PRECIS: an automated pipeline for producing concise reports about proteins

Lord, Phillip; Reich, J.R.; Mitchell, Alex L.; Stevens, Robert; Goble, Carole

doi:10.1109/bibe.2001.974412

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…However it causes difficulties when trying to analyse the annotation computationally for the purpose, for example, of summarising many different SWISS-PROT entries comprising a protein family. 1 While the text is accessible by computer applications, it is not easy to interpret computationally.…”

Section: Introductionmentioning

confidence: 99%

Semantic Similarity Measures as Tools for Exploring the Gene Ontology

Lord

Stevens²,

Brass³

et al. 2002

Biocomputing 2003

133

191

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Many bioinformatics resources hold data in the form of sequences. Often this sequence data is associated with a large amount of annotation. In many cases this data has been hard to model, and has been represented as scientific natural language, which is not readily computationally amenable. The development of the Gene Ontology provides us with a more accessible representation of some of this data. However it is not clear how this data can best be searched, or queried. Recently we have adapted information content based measures for use with the Gene Ontology (GO). In this paper we present detailed investigation of the properties of these measures, and examine various properties of GO, which may have implications for its future design.

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Section: Introductionmentioning

confidence: 99%

Semantic Similarity Measures as Tools for Exploring the Gene Ontology

Lord

Stevens²,

Brass³

et al. 2002

Biocomputing 2003

133

191

View full text Add to dashboard Cite

show abstract

“…There are many different types of annotation ( Wooley and Lin, 2005 ): some is highly structured and organized containing, for instance, links through to other databases, ontology terms, or taxonomic relationships; others include unstructured or semi-structured free text. The free text, or textual annotation , is often considered to be the highest value annotation, although, by its very nature it is also the hardest to represent and analyze computationally ( Lord et al , 2001 ). In this paper, we will consider this form of annotation.…”

Section: Introductionmentioning

confidence: 99%

On patterns and re-use in bioinformatics databases

Bell

Lord

2017

Bioinformatics

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MotivationAs the quantity of data being depositing into biological databases continues to increase, it becomes ever more vital to develop methods that enable us to understand this data and ensure that the knowledge is correct. It is widely-held that data percolates between different databases, which causes particular concerns for data correctness; if this percolation occurs, incorrect data in one database may eventually affect many others while, conversely, corrections in one database may fail to percolate to others. In this paper, we test this widely-held belief by directly looking for sentence reuse both within and between databases. Further, we investigate patterns of how sentences are reused over time. Finally, we consider the limitations of this form of analysis and the implications that this may have for bioinformatics database design.ResultsWe show that reuse of annotation is common within many different databases, and that also there is a detectable level of reuse between databases. In addition, we show that there are patterns of reuse that have previously been shown to be associated with percolation errors.Availability and implementationAnalytical software is available on request.

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PRECIS: an automated pipeline for producing concise reports about proteins

Abstract: Abstract

Cited by 2 publications

References 25 publications

Semantic Similarity Measures as Tools for Exploring the Gene Ontology

Semantic Similarity Measures as Tools for Exploring the Gene Ontology

On patterns and re-use in bioinformatics databases

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