Predicting the Functional Effect of Amino Acid Substitutions and Indels
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{"title"=>"Predicting the Functional Effect of Amino Acid Substitutions and Indels", "type"=>"journal", "authors"=>[{"first_name"=>"Yongwook", "last_name"=>"Choi", "scopus_author_id"=>"7404777272"}, {"first_name"=>"Gregory E.", "last_name"=>"Sims", "scopus_author_id"=>"7101608551"}, {"first_name"=>"Sean", "last_name"=>"Murphy", "scopus_author_id"=>"55440772100"}, {"first_name"=>"Jason R.", "last_name"=>"Miller", "scopus_author_id"=>"55700329200"}, {"first_name"=>"Agnes P.", "last_name"=>"Chan", "scopus_author_id"=>"7403167802"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84867301515", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0046688", "pmid"=>"23056405", "pui"=>"365821708", "scopus"=>"2-s2.0-84867301515", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"ae3046c5-b2c3-3f4b-b49c-3a02cfdfb0d7", "abstract"=>"As next-generation sequencing projects generate massive genome-wide sequence variation data, bioinformatics tools are being developed to provide computational predictions on the functional effects of sequence variations and narrow down the search of casual variants for disease phenotypes. Different classes of sequence variations at the nucleotide level are involved in human diseases, including substitutions, insertions, deletions, frameshifts, and non-sense mutations. Frameshifts and non-sense mutations are likely to cause a negative effect on protein function. Existing prediction tools primarily focus on studying the deleterious effects of single amino acid substitutions through examining amino acid conservation at the position of interest among related sequences, an approach that is not directly applicable to insertions or deletions. Here, we introduce a versatile alignment-based score as a new metric to predict the damaging effects of variations not limited to single amino acid substitutions but also in-frame insertions, deletions, and multiple amino acid substitutions. This alignment-based score measures the change in sequence similarity of a query sequence to a protein sequence homolog before and after the introduction of an amino acid variation to the query sequence. Our results showed that the scoring scheme performs well in separating disease-associated variants (n = 21,662) from common polymorphisms (n = 37,022) for UniProt human protein variations, and also in separating deleterious variants (n = 15,179) from neutral variants (n = 17,891) for UniProt non-human protein variations. In our approach, the area under the receiver operating characteristic curve (AUC) for the human and non-human protein variation datasets is ∼0.85. We also observed that the alignment-based score correlates with the deleteriousness of a sequence variation. In summary, we have developed a new algorithm, PROVEAN (Protein Variation Effect Analyzer), which provides a generalized approach to predict the functional effects of protein sequence variations including single or multiple amino acid substitutions, and in-frame insertions and deletions. The PROVEAN tool is available online at http://provean.jcvi.org.", "link"=>"http://www.mendeley.com/research/predicting-functional-effect-amino-acid-substitutions-indels-1", "reader_count"=>650, "reader_count_by_academic_status"=>{"Unspecified"=>25, "Professor > Associate Professor"=>17, "Librarian"=>1, "Student > Doctoral Student"=>36, "Researcher"=>126, "Student > Ph. D. Student"=>194, "Student > Postgraduate"=>34, "Student > Master"=>107, "Other"=>21, "Student > Bachelor"=>71, "Lecturer"=>5, "Professor"=>13}, "reader_count_by_user_role"=>{"Unspecified"=>25, "Professor > Associate Professor"=>17, "Librarian"=>1, "Student > Doctoral Student"=>36, "Researcher"=>126, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/562601"], "description"=>"<p>For each class, a box plot is shown. The vertical line shows the whole range of delta scores, the thick horizontal line shows the median, and the gray rectangle shows the interquartile range (25%–75%). The PROVEAN score increases and correlates with median transactivation level for the non-functional and partially functional classes of variations.</p>", "links"=>[], "tags"=>["tp53", "variations", "binned", "15", "classes", "transactivation"], "article_id"=>233088, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g007", "stats"=>{"downloads"=>1, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PROVEAN_score_distribution_of_TP53_variations_binned_into_15_classes_based_on_transactivation_levels_/233088", "title"=>"PROVEAN score distribution of TP53 variations binned into 15 classes based on transactivation levels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:51:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/562435"], "description"=>"<p>ROC curves of four different prediction tools for single amino acid substitutions found in human and non-human proteins. All tools show a similar predictive ability with the AUC value of ∼0.85.