We are happy to announce the first major public release of our protein mutation impact analysis system, Open Mutation Miner (OMM), together with a new open access publication: Naderi, N., and R. Witte, "Automated extraction and semantic analysis of mutation impacts from the biomedical literature", BMC Genomics, vol. 13, no. Suppl 4, pp. S10, 06/2012.

OMM is the first comprehensive, fully open source system for extracting and analysing mutation-related information from full-text research papers. Novel features not available in other systems include: the detection of various forms of mutation mentions, in particular mutation series, full mutation impact analysis, including linking impacts with the causative mutation and the affected protein properties, such as molecular functions, kinetic constants, kinetic values, units of measurements, and physical quantities. OMM provides output options in various formats, including populating an OWL ontology, Web service access, structured queries, and interactive use embedded in desktop clients. OMM is robust and scalable: we processed the entire PubMed Open Access Subset (nearly half a million full-text papers) on a standard desktop PC, and larger document sets can be easily processed and indexed on appropriate hardware.

Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually reading through the rich and fast growing repository of biomedical literature is expensive and time-consuming. Text mining methods can help by automatically analysing the literature and extracting mutation-related knowledge into a structured represenation.

Our Open Mutation Miner (OMM) system provides a number of advanced text mining components for mutation mining from full-text research papers, including the detection of various forms of mutation mentions, protein properties, organisms, impact mentions, and the relations between them. OMM provides output options in various formats, including populating an OWL ontology, Web service access, structured queries, and interactive use embedded in desktop clients. It is described and evaluated in detail in our paper, Naderi, N., and R. Witte, "Automated extraction and semantic analysis of mutation impacts from the biomedical literature", BMC Genomics, vol. 13, no. Suppl 4, pp. S10, 06/2012.

Our open source OrganismTagger is a hybrid rule-based/machine-learning system that extracts organism mentions from the biomedical literature, normalizes them to their scientific name, and provides grounding to the NCBI Taxonomy database. Our pipeline provides the flexibility of annotating the species of particular interest to bio-engineers on different corpora, by optionally including detection of common names, acronyms, and strains. The OrganismTagger performance has been evaluated on two manually annotated corpora, OT and Linneaus. On the OT corpus, the OrganismTagger achieves a precision and recall of 95% and 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linneaus-100, the results show a precision and recall of 99% and 97% and grounding with an accuracy of 97.4%. It is described in detail in our publication, Naderi, N., T. Kappler, C. J. O. Baker, and R. Witte, "OrganismTagger: Detection, normalization, and grounding of organism entities in biomedical documents", Bioinformatics, vol. 27, no. 19 Oxford University Press, pp. 2721--2729, August 9, 2011.