Prediction of Human Protein Function from Post-translational Modifications and Localization Features

Jensen, Lars Juhl; Gupta, Ramneek; Blom, Nikolaj; Devos, Damien P.; Tamames, Javier; Keşmir, Can; Nielsen, Henrik; Stærfeldt, Hans Henrik; Rapacki, Krzysztof; Workman, Christopher T.; Andersen, Claus A.; Knudsen, Steen; Krogh, Anders; Valencia, Alfonso; Brunak, Søren

doi:10.1016/s0022-2836(02)00379-0

Cited by 290 publications

(198 citation statements)

References 36 publications

Supporting

Mentioning

183

Contrasting

Unclassified

Order By: Relevance

“…Here we extend this definition to include a more fine-grained and precise definition of protein function as an ensemble of protein features that together describe the different functional capabilities of proteins (e.g., ATP binding, substrate specificity, protein activation or phospho-tyrosine binding). This new definition would not only adapt well to current studies of sequencefunction associations 15,16 , but also lead to a better description of the effects of a mutation affecting such residues (Fig. 2a,b).…”

Section: From Genomic Lesions To Functional Network Perturbationsmentioning

confidence: 94%

Navigating cancer network attractors for tumor-specific therapy

et al. 2012

View full text Add to dashboard Cite

Section: From Genomic Lesions To Functional Network Perturbationsmentioning

confidence: 94%

Navigating cancer network attractors for tumor-specific therapy

et al. 2012

View full text Add to dashboard Cite

“…Because the majority of GSGs and SSGs are not functionally annotated, we used the online server ProtFun2.2 [41,42] to predict their possible protein functions, including cellular roles and GO categories (Fig. 5).…”

Section: Presumed Functions Of Ssgs and Gsgsmentioning

confidence: 99%

Genome-wide identification of lineage-specific genes within Caenorhabditis elegans

et al. 2015

View full text Add to dashboard Cite

With the rapid growth of sequencing technology, a number of genomes and transcriptomes of various species have been sequenced, contributing to the study of lineage-specific genes (LSGs). We identified two sets of LSGs using BLAST: one included Caenorhabditis elegans species-specific genes (1423, SSGs), and the other consisted of Caenorhabditis genus-specific genes (4539, GSGs). The subsequent characterization and analysis of the SSGs and GSGs showed that they have significant differences in evolution and that most LSGs were generated by gene duplication and integration of transposable elements (TEs). We then performed temporal expression profiling and protein function prediction and observed that many SSGs and GSGs are expressed and that genes involved with sex determination, specific stress, immune response, and morphogenesis are over-represented, suggesting that these specific genes may be related to the Caenorhabditis nematodes' special ability to survive in severe and extreme environments.

show abstract

“…In mammalian cells N-glycosidic linkage is the predominant carbohydrate attachment [235]. On the computation level, the different PTMs are identified mainly via extensive motif search [237,238]. The PRO-SITE database is a public repository holding a variety of biologically relevant patterns and profiles, and can be used for fast motif identification [239].…”

Section: Protein Structure and Functional Propertiesmentioning

confidence: 99%

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

et al. 2006

View full text Add to dashboard Cite

Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables -and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

show abstract

Prediction of Human Protein Function from Post-translational Modifications and Localization Features

Cited by 290 publications

References 36 publications

Navigating cancer network attractors for tumor-specific therapy

Navigating cancer network attractors for tumor-specific therapy

Genome-wide identification of lineage-specific genes within Caenorhabditis elegans

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

Contact Info

Product

Resources

About