Habitat-Lite: a GSC case study based on free text terms for environmental metadata.

There is an urgent need to capture metadata on the rapidly growing number of genomic, metagenomic and related sequences, such as 16S ribosomal genes. This need is a major focus within the Genomic Standards Consortium (GSC), and Habitat is a key metadata descriptor in the proposed "Minimum Information about a Genome Sequence" (MIGS) specification. The goal of the work described here is to provide a light-weight, easy-to-use (small) set of terms ("Habitat-Lite") that captures high-level information about habitat while preserving a mapping to the recently launched Environment Ontology (EnvO). Our motivation for building Habitat-Lite is to meet the needs of multiple users, such as annotators curating these data, database providers hosting the data, and biologists and bioinformaticians alike who need to search and employ such data in comparative analyses. Here, we report a case study based on semiautomated identification of terms from GenBank and GOLD. We estimate that the terms in the initial version of Habitat-Lite would provide useful labels for over 60% of the kinds of information found in the GenBank isolation_source field, and around 85% of the terms in the GOLD habitat field. We present a revised version of Habitat-Lite defined within the EnvO Environmental Ontology through a new category, EnvO-Lite-GSC. We invite the community's feedback on its further development to provide a minimum list of terms to capture high-level habitat information and to provide classification bins needed for future studies.

Rapidly retargetable approaches to de-identification in medical records.

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.

Nephele: genotyping via complete composition vectors and MapReduce.

BACKGROUND: Current sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences. RESULTS: Nephele is a suite of tools that uses the complete composition vector algorithm to represent each sequence in the dataset as a vector derived from its constituent k-mers by passing the need for multiple sequence alignment, and affinity propagation clustering to group the sequences into genotypes based on a distance measure over the vectors. Our methods produce results that correlate well with expert-defined clades or genotypes, at a fraction of the computational cost of traditional phylogenetic methods run on traditional hardware. Nephele can use the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. We were able to generate a neighbour-joined tree of over 10,000 16S samples in less than 2 hours. CONCLUSIONS: We conclude that using Nephele can substantially decrease the processing time required for generating genotype trees of tens to hundreds of organisms at genome scale sequence coverage.

An Overview of BioCreative II.5.

We present the results of the BioCreative II.5 evaluation in association with the FEBS Letters experiment, where authors created Structured Digital Abstracts to capture information about protein-protein interactions. The BioCreative II.5 challenge evaluated automatic annotations from 15 text mining teams based on a gold standard created by reconciling annotations from curators, authors, and automated systems. The tasks were to rank articles for curation based on curatable protein-protein interactions; to identify the interacting proteins (using UniProt identifiers) in the positive articles (61); and to identify interacting protein pairs. There were 595 full-text articles in the evaluation test set, including those both with and without curatable protein interactions. The principal evaluation metrics were the interpolated area under the precision/recall curve (AUC iP/R), and (balanced) F-measure. For article classification, the best AUC iP/R was 0.70; for interacting proteins, the best system achieved good macroaveraged recall (0.73) and interpolated area under the precision/recall curve (0.58), after filtering incorrect species and mapping homonymous orthologs; for interacting protein pairs, the top (filtered, mapped) recall was 0.42 and AUC iP/R was 0.29. Ensemble systems improved performance for the interacting protein task.