Automatic extraction of mutations from Medline and cross-validation with OMIM

Abstract

Mutations help us to understand the molecular origins of diseases. Researchers, therefore, both publish and seek disease-relevant mutations in public databases and in scientific literature, e.g. Medline. The retrieval tends to be time-consuming and incomplete. Automated screening of the literature is more efficient. We developed extraction methods (called MEMA) that scan Medline abstracts for mutations. MEMA identified 24,351 singleton mutations in conjunction with a HUGO gene name out of 16,728 abstracts. From a sample of 100 abstracts we estimated the recall for the identification of mutation-gene pairs to 35% at a precision of 93%. Recall for the mutation detection alone was >67% with a precision rate of >96%. This shows that our system produces reliable data. The subset consisting of protein sequence mutations (PSMs) from MEMA was compared to the entries in OMIM (20,503 entries versus 6699, respectively). We found 1826 PSM-gene pairs to be in common to both datasets (cross-validated). This is 27% of all PSM-gene pairs in OMIM and 91% of those pairs from OMIM which co-occur in at least one Medline abstract. We conclude that Medline covers a large portion of the mutations known to OMIM. Another large portion could be artificially produced mutations from mutagenesis experiments. Access to the database of extracted mutation-gene pairs is available through the web pages of the EBI (refer to http://www.ebi. ac.uk/rebholz/index.html).

An examination of the OMIM database for associating mutation to a consensus reference sequence

“…The capture of the reference sequence data from literature before or after publication might be helpful. Recently, several natural language tools have been developed to identify protein names and mutation terms from Medline abstract or full text articles, such as MutationFinder (Caporaso et al, 2007), MEMA (Rebholz-Schuhmann et al, 2004), mSTRAP (Kanagasabai et al, 2007) and MuteXt (Horn et al, 2004). Verifying the extracted mutation information based on corresponding position within the protein sequence will improve their performance.…”

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].…”

“…The advantage of this technique is that it does not require the protein name to be in the same sentence as the mutation. The 2004 study that extracted mutations within MEDLINE abstracts also deciphered between protein-mutation pairs using syntactical and proximity parameters [Rebholz-Schuhmann et al, 2004]. Within our mutation-finding tool in the present study, the occurrences of the protein words are highlighted, and the most commonly occurring protein is selected.…”

Rapid Pattern Development for Concept Recognition Systems: Application to Point Mutations

“…6,[8][9][10][11][12] MuteXt, 8 MEMA, 9 and Mutation GraB 10 attempt to extract mentions of mutations paired with a specific gene or gene product from input texts. OSIRIS 11 is a web-based information retrieval system for compiling the mutation literature using a concept-driven, mutation-recognition approach.…”

“…Like the earlier mutation recognition systems, [8][9][10] MutationFinder applies a set of regular expressions to identify mutation mentions in input texts. Our currently top-performing collection of regular expressions results in a precision of 98.4% and a recall of 81.9% when extracting mutation mentions from completely blind test data.…”