PubMeth: a cancer methylation database combining text-mining and expert annotation

Epigenetics, and more specifically DNA methylation is a fast evolving research area. In almost every cancer type, each month new publications confirm the differentiated regulation of specific genes due to methylation and mention the discovery of novel methylation markers. Therefore, it would be extremely useful to have an annotated, reviewed, sorted and summarized overview of all available data. PubMeth is a cancer methylation database that includes genes that are reported to be methylated in various cancer types. A query can be based either on genes (to check in which cancer types the genes are reported as being methylated) or on cancer types (which genes are reported to be methylated in the cancer (sub) types of interest). The database is freely accessible at http://www.pubmeth.org

A 3d renal proximal tubule on chip model phenocopies Lowe syndrome and Dent II disease tubulopathy

Lowe syndrome and Dent II disease are X‐linked monogenetic diseases characterised by a renal reabsorption defect in the proximal tubules and caused by mutations in the OCRL gene, which codes for an inositol‐5‐phosphatase. The life expectancy of patients suffering from Lowe syndrome is largely reduced because of the development of chronic kidney disease and related complications. There is a need for physiological human in vitro models for Lowe syndrome/Dent II disease to study the underpinning disease mechanisms and to identify and characterise potential drugs and drug targets. Here, we describe a proximal tubule organ on chip model combining a 3D tubule architecture with fluid flow shear stress that phenocopies hallmarks of Lowe syndrome/Dent II dis-ease. We demonstrate the high suitability of our in vitro model for drug target validation. Further-more, using this model, we demonstrate that proximal tubule cells lacking OCRL expression upregulate markers typical for epithelial–mesenchymal transition (EMT), including the transcription factor SNAI2/Slug, and show increased collagen expression and deposition, which potentially contributes to interstitial fibrosis and disease progression as observed in Lowe syndrome and Dent II disease

Identification of histone modifications in biomedical text for supporting epigenomic research

Corinna K, Klinger R, Hofmann-Apitius M. Identification of Histone Modifications in Biomedical Text for Supporting Epigenomic Research. BMC Bioinformatics. 2009;10(Suppl 1):S28

Discovery of DNA methylation markers in cervical cancer using relaxation ranking

<p>Abstract</p> <p>Background</p> <p>To discover cancer specific DNA methylation markers, large-scale screening methods are widely used. The pharmacological unmasking expression microarray approach is an elegant method to enrich for genes that are silenced and re-expressed during functional reversal of DNA methylation upon treatment with demethylation agents. However, such experiments are performed in <it>in vitro </it>(cancer) cell lines, mostly with poor relevance when extrapolating to primary cancers. To overcome this problem, we incorporated data from primary cancer samples in the experimental design. A strategy to combine and rank data from these different data sources is essential to minimize the experimental work in the validation steps.</p> <p>Aim</p> <p>To apply a new relaxation ranking algorithm to enrich DNA methylation markers in cervical cancer.</p> <p>Results</p> <p>The application of a new sorting methodology allowed us to sort high-throughput microarray data from both cervical cancer cell lines and primary cervical cancer samples. The performance of the sorting was analyzed <it>in silico</it>. Pathway and gene ontology analysis was performed on the top-selection and gives a strong indication that the ranking methodology is able to enrich towards genes that might be methylated. Terms like regulation of progression through cell cycle, positive regulation of programmed cell death as well as organ development and embryonic development are overrepresented. Combined with the highly enriched number of imprinted and X-chromosome located genes, and increased prevalence of known methylation markers selected from cervical (the highest-ranking known gene is <it>CCNA1</it>) as well as from other cancer types, the use of the ranking algorithm seems to be powerful in enriching towards methylated genes.</p> <p>Verification of the DNA methylation state of the 10 highest-ranking genes revealed that 7/9 (78%) gene promoters showed DNA methylation in cervical carcinomas. Of these 7 genes, 3 (<it>SST</it>, <it>HTRA3 </it>and <it>NPTX1</it>) are not methylated in normal cervix tissue.</p> <p>Conclusion</p> <p>The application of this new relaxation ranking methodology allowed us to significantly enrich towards methylation genes in cancer. This enrichment is both shown <it>in silico </it>and by experimental validation, and revealed novel methylation markers as proof-of-concept that might be useful in early cancer detection in cervical scrapings.</p

