The increasing availability of whole human genomes due to the improvements in high-throughput sequencing technologies makes the interpretation and annotation of data at a whole-genome scale more and more feasible. However, these tasks critically depend on the availability of knowledge already stored in databases or published in the scientific literature. This scenario requires new reliable and integrative information extraction systems to be available to the biomedical community. In this work we present a hybrid approach for mining the wealth of knowledge stored in the scientific literature. This approach is based on the use of efficient text-mining tools in combination with highly accurate collaborative human curation. BioNotate-2.0 is an open source, modular friendly tool which implements a collaborative semi-automatic annotation platform in which human and automated annotations are efficiently combined. BioNotate-2.0 also builds upon the Semantic Web, facilitating the dissemination of annotated facts into other resources and pipelines. BioNotate-2.0 allows any interested user to run his own annotation efforts or to contribute to an existing annotation project. To access and contribute to the BioNotate-2.0 annotation platform, please check: http://genome2.ugr.es/bionotate2/. BioNotate source code is available at: http://sourceforge.net/projects/bionotate/.

Agglomerating results from studies of individual biological components has shown the potential to produce biomedical discovery and the promise of therapeutic development. Such knowledge integration could be tremendously facilitated by automated text mining for relation extraction in the biomedical literature. Relation extraction systems cannot be developed without substantial datasets annotated with ground truth for benchmarking and training. The creation of such datasets is hampered by the absence of a resource for launching a distributed annotation effort, as well as by the lack of a standardized annotation schema. We have developed an annotation schema and an annotation tool which can be widely adopted so that the resulting annotated corpora from a multitude of disease studies could be assembled into a unified benchmark dataset. The contribution of this paper is threefold. First, we provide an overview of available benchmark corpora and derive a simple annotation schema for specific binary relation extraction problems such as protein-protein and gene-disease relation extraction. Second, we present BioNotate: an open source annotation resource for the distributed creation of a large corpus. Third, we present and make available the results of a pilot annotation effort of the autism disease network.

This work is motivated by the necessity to automate the discovery of structure in vast and ever-growing collection of relational data commonly represented as graphs, for example genomic networks. A novel algorithm, dubbed Graphitour, for structure induction by lossless graph compression is presented and illustrated by a clear and broadly known case of nested structure in a DNA molecule. This work extends to graphs some well established approaches to grammatical inference previously applied only to strings. The bottom-up graph compression problem is related to the maximum cardinality (non-bipartite) maximum cardinality matching problem. The algorithm accepts a variety of graph types including directed graphs and graphs with labeled nodes and arcs. The resulting structure could be used for representation and classification of graphs

OBJECTIVE: This paper describes a successful approach to de-identification that was developed to participate in a recent AMIA-sponsored challenge evaluation. METHOD: Our approach focused on rapid adaptation of existing toolkits for named entity recognition using two existing toolkits, Carafe and LingPipe. RESULTS: The "out of the box" Carafe system achieved a very good score (phrase F-measure of 0.9664) with only four hours of work to adapt it to the de-identification task. With further tuning, we were able to reduce the token-level error term by over 36% through task-specific feature engineering and the introduction of a lexicon, achieving a phrase F-measure of 0.9736. CONCLUSIONS: We were able to achieve good performance on the de-identification task by the rapid retargeting of existing toolkits. For the Carafe system, we developed a method for tuning the balance of recall vs. precision, as well as a confidence score that correlated well with the measured F-score.

SUMMARY: We have created a tool for ortholog and phylogenetic profile retrieval called Roundup. Roundup is backed by a massive repository of orthologs and associated evolutionary distances that was built using the reciprocal smallest distance algorithm, an approach that has been shown to improve upon alternative approaches of ortholog detection, such as reciprocal blast. Presently, the Roundup repository contains all possible pair-wise comparisons for over 250 genomes, including 32 Eukaryotes, more than doubling the coverage of any similar resource. The orthologs are accessible through an intuitive web interface that allows searches by genome or gene identifier, presenting results as phylogenetic profiles together with gene and molecular function annotations. Results may be downloaded as phylogenetic matrices for subsequent analysis, including the construction of whole-genome phylogenies based on gene-content data. AVAILABILITY: http://rodeo.med.harvard.edu/tools/roundup.

MOTIVATION: Compositionally homogeneous segments of genomic DNA often correspond to meaningful biological units. Simple sliding window analysis is usually insufficient for compositional segmentation of natural sequences. Hidden Markov models (HMM) with a small number of states are a natural language for description of compositional properties of chromosome-size DNA sequences. RESULTS: The algorithms were applied to yeast Saccharomyces cerevisiae chromosomes (YC) I, III, IV, VI and IX. The optimal number of HMM states is found to be four. The optimal four-state HMMs for all chromosomes are very similar, as well as the reconstructed segmentations. In most cases the models with k + 1 states are obtained by 'splitting' one of the states in the model with k states, and the corresponding increase of the level of detail in segmentation. The high AT states usually correspond to intergenic regions. We also explore the model's likelihood landscape and analyze the dynamics of the optimization process, thus addressing the problem of reliability of the obtained optima and efficiency of the algorithms.