</p>", "links"=>[], "tags"=>["curves", "tools", "amino", "substitutions", "non-human", "predictive", "auc"], "article_id"=>232923, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_of_four_different_prediction_tools_for_single_amino_acid_substitutions_found_in_human_and_non_human_proteins_All_tools_show_a_similar_predictive_ability_with_the_AUC_value_of_8764_0_85_/232923", "title"=>"ROC curves of four different prediction tools for single amino acid substitutions found in human and non-human proteins. All tools show a similar predictive ability with the AUC value of ∼0.85.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:48:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/563024"], "description"=>"<p>Prediction accuracy for the UniProt human protein variations given a PROVEAN score threshold of −2.282.</p>", "links"=>[], "tags"=>["uniprot", "variations", "provean"], "article_id"=>233516, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.t002", "stats"=>{"downloads"=>6, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_accuracy_for_the_UniProt_human_protein_variations_given_a_PROVEAN_score_threshold_of_8722_2_282_/233516", "title"=>"Prediction accuracy for the UniProt human protein variations given a PROVEAN score threshold of −2.282.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-08 00:58:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/562872"], "description"=>"<p>For simplicity, only the top three clusters were included in building the supporting sequence set in this example.</p>", "links"=>[], "tags"=>["provean"], "article_id"=>233370, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g010", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computing_the_PROVEAN_score_/233370", "title"=>"Computing the PROVEAN score.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:56:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/562168"], "description"=>"<p>An overview of the PROVEAN procedure.</p>", "links"=>[], "tags"=>["overview", "provean"], "article_id"=>232660, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_overview_of_the_PROVEAN_procedure_/232660", "title"=>"An overview of the PROVEAN procedure.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:44:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/562998"], "description"=>"<p>Number of human protein variations collected from the UniProt/Swiss-Prot database.</p>", "links"=>[], "tags"=>["variations"], "article_id"=>233492, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_human_protein_variations_collected_from_the_UniProt_Swiss_Prot_database_/233492", "title"=>"Number of human protein variations collected from the UniProt/Swiss-Prot database.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-08 00:58:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/562792"], "description"=>"<p>(A) Distribution of the 11,990 human proteins based on the number of supporting sequences used for PROVEAN prediction. (B) Prediction accuracy achieved with respect to the number of supporting sequences. The observed accuracy is consistently above 73%, except in cases when the number of supporting sequences drops below 50.</p>", "links"=>[], "tags"=>["supporting", "sequences", "uniprot", "proteins", "carrying", "deleterious"], "article_id"=>233282, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g009", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_supporting_sequences_used_for_the_Uniprot_human_proteins_carrying_neutral_or_deleterious_variants_/233282", "title"=>"Number of supporting sequences used for the Uniprot human proteins carrying neutral or deleterious variants.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:54:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/562062"], "description"=>"<p>The amino acid residue replaced, deleted, or inserted is indicated by an arrow, and the difference between two alignments is indicated by a rectangle. Low delta scores are interpreted as deleterious, and high delta scores are interpreted as neutral. The BLOSUM62 and gap penalties of 10 for opening and 1 for extension were used.