Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations.

Background: Here we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools.

Results: In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp.

CD8 T-cell reactivity to islet antigens is unique to type 1 while CD4 T-cell reactivity exists in both type 1 and type 2 diabetes.

Previous cross-sectional analyses demonstrated that CD8(+) and CD4(+) T-cell reactivity to islet-specific antigens was more prevalent in T1D subjects than in healthy donors (HD). Here, we examined T1D-associated epitope-specific CD4(+) T-cell cytokine production and autoreactive CD8(+) T-cell frequency on a monthly basis for one year in 10 HD, 33 subjects with T1D, and 15 subjects with T2D. Autoreactive CD4(+) T-cells from both T1D and T2D subjects produced more IFN-γ when stimulated than cells from HD.

Learning virulent proteins from integrated query networks.

Background: Methods of weakening and attenuating pathogens' abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categories.

Results: A lightweight method for data integration is used, which links information regarding a protein via a path-based query graph.

Computational analysis of pathogen-borne metallo β-lactamases reveals discriminating structural features between B1 types.

Background: Genes conferring antibiotic resistance to groups of bacterial pathogens are cause for considerable concern, as many once-reliable antibiotics continue to see a reduction in efficacy. The recent discovery of the metallo β-lactamase blaNDM-1 gene, which appears to grant antibiotic resistance to a variety of Enterobacteriaceae via a mobile plasmid, is one example of this distressing trend. The following work describes a computational analysis of pathogen-borne MBLs that focuses on the structural aspects of characterized proteins.

A cascade of classifiers for extracting medication information from discharge summaries.

Background: Extracting medication information from clinical records has many potential applications, and recently published research, systems, and competitions reflect an interest therein. Much of the early extraction work involved rules and lexicons, but more recently machine learning has been applied to the task.

Methods: We present a hybrid system consisting of two parts.

Community annotation experiment for ground truth generation for the i2b2 medication challenge.

Objective: Within the context of the Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records, the authors (also referred to as 'the i2b2 medication challenge team' or 'the i2b2 team' for short) organized a community annotation experiment.

Design: For this experiment, the authors released annotation guidelines and a small set of annotated discharge summaries. They asked the participants of the Third i2b2 Workshop to annotate 10 discharge summaries per person; each discharge summary was annotated by two annotators from two different teams, and a third annotator from a third team resolved disagreements.

Extracting medication information from clinical text.

The Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records focused on the identification of medications, their dosages, modes (routes) of administration, frequencies, durations, and reasons for administration in discharge summaries. This challenge is referred to as the medication challenge. For the medication challenge, i2b2 released detailed annotation guidelines along with a set of annotated discharge summaries.

Supporting retrieval of diverse biomedical data using evidence-aware queries.

Though there have been many advances in providing access to linked and integrated biomedical data across repositories, developing methods which allow users to specify ambiguous and exploratory queries over disparate sources remains a challenge to extracting well-curated or diversely-supported biological information. In the following work, we discuss the concepts of data coverage and evidence in the context of integrated sources. We address diverse information retrieval via a simple framework for representing coverage and evidence that operates in parallel with an arbitrary schema, and a language upon which queries on the schema and framework may be executed.

Evaluation of probabilistic and logical inference for a SNP annotation system.

Genome wide association studies (GWAS) are an important approach to understanding the genetic mechanisms behind human diseases. Single nucleotide polymorphisms (SNPs) are the predominant markers used in genome wide association studies, and the ability to predict which SNPs are likely to be functional is important for both a priori and a posteriori analyses of GWA studies. This article describes the design, implementation and evaluation of a family of systems for the purpose of identifying SNPs that may cause a change in phenotypic outcomes.

Learning pathogenic proteins across fractured and heterogeneous data.

In the following work, we test a generalized approach to integrating, transforming and learning data from disparate data sources for the classification of bacterial proteins involved in pathogenesis. We rely on the implicit inter-linkages between biological databases to draw relevant records, and leverage statistical learning methods to infer classification based on abundant, albeit noisy, data. Results suggest that types of public biological information have varying degrees of effectiveness in predictive data mining.

Biomediator data integration and inference for functional annotation of anonymous sequences.

Scientists working on genomics projects are often faced with the difficult task of sifting through large amounts of biological information dispersed across various online data sources that are relevant to their area or organism of research. Gene annotation, the process of identifying the functional role of a possible gene, in particular has become increasingly more time-consuming and laborious to conduct as more genomes are sequenced and the number of candidate genes continues to increase at near-exponential pace; genes are left un-annotated, or worse, incorrectly annotated. Many groups have attempted to address the annotation backlog through automated annotation systems that are geared toward specific organisms, and which may thus not possess the necessary flexibility and scalability to annotate other genomes.

The genome of the kinetoplastid parasite, Leishmania major.

Leishmania species cause a spectrum of human diseases in tropical and subtropical regions of the world. We have sequenced the 36 chromosomes of the 32.8-megabase haploid genome of Leishmania major (Friedlin strain) and predict 911 RNA genes, 39 pseudogenes, and 8272 protein-coding genes, of which 36% can be ascribed a putative function.

The genome sequence of Trypanosoma cruzi, etiologic agent of Chagas disease.

Whole-genome sequencing of the protozoan pathogen Trypanosoma cruzi revealed that the diploid genome contains a predicted 22,570 proteins encoded by genes, of which 12,570 represent allelic pairs. Over 50% of the genome consists of repeated sequences, such as retrotransposons and genes for large families of surface molecules, which include trans-sialidases, mucins, gp63s, and a large novel family (>1300 copies) of mucin-associated surface protein (MASP) genes. Analyses of the T.

Comparative genomics of trypanosomatid parasitic protozoa.

A comparison of gene content and genome architecture of Trypanosoma brucei, Trypanosoma cruzi, and Leishmania major, three related pathogens with different life cycles and disease pathology, revealed a conserved core proteome of about 6200 genes in large syntenic polycistronic gene clusters. Many species-specific genes, especially large surface antigen families, occur at nonsyntenic chromosome-internal and subtelomeric regions. Retroelements, structural RNAs, and gene family expansion are often associated with syntenic discontinuities that-along with gene divergence, acquisition and loss, and rearrangement within the syntenic regions-have shaped the genomes of each parasite.