The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Konstantinos Mavromatis - One of the best experts on this subject based on the ideXlab platform.
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erratum to the standard operating procedure of the doe jgi Microbial Genome annotation pipeline mgap v 4
Standards in Genomic Sciences, 2016Co-Authors: Marcel Huntemann, Konstantinos Mavromatis, Ernest Szeto, Natalia Ivanova, James H Tripp, David Paezespino, Krishnaveni Palaniappan, Manoj Pillay, Imin A ChenAbstract:After publication of this study [1], we noticed that the Strain ID summary was not removed from the PDF due to a copyediting error. The original version of this article was corrected. The publisher apologizes for any inconvenience caused.
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the standard operating procedure of the doe jgi Microbial Genome annotation pipeline mgap v 4
Standards in Genomic Sciences, 2015Co-Authors: Marcel Huntemann, Konstantinos Mavromatis, Ernest Szeto, Natalia Ivanova, James H Tripp, David Paezespino, Krishnaveni Palaniappan, Manoj Pillay, Imin A ChenAbstract:The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of Microbial Genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.
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improving Microbial Genome annotations in an integrated database context
PLOS ONE, 2013Co-Authors: Imin A Chen, Konstantinos Mavromatis, Victor Markowitz, Nikos C. Kyrpides, Ken Chu, Iain Anderson, Natalia IvanovaAbstract:Effective comparative analysis of Microbial Genomes requires a consistent and complete view of biological data. Consistency regards the biological coherence of annotations, while completeness regards the extent and coverage of functional characterization for Genomes. We have developed tools that allow scientists to assess and improve the consistency and completeness of Microbial Genome annotations in the context of the Integrated Microbial Genomes (IMG) family of systems. All publicly available Microbial Genomes are characterized in IMG using different functional annotation and pathway resources, thus providing a comprehensive framework for identifying and resolving annotation discrepancies. A rule based system for predicting phenotypes in IMG provides a powerful mechanism for validating functional annotations, whereby the phenotypic traits of an organism are inferred based on the presence of certain metabolic reactions and pathways and compared to experimentally observed phenotypes. The IMG family of systems are available at http://img.jgi.doe.gov/.
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the fast changing landscape of sequencing technologies and their impact on Microbial Genome assemblies and annotation
PLOS ONE, 2012Co-Authors: Konstantinos Mavromatis, Alicia Clum, Alex Copeland, Miriam Land, Thomas Brettin, Daniel Quest, Lynne A Goodwin, Tanja Woyke, Alla Lapidus, Hanspeter KlenkAbstract:Background: The emergence of next generation sequencing (NGS) has provided the means for rapid and high throughput sequencing and data generation at low cost, while concomitantly creating a new set of challenges. The number of available assembled Microbial Genomes continues to grow rapidly and their quality reflects the quality of the sequencing technology used, but also of the analysis software employed for assembly and annotation. Methodology/Principal Findings: In this work, we have explored the quality of the Microbial draft Genomes across various sequencing technologies. We have compared the draft and finished assemblies of 133 Microbial Genomes sequenced at the Department of Energy-Joint Genome Institute and finished at the Los Alamos National Laboratory using a variety of combinations of sequencing technologies, reflecting the transition of the institute from Sanger-based sequencing platforms to NGS platforms. The quality of the public assemblies and of the associated gene annotations was evaluated using various metrics. Results obtained with the different sequencing technologies, as well as their effects on downstream processes, were analyzed. Our results demonstrate that the Illumina HiSeq 2000 sequencing system, the primary sequencing technology currently used for de novo Genome sequencing and assembly at JGI, has various advantages in terms of total sequencemore » throughput and cost, but it also introduces challenges for the downstream analyses. In all cases assembly results although on average are of high quality, need to be viewed critically and consider sources of errors in them prior to analysis. Conclusion: These data follow the evolution of Microbial sequencing and downstream processing at the JGI from draft Genome sequences with large gaps corresponding to missing genes of significant biological role to assemblies with multiple small gaps (Illumina) and finally to assemblies that generate almost complete Genomes (Illumina+PacBio).« less
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the doe jgi standard operating procedure for the annotations of Microbial Genomes
Standards in Genomic Sciences, 2009Co-Authors: Konstantinos Mavromatis, Imin A Chen, Ernest Szeto, Victor Markowitz, Natalia Ivanova, Nikos C. KyrpidesAbstract:The DOE-JGI Microbial Annotation Pipeline (DOE-JGI MAP) supports gene prediction and/or functional annotation of Microbial Genomes towards comparative analysis with the Integrated Microbial Genome (IMG) system. DOE-JGI MAP annotation is applied on nucleotide sequence datasets included in the IMG-ER (Expert Review) version of IMG via the IMG ER submission site. Users can submit the sequence datasets consisting of one or more contigs in a multi-fasta file. DOE-JGI MAP annotation includes prediction of protein coding and RNA genes, as well as repeats and assignment of product names to these genes.
