The GOA database: Gene Ontology annotation updates for 2015

Huntley, Rachael P.; Sawford, Tony; Mutowo, Prudence; Shypitsyna, Aleksandra; Bonilla, Carlos A.; Martin, Maria Jesus; O′Donovan, Claire

doi:10.1093/nar/gku1113

Cited by 550 publications

(479 citation statements)

References 24 publications

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“…and they are often perceived as being less useful, and certainly less publishable. As a result, currently less than 1 % of all experimental annotations are negative ones in UniProt-GOA [ 37 ]. This imbalance causes problems with training of machine learning algorithms [ 38 ].…”

Section: Propagation Biasmentioning

confidence: 99%

Gene Ontology: Pitfalls, Biases, and Remedies

Gaudet

Dessimoz

2016

Methods in Molecular Biology

144

103

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The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is suffi ciently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifi ers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices.

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Section: Propagation Biasmentioning

confidence: 99%

Gene Ontology: Pitfalls, Biases, and Remedies

Gaudet

Dessimoz

2016

Methods in Molecular Biology

144

103

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“…Human tissue expression data were obtained from Protein Atlas 26 website API and median values from GTEx 25 downloadable files. Human GO terms are directly downloaded from the QuickGO 24 web pages. Rat expression data and GO terms are from RGD downloadable files.…”

Section: Server and Data Storingmentioning

confidence: 99%

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

Wang¹,

Al‐Ouran²,

Hu³

et al. 2017

The American Journal of Human Genetics

198

146

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One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.

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“…It is thus used by many portals (RefSeq, UniProt, KEGG, PDB, TAIR, etc.). Gene Ontology Annotation [50] is a portal dedicated to the data annotation of various interest organisms by using GO. AmiGO [51] is a portal that provides access to GO website, it contains many cross-references with other information systems.…”

Section: Biological and Bioinformatic Ontologiesmentioning

confidence: 99%