Mutation Mining—A Prospector's Tale

Abstract

Protein structure visualization tools render images that allow the user to explore structural features of a protein.Context specific information relating to a particular protein or protein family is, however, not easily integrated and must be uploaded from databases or provided through manual curation of input files. Protein Engineers spend considerable time iteratively reviewing both literature and protein structure visualizations manually annotated with mutated residues.Meanwhile, text mining tools are increasingly used to extract specific units of raw text from scientific literature and have demonstrated the potential to support the activities of Protein Engineers.The transfer of mutation specific raw-text annotations to protein structures requires integrated data processing pipelines that can co-ordinate information retrieval, information extraction, protein sequence retrieval, sequence alignment and mutant residue mapping. We describe the Mutation Miner pipeline designed for this purpose and present case study evaluations of the key steps in the process. Starting with literature about mutations made to protein families; haloalkane dehalogenase, bi-phenyl dioxygenase, and xylanase we enumerate relevant documents available for text Funding project: Ontologies, the semantic web and intelligent systems for genomics Génome Québec, 630, boul. René-Lévesque Ouest, bureau 2660, Montréal (Québec) H3B 1S6, mining analysis, the available electronic formats, and the number of mutations made to a given protein family. We review the efficiency of NLP driven protein sequence retrieval from databases and report on the effectiveness of Mutation Miner in mapping annotations to protein structure visualizations. We highlight the feasibility and practicability of the approach.

Mutationmapper: A Tool to Aid the Mapping of Protein Mutation Data

“…Once abstracts are retrieved and converted to raw text, the next stage is to extract possible mutation data. For that purpose we used the freely available MutationFinder program [7]. It splits text at the sentence level and applies sets of regular expressions to identify and extract mutations.…”

“…The alternatives to automatic retrieval are manually curated systems such as PMD [6], but this requires huge effort that is probably not sustainable in the longer term. Several algorithms [7]–[9] have been developed to extract point mutations from the literature but the challenge is to relate the mutation to the associated protein. To that end we have developed an application called MutationMapper that uses an integrated pipeline to extract point mutations from published text and map them on to the associated protein.…”

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

“…So, while the application of MuteXt to specific superfamilies was shown to be successful, such results may not be accurate for variants in other genes and thus will require additional manual curation to eliminate false positives[93]. Additional tools have been created for the task of extracting gene and variant mentions in text (MutationFinder (variant only)[94], Mutation GraB[95], Mutator[96], Osiris[97], Mutation Miner[98], CoagMDB[99]). However, additional methods are still needed to find phenotypic associations for variants in text.…”

CoagMDB: a database analysis of missense mutations within four conserved domains in five vitamin K-dependent coagulation serine proteases using a text-mining tool

“…A 2004 study developed regular expressions to extract mutations from MEDLINE abstracts [Rebholz-Schuhmann et al, 2004]. Two subsequent studies used pipelines to extract mutations automatically from full-length publications [Baker and Witte, 2006;Lee et al, 2007]. Both of these pipelines focused on extracting mutagenesis data from fulllength publications and were applied to specific protein families such as the protein tyrosine kinases.…”