Imin A Chen - One of the best experts on this subject based on the ideXlab platform.
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erratum to the standard operating procedure of the doe jgi Microbial Genome annotation pipeline mgap v 4
Standards in Genomic Sciences, 2016Co-Authors: Marcel Huntemann, Konstantinos Mavromatis, Ernest Szeto, Natalia Ivanova, James H Tripp, David Paezespino, Krishnaveni Palaniappan, Manoj Pillay, Imin A ChenAbstract:After publication of this study [1], we noticed that the Strain ID summary was not removed from the PDF due to a copyediting error. The original version of this article was corrected. The publisher apologizes for any inconvenience caused.
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the standard operating procedure of the doe jgi Microbial Genome annotation pipeline mgap v 4
Standards in Genomic Sciences, 2015Co-Authors: Marcel Huntemann, Konstantinos Mavromatis, Ernest Szeto, Natalia Ivanova, James H Tripp, David Paezespino, Krishnaveni Palaniappan, Manoj Pillay, Imin A ChenAbstract:The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of Microbial Genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.
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img 4 version of the integrated Microbial Genomes comparative analysis system
Nucleic Acids Research, 2014Co-Authors: Victor M Markowitz, Imin A Chen, Ernest Szeto, Manoj Pillay, Ken Chu, Tanja Woyke, Krishna Palaniappan, Anna Ratner, Jinghua Huang, Marcel HuntemannAbstract:The Integrated Microbial Genomes (IMG) data warehouse integrates Genomes from all three domains of life, as well as plasmids, viruses and Genome fragments. IMG provides tools for analyzing and reviewing the structural and functional annotations of Genomes in a comparative context. IMG’s data content and analytical capabilities have increased continuously since its first version released in 2005. Since the last report published in the 2012 NAR Database Issue, IMG’s annotation and data integration pipelines have evolved while new tools have been added for recording and analyzing single cell Genomes, RNA Seq and biosynthetic cluster data. Different IMG datamarts provide support for the analysis of publicly available Genomes (IMG/W: http://img.jgi.doe.gov/w), expert review of Genome annotations (IMG/ER: http://img.jgi.doe.gov/er) and teaching and training in the area of Microbial Genome analysis (IMG/EDU: http://img.jgi.doe.gov/edu).
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improving Microbial Genome annotations in an integrated database context
PLOS ONE, 2013Co-Authors: Imin A Chen, Konstantinos Mavromatis, Victor Markowitz, Nikos C. Kyrpides, Ken Chu, Iain Anderson, Natalia IvanovaAbstract:Effective comparative analysis of Microbial Genomes requires a consistent and complete view of biological data. Consistency regards the biological coherence of annotations, while completeness regards the extent and coverage of functional characterization for Genomes. We have developed tools that allow scientists to assess and improve the consistency and completeness of Microbial Genome annotations in the context of the Integrated Microbial Genomes (IMG) family of systems. All publicly available Microbial Genomes are characterized in IMG using different functional annotation and pathway resources, thus providing a comprehensive framework for identifying and resolving annotation discrepancies. A rule based system for predicting phenotypes in IMG provides a powerful mechanism for validating functional annotations, whereby the phenotypic traits of an organism are inferred based on the presence of certain metabolic reactions and pathways and compared to experimentally observed phenotypes. The IMG family of systems are available at http://img.jgi.doe.gov/.
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the doe jgi standard operating procedure for the annotations of Microbial Genomes
Standards in Genomic Sciences, 2009Co-Authors: Konstantinos Mavromatis, Imin A Chen, Ernest Szeto, Victor Markowitz, Natalia Ivanova, Nikos C. KyrpidesAbstract:The DOE-JGI Microbial Annotation Pipeline (DOE-JGI MAP) supports gene prediction and/or functional annotation of Microbial Genomes towards comparative analysis with the Integrated Microbial Genome (IMG) system. DOE-JGI MAP annotation is applied on nucleotide sequence datasets included in the IMG-ER (Expert Review) version of IMG via the IMG ER submission site. Users can submit the sequence datasets consisting of one or more contigs in a multi-fasta file. DOE-JGI MAP annotation includes prediction of protein coding and RNA genes, as well as repeats and assignment of product names to these genes.