</p>", "links"=>[], "tags"=>["computing", "interpreting", "delta", "alignment", "scores", "deleterious", "substitution", "deletion", "insertion", "homologous"], "article_id"=>232536, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g001", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_computing_and_interpreting_delta_alignment_scores_for_six_different_known_variations_A_deleterious_substitution_MIM_151623_B_neutral_substitution_dbSNP_rs1042522_C_deleterious_deletion_MIM_219700_D_neutral_deletion_dbSNP_rs72471101_E_deleteri/232536", "title"=>"Examples of computing and interpreting delta alignment scores for six different known variations, (A) deleterious substitution (MIM:151623), (B) neutral substitution (dbSNP:rs1042522), (C) deleterious deletion (MIM:219700), (D) neutral deletion (dbSNP:rs72471101), (E) deleterious insertion (MIM:164200), and (F) neutral insertion (dbSNP:rs10625857) with respect to the selected homologous proteins.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:42:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/562264"], "description"=>"<p>For all classes of variations including substitutions, indels, and replacements, the distribution shows a distinct separation between the deleterious and neutral variations.</p>", "links"=>[], "tags"=>["deleterious"], "article_id"=>232763, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PROVEAN_score_distribution_for_deleterious_and_neutral_human_protein_variations_/232763", "title"=>"PROVEAN score distribution for deleterious and neutral human protein variations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:46:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/563064"], "description"=>"<p>Number of the UniProt non-human protein variations and prediction accuracy given a PROVEAN score threshold of −2.282.</p>", "links"=>[], "tags"=>["uniprot", "non-human", "variations", "provean"], "article_id"=>233554, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.t003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_the_UniProt_non_human_protein_variations_and_prediction_accuracy_given_a_PROVEAN_score_threshold_of_8722_2_282_/233554", "title"=>"Number of the UniProt non-human protein variations and prediction accuracy given a PROVEAN score threshold of −2.282.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-08 00:59:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/562356"], "description"=>"<p>Only mutations annotated as “disease-causing” were collected from the HGMD. The distribution shows a distinct separation between the two datasets.</p>", "links"=>[], "tags"=>["deletions", "insertions", "mutation", "1000", "genomes"], "article_id"=>232850, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PROVEAN_score_distribution_of_deletions_and_insertions_collected_from_the_Human_Gene_Mutation_Database_HGMD_and_the_1000_Genomes_Project_/232850", "title"=>"PROVEAN score distribution of deletions and insertions collected from the Human Gene Mutation Database (HGMD) and the 1000 Genomes Project.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:47:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/562531"], "description"=>"<p>The AUC values are shown in the legend. The top two performers for LacI were PolyPhen-2 and PROVEAN, and those for TP53 were Mutation Assessor and PROVEAN.</p>", "links"=>[], "tags"=>["curves", "tools", "amino", "substitutions", "lac", "repressor", "tp53", "suppressor"], "article_id"=>233016, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g006", "stats"=>{"downloads"=>3, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_of_different_prediction_tools_for_single_amino_acid_substitutions_in_A_E_coli_lac_repressor_protein_and_B_human_TP53_tumor_suppressor_protein_/233016", "title"=>"ROC curves of different prediction tools for single amino acid substitutions in (A) <i>E. coli</i> lac repressor protein and (B) human TP53 tumor suppressor protein.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:50:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/298310", "https://ndownloader.figshare.com/files/298352", "https://ndownloader.figshare.com/files/298407", "https://ndownloader.figshare.com/files/298469", "https://ndownloader.figshare.com/files/298510", "https://ndownloader.figshare.com/files/298568", "https://ndownloader.figshare.com/files/298622", "https://ndownloader.figshare.com/files/298673", "https://ndownloader.figshare.com/files/298724", "https://ndownloader.figshare.com/files/298780", "https://ndownloader.figshare.com/files/298814"], "description"=>"<div><p>As next-generation sequencing projects generate massive genome-wide sequence variation data, bioinformatics tools are being developed to provide computational predictions on the functional effects of sequence variations and narrow down the search of casual variants for disease phenotypes. Different classes of sequence variations at the nucleotide level are involved in human diseases, including substitutions, insertions, deletions, frameshifts, and non-sense mutations. Frameshifts