MOTIVATION: The exponential growth of scientific literature has resulted in a massive amount of unstructured natural language data that cannot be directly handled by means of bioinformatics tools. Such tools generally require structured data, often generated through a cumbersome process of manual literature curation. Herein, we present MyMiner, a free and user-friendly text annotation tool aimed to assist in carrying out the main biocuration tasks and to provide labelled data for the development of text mining systems. MyMiner allows easy classification and labelling of textual data according to user-specified classes as well as predefined biological entities. The usefulness and efficiency of this application have been tested for a range of real-life annotation scenarios of various research topics. AVAILABILITY: http://myminer.armi.monash.edu.au.

MOTIVATION: With rapidly expanding protein structure databases, efficiently retrieving structures similar to a given protein is an important problem. It involves two major issues: (i) effective protein structure representation that captures inherent relationship between fragments and facilitates efficient comparison between the structures and (ii) effective framework to address different retrieval requirements. Recently, researchers proposed vector space model of proteins using bag of fragments representation (FragBag), which corresponds to the basic information retrieval model. RESULTS: In this article, we propose an improved representation of protein structures using latent dirichlet allocation topic model. Another important requirement is to retrieve proteins, whether they are either close or remote homologs. In order to meet diverse objectives, we propose multi-viewpoint based framework that combines multiple representations and retrieval techniques. We compare the proposed representation and retrieval framework on the benchmark dataset developed by Kolodny and co-workers. The results indicate that the proposed techniques outperform state-of-the-art methods. AVAILABILITY: http://www.cse.iitm.ac.in/~ashishvt/research/protein-lda/. CONTACT: [email protected].

BACKGROUND: Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them. RESULTS: A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%. CONCLUSIONS: The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.

BACKGROUND: The structure conservation in various α-helix subclasses reveals the sequence and context dependent factors causing distortions in the α-helix. The sequence-structure relationship in these subclasses can be used to predict structural variations in α-helix purely based on its sequence. We train support vector machine(SVM) with dot product kernel function to discriminate between regular α-helix and non-regular α-helices purely based on the sequences, which are represented with various overall and position specific propensities of amino acids. RESULTS: We characterize the structural distortions in five α-helix subclasses. The sequence structure correlation in the subclasses reveals that the increased propensity of proline, histidine, serine, aspartic acid and aromatic amino acids are responsible for the distortions in regular α-helix. The N-terminus of regular α-helix prefers neutral and acidic polar amino acids, while the C-terminus prefers basic polar amino acid. Proline is preferred in the first turn of regular α-helix, while it is preferred to produce kinked and curved subclasses. The SVM discriminates between regular α-helix and the rest with precision of 80.97% and recall of 88.05%. CONCLUSIONS: The correlation between structural variation in helices and their sequences is manifested by the performance of SVM based on sequence features. The results presented here are useful for computational design of helices. The results are also useful for prediction of structural perturbations in helix sequence purely based on its sequence.

Substantial text mining efforts are being devoted to detect protein mentions and protein-protein interaction (PPI) relations from scientific articles [1,2]. In this context, the BioCreative challenge showed that the correct identification of the individual interactor proteins is still a challenging task, especially when using full text articles [2]. A systematic analysis of particularities of protein mentions in the context of interaction descriptions was nonetheless missing. Experimental biologists often use specific fusion proteins or protein-tags such as -GST, -His, -Myc, FLAG-, antibodies or fluorescent protein (GFP, YFP, CFP and RFP) tags to detect and visualize interactions. These tags are often mentioned as affixes of the target proteins in the literature. The importance of affixes in biomedical text mining had been addressed in case of affixal negation expressions [3], to consider general posttranslational modifications of proteins [4] and can be observed in trigger verbs used for interaction extraction [5]. We carried out a detailed study on the presence of common affixes belonging to interactor protein mentions in full text sentences considered by database curators as evidential support for experimentally characterized physical protein interactions. Furthermore, we tried to determine whether specific affixes might be useful to detect PPI relevant articles and to correlate affix mentions with particular interaction detection methods. Based on examination of over 3,000 of the previously referred interaction evidence passages we have compiled a collection of 277 interaction relevant affixes (89 suffixes, 176 prefixes and 12 that could be both), which were structured into 36 affix tag classes (26 super-affix and 10 combined or sub-affix classes). Figure 1A shows the frequency of mention of each of the affix tag classes. In the resulting PPI affix dictionary (PPIAD), each affix tag class has been manually linked to experimental qualifiers represented by associated PSI-MI ontology [5] concepts by considering their concept definitions. Additionally, statistical associations of affix tag classes to PSI-MI interaction detection method concepts have been derived through curator-based annotations of the evidence passages. To overcome the limited scope and lexical coverage of terms contained in the PSI-MI ontology we build the BioMethod Lexicon, a collection of experimental method terms important for protein interaction and gene regulation relations, and characterized method term co-mentions with affix tag classes. Within a total set of 6,300 interaction evidence sentences, 1,946 (31 %) mentioned at least one interaction relevant affix, which shows that it is a relatively common feature of interaction descriptions. Using statistical analysis of associations between affix classes and interaction detection method annotations (Chi-square test) we discovered that some of the affix classes showed strong associations to interaction methods, such as between: MI:0096 AF_21 (MI: pull down and PPIAD: gst_pull_down_tag), MI:0676 AF_6 (tandem affinity purification and Tandem_Affinity_Purification_tag), MI:0018 AF_10 (two hybrid and Gal4_tag), MI:0006 AF_4 (anti bait coimmunoprecipitation and Antibody_tag), MI:0055 AF_15 (fluorescent * Correspondence: [email protected] Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Madrid, Spain Full list of author information is available at the end of the article Krallinger et al. BMC Bioinformatics 2010, 11(Suppl 5):O1 http://www.biomedcentral.com/1471-2105/11/S5/O1

MOTIVATION: Characterization of the restricted nature of the protein local conformational space has remained a challenge, thereby necessitating a computationally expensive conformational search in protein modeling. Moreover, owing to the lack of unilateral structural descriptors, conventional data mining techniques, such as clustering and classification, have not been applied in protein structure analysis. RESULTS: We first map the local conformations in a fixed dimensional space by using a carefully selected suite of geometric invariants (GIs) and then reduce the number of dimensions via principal component analysis (PCA). Distribution of the conformations in the space spanned by the first four PCs is visualized as a set of conditional bivariate probability distribution plots, where the peaks correspond to the preferred conformations. The locations of the different canonical structures in the PC-space have been interpreted in the context of the weights of the GIs to the first four PCs. Clustering of the available conformations reveals that the number of preferred local conformations is several orders of magnitude smaller than that suggested previously. SUPPLEMENTARY INFORMATION: www.it.iitb.ac.in/~ashish/bioinfo2005/.