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cafa2 benchmarking matlab scripts  (MathWorks Inc)


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    MathWorks Inc cafa2 benchmarking matlab scripts
    eggNOG-mapper versus BLAST based Gene Ontology annotations. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode (brighter colors) and BLAST (dimmed colors). Left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Within each plot, consecutive pairs of horizontal bars represent different BLAST E -value cutoffs ranging from 1E-03 to 1E-40, with sequence matches under this cutoff being excluded from both BLAST and eggNOG-mapper hits. Middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria holds (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain terms, <t>CAFA2</t> approach). Right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).
    Cafa2 Benchmarking Matlab Scripts, supplied by MathWorks Inc, 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/cafa2 benchmarking matlab scripts/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    cafa2 benchmarking matlab scripts - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper"

    Article Title: Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper

    Journal: Molecular Biology and Evolution

    doi: 10.1093/molbev/msx148

    eggNOG-mapper versus BLAST based Gene Ontology annotations. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode (brighter colors) and BLAST (dimmed colors). Left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Within each plot, consecutive pairs of horizontal bars represent different BLAST E -value cutoffs ranging from 1E-03 to 1E-40, with sequence matches under this cutoff being excluded from both BLAST and eggNOG-mapper hits. Middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria holds (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain terms, CAFA2 approach). Right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).
    Figure Legend Snippet: eggNOG-mapper versus BLAST based Gene Ontology annotations. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode (brighter colors) and BLAST (dimmed colors). Left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Within each plot, consecutive pairs of horizontal bars represent different BLAST E -value cutoffs ranging from 1E-03 to 1E-40, with sequence matches under this cutoff being excluded from both BLAST and eggNOG-mapper hits. Middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria holds (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain terms, CAFA2 approach). Right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).

    Techniques Used: Comparison, Derivative Assay, Sequencing

    eggNOG-mapper versus InterProScan. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode and with default parameters (brighter colors) and InterProScan (dimmed colors) with default parameters and without further restrictions. The left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Consecutive pairs of horizontal bars represent each species in the benchmark. The middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria hold (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain assignments, CAFA2 approach). The right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).
    Figure Legend Snippet: eggNOG-mapper versus InterProScan. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode and with default parameters (brighter colors) and InterProScan (dimmed colors) with default parameters and without further restrictions. The left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Consecutive pairs of horizontal bars represent each species in the benchmark. The middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria hold (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain assignments, CAFA2 approach). The right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).

    Techniques Used: Comparison, Derivative Assay

    eggNOG-mapper under the CAFA2 benchmark. Evaluation of eggNOG-mapper using CAFA2 benchmark data set. Evaluation was carried out on No-Knowledge (NK) benchmark sequences in the partial mode. The coverage of each method is shown within its performance bar. Accuracy of the methods is represented by the F -max measure ( F -max = 1 being a perfect predictor). eggNOG-mapper results (DIAMOND mode) are shown in green. For details on the other methods shown, refer to .
    Figure Legend Snippet: eggNOG-mapper under the CAFA2 benchmark. Evaluation of eggNOG-mapper using CAFA2 benchmark data set. Evaluation was carried out on No-Knowledge (NK) benchmark sequences in the partial mode. The coverage of each method is shown within its performance bar. Accuracy of the methods is represented by the F -max measure ( F -max = 1 being a perfect predictor). eggNOG-mapper results (DIAMOND mode) are shown in green. For details on the other methods shown, refer to .

    Techniques Used:



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    cafa2 benchmarking matlab scripts  (MathWorks Inc)
    90
    MathWorks Inc cafa2 benchmarking matlab scripts
    eggNOG-mapper versus BLAST based Gene Ontology annotations. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode (brighter colors) and BLAST (dimmed colors). Left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Within each plot, consecutive pairs of horizontal bars represent different BLAST E -value cutoffs ranging from 1E-03 to 1E-40, with sequence matches under this cutoff being excluded from both BLAST and eggNOG-mapper hits. Middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria holds (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain terms, <t>CAFA2</t> approach). Right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).
    Cafa2 Benchmarking Matlab Scripts, supplied by MathWorks Inc, 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/cafa2 benchmarking matlab scripts/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    cafa2 benchmarking matlab scripts - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

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    eggNOG-mapper versus BLAST based Gene Ontology annotations. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode (brighter colors) and BLAST (dimmed colors). Left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Within each plot, consecutive pairs of horizontal bars represent different BLAST E -value cutoffs ranging from 1E-03 to 1E-40, with sequence matches under this cutoff being excluded from both BLAST and eggNOG-mapper hits. Middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria holds (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain terms, CAFA2 approach). Right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).

