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1.  MESSA: MEta-Server for protein Sequence Analysis 
BMC Biology  2012;10:82.
Background
Computational sequence analysis, that is, prediction of local sequence properties, homologs, spatial structure and function from the sequence of a protein, offers an efficient way to obtain needed information about proteins under study. Since reliable prediction is usually based on the consensus of many computer programs, meta-severs have been developed to fit such needs. Most meta-servers focus on one aspect of sequence analysis, while others incorporate more information, such as PredictProtein for local sequence feature predictions, SMART for domain architecture and sequence motif annotation, and GeneSilico for secondary and spatial structure prediction. However, as predictions of local sequence properties, three-dimensional structure and function are usually intertwined, it is beneficial to address them together.
Results
We developed a MEta-Server for protein Sequence Analysis (MESSA) to facilitate comprehensive protein sequence analysis and gather structural and functional predictions for a protein of interest. For an input sequence, the server exploits a number of select tools to predict local sequence properties, such as secondary structure, structurally disordered regions, coiled coils, signal peptides and transmembrane helices; detect homologous proteins and assign the query to a protein family; identify three-dimensional structure templates and generate structure models; and provide predictive statements about the protein's function, including functional annotations, Gene Ontology terms, enzyme classification and possible functionally associated proteins. We tested MESSA on the proteome of Candidatus Liberibacter asiaticus. Manual curation shows that three-dimensional structure models generated by MESSA covered around 75% of all the residues in this proteome and the function of 80% of all proteins could be predicted.
Availability
MESSA is free for non-commercial use at http://prodata.swmed.edu/MESSA/
doi:10.1186/1741-7007-10-82
PMCID: PMC3519821  PMID: 23031578
2.  MochiView: versatile software for genome browsing and DNA motif analysis 
BMC Biology  2010;8:49.
Background
As high-throughput technologies rapidly generate genome-scale data, it becomes increasingly important to visually integrate these data so that specific hypotheses can be formulated and tested.
Results
We present MochiView, a platform-independent Java software that integrates browsing of genomic sequences, features, and data with DNA motif visualization and analysis in a visually-appealing and user-friendly application.
Conclusions
While highly versatile, the software is particularly useful for organizing, exploring, and analyzing large genomic data sets, such as those from deep RNA sequencing, chromatin immunoprecipitation experiments (ChIP-Seq and ChIP-Chip), and transcriptional profiling. MochiView provides an extensive suite of utilities to identify and to explore connections between these data sets and short sequence motifs present in DNA or RNA.
doi:10.1186/1741-7007-8-49
PMCID: PMC2867778  PMID: 20409324
3.  Broadening the horizon – level 2.5 of the HUPO-PSI format for molecular interactions 
BMC Biology  2007;5:44.
Background
Molecular interaction Information is a key resource in modern biomedical research. Publicly available data have previously been provided in a broad array of diverse formats, making access to this very difficult. The publication and wide implementation of the Human Proteome Organisation Proteomics Standards Initiative Molecular Interactions (HUPO PSI-MI) format in 2004 was a major step towards the establishment of a single, unified format by which molecular interactions should be presented, but focused purely on protein-protein interactions.
Results
The HUPO-PSI has further developed the PSI-MI XML schema to enable the description of interactions between a wider range of molecular types, for example nucleic acids, chemical entities, and molecular complexes. Extensive details about each supported molecular interaction can now be captured, including the biological role of each molecule within that interaction, detailed description of interacting domains, and the kinetic parameters of the interaction. The format is supported by data management and analysis tools and has been adopted by major interaction data providers. Additionally, a simpler, tab-delimited format MITAB2.5 has been developed for the benefit of users who require only minimal information in an easy to access configuration.
Conclusion
The PSI-MI XML2.5 and MITAB2.5 formats have been jointly developed by interaction data producers and providers from both the academic and commercial sector, and are already widely implemented and well supported by an active development community. PSI-MI XML2.5 enables the description of highly detailed molecular interaction data and facilitates data exchange between databases and users without loss of information. MITAB2.5 is a simpler format appropriate for fast Perl parsing or loading into Microsoft Excel.
doi:10.1186/1741-7007-5-44
PMCID: PMC2189715  PMID: 17925023

Results 1-3 (3)