Transfer RNAs (tRNAs) and small nucleolar RNAs (snoRNAs) are two of the largest classes of non-protein-coding RNAs. Conventional gene finders that detect protein-coding genes do not find tRNA and snoRNA genes because they lack the codon structure and statistical signatures of protein-coding genes. Previously, we developed tRNAscan-SE, snoscan and snoGPS for the detection of tRNAs, methylation-guide snoRNAs and pseudouridylation-guide snoRNAs, respectively. tRNAscan-SE is routinely applied to completed genomes, resulting in the identification of thousands of tRNA genes. Snoscan has successfully detected methylation-guide snoRNAs in a variety of eukaryotes and archaea, and snoGPS has identified novel pseudouridylation-guide snoRNAs in yeast and mammals. Although these programs have been quite successful at RNA gene detection, their use has been limited by the need to install and configure the software packages on UNIX workstations. Here, we describe online implementations of these RNA detection tools that make these programs accessible to a wider range of research biologists. The tRNAscan-SE, snoscan and snoGPS servers are available at , and , respectively.

The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort.[Supplemental material is available online at www.genome.org. Bioperl is available as open-source software free of charge and is licensed under the Perl Artistic License (http://www.perl.com/pub/a/language/misc/Artistic.html). It is available for download at http://www.bioperl.org. Support inquiries should be addressed to bioperl-l@bioperl.org.]

BackgroundThere is little systematic operational guidance about how best to develop complex interventions to reduce the gap between practice and evidence. This article is one in a Series of articles documenting the development and use of the Theoretical Domains Framework (TDF) to advance the science of implementation research.MethodsThe intervention was developed considering three main components: theory, evidence, and practical issues. We used a four-step approach, consisting of guiding questions, to direct the choice of the most appropriate components of an implementation intervention: Who needs to do what, differently? Using a theoretical framework, which barriers and enablers need to be addressed? Which intervention components (behaviour change techniques and mode(s) of delivery) could overcome the modifiable barriers and enhance the enablers? And how can behaviour change be measured and understood?ResultsA complex implementation intervention was designed that aimed to improve acute low back pain management in primary care. We used the TDF to identify the barriers and enablers to the uptake of evidence into practice and to guide the choice of intervention components. These components were then combined into a cohesive intervention. The intervention was delivered via two facilitated interactive small group workshops. We also produced a DVD to distribute to all participants in the intervention group. We chose outcome measures in order to assess the mediating mechanisms of behaviour change.ConclusionsWe have illustrated a four-step systematic method for developing an intervention designed to change clinical practice based on a theoretical framework. The method of development provides a systematic framework that could be used by others developing complex implementation interventions. While this framework should be iteratively adjusted and refined to suit other contexts and settings, we believe that the four-step process should be maintained as the primary framework to guide researchers through a comprehensive intervention development process.

Diabetes mellitus is one of the most common endocrine disorders affecting almost 6% of the world's population. The number of diabetic patients will reach 300 million in 2025 (International Diabetes Federation, 2001). More than 97% of these patients will have type II diabetes. The projected increase in the number of diabetic patients will strain the capabilities of healthcare providers the world over. Thus it is of paramount importance to revisit the causes and epidemiology of diabetes mellitus. Diabetes mellitus is caused by both environmental and genetic factors. The environmental factors that may lead to the development of diabetes mellitus include physical inactivity, drugs and toxic agents, obesity, viral infection, and location. While type I diabetes is not a genetically predestined disease, an increased susceptibility can be inherited. Genetic susceptibility plays a crucial role in the etiology and manifestation of type II diabetes, with concordance in monozygotic twins approaching 100%. Genetic factors may have to be modified by environmental factors for diabetes mellitus to become overt. An individual with a susceptible gene may become diabetic if environmental factors modify the expression of these genes. Since there is an increase in the trend at which diabetes prevail, it is evident that environmental factors are playing a more increasing role in the cause of diabetes mellitus. The incidence of type I diabetes ranged from 1.9 to 7.0/100,000/yr in Africa, 0.13 to 10/100,000/yr in Asia, approximately 4.4/100,000/yr in Australasia, 3.4 to 36/100,000/yr in Europe, 2.62 to 20.18/100,000/yr in the Middle East, 7.61 to 25.7/100,000/yr in North America, and 1.27 to 18/100,000/yr in South America. The epidemiology of type II diabetes is equally bleak. The prevalence of type II diabetes ranged from 0.3 to 17.9% in Africa, 1.2 to 14.6% in Asia, 0.7 to 11.6% in Europe, 4.6 to 40% in the Middle East, 6.69 to 28.2% in North America, and 2.01 to 17.4% in South America.

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