Tulio De Oliveira - One of the best experts on this subject based on the ideXlab platform.
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current affairs of Microbial Genome wide association studies approaches bottlenecks and analytical pitfalls
Frontiers in Microbiology, 2020Co-Authors: James Emmanuel San, Shakuntala Baichoo, Aquillah M Kanzi, Yumna Moosa, Richard J Lessells, Vagner Fonseca, John J O Mogaka, Robert A Power, Tulio De OliveiraAbstract:Microbial Genome-wide association studies (mGWAS) are a new and exciting research field that is adapting human GWAS methods to understand how variations in Microbial Genomes affect host or pathogen phenotypes, such as drug resistance, virulence, host specificity and prognosis. Several computational tools and methods have been developed or adapted from human GWAS to facilitate the discovery of novel mutations and structural variations that are associated with the phenotypes of interest. However, no comprehensive, end-to-end, user-friendly tool is currently available. The development of a broadly applicable pipeline presents a real opportunity among computational biologists. Here, (i) we review the prominent and promising tools, (ii) discuss analytical pitfalls and bottlenecks in mGWAS, (iii) provide insights into the selection of appropriate tools, (iv) highlight the gaps that still need to be filled and how users and developers can work together to overcome these bottlenecks. Use of mGWAS research can inform drug repositioning decisions as well as accelerate the discovery and development of more effective vaccines and antiMicrobials for pressing infectious diseases of global health significance, such as HIV, TB, influenza, and malaria.
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Microbial Genome wide association studies lessons from human gwas
Nature Reviews Genetics, 2017Co-Authors: Robert Power, Julian Parkhill, Tulio De OliveiraAbstract:The reduced costs of sequencing have led to whole-Genome sequences for a large number of microorganisms, enabling the application of Microbial Genome-wide association studies (GWAS). Given the successes of human GWAS in understanding disease aetiology and identifying potential drug targets, Microbial GWAS are likely to further advance our understanding of infectious diseases. These advances include insights into pressing global health problems, such as antibiotic resistance and disease transmission. In this Review, we outline the methodologies of GWAS, the current state of the field of Microbial GWAS, and how lessons from human GWAS can direct the future of the field.
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Microbial Genome wide association studies lessons from human gwas
bioRxiv, 2016Co-Authors: Robert Power, Julian Parkhill, Tulio De OliveiraAbstract:The reduced costs of sequencing have led to the availability of whole Genome sequences for a large number of microorganisms, enabling the application of Microbial Genome wide association studies (GWAS). Given the successes of human GWAS in understanding disease aetiology and identifying potential drug targets, Microbial GWAS is likely to further advance our understanding of infectious diseases. By building on the success of GWAS, Microbial GWAS have the potential to rapidly provide important insights into pressing global health problems, such as antibiotic resistance and disease transmission. In this review, we outline the methodologies of GWAS, the state of the field of Microbial GWAS today, and how lessons from GWAS can direct the future of the field.
Natalia Ivanova - One of the best experts on this subject based on the ideXlab platform.
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erratum to the standard operating procedure of the doe jgi Microbial Genome annotation pipeline mgap v 4
Standards in Genomic Sciences, 2016Co-Authors: Marcel Huntemann, Konstantinos Mavromatis, Ernest Szeto, Natalia Ivanova, James H Tripp, David Paezespino, Krishnaveni Palaniappan, Manoj Pillay, Imin A ChenAbstract:After publication of this study [1], we noticed that the Strain ID summary was not removed from the PDF due to a copyediting error. The original version of this article was corrected. The publisher apologizes for any inconvenience caused.
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the standard operating procedure of the doe jgi Microbial Genome annotation pipeline mgap v 4
Standards in Genomic Sciences, 2015Co-Authors: Marcel Huntemann, Konstantinos Mavromatis, Ernest Szeto, Natalia Ivanova, James H Tripp, David Paezespino, Krishnaveni Palaniappan, Manoj Pillay, Imin A ChenAbstract:The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of Microbial Genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.
