The Experts below are selected from a list of 58710 Experts worldwide ranked by ideXlab platform

Benjamin J Callahan - One of the best experts on this subject based on the ideXlab platform.

  • simple statistical identification and removal of contaminant sequences in Marker Gene and metagenomics data
    Microbiome, 2018
    Co-Authors: Nicole M Davis, Diana M Proctor, Susan Holmes, David A Relman, Benjamin J Callahan
    Abstract:

    The accuracy of microbial community surveys based on Marker-Gene and metagenomic sequencing (MGS) suffers from the presence of contaminants—DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls. Decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and Marker-Gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Decontam improves the quality of metagenomic and Marker-Gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to Generate more accurate profiles of microbial communities at little to no additional cost.

  • simple statistical identification and removal of contaminant sequences in Marker Gene and metagenomics data
    bioRxiv, 2018
    Co-Authors: Nicole M Davis, Diana M Proctor, Susan Holmes, David A Relman, Benjamin J Callahan
    Abstract:

    Background: The accuracy of microbial community surveys based on Marker-Gene and metagenomic sequencing (MGS) suffers from the presence of contaminants - DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam (https://github.com/benjjneb/decontam), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples, and are often found in negative controls. Results: decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and Marker-Gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome, and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Conclusions: decontam improves the quality of metagenomic and Marker-Gene sequencing by identifying and removing contaminant DNA sequences. decontam integrates easily with existing MGS workflows, and allows researchers to Generate more accurate profiles of microbial communities at little to no additional cost.

  • exact sequence variants should replace operational taxonomic units in Marker Gene data analysis
    The ISME Journal, 2017
    Co-Authors: Benjamin J Callahan, Paul J Mcmurdie, Susan Holmes
    Abstract:

    Recent advances have made it possible to analyze high-throughput Marker-Gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently such that amplicon sequence variants (ASVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced Gene region. The benefits of finer resolution are immediately apparent, and arguments for ASV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits that derive from the status of ASVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how these features grant ASVs the combined advantages of closed-reference OTUs—including computational costs that scale linearly with study size, simple merging between independently processed data sets, and forward prediction—and of de novo OTUs—including accurate measurement of diversity and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that ASVs should replace OTUs as the standard unit of Marker-Gene analysis and reporting.

  • exact sequence variants should replace operational taxonomic units in Marker Gene data analysis
    bioRxiv, 2017
    Co-Authors: Benjamin J Callahan, Paul J Mcmurdie, Susan Holmes
    Abstract:

    Recent advances have made it possible to analyze high-throughput Marker-Gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently that sequence variants (SVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced Gene region. The benefits of finer taxonomic resolution are immediately apparent, and arguments for SV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits deriving from the status of SVs as consistent labels with intrinsic biological meaning identified independently from a reference database . Here we discuss how those features grant SVs the combined advantages of closed-reference OTUs -- including computational costs that scale linearly with study size, simple merging between independently processed datasets, and forward prediction -- and of de novo OTUs -- including accurate diversity measurement and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that SVs should replace OTUs as the standard unit of Marker Gene analysis and reporting.

Susan Holmes - One of the best experts on this subject based on the ideXlab platform.

  • simple statistical identification and removal of contaminant sequences in Marker Gene and metagenomics data
    Microbiome, 2018
    Co-Authors: Nicole M Davis, Diana M Proctor, Susan Holmes, David A Relman, Benjamin J Callahan
    Abstract:

    The accuracy of microbial community surveys based on Marker-Gene and metagenomic sequencing (MGS) suffers from the presence of contaminants—DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls. Decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and Marker-Gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Decontam improves the quality of metagenomic and Marker-Gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to Generate more accurate profiles of microbial communities at little to no additional cost.

  • simple statistical identification and removal of contaminant sequences in Marker Gene and metagenomics data
    bioRxiv, 2018
    Co-Authors: Nicole M Davis, Diana M Proctor, Susan Holmes, David A Relman, Benjamin J Callahan
    Abstract:

    Background: The accuracy of microbial community surveys based on Marker-Gene and metagenomic sequencing (MGS) suffers from the presence of contaminants - DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam (https://github.com/benjjneb/decontam), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples, and are often found in negative controls. Results: decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and Marker-Gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome, and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Conclusions: decontam improves the quality of metagenomic and Marker-Gene sequencing by identifying and removing contaminant DNA sequences. decontam integrates easily with existing MGS workflows, and allows researchers to Generate more accurate profiles of microbial communities at little to no additional cost.

  • exact sequence variants should replace operational taxonomic units in Marker Gene data analysis
    The ISME Journal, 2017
    Co-Authors: Benjamin J Callahan, Paul J Mcmurdie, Susan Holmes
    Abstract:

    Recent advances have made it possible to analyze high-throughput Marker-Gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently such that amplicon sequence variants (ASVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced Gene region. The benefits of finer resolution are immediately apparent, and arguments for ASV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits that derive from the status of ASVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how these features grant ASVs the combined advantages of closed-reference OTUs—including computational costs that scale linearly with study size, simple merging between independently processed data sets, and forward prediction—and of de novo OTUs—including accurate measurement of diversity and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that ASVs should replace OTUs as the standard unit of Marker-Gene analysis and reporting.

  • exact sequence variants should replace operational taxonomic units in Marker Gene data analysis
    bioRxiv, 2017
    Co-Authors: Benjamin J Callahan, Paul J Mcmurdie, Susan Holmes
    Abstract:

    Recent advances have made it possible to analyze high-throughput Marker-Gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently that sequence variants (SVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced Gene region. The benefits of finer taxonomic resolution are immediately apparent, and arguments for SV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits deriving from the status of SVs as consistent labels with intrinsic biological meaning identified independently from a reference database . Here we discuss how those features grant SVs the combined advantages of closed-reference OTUs -- including computational costs that scale linearly with study size, simple merging between independently processed datasets, and forward prediction -- and of de novo OTUs -- including accurate diversity measurement and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that SVs should replace OTUs as the standard unit of Marker Gene analysis and reporting.

