| 1. |
- Gould, Cathryn M, et al.
(författare)
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ELM : the status of the 2010 eukaryotic linear motif resource.
- 2010
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 38
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Tidskriftsartikel (refereegranskat)abstract
- Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a 'Bar Code' format, which also displays known instances from homologous proteins through a novel 'Instance Mapper' protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation.
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| 2. |
- Via, Allegra, et al.
(författare)
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Best practices in bioinformatics training for life scientists
- 2013
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; 14:5, s. 528-537
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Tidskriftsartikel (refereegranskat)abstract
- The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists.
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