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perl module implementing the gepa algorithm  (SourceForge net)

 
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    SourceForge net perl module implementing the gepa algorithm
    <t>GEPA</t> <t>algorithm</t> identified lineage-enriched genes from RNA-seq data. ( a ) Schematic representation of the “GEPA” algorithm workflow to identify the lineage-enriched patterns of the genes. ( b ) Estimation of false positive and false negative ratio of GEPA algorithm at different thresholds of FPKM fold change. ( c ) Distribution of the genes across the expression pattern categories. Lineage-enriched patterns are indicated at left side. In the same row, rectangles filled with blue are at least 2.5 fold higher than those in light gray. The bars indicating the number of genes are color coded. Blue for single lineage-enriched groups. Green, yellow and orange for two, three and four lineage-enriched groups, respectively. Light blue for “Gradient” group and purple for “Even” group. ( d ) qRT-PCR validation of the signature genes for lineage-enriched categories. Gene name and expression pattern defined by GEPA (in brackets) were shown above the plots.
    Perl Module Implementing The Gepa Algorithm, supplied by SourceForge net, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/perl module implementing the gepa algorithm/product/SourceForge net
    Average 90 stars, based on 1 article reviews
    perl module implementing the gepa algorithm - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Comparative Transcriptomic Analysis of Multiple Cardiovascular Fates from Embryonic Stem Cells Predicts Novel Regulators in Human Cardiogenesis"

    Article Title: Comparative Transcriptomic Analysis of Multiple Cardiovascular Fates from Embryonic Stem Cells Predicts Novel Regulators in Human Cardiogenesis

    Journal: Scientific Reports

    doi: 10.1038/srep09758

    GEPA algorithm identified lineage-enriched genes from RNA-seq data. ( a ) Schematic representation of the “GEPA” algorithm workflow to identify the lineage-enriched patterns of the genes. ( b ) Estimation of false positive and false negative ratio of GEPA algorithm at different thresholds of FPKM fold change. ( c ) Distribution of the genes across the expression pattern categories. Lineage-enriched patterns are indicated at left side. In the same row, rectangles filled with blue are at least 2.5 fold higher than those in light gray. The bars indicating the number of genes are color coded. Blue for single lineage-enriched groups. Green, yellow and orange for two, three and four lineage-enriched groups, respectively. Light blue for “Gradient” group and purple for “Even” group. ( d ) qRT-PCR validation of the signature genes for lineage-enriched categories. Gene name and expression pattern defined by GEPA (in brackets) were shown above the plots.
    Figure Legend Snippet: GEPA algorithm identified lineage-enriched genes from RNA-seq data. ( a ) Schematic representation of the “GEPA” algorithm workflow to identify the lineage-enriched patterns of the genes. ( b ) Estimation of false positive and false negative ratio of GEPA algorithm at different thresholds of FPKM fold change. ( c ) Distribution of the genes across the expression pattern categories. Lineage-enriched patterns are indicated at left side. In the same row, rectangles filled with blue are at least 2.5 fold higher than those in light gray. The bars indicating the number of genes are color coded. Blue for single lineage-enriched groups. Green, yellow and orange for two, three and four lineage-enriched groups, respectively. Light blue for “Gradient” group and purple for “Even” group. ( d ) qRT-PCR validation of the signature genes for lineage-enriched categories. Gene name and expression pattern defined by GEPA (in brackets) were shown above the plots.

    Techniques Used: RNA Sequencing, Expressing, Quantitative RT-PCR, Biomarker Discovery

