable user upload of GFF files for quick view of genome annotation comparisons. The toolkit page integrates the EMBOSS package and has a gene prediction tool. Annotations for the organisms are updated once every six months to ensure quality. The database resource is available at www.eumicrobedb.org.

scholarly journals Build a Bioinformatic Analysis Platform and Apply it to Routine Analysis of Microbial Genomics and Comparative Genomics

2021 ◽  
Author(s):  
Hualin Liu ◽  
Bingyue Xin ◽  
Jinshui Zheng ◽  
Hao Zhong ◽  
Yun Yu ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Gene Prediction ◽  
Distance Estimation ◽  
Variant Calling ◽  
Genomic Analysis ◽  
Orthologous Gene ◽  
Bioinformatic Analysis ◽  
Comparative Genomic ◽  
Operation Process ◽  
Analysis Platform

Abstract More and more frequently, genomics and comparative genomics have been used as routine methods for general microbiological research. However, using several tools or even writing some scripts are required for completing a simple analysis, which is complicated for most biological researchers. To simplify the operation process, particularly for the convenience of microbiologists, here we have developed PGCGAP, a comprehensive, malleable, and easily installed prokaryotic genomic and comparative genomic analysis pipeline. PGCGAP implements genome assembly, gene prediction and annotation, genome and metagenome distance estimation, phylogenetic analysis, COG annotation, pan-genome analysis, inference of orthologous gene groups, variant calling and annotation, and screening for antimicrobial and virulence genes. Although we have tried our best to simplify the installation and usage of PGCGAP, it may be difficult for non-bioinformaticians to master it. Therefore, a protocol was created to help microbiologists without any experience in bioinformatics to establish their bioinformatics platform and perform routine analyses. This protocol shows how to choose the equipment to install a Linux subsystem on a laptop with a Windows 10 system, to install the PGCGAP and perform all analyses with an example dataset. The protocol requires a basic understanding of Linux, so an additional web page was written to help uninitiated users learn Linux and whole-genome sequencing (https://github.com/liaochenlanruo/pgcgap/wiki/Learning-bioinformatics or http://bcam.hzau.edu.cn/linuxwgs.php).

scholarly journals Build a Bioinformatics Analysis Platform and Apply it to Routine Analysis of Microbial Genomics and Comparative Genomics

2020 ◽  
Author(s):  
Hualin Liu ◽  
Bingyue Xin ◽  
Jinshui Zheng ◽  
Hao Zhong ◽  
Yun Yu ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Bioinformatics Analysis ◽  
Gene Prediction ◽  
Orthologous Gene ◽  
Routine Analysis ◽  
Simple Analysis ◽  
Analysis Pipeline ◽  
Operation Process ◽  
Comparative Genomics Analysis ◽  
Analysis Platform

Abstract Genomics and comparative genomics have been increasingly used as routine methods for general microbiological researches. However, it is usually necessary to call several tools or even write some scripts to complete some simple analysis, which is complicated for most biological researchers. To simplify the operation process, especially for the convenience of microbiologists in the analysis, here we have developed PGCGAP, a comprehensive, malleable and easily-installed prokaryotic genomics and comparative genomics analysis pipeline, which implements genome assembly, gene prediction and annotation, average nucleotide identity (ANI) calculation, phylogenetic analysis, COG annotation, pan-genome analysis, inference of orthologous gene groups, variants calling and annotation and screening for antimicrobial and virulence genes. Although we have tried our best to simplify the installation and usage of PGCGAP, it may be difficult for non-bioinformatician users to master it. So, a protocol was created to help microbiologists without any experience in bioinformatics to establish their own bioinformatics platform and perform routine analysis. This protocol shows how to choose equipment, to install a Linux subsystem on a laptop with windows 10 system, to install PGCGAP and perform all analysis with an example dataset. The protocol requires a basic understanding of Linux, so an additional web page was written to help uninitiated users learn Linux and whole-genome sequencing (http://bcam.hzau.edu.cn/linuxwgs.php).

scholarly journals Build a Bioinformatics Analysis Platform and Apply it to Routine Analysis of Microbial Genomics and Comparative Genomics

2020 ◽  
Author(s):  
Hualin Liu ◽  
Bingyue Xin ◽  
Jinshui Zheng ◽  
Hao Zhong ◽  
Yun Yu ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Bioinformatics Analysis ◽  
Gene Prediction ◽  
Orthologous Gene ◽  
Routine Analysis ◽  
Simple Analysis ◽  
Analysis Pipeline ◽  
Operation Process ◽  
Comparative Genomics Analysis ◽  
Analysis Platform

