Gene Characterization Index: Assessing the Depth of Gene Annotation

Abstract

BackgroundWe introduce the Gene Characterization Index, a bioinformatics method for scoring the extent to which a protein-encoding gene is functionally described. Inherently a reflection of human perception, the Gene Characterization Index is applied for assessing the characterization status of individual genes, thus serving the advancement of both genome annotation and applied genomics research by rapid and unbiased identification of groups of uncharacterized genes for diverse applications such as directed functional studies and delineation of novel drug targets.Methodology/Principal FindingsThe scoring procedure is based on a global survey of researchers, who assigned characterization scores from 1 (poor) to 10 (extensive) for a sample of genes based on major online resources. By evaluating the survey as training data, we developed a bioinformatics procedure to assign gene characterization scores to all genes in the human genome. We analyzed snapshots of functional genome annotation over a period of 6 years to assess temporal changes reflected by the increase of the average Gene Characterization Index. Applying the Gene Characterization Index to genes within pharmaceutically relevant classes, we confirmed known drug targets as high-scoring genes and revealed potentially interesting novel targets with low characterization indexes. Removing known drug targets and genes linked to sequence-related patent filings from the entirety of indexed genes, we identified sets of low-scoring genes particularly suited for further experimental investigation.Conclusions/SignificanceThe Gene Characterization Index is intended to serve as a tool to the scientific community and granting agencies for focusing resources and efforts on unexplored areas of the genome. The Gene Characterization Index is available from http://cisreg.ca/gci/.

Systematic Identification and Characterization of Novel Human Skin-Associated Genes Encoding Membrane and Secreted Proteins

“…By including 104 tissue and cell types (listed in Table S2), our analysis excluded broadly expressed “housekeeping” genes and focused instead on genes that participate in functions and pathways restricted to skin. The use of genome-wide arrays provided data for genes not included in many prior studies, including ∼20% human genes that encode proteins with unknown function [9]. Importantly, a subset of SAGs identified in this study represents uncharacterized genes, as described below.…”

GIFtS: annotation landscape analysis with GeneCards

“…GAQ uses only one information source, and in contrast to GIFtS, focuses on assessing the annotation for an entire set of gene entries. The third tool, the Gene Characterization Index (GCI) [ 3 ] assesses the characterization status of individual human genes using annotations from six information sources, combined with the count of articles per each gene based on data from SwissProt [ 27 ] and NCBI Entrez Gene [ 14 ]. It is based on an original concept, whereby the perception of human researchers about the annotation status of a subset of genes is used as training data for developing a procedure to assign scores to all human genes.…”

“…Therefore, it is important to develop bioinformatics tools such as GIFtS which are applicable to all types of genes. However, in a world of fleeting gene annotations, it appears that GCI and GIFtS are complementary to each other: while GCI contains about 33,000 entries, all defined as protein coding [ 3 ], GIFtS (via GeneCards) addresses only ~22,000 genes defined to be protein coding (based on uniprot, refseq, and/or ensembl evidence), along with another ~10,000 non-protein-coding genes (categorized as pseudogenes, RNA genes, or disorder loci), and another ~10,000 which are still uncategorized). Future scrutiny could help resolve this dichotomy.…”

“…Thus, a quantitative tool for assessing annotation depth is important for directing ongoing research and for analyzing the emerging results. Efforts in this field have included the Genome Annotation Scores (GAS) algorithm [ 1 ], which demonstrates a quantitative methodology of assigning annotations scores at the whole genome level, the GO Annotation Quality (GAQ) score, which gives a quantitative measure of GO annotations [ 2 ], and the Gene Characterization Index (GCI), which scores the extent to which a gene's functionality is described, based largely on the quantification of human perception, and applied only to protein-encoding genes [ 3 ]. We now introduce the GeneCards Inferred Functionality Scores (GIFtS) tool [ 4 ], which utilizes the wealth of gene annotation within GeneCards [ 5 ] to quantify the degree of functional knowledge about >50,000 GeneCards entries.…”

Medical informatics: transition from data acquisition to data analysis by means of bioinformatics tools and resources

“…Transcribed mRNA and expressed protein from the new gene can also be specified and the cellular location of the protein would be predictable. Gene characterisation index has recently been developed for scoring the extent to which a gene is described (Kemmer et al, 2008). Using this bioinformatics tool, research scientists are able to compare levels of understanding of multiple genes.…”