Mining GO annotations for improving annotation consistency.
Ontology highlight
ABSTRACT:
SUBMITTER: Faria D
PROVIDER: S-EPMC3405096 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The removal of annotation from biological databases is often perceived as an indicator of erroneous annotation. As a corollary, annotation stability is considered to be a measure of reliability. However, diverse data-driven events can affect the stability of annotations in both primary protein sequence databases and the protein family databases that are built upon the sequence databases and used to help annotate them. Here, we describe some of these events and their consequences for the InterPro database, and demonstrate that annotation removal or reassignment is not always linked to incorrect annotation by the curator. Database URL: http://www.ebi.ac.uk/interpro.
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Project description:A major challenge for protein databases is reconciling information from diverse sources. This is especially difficult when some information consists of secondary, human-interpreted rather than primary data. For example, the Swiss-Prot database contains curated annotations of subcellular location that are based on predictions from protein sequence, statements in scientific articles, and published experimental evidence. The Human Protein Atlas (HPA) consists of millions of high-resolution microscopic images that show protein spatial distribution on a cellular and subcellular level. These images are manually annotated with protein subcellular locations by trained experts. The image annotations in HPA can capture the variation of subcellular location across different cell lines, tissues, or tissue states. Systematic investigation of the consistency between HPA and Swiss-Prot assignments of subcellular location, which is important for understanding and utilizing protein location data from the two databases, has not been described previously. In this paper, we quantitatively evaluate the consistency of subcellular location annotations between HPA and Swiss-Prot at multiple levels, as well as variation of protein locations across cell lines and tissues. Our results show that annotations of these two databases differ significantly in many cases, leading to proposed procedures for deriving and integrating the protein subcellular location data. We also find that proteins having highly variable locations are more likely to be biomarkers of diseases, providing support for incorporating analysis of subcellular location in protein biomarker identification and screening.
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Project description:Unified, structured vocabularies and classifications freely provided by the Gene Ontology (GO) Consortium are widely accepted in most of the large scale gene annotation projects. Consequently, many tools have been created for use with the GO ontologies. WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO annotation results. Different from other commercial software for creating chart, WEGO is designed to deal with the directed acyclic graph structure of GO to facilitate histogram creation of GO annotation results. WEGO has been used widely in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the daily tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. WEGO, along with the two other tools, namely External to GO Query and GO Archive Query, are freely available for all users at http://wego.genomics.org.cn. There are two available mirror sites at http://wego2.genomics.org.cn and http://wego.genomics.com.cn. Any suggestions are welcome at wego@genomics.org.cn.
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Project description:BACKGROUND:Annotation of proteins with gene ontology (GO) terms is ongoing work and a complex task. Manual GO annotation is precise and precious, but it is time-consuming. Therefore, instead of curated annotations most of the proteins come with uncurated annotations, which have been generated automatically. Text-mining systems that use literature for automatic annotation have been proposed but they do not satisfy the high quality expectations of curators. RESULTS:In this paper we describe an approach that links uncurated annotations to text extracted from literature. The selection of the text is based on the similarity of the text to the term from the uncurated annotation. Besides substantiating the uncurated annotations, the extracted texts also lead to novel annotations. In addition, the approach uses the GO hierarchy to achieve high precision. Our approach is integrated into GOAnnotator, a tool that assists the curation process for GO annotation of UniProt proteins. CONCLUSION:The GO curators assessed GOAnnotator with a set of 66 distinct UniProt/SwissProt proteins with uncurated annotations. GOAnnotator provided correct evidence text at 93% precision. This high precision results from using the GO hierarchy to only select GO terms similar to GO terms from uncurated annotations in GOA. Our approach is the first one to achieve high precision, which is crucial for the efficient support of GO curators. GOAnnotator was implemented as a web tool that is freely available at http://xldb.di.fc.ul.pt/rebil/tools/goa/.
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Project description:Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation transfer by identifying sequence-similar proteins with known function or through prediction methods using evolutionary information. Here, we propose predicting GO terms through annotation transfer based on proximity of proteins in the SeqVec embedding rather than in sequence space. These embeddings originate from deep learned language models (LMs) for protein sequences (SeqVec) transferring the knowledge gained from predicting the next amino acid in 33 million protein sequences. Replicating the conditions of CAFA3, our method reaches an Fmax of 37 ± 2%, 50 ± 3%, and 57 ± 2% for BPO, MFO, and CCO, respectively. Numerically, this appears close to the top ten CAFA3 methods. When restricting the annotation transfer to proteins with < 20% pairwise sequence identity to the query, performance drops (Fmax BPO 33 ± 2%, MFO 43 ± 3%, CCO 53 ± 2%); this still outperforms naïve sequence-based transfer. Preliminary results from CAFA4 appear to confirm these findings. Overall, this new concept is likely to change the annotation of proteins, in particular for proteins from smaller families or proteins with intrinsically disordered regions.
| S-EPMC7806674 | biostudies-literature
Project description:BackgroundIn systems biology, it is of great interest to identify previously unreported associations between genes. Recently, biomedical literature has been considered as a valuable resource for this purpose. While classical clustering algorithms have popularly been used to investigate associations among genes, they are not tuned for the literature mining data and are also based on strong assumptions, which are often violated in this type of data. For example, these approaches often assume homogeneity and independence among observations. However, these assumptions are often violated due to both redundancies in functional descriptions and biological functions shared among genes. Latent block models can be alternatives in this case but they also often show suboptimal performances, especially when signals are weak. In addition, they do not allow to utilize valuable prior biological knowledge, such as those available in existing databases.ResultsIn order to address these limitations, here we propose PALMER, a constrained latent block model that allows to identify indirect relationships among genes based on the biomedical literature mining data. By automatically associating relevant Gene Ontology terms, PALMER facilitates biological interpretation of novel findings without laborious downstream analyses. PALMER also allows researchers to utilize prior biological knowledge about known gene-pathway relationships to guide identification of gene-gene associations. We evaluated PALMER with simulation studies and applications to studies of pathway-modulating genes relevant to cancer signaling pathways, while utilizing biological pathway annotations available in the KEGG database as prior knowledge.ConclusionsWe showed that PALMER outperforms traditional latent block models and it provides reliable identification of novel gene-gene associations by utilizing prior biological knowledge, especially when signals are weak in the biomedical literature mining dataset. We believe that PALMER and its relevant user-friendly software will be powerful tools that can be used to improve existing pathway annotations and identify novel pathway-modulating genes.