Abstract Genomics and comparative genomics have been increasingly used as routine methods for general microbiological researches. However, it is usually necessary to call several tools or even write some scripts to complete some simple analysis, which is complicated for most biological researchers. To simplify the operation process, especially for the convenience of microbiologists in the analysis, here we have developed PGCGAP, a comprehensive, malleable and easily-installed prokaryotic genomics and comparative genomics analysis pipeline, which implements genome assembly, gene prediction and annotation, average nucleotide identity (ANI) calculation, phylogenetic analysis, COG annotation, pan-genome analysis, inference of orthologous gene groups, variants calling and annotation and screening for antimicrobial and virulence genes. Although we have tried our best to simplify the installation and usage of PGCGAP, it may be difficult for non-bioinformatician users to master it. So, a protocol was created to help microbiologists without any experience in bioinformatics to establish their own bioinformatics platform and perform routine analysis. This protocol shows how to choose equipment, to install a Linux subsystem on a laptop with windows 10 system, to install PGCGAP and perform all analysis with an example dataset. The protocol requires a basic understanding of Linux, so an additional web page was written to help uninitiated users learn Linux and whole-genome sequencing (https://github.com/liaochenlanruo/pgcgap/wiki/Learning-bioinformatics or http://bcam.hzau.edu.cn/linuxwgs.php).

scholarly journals Build a Bioinformatics Analysis Platform and Apply it to Routine Analysis of Microbial Genomics and Comparative Genomics

2020 ◽  
Author(s):  
Hualin Liu ◽  
Bingyue Xin ◽  
Jinshui Zheng ◽  
Hao Zhong ◽  
Yun Yu ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Bioinformatics Analysis ◽  
Gene Prediction ◽  
Orthologous Gene ◽  
Routine Analysis ◽  
Simple Analysis ◽  
Analysis Pipeline ◽  
Operation Process ◽  
Comparative Genomics Analysis ◽  
Analysis Platform

Abstract Genomics and comparative genomics have been increasingly used as routine methods for general microbiological researches. However, it is usually necessary to call several tools or even write some scripts to complete some simple analysis, which is complicated for most biological researchers. To simplify the operation process, especially for the convenience of microbiologists in the analysis, here we have developed PGCGAP, a comprehensive, malleable and easily-installed prokaryotic genomics and comparative genomics analysis pipeline, which implements genome assembly, gene prediction and annotation, average nucleotide identity (ANI) calculation, phylogenetic analysis, COG annotation, pan-genome analysis, inference of orthologous gene groups, variants calling and annotation and screening for antimicrobial and virulence genes. Although we have tried our best to simplify the installation and usage of PGCGAP, it may be difficult for non-bioinformatician users to master it. So, a protocol was created to help microbiologists without any experience in bioinformatics to establish their own bioinformatics platform and perform routine analysis. This protocol shows how to choose equipment, to install a Linux subsystem on a laptop with windows 10 system, to install PGCGAP and perform all analysis with an example dataset. The protocol requires a basic understanding of Linux, so an additional web page was written to help uninitiated users learn Linux and whole-genome sequencing (https://github.com/liaochenlanruo/pgcgap/wiki/Learning-bioinformatics).

scholarly journals Build a Bioinformatics Analysis Platform and Apply it to Routine Analysis of Microbial Genomics and Comparative Genomics

2020 ◽  
Author(s):  
Hualin Liu ◽  
Bingyue Xin ◽  
Jinshui Zheng ◽  
Hao Zhong ◽  
Yun Yu ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Bioinformatics Analysis ◽  
Gene Prediction ◽  
Orthologous Gene ◽  
Routine Analysis ◽  
Simple Analysis ◽  
Analysis Pipeline ◽  
Operation Process ◽  
Comparative Genomics Analysis ◽  
Analysis Platform

Abstract Genomics and comparative genomics have been increasingly used as routine methods for general microbiological researches. However, it is usually necessary to call several tools or even write some scripts to complete some simple analysis, which is complicated for most biological researchers. To simplify the operation process, especially for the convenience of microbiologists in the analysis, here we have developed PGCGAP, a comprehensive, malleable and easily-installed prokaryotic genomics and comparative genomics analysis pipeline, which implements genome assembly, gene prediction and annotation, average nucleotide identity (ANI) calculation, phylogenetic analysis, COG annotation, pan-genome analysis, inference of orthologous gene groups, variants calling and annotation and screening for antimicrobial and virulence genes. Although we have tried our best to simplify the installation and usage of PGCGAP, it may be difficult for non-bioinformatician users to master it. So, a protocol was created to help microbiologists without any experience in bioinformatics to establish their own bioinformatics platform and perform routine analysis. This protocol shows how to choose equipment, to install a Linux subsystem on a laptop with windows 10 system, to install PGCGAP and perform all analysis with an example dataset. The protocol requires a basic understanding of Linux, so an additional web page was written to help uninitiated users learn Linux and whole-genome sequencing (http://bcam.hzau.edu.cn/linuxwgs.php).

scholarly journals InsectOR – webserver for sensitive identification of insect olfactory receptor genes from non-model genomes

2020 ◽  
Author(s):  
Snehal D. Karpe ◽  
Vikas Tiwari ◽  
Sowdhamini Ramanathan
Keyword(s):  
Genome Annotation ◽  
Gene Prediction ◽  
Web Server ◽  
Olfactory Receptors ◽  
Rapid Prediction ◽  
Protein Receptors ◽  
Olfactory Receptor Genes ◽  
Or Gene ◽  
Or Genes ◽  
The Web