Linking genes to literature: text mining, information extraction, and retrieval applications for biology

Efficient access to information contained in online scientific literature collections is essential for life science research, playing a crucial role from the initial stage of experiment planning to the final interpretation and communication of the results. The biological literature also constitutes the main information source for manual literature curation used by expert-curated databases. Following the increasing popularity of web-based applications for analyzing biological data, new text-mining and information extraction strategies are being implemented. These systems exploit existing regularities in natural language to extract biologically relevant information from electronic texts automatically. The aim of the BioCreative challenge is to promote the development of such tools and to provide insight into their performance. This review presents a general introduction to the main characteristics and applications of currently available text-mining systems for life sciences in terms of the following: the type of biological information demands being addressed; the level of information granularity of both user queries and results; and the features and methods commonly exploited by these applications. The current trend in biomedical text mining points toward an increasing diversification in terms of application types and techniques, together with integration of domain-specific resources such as ontologies. Additional descriptions of some of the systems discussed here are available on the internet

Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011

We present the preparation, resources, results and analysis of three tasks of the BioNLP Shared Task 2011: the main tasks on Infectious Diseases (ID) and Epigenetics and Post-translational Modifications (EPI), and the supporting task on Entity Relations (REL). The two main tasks represent extensions of the event extraction model introduced in the BioNLP Shared Task 2009 (ST'09) to two new areas of biomedical scientific literature, each motivated by the needs of specific biocuration tasks. The ID task concerns the molecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria. The EPI task is dedicated to the extraction of statements regarding chemical modifications of DNA and proteins, with particular emphasis on changes relating to the epigenetic control of gene expression. By contrast to these two application-oriented main tasks, the REL task seeks to support extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems. Seven groups participated in each of the two main tasks and four groups in the supporting task. The participating systems indicated advances in the capability of event extraction methods and demonstrated generalization in many aspects: from abstracts to full texts, from previously considered subdomains to new ones, and from the ST'09 extraction targets to other entities and events. The highest performance achieved in the supporting task REL, 58% F-score, is broadly comparable with levels reported for other relation extraction tasks. For the ID task, the highest-performing system achieved 56% F-score, comparable to the state-of-the-art performance at the established ST'09 task. In the EPI task, the best result was 53% F-score for the full set of extraction targets and 69% F-score for a reduced set of core extraction targets, approaching a level of performance sufficient for user-facing applications. In this study, we extend on previously reported results and perform further analyses of the outputs of the participating systems. We place specific emphasis on aspects of system performance relating to real-world applicability, considering alternate evaluation metrics and performing additional manual analysis of system outputs. We further demonstrate that the strengths of extraction systems can be combined to improve on the performance achieved by any system in isolation. The manually annotated corpora, supporting resources, and evaluation tools for all tasks are available from http://www.bionlp-st.org and the tasks continue as open challenges for all interested parties

Critical evaluation of the use of bioinformatics as a theoretical tool to find high-potential sources of ACE inhibitory peptides

A bioinformatics analysis to screen for high-potential sources of angiotensin converting enzyme (ACE) inhibitory peptides was conducted in the area of insect muscle proteins. Vertebrate muscle proteins are reported as good sources of ACE inhibitory peptides, while the research on invertebrate muscle proteins is limited. A phylogenetic tree constructed with actin sequences of both vertebrate and invertebrate species indicated a high homology. Furthermore, a quantitative in silico ACE inhibition analysis suggested that actin proteins of invertebrates have potentials as new sources of ACE inhibitory peptides. On one insect, Bombyx mori, a more detailed in silico analysis was done followed by a small experimental study. The in silico analysis indicated B. mori as a high-potential source of ACE inhibitory peptides and this was supported by the ACE inhibitory activity of the partially purified actin preparation. In conclusion, in food science, in silico analysis can be used as fast initial screening tool to look for high-potential sources of ACE inhibitory peptides and other peptidic bioactivities