3 research outputs found
The ontology-based answers (OBA) service: A connector for embedded usage of ontologies in applications
No full textThe semantic web depends on the use of ontologies to let electronic systems interpret contextualinformation. Optimally, the handling and access of ontologies should be completely transparent to theuser. As a means to this end, we have developed a service that attempts to bridge the gap betweenexperts in a certain knowledge domain, ontologists and application developers. The ontology-basedanswers (OBA) service introduced here can be embedded into custom applications to grant access to theclasses of ontologies and their relations as most important structural features as well as to informationencoded in the relations between ontology classes. Thus computational biologists can benefit fromontologies without detailed knowledge about the respective ontology. The content of ontologies ismapped to a graph of connected objects which is compatible to the object-oriented programmingstyle in Java. Semantic functions implement knowledge about the complex semantics of anontology beyond the class hierarchy and partOf-relations. By using these OBA functions anapplication can, for example, provide a semantic search function, or (in the examples outlined) mapan anatomical structure to the organs it belongs to. The semantic functions relieve the applicationdeveloper from the necessity of acquiring in-depth knowledge about the semantics and curationguidelines of the used ontologies by implementing the required knowledge. The architecture of theOBA service encapsulates the logic to process ontologies in order to achieve a separation from theapplication logic. A public server with the current plugins is available and can be used with theprovided connector in a custom application in scenarios analogous to the presented use cases. Theserver and the client are freely available if a project requires the use of custom plugins or nonpublicontologies.The OBA service and further documentation is available at: http://www.bioinf.med.unigoettingen.de/projects/ob
Computational identification of key regulators in two different colorectal cancer cell lines
No full textTranscription factors (TFs) are gene regulatory proteins that are essential for an effective regulation of the transcriptional machinery. Today, it is known that their expression plays an important role in several types of cancer. Computational identification of key players in specific cancer cell lines is still an open challenge in cancer research. In this study, we present a systematic approach which combines colorectal cancer (CRC) cell lines, namely 1638N-T1 and CMT-93, and well-established computational methods in order to compare these cell lines on the level of transcriptional regulation as well as on a pathway level, i.e., the cancer cell-intrinsic pathway repertoire. For this purpose, we firstly applied the Trinity platform to detect signature genes, and then applied analyses of the geneXplain platform to these for detection of upstream transcriptional regulators and their regulatory networks. We created a CRC-specific position weight matrix (PWM) library based on the TRANSFAC database (release 2014.1) to minimize the rate of false predictions in the promoter analyses. Using our proposed workflow, we specifically focused on revealing the similarities and differences in transcriptional regulation between the two CRC cell lines, and report a number of well-known, cancer-associated TFs with significantly enriched binding sites in the promoter regions of the signature genes. We show that, although the signature genes of both cell lines show no overlap, they may still be regulated by common transcription factors in CRC. Based on our findings, we suggest that canonical Wnt signaling is activated in 1638N-T1, but inhibited in CMT-93 through cross-talks of Wnt signaling with the VDR signaling pathway and/or LXR-related pathways. Furthermore, our findings provide indication of several master regulators being present such as MLK3 and Mapk1 (ERK2) which might be important in cell proliferation, migration, and invasion of 1638N-T1 and CMT-93, respectively. Taken together, we provide new insights into the invasive potential of these cell lines, which can be used for development of effective cancer therapy
Computational detection of stage-specific transcription factor clusters during heart development
No full textTranscription factors (TFs) regulate gene expression in living organisms. In higher organisms, TFs often interact in non-random combinations with each other to control gene transcription. Understanding the interactions is key to decipher mechanisms underlying tissue development. The aim of this study was to analyze co-occurring transcription factor binding sites (TFBSs) in a time series dataset from a new cell-culture model of human heart muscle development in order to identify common as well as specific co-occurring TFBS pairs in the promoter regions of regulated genes which can be essential to enhance cardiac tissue developmental processes. To this end, we separated available RNAseq dataset into five temporally defined groups: i) mesoderm induction stage; ii) early cardiac specification stage; iii) late cardiac specification stage; iv) early cardiac maturation stage; v) late cardiac maturation stage, where each of these stages is characterized by unique differentially expressed genes (DEGs). To identify TFBS pairs for each stage, we applied the MatrixCatch algorithm, which is a successful method to deduce experimentally described TFBS pairs in the promoters of the DEGs. Although DEGs in each stage are distinct, our results show that the TFBS pair networks predicted by MatrixCatch for all stages are quite similar. Thus, we extend the results of MatrixCatch utilizing a Markov clustering algorithm (MCL) to perform network analysis. Using our extended approach, we are able to separate the TFBS pair networks in several clusters to highlight stage-specific co-occurences between TFBSs. Our approach has revealed clusters that are either common (NFAT or HMGIY clusters) or specific (SMAD or AP-1 clusters) for the individual stages. Several of these clusters are likely to play an important role during the cardiomyogenesis. Further, we have shown that the related TFs of TFBSs in the clusters indicate potential synergistic or antagonistic interactions to switch between different stages. Additionally, our results suggest that cardiomyogenesis follows the hourglass model which was already proven for Arabidopsis and some vertebrates. This investigation helps us to get a better understanding of how each stage of cardiomyogenesis is affected by different combination of TFs
