The thirteenth NETTAB workshop, NETTAB 2013, was devoted to semantic, social, and mobile applications for bioinformatics and biomedical laboratories. Topics included issues, methods, algorithms, and technologies for the design and development of tools and platforms able to provide semantic, social, and mobile applications supporting bioinformatics and the activities carried out in a biomedical laboratory. About 30 scientific contributions were presentedat NETTAB 2013, including keynote and tutorial talks, oral communications, and posters. Best contributions presented at the workshop were later submitted to a special Call for this Supplement. Here, we provide an overview of the workshop and introduce manuscripts that have been accepted for publication in this Supplement.

Network Tools and Applications in Biology (NETTAB) [ 1 ] is a series of workshops focused on the most promising and innovative Information and Communication Technologies (ICT) tools and to their usefulness in Bioinformatics. These workshops aim at introducing participants to innovative network standards and technologies that are being applied to the biology field. To this end, each year a special emphasis is given to a focus theme. Workshops also include special sessions devoted both to the general theme of the series of workshops, i.e. “Network Tools and Applications in Biology”, and to further topics selected by local organizers.

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.

MOTIVATION: DNA repeats are a common feature of most genomic sequences. Their de novo identification is still difficult despite being a crucial step in genomic analysis and oligonucleotides design. Several efficient algorithms based on word counting are available, but too short words decrease specificity while long words decrease sensitivity, particularly in degenerated repeats. RESULTS: The Repeat Analysis Program (RAP) is based on a new word-counting algorithm optimized for high resolution repeat identification using gapped words. Many different overlapping gapped words can be counted at the same genomic position, thus producing a better signal than the single ungapped word. This results in better specificity both in terms of low-frequency detection, being able to identify sequences repeated only once, and highly divergent detection, producing a generally high score in most intron sequences. AVAILABILITY: The program is freely available for non-profit organizations, upon request to the authors. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: The program has been tested on the Caenorhabditis elegans genome using word lengths of 12, 14 and 16 bases. The full analysis has been implemented in the UCSC Genome Browser and is accessible at http://genome.cribi.unipd.it.

The initial steps toward a bioinformatics resourceome are clear. First, an overall ontology with the high-level concepts (algorithms, databases, organizations, papers, people, etc.) must be created, with a set of standard attributes and a standard set of relations between these concepts (e.g., people publish papers, papers describe algorithms or databases, organizations house people, etc.). The initial ontology should be compact and built for distributed collaborative extension. Second, a mechanism for people to extend this ontology with subconcepts in order to describe their own resources should be designed. The precise location of a tool within a taxonomy is not critical—the author will place it somewhere based on the location of similar/competing resources or based on a best-informed guess. Others may create links to the resource from other appropriate locations in the taxonomy in order to ensure that competing interpretations of the appropriate conceptual location for the resource are accommodated. Third, the formats for the ontologies and the resource descriptions should be published so enterprising software engineers can create interfaces for surfing, searching, and viewing the resources. The resulting distributed system of resource descriptions would be extensible, robust, and useful to the entire biomedical research community.

Abstract Summary: TRAIT is a knowledgebase integrating information on transcripts with related data from genome, proteins, ortholog genes and diseases. It was initially built as a system to manage an EST-based gene discovery project on human skeletal muscle, which yielded over 4500 independent sequence clusters. Transcripts are annotated using automatic as well as manual procedures, linking known transcripts to public databases and unknown transcripts to tables of predicted features. Data are stored in a MySQL database. Complex queries are automatically built by means of a user-friendly web interface that allows the concurrent selection of many fields such as ontology, expression level, map position and protein domains. The results are parsed by the system and returned in a ranked order, in respect to the number of satisfied criteria. Availability: http://muscle.cribi.unipd.it and http://muscle.cribi.unipd.it/features/querystrait.html Contact: [email protected]; [email protected] * To whom correspondence should be addressed.

MOTIVATION: Protein and DNA are generally represented by sequences of letters. In a number of circumstances simplified alphabets (where one or more letters would be represented by the same symbol) have proved their potential utility in several fields of bioinformatics including searching for patterns occurring at an unexpected rate, studying protein folding and finding consensus sequences in multiple alignments. The main issue addressed in this paper is the possibility of finding a general approach that would allow an exhaustive analysis of all the possible simplified alphabets, using substitution matrices like PAM and BLOSUM as a measure for scoring. RESULTS: The computational approach presented in this paper has led to a computer program called AlphaSimp (Alphabet Simplifier) that can perform an exhaustive analysis of the possible simplified amino acid alphabets, using a branch and bound algorithm together with standard or user-defined substitution matrices. The program returns a ranked list of the highest-scoring simplified alphabets. When the extent of the simplification is limited and the simplified alphabets are maintained above ten symbols the program is able to complete the analysis in minutes or even seconds on a personal computer. However, the performance becomes worse, taking up to several hours, for highly simplified alphabets. AVAILABILITY: AlphaSimp and other accessory programs are available at http://bioinformatics.cribi.unipd.it/alphasimp