    Journal: Molecular Biology and Evolution

    Article Title: Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper

    doi: 10.1093/molbev/msx148

    Figure Lengend Snippet: eggNOG-mapper versus BLAST based Gene Ontology annotations. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode (brighter colors) and BLAST (dimmed colors). Left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Within each plot, consecutive pairs of horizontal bars represent different BLAST E -value cutoffs ranging from 1E-03 to 1E-40, with sequence matches under this cutoff being excluded from both BLAST and eggNOG-mapper hits. Middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria holds (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain terms, CAFA2 approach). Right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).

    Article Snippet: Predicted eggNOG-mapper annotations were evaluated using the official CAFA2 benchmarking MatLab scripts in partial mode .

    Techniques: Comparison, Derivative Assay, Sequencing

    eggNOG-mapper versus InterProScan. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode and with default parameters (brighter colors) and InterProScan (dimmed colors) with default parameters and without further restrictions. The left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Consecutive pairs of horizontal bars represent each species in the benchmark. The middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria hold (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain assignments, CAFA2 approach). The right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).

    Journal: Molecular Biology and Evolution

    Article Title: Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper

    doi: 10.1093/molbev/msx148

    Figure Lengend Snippet: eggNOG-mapper versus InterProScan. Comparison of the annotation results for five model species using eggNOG-mapper in HMMER mode and with default parameters (brighter colors) and InterProScan (dimmed colors) with default parameters and without further restrictions. The left panel shows the per-protein average proportion of true positive GO term assignments (TP, green, experimentally validated) to false positive term assignments (FP, red, derived from taxonomic exclusion criteria). Consecutive pairs of horizontal bars represent each species in the benchmark. The middle panel shows the per-protein average number of true positive GO term assignments (green), false positive term assignments (red), and assignments of GO terms where neither curated evidence nor taxonomic exclusion criteria hold (grey). Next to the plot is shown the ratio of true positive term assignments (TP-ratio) over the total number of assignments (including false and uncertain assignments, CAFA2 approach). The right panel shows the percentage of each proteome that receives annotation, indicating the fraction of proteins that were annotated exclusively with curated true positive terms (TP, blue); proteins annotated with curated terms but also false or uncertain assignments (purple); and proteins that only received false or uncertain assignments (orange, proportion used to compute the no-TP ratio column).

    Article Snippet: Predicted eggNOG-mapper annotations were evaluated using the official CAFA2 benchmarking MatLab scripts in partial mode .

    Techniques: Comparison, Derivative Assay

    eggNOG-mapper under the CAFA2 benchmark. Evaluation of eggNOG-mapper using CAFA2 benchmark data set. Evaluation was carried out on No-Knowledge (NK) benchmark sequences in the partial mode. The coverage of each method is shown within its performance bar. Accuracy of the methods is represented by the F -max measure ( F -max = 1 being a perfect predictor). eggNOG-mapper results (DIAMOND mode) are shown in green. For details on the other methods shown, refer to .

    Journal: Molecular Biology and Evolution

    Article Title: Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper

    doi: 10.1093/molbev/msx148

    Figure Lengend Snippet: eggNOG-mapper under the CAFA2 benchmark. Evaluation of eggNOG-mapper using CAFA2 benchmark data set. Evaluation was carried out on No-Knowledge (NK) benchmark sequences in the partial mode. The coverage of each method is shown within its performance bar. Accuracy of the methods is represented by the F -max measure ( F -max = 1 being a perfect predictor). eggNOG-mapper results (DIAMOND mode) are shown in green. For details on the other methods shown, refer to .

    Article Snippet: Predicted eggNOG-mapper annotations were evaluated using the official CAFA2 benchmarking MatLab scripts in partial mode .

    Techniques: