Annotation

About: Annotation is a research topic. Over the lifetime, 6719 publications have been published within this topic receiving 203463 citations. The topic is also known as: note & markup.

Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms

Abstract: Predicting protein function has become increasingly demanding in the era of next generation sequencing technology The task to assign a curator-reviewed function to every single sequence is impracticable Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score The algorithm is based on the original idea developed in a previous tool called Argot The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation The server is freely accessible at http://wwwmedcompmedicinaunipdit/Argot2

Comparative analysis of grapevine whole-genome gene predictions, functional annotation, categorization and integration of the predicted gene sequences

Abstract: The first draft assembly and gene prediction of the grapevine genome (8X base coverage) was made available to the scientific community in 2007, and functional annotation was developed on this gene prediction. Since then additional Sanger sequences were added to the 8X sequences pool and a new version of the genomic sequence with superior base coverage (12X) was produced. In order to more efficiently annotate the function of the genes predicted in the new assembly, it is important to build on as much of the previous work as possible, by transferring 8X annotation of the genome to the 12X version. The 8X and 12X assemblies and gene predictions of the grapevine genome were compared to answer the question, “Can we uniquely map 8X predicted genes to 12X predicted genes?” The results show that while the assemblies and gene structure predictions are too different to make a complete mapping between them, most genes (18,725) showed a one-to-one relationship between 8X predicted genes and the last version of 12X predicted genes. In addition, reshuffled genomic sequence structures appeared. These highlight regions of the genome where the gene predictions need to be taken with caution. Based on the new grapevine gene functional annotation and in-depth functional categorization, twenty eight new molecular networks have been created for VitisNet while the existing networks were updated. The outcomes of this study provide a functional annotation of the 12X genes, an update of VitisNet, the system of the grapevine molecular networks, and a new functional categorization of genes. Data are available at the VitisNet website ( http://www.sdstate.edu/ps/research/vitis/pathways.cfm ).

ORegAnno 3.0: a community-driven resource for curated regulatory annotation

Abstract: The Open Regulatory Annotation database (ORegAnno) is a resource for curated regulatory annotation. It contains information about regulatory regions, transcription factor binding sites, RNA binding sites, regulatory variants, haplotypes, and other regulatory elements. ORegAnno differentiates itself from other regulatory resources by facilitating crowd-sourced interpretation and annotation of regulatory observations from the literature and highly curated resources. It contains a comprehensive annotation scheme that aims to describe both the elements and outcomes of regulatory events. Moreover, ORegAnno assembles these disparate data sources and annotations into a single, high quality catalogue of curated regulatory information. The current release is an update of the database previously featured in the NAR Database Issue, and now contains 1 948 307 records, across 18 species, with a combined coverage of 334 215 080 bp. Complete records, annotation, and other associated data are available for browsing and download at http://www.oreganno.org/.

Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT

Abstract: Motivation: The gap between the amount of newly submitted protein data and reliable functional annotation in public databases is growing. Traditional manual annotation by literature curation and sequence analysis tools without the use of automated annotation systems is not able to keep up with the ever increasing quantity of data that is submitted. Automated supplements to manually curated databases such as TrEMBL or GenPept cover raw data but provide only limited annotation. To improve this situation automatic tools are needed that support manual annotation, automatically increase the amount of reliable information and help to detect inconsistencies in manually generated annotations. Results: A standard data mining algorithm was successfully applied to gain knowledge about the Keyword annotation in SWISS-PROT. 11 306 rules were generated, which are provided in a database and can be applied to yet unannotated protein sequences and viewed using a web browser. They rely on the taxonomy of the organism, in which the protein was found and on signature matches of its sequence. The statistical evaluation of the generated rules by cross-validation suggests that by applying them on arbitrary proteins 33% of their keyword annotation can be generated with an error rate of 1.5%. The coverage rate of the keyword annotation can be increased to 60% by tolerating a higher error rate of 5%. Availability: The results of the automatic data mining process can be browsed on http://golgi.ebi.ac.uk:8080/ Spearmint/ Source code is available upon request.