    LEGs identified by GEPA predict novel regulatory genes and pathways in human cardiovascular differentiation. ( a ) Percent of the genes with or without annotation in “cardiovascular development and function” from Ingenuity knowledge database in the LEG groups identified by GEPA. ( b ) Our GEPA analysis of top 100 novel cardiac regulatory genes previously predicted by Paige et al. when considering “expression only” or “H3K4me3+H3K27me3+expression “ at T5, T9 and T14 of CM differentiation, respectively. ( c ) Examples of predicted functional genes by GEPA. Dynamic expression of these genes is shown in heatmap. Known cardiac regulators are highlighted in blue. Our predicted candidates, which are overlapping with chromatin dynamics-based predictions by Paige et al. and Wamstad et al ., are shown in green . Novel regulatory genes solely predicted by GEPA are labeled in red. ( d ) Predicted novel regulatory pathways in cardiovascular differentiation using GEPA and Inginuity IPA pathway enrichment analysis. ( e ) A heat-map showing the lineage-specific expression pattern of ephrin and ephrin receptor genes during cardiovascular differentiation. To indicate the lineage-specificity, the relative gene expression was shown as percent in the sum of all the cell types in the heatmap. ( f ) Illustrative Ephrin/Ephrin signaling pathway imposed on a pathway map based on Ingenuity IPA showing localizations of the LEGs. Color code of the molecule indicates its lineage specificity. Blue indicates enrichment in “MCP” ; Orange, both “MCP” and “MCP&CM”; Green, “MCP&CM”.
    Figure Legend Snippet: LEGs identified by GEPA predict novel regulatory genes and pathways in human cardiovascular differentiation. ( a ) Percent of the genes with or without annotation in “cardiovascular development and function” from Ingenuity knowledge database in the LEG groups identified by GEPA. ( b ) Our GEPA analysis of top 100 novel cardiac regulatory genes previously predicted by Paige et al. when considering “expression only” or “H3K4me3+H3K27me3+expression “ at T5, T9 and T14 of CM differentiation, respectively. ( c ) Examples of predicted functional genes by GEPA. Dynamic expression of these genes is shown in heatmap. Known cardiac regulators are highlighted in blue. Our predicted candidates, which are overlapping with chromatin dynamics-based predictions by Paige et al. and Wamstad et al ., are shown in green . Novel regulatory genes solely predicted by GEPA are labeled in red. ( d ) Predicted novel regulatory pathways in cardiovascular differentiation using GEPA and Inginuity IPA pathway enrichment analysis. ( e ) A heat-map showing the lineage-specific expression pattern of ephrin and ephrin receptor genes during cardiovascular differentiation. To indicate the lineage-specificity, the relative gene expression was shown as percent in the sum of all the cell types in the heatmap. ( f ) Illustrative Ephrin/Ephrin signaling pathway imposed on a pathway map based on Ingenuity IPA showing localizations of the LEGs. Color code of the molecule indicates its lineage specificity. Blue indicates enrichment in “MCP” ; Orange, both “MCP” and “MCP&CM”; Green, “MCP&CM”.

    Techniques Used: Expressing, Functional Assay, Labeling, Gene Expression



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    perl module implementing the gepa algorithm  (SourceForge net)
    90
    SourceForge net perl module implementing the gepa algorithm
    <t>GEPA</t> <t>algorithm</t> identified lineage-enriched genes from RNA-seq data. ( a ) Schematic representation of the “GEPA” algorithm workflow to identify the lineage-enriched patterns of the genes. ( b ) Estimation of false positive and false negative ratio of GEPA algorithm at different thresholds of FPKM fold change. ( c ) Distribution of the genes across the expression pattern categories. Lineage-enriched patterns are indicated at left side. In the same row, rectangles filled with blue are at least 2.5 fold higher than those in light gray. The bars indicating the number of genes are color coded. Blue for single lineage-enriched groups. Green, yellow and orange for two, three and four lineage-enriched groups, respectively. Light blue for “Gradient” group and purple for “Even” group. ( d ) qRT-PCR validation of the signature genes for lineage-enriched categories. Gene name and expression pattern defined by GEPA (in brackets) were shown above the plots.
    Perl Module Implementing The Gepa Algorithm, supplied by SourceForge net, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/perl module implementing the gepa algorithm/product/SourceForge net
    Average 90 stars, based on 1 article reviews
    perl module implementing the gepa algorithm - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    GEPA algorithm identified lineage-enriched genes from RNA-seq data. ( a ) Schematic representation of the “GEPA” algorithm workflow to identify the lineage-enriched patterns of the genes. ( b ) Estimation of false positive and false negative ratio of GEPA algorithm at different thresholds of FPKM fold change. ( c ) Distribution of the genes across the expression pattern categories. Lineage-enriched patterns are indicated at left side. In the same row, rectangles filled with blue are at least 2.5 fold higher than those in light gray. The bars indicating the number of genes are color coded. Blue for single lineage-enriched groups. Green, yellow and orange for two, three and four lineage-enriched groups, respectively. Light blue for “Gradient” group and purple for “Even” group. ( d ) qRT-PCR validation of the signature genes for lineage-enriched categories. Gene name and expression pattern defined by GEPA (in brackets) were shown above the plots.