| S-EPMC7532116 | biostudies-literature
Project description:BackgroundThe Gene Ontology (GO) is an ontology representing molecular biology concepts related to genes and their products. Current annotations from the GO Consortium tend to be highly specific, and contemporary genome-scale studies often return a long list of genes of potential interest, such as genes in a cancer tumor that are differentially expressed than those found in normal tissue. It is therefore a challenging task to reveal, at a conceptual level, the major functional themes in which genes are involved. Presently, there is a need for tools capable of revealing such themes through mining and representing semantic information in an objective and quantitative manner.MethodsIn this study, we utilized the hierarchical organization of the GO to derive a more abstract representation of the major biological processes of a list of genes based on their annotations. We cast the task as follows: given a list of genes, identify non-disjoint, functionally coherent subsets, such that the functions of the genes in a subset are summarized by an informative GO term that accurately captures the semantic information of the original annotations.ResultsWe evaluated different metrics for assessing information loss when merging GO terms, and different statistical schemes to assess the functional coherence of a set of genes. We found that the best discriminative power was achieved by using a combination of the information-content-based measure as the information-loss metric, and the graph-based statistics derived from a Steiner tree connecting genes in an augmented GO graph.ConclusionsOur methods provide an objective and quantitative approach to capturing the major directions of gene functions in a context-specific fashion.
| S-EPMC4042834 | biostudies-literature
Project description:Gene Ontology (GO) is a community effort to represent functional features of gene products. GO annotations (GOA) provide functional associations between GO terms and gene products. Due to resources limitation, only a small portion of annotations are manually checked by curators, and the others are electronically inferred. Although quality control techniques have been applied to ensure the quality of annotations, the community consistently report that there are still considerable noisy (or incorrect) annotations. Given the wide application of annotations, however, how to identify noisy annotations is an important but yet seldom studied open problem.We introduce a novel approach called NoGOA to predict noisy annotations. NoGOA applies sparse representation on the gene-term association matrix to reduce the impact of noisy annotations, and takes advantage of sparse representation coefficients to measure the semantic similarity between genes. Secondly, it preliminarily predicts noisy annotations of a gene based on aggregated votes from semantic neighborhood genes of that gene. Next, NoGOA estimates the ratio of noisy annotations for each evidence code based on direct annotations in GOA files archived on different periods, and then weights entries of the association matrix via estimated ratios and propagates weights to ancestors of direct annotations using GO hierarchy. Finally, it integrates evidence-weighted association matrix and aggregated votes to predict noisy annotations. Experiments on archived GOA files of six model species (H. sapiens, A. thaliana, S. cerevisiae, G. gallus, B. Taurus and M. musculus) demonstrate that NoGOA achieves significantly better results than other related methods and removing noisy annotations improves the performance of gene function prediction.The comparative study justifies the effectiveness of integrating evidence codes with sparse representation for predicting noisy GO annotations. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=NoGOA .
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Project description:The integration between BioDAS ProServer and Automated Sequence Annotation Pipeline (ASAP) provides an interface for querying diverse annotation sources, chaining and linking results, and standardizing the output using the Distributed Annotation System (DAS) protocol. This interface allows pipeline plans in ASAP to be integrated into any system using HTTP and also allows the information returned by ASAP to be included in the DAS registry for use in any DAS-aware system. Three example implementations have been developed: the first accesses TRANSFAC information to automatically create gene sets for the Coordinated Gene Activity in Pattern Sets (CoGAPS) algorithm; the second integrates annotations from multiple array platforms and provides unified annotations in an R environment; and the third wraps the UniProt database for integration with the SPICE DAS client.Source code for ASAP 2.7 and the DAS 1.6 interface is available under the GNU public license. Proserver 2.20 is free software available from SourceForge. Scripts for installation and configuration on Linux are provided at our website: http://www.rits.onc.jhmi.edu/dbb/custom/A6/
| S-EPMC3476339 | biostudies-literature
Project description:BackgroundDecoding transcriptional regulatory networks and the genomic cis-regulatory logic implemented in their control nodes is a fundamental challenge in genome biology. High-throughput computational and experimental analyses of regulatory networks and sequences rely heavily on positive control data from prior small-scale experiments, but the vast majority of previously discovered regulatory data remains locked in the biomedical literature.ResultsWe develop text-mining strategies to identify relevant publications and extract sequence information to assist the regulatory annotation process. Using a vector space model to identify Medline abstracts from papers likely to have high cis-regulatory content, we demonstrate that document relevance ranking can assist the curation of transcriptional regulatory networks and estimate that, minimally, 30,000 papers harbor unannotated cis-regulatory data. In addition, we show that DNA sequences can be extracted from primary text with high cis-regulatory content and mapped to genome sequences as a means of identifying the location, organism and target gene information that is critical to the cis-regulatory annotation process.ConclusionOur results demonstrate that text-mining technologies can be successfully integrated with genome annotation systems, thereby increasing the availability of annotated cis-regulatory data needed to catalyze advances in the field of gene regulation.
| S-EPMC2374703 | biostudies-literature