Showing papers by "Hans-Peter Lenhof published in 2017"
TL;DR: The miRNA Pathway Dictionary Database (miRPathDB) is introduced, freely accessible at https://mpd.bioinf.uni-sb.de/ and aims to complement available target pathway web-servers by providing researchers easy access to the information which pathways are regulated by a miRNA, which miRNAs target a pathway and how specific these regulations are.
Abstract: In the last decade, miRNAs and their regulatory mechanisms have been intensively studied and many tools for the analysis of miRNAs and their targets have been developed. We previously presented a dictionary on single miRNAs and their putative target pathways. Since then, the number of miRNAs has tripled and the knowledge on miRNAs and targets has grown substantially. This, along with changes in pathway resources such as KEGG, leads to an improved understanding of miRNAs, their target genes and related pathways. Here, we introduce the miRNA Pathway Dictionary Database (miRPathDB), freely accessible at https://mpd.bioinf.uni-sb.de/ With the database we aim to complement available target pathway web-servers by providing researchers easy access to the information which pathways are regulated by a miRNA, which miRNAs target a pathway and how specific these regulations are. The database contains a large number of miRNAs (2595 human miRNAs), different miRNA target sets (14 773 experimentally validated target genes as well as 19 281 predicted targets genes) and a broad selection of functional biochemical categories (KEGG-, WikiPathways-, BioCarta-, SMPDB-, PID-, Reactome pathways, functional categories from gene ontology (GO), protein families from Pfam and chromosomal locations totaling 12 875 categories). In addition to Homo sapiens, also Mus musculus data are stored and can be compared to human target pathways.
100 citations
TL;DR: The main goal of this study is to provide a general overview on the results, to highlight a selection of relevant results on miRNAs, miRNA seeds, target genes and target pathways and to raise awareness for artifacts in respective comparisons.
Abstract: // Tim Kehl 1, * , Christina Backes 2, * , Fabian Kern 2 , Tobias Fehlmann 2 , Nicole Ludwig 3 , Eckart Meese 3 , Hans-Peter Lenhof 1 and Andreas Keller 2, * 1 Center for Bioinformatics, Saarland University, Saarland Informatics Campus, Saarbrucken, Germany 2 Chair for Clinical Bioinformatics, Saarland University, Saarland Informatics Campus, Saarbrucken, Germany 3 Department of Human Genetics, Saarland University, Homburg, Germany * These authors have contributed equally to this work Correspondence to: Andreas Keller, email: andreas.keller@ccb.uni-saarland.de Keywords: non-coding RNA; systems biology; target gene; microRNA Received: July 27, 2017 Accepted: September 21, 2017 Published: November 09, 2017 ABSTRACT miRNAs are typically repressing gene expression by binding to the 3’ UTR, leading to degradation of the mRNA. This process is dominated by the eight-base seed region of the miRNA. Further, miRNAs are known not only to target genes but also to target significant parts of pathways. A logical line of thoughts is: miRNAs with similar (seed) sequence target similar sets of genes and thus similar sets of pathways. By calculating similarity scores for all 3.25 million pairs of 2,550 human miRNAs, we found that this pattern frequently holds, while we also observed exceptions. Respective results were obtained for both, predicted target genes as well as experimentally validated targets. We note that miRNAs target gene set similarity follows a bimodal distribution, pointing at a set of 282 miRNAs that seems to target genes with very high specificity. Further, we discuss miRNAs with different (seed) sequences that nonetheless regulate similar gene sets or pathways. Most intriguingly, we found miRNA pairs that regulate different gene sets but similar pathways such as miR-6886-5p and miR-3529-5p. These are jointly targeting different parts of the MAPK signaling cascade. The main goal of this study is to provide a general overview on the results, to highlight a selection of relevant results on miRNAs, miRNA seeds, target genes and target pathways and to raise awareness for artifacts in respective comparisons. The full set of information that allows to infer detailed results on each miRNA has been included in miRPathDB, the miRNA target pathway database ( https://mpd.bioinf.uni-sb.de ).
89 citations
TL;DR: RegulatorTrail is presented, a web service that provides rich functionality for the identification and prioritization of key transcriptional regulators that have a strong impact on natural and pathogenic processes, and successfully identified regulators that might explain the increased malignancy in metastatic melanoma compared to primary tumors, as well as important regulators in macrophages.
Abstract: Transcriptional regulators such as transcription factors and chromatin modifiers play a central role in most biological processes. Alterations in their activities have been observed in many diseases, e.g. cancer. Hence, it is of utmost importance to evaluate and assess the effects of transcriptional regulators on natural and pathogenic processes. Here, we present RegulatorTrail, a web service that provides rich functionality for the identification and prioritization of key transcriptional regulators that have a strong impact on, e.g. pathological processes. RegulatorTrail offers eight methods that use regulator binding information in combination with transcriptomic or epigenomic data to infer the most influential regulators. Our web service not only provides an intuitive web interface, but also a well-documented RESTful API that allows for a straightforward integration into third-party workflows. The presented case studies highlight the capabilities of our web service and demonstrate its potential for the identification of influential regulators: we successfully identified regulators that might explain the increased malignancy in metastatic melanoma compared to primary tumors, as well as important regulators in macrophages. RegulatorTrail is freely accessible at: https://regulatortrail.bioinf.uni-sb.de/.
15 citations