    Journal: Scientific Reports

    Article Title: Comparative Transcriptomic Analysis of Multiple Cardiovascular Fates from Embryonic Stem Cells Predicts Novel Regulators in Human Cardiogenesis

    doi: 10.1038/srep09758

    Figure Lengend Snippet: GEPA algorithm identified lineage-enriched genes from RNA-seq data. ( a ) Schematic representation of the “GEPA” algorithm workflow to identify the lineage-enriched patterns of the genes. ( b ) Estimation of false positive and false negative ratio of GEPA algorithm at different thresholds of FPKM fold change. ( c ) Distribution of the genes across the expression pattern categories. Lineage-enriched patterns are indicated at left side. In the same row, rectangles filled with blue are at least 2.5 fold higher than those in light gray. The bars indicating the number of genes are color coded. Blue for single lineage-enriched groups. Green, yellow and orange for two, three and four lineage-enriched groups, respectively. Light blue for “Gradient” group and purple for “Even” group. ( d ) qRT-PCR validation of the signature genes for lineage-enriched categories. Gene name and expression pattern defined by GEPA (in brackets) were shown above the plots.

    Article Snippet: Lists of predicted cardiac development regulators based on histone modification markers and expression levels were from Paige et al . . A Perl module implementing the GEPA algorithm can be accessed through http://sourceforge.net/projects/gepa/files/ .

    Techniques: RNA Sequencing, Expressing, Quantitative RT-PCR, Biomarker Discovery

    LEGs identified by GEPA predict novel regulatory genes and pathways in human cardiovascular differentiation. ( a ) Percent of the genes with or without annotation in “cardiovascular development and function” from Ingenuity knowledge database in the LEG groups identified by GEPA. ( b ) Our GEPA analysis of top 100 novel cardiac regulatory genes previously predicted by Paige et al. when considering “expression only” or “H3K4me3+H3K27me3+expression “ at T5, T9 and T14 of CM differentiation, respectively. ( c ) Examples of predicted functional genes by GEPA. Dynamic expression of these genes is shown in heatmap. Known cardiac regulators are highlighted in blue. Our predicted candidates, which are overlapping with chromatin dynamics-based predictions by Paige et al. and Wamstad et al ., are shown in green . Novel regulatory genes solely predicted by GEPA are labeled in red. ( d ) Predicted novel regulatory pathways in cardiovascular differentiation using GEPA and Inginuity IPA pathway enrichment analysis. ( e ) A heat-map showing the lineage-specific expression pattern of ephrin and ephrin receptor genes during cardiovascular differentiation. To indicate the lineage-specificity, the relative gene expression was shown as percent in the sum of all the cell types in the heatmap. ( f ) Illustrative Ephrin/Ephrin signaling pathway imposed on a pathway map based on Ingenuity IPA showing localizations of the LEGs. Color code of the molecule indicates its lineage specificity. Blue indicates enrichment in “MCP” ; Orange, both “MCP” and “MCP&CM”; Green, “MCP&CM”.

    Journal: Scientific Reports

    Article Title: Comparative Transcriptomic Analysis of Multiple Cardiovascular Fates from Embryonic Stem Cells Predicts Novel Regulators in Human Cardiogenesis

    doi: 10.1038/srep09758

    Figure Lengend Snippet: LEGs identified by GEPA predict novel regulatory genes and pathways in human cardiovascular differentiation. ( a ) Percent of the genes with or without annotation in “cardiovascular development and function” from Ingenuity knowledge database in the LEG groups identified by GEPA. ( b ) Our GEPA analysis of top 100 novel cardiac regulatory genes previously predicted by Paige et al. when considering “expression only” or “H3K4me3+H3K27me3+expression “ at T5, T9 and T14 of CM differentiation, respectively. ( c ) Examples of predicted functional genes by GEPA. Dynamic expression of these genes is shown in heatmap. Known cardiac regulators are highlighted in blue. Our predicted candidates, which are overlapping with chromatin dynamics-based predictions by Paige et al. and Wamstad et al ., are shown in green . Novel regulatory genes solely predicted by GEPA are labeled in red. ( d ) Predicted novel regulatory pathways in cardiovascular differentiation using GEPA and Inginuity IPA pathway enrichment analysis. ( e ) A heat-map showing the lineage-specific expression pattern of ephrin and ephrin receptor genes during cardiovascular differentiation. To indicate the lineage-specificity, the relative gene expression was shown as percent in the sum of all the cell types in the heatmap. ( f ) Illustrative Ephrin/Ephrin signaling pathway imposed on a pathway map based on Ingenuity IPA showing localizations of the LEGs. Color code of the molecule indicates its lineage specificity. Blue indicates enrichment in “MCP” ; Orange, both “MCP” and “MCP&CM”; Green, “MCP&CM”.

    Article Snippet: Lists of predicted cardiac development regulators based on histone modification markers and expression levels were from Paige et al . . A Perl module implementing the GEPA algorithm can be accessed through http://sourceforge.net/projects/gepa/files/ .

    Techniques: Expressing, Functional Assay, Labeling, Gene Expression