and non-sense mutations are likely to cause a negative effect on protein function. Existing prediction tools primarily focus on studying the deleterious effects of single amino acid substitutions through examining amino acid conservation at the position of interest among related sequences, an approach that is not directly applicable to insertions or deletions. Here, we introduce a versatile alignment-based score as a new metric to predict the damaging effects of variations not limited to single amino acid substitutions but also in-frame insertions, deletions, and multiple amino acid substitutions. This alignment-based score measures the change in sequence similarity of a query sequence to a protein sequence homolog before and after the introduction of an amino acid variation to the query sequence. Our results showed that the scoring scheme performs well in separating disease-associated variants (n = 21,662) from common polymorphisms (n = 37,022) for UniProt human protein variations, and also in separating deleterious variants (n = 15,179) from neutral variants (n = 17,891) for UniProt non-human protein variations. In our approach, the area under the receiver operating characteristic curve (AUC) for the human and non-human protein variation datasets is ∼0.85. We also observed that the alignment-based score correlates with the deleteriousness of a sequence variation. In summary, we have developed a new algorithm, PROVEAN (<b>Pro</b>tein <b>V</b>ariation <b>E</b>ffect <b>An</b>alyzer), which provides a generalized approach to predict the functional effects of protein sequence variations including single or multiple amino acid substitutions, and in-frame insertions and deletions. The PROVEAN tool is available online at <a href=\"http://provean.jcvi.org\">http://provean.jcvi.org</a>.</p> </div>", "links"=>[], "tags"=>["predicting", "amino", "substitutions", "indels"], "article_id"=>118821, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046688.s001", "https://dx.doi.org/10.1371/journal.pone.0046688.s002", "https://dx.doi.org/10.1371/journal.pone.0046688.s003", "https://dx.doi.org/10.1371/journal.pone.0046688.s004", "https://dx.doi.org/10.1371/journal.pone.0046688.s005", "https://dx.doi.org/10.1371/journal.pone.0046688.s006", "https://dx.doi.org/10.1371/journal.pone.0046688.s007", "https://dx.doi.org/10.1371/journal.pone.0046688.s008", "https://dx.doi.org/10.1371/journal.pone.0046688.s009", "https://dx.doi.org/10.1371/journal.pone.0046688.s010", "https://dx.doi.org/10.1371/journal.pone.0046688.s011"], "stats"=>{"downloads"=>21, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Predicting_the_Functional_Effect_of_Amino_Acid_Substitutions_and_Indels/118821", "title"=>"Predicting the Functional Effect of Amino Acid Substitutions and Indels", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-10-08 02:27:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/562966"], "description"=>"<p>“Balanced accuracy” is a simple average of sensitivity and specificity, that is, (sensitivity+specificity)/2. The “No prediction” column shows the number of variants for which the tool fails to provide a prediction.</p>", "links"=>[], "tags"=>["classification", "tools", "amino", "substitutions", "non-human"], "article_id"=>233459, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.t005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Binary_classification_performance_of_four_different_tools_for_single_amino_acid_substitutions_in_human_and_non_human_proteins_/233459", "title"=>"Binary classification performance of four different tools for single amino acid substitutions in human and non-human proteins.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-08 00:57:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/562726"], "description"=>"<p>In general, an increase in score (i.e. less deleterious effects) correlates with an increase in cholesterol efflux activity.</p>", "links"=>[], "tags"=>["efflux", "provean", "scores", "abca1"], "article_id"=>233217, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.g008", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_of_cholesterol_efflux_values_with_the_PROVEAN_scores_for_ABCA1_variations_/233217", "title"=>"Correlation of cholesterol efflux values with the PROVEAN scores for ABCA1 variations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:53:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/562933"], "description"=>"<p>Number of deletions and insertions collected from the Human Gene Mutation Database and the 1000 Genomes Project and used for validation of PROVEAN.