AbstractInsect Olfactory Receptors (ORs) are diverse family of membrane protein receptors responsible for most of the insect olfactory perception and communication, and hence they are of utmost importance for developing repellents or pesticides. Hence, accurate gene prediction of insect ORs from newly sequenced genomes is an important but challenging task. We have developed a dedicated web-server, ‘insectOR’, to predict and validate insect OR genes using multiple gene prediction algorithms, accompanied by relevant validations. It is possible to employ this sever nearly automatically and perform rapid prediction of the OR gene loci from thousands of OR-protein-to-genome alignments, resolve gene boundaries for tandem OR genes and refine them further to provide more complete OR gene models. InsectOR outperformed the popular genome annotation pipelines (MAKER and NCBI eukaryotic genome annotation) in terms of overall sensitivity at base, exon and locus level, when tested on two distantly related insect genomes. It displayed more than 95% nucleotide level precision in both tests. Finally, given the same input data and parameters, InsectOR missed less than 2% gene loci, in contrast to 55% loci missed by MAKER for Drosophila melanogaster. The web-server is freely available on the web at http://caps.ncbs.res.in/insectOR/. All major browsers are supported. Website is implemented in Python with Jinja2 for templating and bootstrap framework which uses HTML, CSS and JavaScript/Ajax. The core pipeline is written in Perl.

Automated Genome Annotation and Comparative Genomics

Biotechnology ◽  
2008 ◽  
pp. 361-378
Author(s):  
Shuba Gopal ◽  
Terry Gaasterland
Keyword(s):  
Comparative Genomics ◽  
Genome Annotation

scholarly journals Genome Sequence Data of the Soybean Pathogen Stagonosporopsis vannaccii: A Resource for Studies on Didymellaceae Evolution

2020 ◽  
Vol 33 (8) ◽  
pp. 1022-1024
Author(s):  
Giovanni Cafà ◽  
Thaís Regina Boufleur ◽  
Renata Rebellato Linhares de Castro ◽  
Nelson Sidnei Massola ◽  
Riccardo Baroncelli
Keyword(s):  
Comparative Genomics ◽  
Genome Sequence ◽  
Plant Pathogens ◽  
Sequence Data ◽  
Gene Prediction ◽  
Whole Genome Sequence ◽  
Future Research ◽  
Whole Genome ◽  
Genome Sequence Data ◽  
Associated Species

The genus Stagonosporopsis is classified within the Didymellaceae family and has around 40 associated species. Among them, several species are important plant pathogens responsible for significant losses in economically important crops worldwide. Stagonosporopsis vannaccii is a newly described species pathogenic to soybean. Here, we present the draft whole-genome sequence, gene prediction, and annotation of S. vannaccii isolate LFN0148 (also known as IMI 507030). To our knowledge, this is the first genome sequenced of this species and represents a new useful source for future research on fungal comparative genomics studies.

scholarly journals Integration of Data in Pathogenomics: Three Layers of cellular complexity and an XML-based Framework

2004 ◽  
Vol 1 (1) ◽  
pp. 52-63
Author(s):  
M. Dünßer ◽  
R. Lampidis ◽  
S. Schmidt ◽  
D. Seipel ◽  
T. Dandekar
Keyword(s):  
Comparative Genomics ◽  
Data Integration ◽  
Genome Annotation ◽  
Regulatory Elements ◽  
Protein Domain ◽  
Mode Analysis ◽  
Sequence Annotation ◽  
Elementary Mode Analysis ◽  
Metabolic Network Analysis ◽  
Domain Annotation

Summary Integration of data in pathogenomics is achieved here considering three different levels of cellular complexity: (i) genome and comparative genomics, (ii) enzyme cascades and pathway analysis, (iii) networks including metabolic network analysis.After direct sequence annotation exploiting tools for protein domain annotation (e.g. AnDOM) and analysis of regulatory elements (e.g. the RNA analyzer tool) the analysis results from extensive comparative genomics are integrated for the first level, pathway alignment adds data for the pathway level, elementary mode analysis and metabolite databanks add to the third level of cellular complexity. For efficient data integration of all data the XML based platform myBSMLStudio2003 is discussed and developed here. It integrates XQuery capabilities, automatic scripting updates for sequence annotation and a JESS expert system shell for functional annotation. In the context of genome annotation platforms in place (GenDB, PEDANT) these different tools and approaches presented here allow improved functional genome annotation as well as data integration in pathogenomics.

scholarly journals MicroScope: a platform for microbial genome annotation and comparative genomics

Database ◽  
2009 ◽  
Vol 2009 ◽  
Author(s):  
D. Vallenet ◽  
S. Engelen ◽  
D. Mornico ◽  
S. Cruveiller ◽  
L. Fleury ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Genome Annotation ◽  
Microbial Genome
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