Bioinformatics: Projects

Development of a pulsed SILAC based masspectromic approach to unravel proteomes of organisms with unknown genomes

Beside typical model organisms like mouse, rat and drosophila numerous organisms with unique characteristics exist that make them interesting for specific research topics. Proteomes of these niche model organisms are most often poorly characterized despite progress in the characterization of their genomes. This problem is exacerbated by the presence of large numbers of EST clones that lack homologues in other species making it difficult to identify new proteins irrespective of whether such molecules are involved in species-specific biological processes or not. One of these niche organisms is the red spotted newt Notophthalmus viridescens capable of wide range regenerative capacities including complete appendage-, lens- and heart regeneration without scarring. To date, less than 11.000 ESTs and 100 protein sequences have been identifed [Borchardt et al. 2010]. We have used a pulsed SILAC-based mass spectrometry method to identify expressed proteins. This method is based on the detection of paired peptides after 13C6–lysine incorporation into proteins in vivo [Looso et al. 2010] and increases peptide identification rates of cross species database search by 30% to 50% (depends on sequence database). Our method was applied to identify peptide pairs in foreign organism databases and a newt EST database. Combined protein identification lists represent detected peptide pairs for more than 4000 non redundant proteins in the regenerating heart. By this approach we massively increased the confidence and number of protein identifications in newt EST and cross-species database searches during heart regeneration. Furthermore, identified peptide pairs from NCBI ambystoma, NCBI xenopus and newt EST libraries indicate a group of proteins exclusive to amphibian species. These exclusive proteins share the characteristic to be derived from ESTs, do not have any significant sequence similarity to any known, non amphibian species, and code for a significant scoring peptide. Since members of this protein group, as well as a number of annotated proteins, show significant differences in their quantitative peptide appearance and also by RT-PCR, we can conclude that such proteins can play a crucial role during heart regeneration. Therefore we reason that pulsed in vivo SILAC represents a versatile tool to identify known proteins from foreign organism databases and also new proteins in species for which only limited sequence information existed heretofore.

Full automatic annotation routine for high throughput sequencing approaches

Annotation of sequence data, derived from high throughput sequencing approaches, is usually done by homology searches. Since we host several sequence databases, dilated by sequencing approaches regularly, the annotation workflow is a frequently used workstep. Hence we established a full automatic annotation routine, based on single sequence reads. Sequences get BLASTed live at NCBI by multiple algorithms and databases, generating parsed annotation tables for database import.

Development of a Gene Ontology analysis tool, visualizing dependencies within the experimental setting

Several tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless, identification of highly specific GO-terms in complex data sets is cumbersome and the display of GO term assignments and GO enrichment analysis by simple tables or pie charts is not optimal. Valuable information such as the hierarchical position of a single GO term within the GO tree (topological ordering), or enrichment within a complex set of biological experiments is not displayed. We developed a novel method, which we name PCA2GO, that allows GO analysis in complex multidimensional experimental settings. We employed principal component analysis (PCA) and developed a new enrichment score, which takes the relative enrichment of certain GO terms and their specificity (hierarchical position) within the GO graph into account. Our score enables us to identify more specific GO-terms (i.e. those positioned further down the GO-graph than other common tools used for this purpose). Importantly, PCA2GO does not depend on specialized experimental settings and allows visualization and detection of multidimensional dependencies both within the acyclic graph (GO tree) and the experimental settings. PCA2GO analysis of a fractionated cardiomyocyte protein dataset, which was identified by enhanced liquid chromatography-mass spectrometry (GeLC-MS) enabled us to detect distinct groups of proteins, which accurately reflect properties of biochemical cell fractions [Bruckskotten et al. 2010].

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