</p>", "links"=>[], "tags"=>["deletions", "insertions", "mutation", "1000", "genomes", "validation"], "article_id"=>233426, "categories"=>["Biological Sciences", "Biochemistry", "Genetics"], "users"=>["Yongwook Choi", "Gregory E. Sims", "Sean Murphy", "Jason R. Miller", "Agnes P. Chan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046688.t004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_deletions_and_insertions_collected_from_the_Human_Gene_Mutation_Database_and_the_1000_Genomes_Project_and_used_for_validation_of_PROVEAN_/233426", "title"=>"Number of deletions and insertions collected from the Human Gene Mutation Database and the 1000 Genomes Project and used for validation of PROVEAN.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-08 00:57:06"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"23", "full-text"=>"22", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"3"}
  • {"unique-ip"=>"21", "full-text"=>"19", "pdf"=>"9", "abstract"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"8", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2013", "month"=>"2"}
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  • {"unique-ip"=>"26", "full-text"=>"21", "pdf"=>"24", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2012", "month"=>"11"}
  • {"unique-ip"=>"25", "full-text"=>"25", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"9", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"5"}
  • {"unique-ip"=>"17", "full-text"=>"13", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"11", "cited-by"=>"0", "year"=>"2013", "month"=>"6"}
  • {"unique-ip"=>"14", "full-text"=>"11", "pdf"=>"6", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"11", "cited-by"=>"1", "year"=>"2013", "month"=>"7"}
  • {"unique-ip"=>"16", "full-text"=>"20", "pdf"=>"10", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"12", "cited-by"=>"0", "year"=>"2013", "month"=>"8"}
  • {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"9"}
  • {"unique-ip"=>"23", "full-text"=>"24", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2013", "month"=>"10"}
  • {"unique-ip"=>"15", "full-text"=>"12", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"11"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"12"}
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  • {"unique-ip"=>"15", "full-text"=>"27", "pdf"=>"10", "abstract"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"8", "cited-by"=>"0", "year"=>"2014", "month"=>"3"}
  • {"unique-ip"=>"21", "full-text"=>"20", "pdf"=>"10", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2014", "month"=>"5"}
  • {"unique-ip"=>"14", "full-text"=>"18", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2014", "month"=>"6"}
  • {"unique-ip"=>"20", "full-text"=>"41", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"8", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2014", "month"=>"4"}
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  • {"unique-ip"=>"45", "full-text"=>"53", "pdf"=>"17", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"2", "cited-by"=>"1", "year"=>"2015", "month"=>"5"}
  • {"unique-ip"=>"48", "full-text"=>"50", "pdf"=>"18", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"15", "supp-data"=>"14", "cited-by"=>"1", "year"=>"2015", "month"=>"6"}
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  • {"unique-ip"=>"42", "full-text"=>"44", "pdf"=>"13", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"8", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2015", "month"=>"3"}
  • {"unique-ip"=>"34", "full-text"=>"36", "pdf"=>"16", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"2"}
  • {"unique-ip"=>"32", "full-text"=>"30", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"9", "cited-by"=>"0", "year"=>"2015", "month"=>"8"}
  • {"unique-ip"=>"44", "full-text"=>"45", "pdf"=>"14", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2015", "month"=>"9"}
  • {"unique-ip"=>"42", "full-text"=>"32", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"6", "cited-by"=>"0", "year"=>"2015", "month"=>"10"}
  • {"unique-ip"=>"35", "full-text"=>"21", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2014", "month"=>"7"}
  • {"unique-ip"=>"39", "full-text"=>"31", "pdf"=>"15", "abstract"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2014", "month"=>"8"}
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Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Genetics", "average_usage"=>[333, 576, 707, 814, 908, 1004, 1104, 1197, 1280, 1370, 1449, 1531, 1603, 1673, 1742, 1817, 1886, 1954, 2025, 2098, 2171, 2234, 2304, 2365, 2431]}, {"subject_area"=>"/Physical sciences/Chemistry", "average_usage"=>[302, 508, 622, 720, 804, 888, 973, 1054, 1141, 1219, 1299, 1370, 1442, 1511, 1574, 1644, 1711, 1782, 1846, 1911, 1971, 2030, 2097, 2155, 2217]}]}
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