Diana M Proctor - One of the best experts on this subject based on the ideXlab platform.

  • simple statistical identification and removal of contaminant sequences in Marker Gene and metagenomics data
    Microbiome, 2018
    Co-Authors: Nicole M Davis, Diana M Proctor, Susan Holmes, David A Relman, Benjamin J Callahan
    Abstract:

    The accuracy of microbial community surveys based on Marker-Gene and metagenomic sequencing (MGS) suffers from the presence of contaminants—DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls. Decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and Marker-Gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Decontam improves the quality of metagenomic and Marker-Gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to Generate more accurate profiles of microbial communities at little to no additional cost.

  • simple statistical identification and removal of contaminant sequences in Marker Gene and metagenomics data
    bioRxiv, 2018
    Co-Authors: Nicole M Davis, Diana M Proctor, Susan Holmes, David A Relman, Benjamin J Callahan
    Abstract:

    Background: The accuracy of microbial community surveys based on Marker-Gene and metagenomic sequencing (MGS) suffers from the presence of contaminants - DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam (https://github.com/benjjneb/decontam), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples, and are often found in negative controls. Results: decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and Marker-Gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome, and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Conclusions: decontam improves the quality of metagenomic and Marker-Gene sequencing by identifying and removing contaminant DNA sequences. decontam integrates easily with existing MGS workflows, and allows researchers to Generate more accurate profiles of microbial communities at little to no additional cost.

Jingfu Li - One of the best experts on this subject based on the ideXlab platform.

  • inducible excision of selectable Marker Gene from transgenic plants by the cre lox site specific recombination system
    Transgenic Research, 2005
    Co-Authors: Yong Wang, Bojun Chen, Yuanlei Hu, Jingfu Li
    Abstract:

    In a plant transformation process, it is necessary to use Marker Genes that allow the selection of reGenerated transgenic plants. However, selectable Marker Genes are Generally superfluous once an intact transgenic plant has been established. Furthermore, they may cause regulatory difficulties for approving transgenic crop release and commercialization. We constructed a binary expression vector with the Cre/lox system with a view to eliminating a Marker Gene from transgenic plants conveniently. In the vector, recombinase Gene cre under the control of heat shock promoter and selectable Marker Gene nptII under the control of CaMV35S promoter were placed between two lox P sites in direct orientation, while the Gene of interest was inserted outside of the lox P sites. By using this vector, both cre and nptII Genes were eliminated from most of the reGenerated plants of primary transformed tobacco through heat shock treatment, while the Gene of interest was retained and stably inherited. This autoexcision strategy, mediated by the Cre/lox system and subjected to heat shock treatment to eliminate a selectable Marker Gene, is easy to adopt and provides a promising approach to Generate Marker-free transgenic plants.

  • Inducible excision of selectable Marker Gene from transgenic plants by the cre/lox site-specific recombination system.
    Transgenic Research, 2005
    Co-Authors: Yong Wang, Bojun Chen, Yuanlei Hu, Jingfu Li
    Abstract:

    In a plant transformation process, it is necessary to use Marker Genes that allow the selection of reGenerated transgenic plants. However, selectable Marker Genes are Generally superfluous once an intact transgenic plant has been established. Furthermore, they may cause regulatory difficulties for approving transgenic crop release and commercialization. We constructed a binary expression vector with the Cre/lox system with a view to eliminating a Marker Gene from transgenic plants conveniently. In the vector, recombinase Gene cre under the control of heat shock promoter and selectable Marker Gene nptII under the control of CaMV35S promoter were placed between two lox P sites in direct orientation, while the Gene of interest was inserted outside of the lox P sites. By using this vector, both cre and nptII Genes were eliminated from most of the reGenerated plants of primary transformed tobacco through heat shock treatment, while the Gene of interest was retained and stably inherited. This autoexcision strategy, mediated by the Cre/lox system and subjected to heat shock treatment to eliminate a selectable Marker Gene, is easy to adopt and provides a promising approach to Generate Marker-free transgenic plants.

Nicole M Davis - One of the best experts on this subject based on the ideXlab platform.

  • simple statistical identification and removal of contaminant sequences in Marker Gene and metagenomics data
    Microbiome, 2018
    Co-Authors: Nicole M Davis, Diana M Proctor, Susan Holmes, David A Relman, Benjamin J Callahan
    Abstract:

    The accuracy of microbial community surveys based on Marker-Gene and metagenomic sequencing (MGS) suffers from the presence of contaminants—DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls. Decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and Marker-Gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Decontam improves the quality of metagenomic and Marker-Gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to Generate more accurate profiles of microbial communities at little to no additional cost.

  • simple statistical identification and removal of contaminant sequences in Marker Gene and metagenomics data
    bioRxiv, 2018
    Co-Authors: Nicole M Davis, Diana M Proctor, Susan Holmes, David A Relman, Benjamin J Callahan
    Abstract:

    Background: The accuracy of microbial community surveys based on Marker-Gene and metagenomic sequencing (MGS) suffers from the presence of contaminants - DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam (https://github.com/benjjneb/decontam), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples, and are often found in negative controls. Results: decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and Marker-Gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome, and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Conclusions: decontam improves the quality of metagenomic and Marker-Gene sequencing by identifying and removing contaminant DNA sequences. decontam integrates easily with existing MGS workflows, and allows researchers to Generate more accurate profiles of microbial communities at little to no additional cost.