Reconstructing signaling pathways using regular language constrained paths
| dc.contributor.author | Wagner, Mitchell J. | en |
| dc.contributor.author | Pratapa, Aditya | en |
| dc.contributor.author | Murali, T. M. | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2019-08-22T17:03:31Z | en |
| dc.date.available | 2019-08-22T17:03:31Z | en |
| dc.date.issued | 2019-07-15 | en |
| dc.description.abstract | Motivation High-quality curation of the proteins and interactions in signaling pathways is slow and painstaking. As a result, many experimentally detected interactions are not annotated to any pathways. A natural question that arises is whether or not it is possible to automatically leverage existing pathway annotations to identify new interactions for inclusion in a given pathway. Results We present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors within a background interaction network. The key idea underlying RegLinker is the use of regular language constraints to control the number of non-pathway interactions that are present in the computed paths. We systematically evaluate RegLinker and five alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker recovers withheld pathway proteins and interactions with the best precision and recall. We used RegLinker to propose new extensions to the pathways. We discuss the literature that supports the inclusion of these proteins in the pathways. These results show the broad potential of automated analysis to attenuate difficulties of traditional manual inquiry. Availability and implementation https://github.com/Murali-group/RegLinker. Supplementary information Supplementary data are available at Bioinformatics online. | en |
| dc.description.notes | This work was supported by grants from the National Science Foundation [CCF-1617678, DBI-1759858] and the National Institute of General Medical Sciences [R01-GM095955] to T.M.M. We also acknowledge support from the Computational Tissue Engineering interdisciplinary graduate education program at Virginia Tech. | en |
| dc.description.sponsorship | National Science Foundation [CCF-1617678, DBI-1759858]; National Institute of General Medical Sciences [R01-GM095955]; Computational Tissue Engineering interdisciplinary graduate education program at Virginia Tech | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1093/bioinformatics/btz360 | en |
| dc.identifier.eissn | 1460-2059 | en |
| dc.identifier.issn | 1367-4803 | en |
| dc.identifier.issue | 14 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/93221 | en |
| dc.identifier.volume | 35 | en |
| dc.language.iso | en | en |
| dc.rights | Creative Commons Attribution-NonCommercial 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | en |
| dc.title | Reconstructing signaling pathways using regular language constrained paths | en |
| dc.title.serial | Bioinformatics | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |
| dc.type.dcmitype | StillImage | en |
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