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improving Microbial Genome annotations in an integrated database context
PLOS ONE, 2013Co-Authors: Imin A Chen, Konstantinos Mavromatis, Victor Markowitz, Nikos C. Kyrpides, Ken Chu, Iain Anderson, Natalia IvanovaAbstract:Effective comparative analysis of Microbial Genomes requires a consistent and complete view of biological data. Consistency regards the biological coherence of annotations, while completeness regards the extent and coverage of functional characterization for Genomes. We have developed tools that allow scientists to assess and improve the consistency and completeness of Microbial Genome annotations in the context of the Integrated Microbial Genomes (IMG) family of systems. All publicly available Microbial Genomes are characterized in IMG using different functional annotation and pathway resources, thus providing a comprehensive framework for identifying and resolving annotation discrepancies. A rule based system for predicting phenotypes in IMG provides a powerful mechanism for validating functional annotations, whereby the phenotypic traits of an organism are inferred based on the presence of certain metabolic reactions and pathways and compared to experimentally observed phenotypes. The IMG family of systems are available at http://img.jgi.doe.gov/.
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the doe jgi standard operating procedure for the annotations of Microbial Genomes
Standards in Genomic Sciences, 2009Co-Authors: Konstantinos Mavromatis, Imin A Chen, Ernest Szeto, Victor Markowitz, Natalia Ivanova, Nikos C. KyrpidesAbstract:The DOE-JGI Microbial Annotation Pipeline (DOE-JGI MAP) supports gene prediction and/or functional annotation of Microbial Genomes towards comparative analysis with the Integrated Microbial Genome (IMG) system. DOE-JGI MAP annotation is applied on nucleotide sequence datasets included in the IMG-ER (Expert Review) version of IMG via the IMG ER submission site. Users can submit the sequence datasets consisting of one or more contigs in a multi-fasta file. DOE-JGI MAP annotation includes prediction of protein coding and RNA genes, as well as repeats and assignment of product names to these genes.
Ernest Szeto - One of the best experts on this subject based on the ideXlab platform.
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erratum to the standard operating procedure of the doe jgi Microbial Genome annotation pipeline mgap v 4
Standards in Genomic Sciences, 2016Co-Authors: Marcel Huntemann, Konstantinos Mavromatis, Ernest Szeto, Natalia Ivanova, James H Tripp, David Paezespino, Krishnaveni Palaniappan, Manoj Pillay, Imin A ChenAbstract:After publication of this study [1], we noticed that the Strain ID summary was not removed from the PDF due to a copyediting error. The original version of this article was corrected. The publisher apologizes for any inconvenience caused.
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the standard operating procedure of the doe jgi Microbial Genome annotation pipeline mgap v 4
Standards in Genomic Sciences, 2015Co-Authors: Marcel Huntemann, Konstantinos Mavromatis, Ernest Szeto, Natalia Ivanova, James H Tripp, David Paezespino, Krishnaveni Palaniappan, Manoj Pillay, Imin A ChenAbstract:The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of Microbial Genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.
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img 4 version of the integrated Microbial Genomes comparative analysis system
Nucleic Acids Research, 2014Co-Authors: Victor M Markowitz, Imin A Chen, Ernest Szeto, Manoj Pillay, Ken Chu, Tanja Woyke, Krishna Palaniappan, Anna Ratner, Jinghua Huang, Marcel HuntemannAbstract:The Integrated Microbial Genomes (IMG) data warehouse integrates Genomes from all three domains of life, as well as plasmids, viruses and Genome fragments. IMG provides tools for analyzing and reviewing the structural and functional annotations of Genomes in a comparative context. IMG’s data content and analytical capabilities have increased continuously since its first version released in 2005. Since the last report published in the 2012 NAR Database Issue, IMG’s annotation and data integration pipelines have evolved while new tools have been added for recording and analyzing single cell Genomes, RNA Seq and biosynthetic cluster data. Different IMG datamarts provide support for the analysis of publicly available Genomes (IMG/W: http://img.jgi.doe.gov/w), expert review of Genome annotations (IMG/ER: http://img.jgi.doe.gov/er) and teaching and training in the area of Microbial Genome analysis (IMG/EDU: http://img.jgi.doe.gov/edu).
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the doe jgi standard operating procedure for the annotations of Microbial Genomes
Standards in Genomic Sciences, 2009Co-Authors: Konstantinos Mavromatis, Imin A Chen, Ernest Szeto, Victor Markowitz, Natalia Ivanova, Nikos C. KyrpidesAbstract:The DOE-JGI Microbial Annotation Pipeline (DOE-JGI MAP) supports gene prediction and/or functional annotation of Microbial Genomes towards comparative analysis with the Integrated Microbial Genome (IMG) system. DOE-JGI MAP annotation is applied on nucleotide sequence datasets included in the IMG-ER (Expert Review) version of IMG via the IMG ER submission site. Users can submit the sequence datasets consisting of one or more contigs in a multi-fasta file. DOE-JGI MAP annotation includes prediction of protein coding and RNA genes, as well as repeats and assignment of product names to these genes.