MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks
Abstract Background Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-12-01
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| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12920-019-0582-8 |
| _version_ | 1831540926189666304 |
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| author | Ying Hui Pi-Jing Wei Junfeng Xia Yu-Tian Wang Chun-Hou Zheng |
| author_facet | Ying Hui Pi-Jing Wei Junfeng Xia Yu-Tian Wang Chun-Hou Zheng |
| author_sort | Ying Hui |
| collection | DOAJ |
| description | Abstract Background Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. Methods We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. Results We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. Conclusions We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation’s effect on the transcriptional network, but also assesses the differential expression’s effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes. |
| first_indexed | 2024-12-17T00:07:30Z |
| format | Article |
| id | doaj.art-f4f66709c2434671ae31c355cb73348a |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-12-17T00:07:30Z |
| publishDate | 2019-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-f4f66709c2434671ae31c355cb73348a2022-12-21T22:10:55ZengBMCBMC Medical Genomics1755-87942019-12-0112S711010.1186/s12920-019-0582-8MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networksYing Hui0Pi-Jing Wei1Junfeng Xia2Yu-Tian Wang3Chun-Hou Zheng4Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui UniversityKey Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui UniversityInstitute of Physical Science and Information Technology, Anhui UniversitySchool of Software Engineering, Qufu Normal UniversityKey Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui UniversityAbstract Background Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. Methods We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. Results We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. Conclusions We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation’s effect on the transcriptional network, but also assesses the differential expression’s effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.https://doi.org/10.1186/s12920-019-0582-8Driver genesCancerTranscriptional networks |
| spellingShingle | Ying Hui Pi-Jing Wei Junfeng Xia Yu-Tian Wang Chun-Hou Zheng MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks BMC Medical Genomics Driver genes Cancer Transcriptional networks |
| title | MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks |
| title_full | MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks |
| title_fullStr | MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks |
| title_full_unstemmed | MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks |
| title_short | MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks |
| title_sort | mecorank cancer driver genes discovery simultaneously evaluating the impact of snvs and differential expression on transcriptional networks |
| topic | Driver genes Cancer Transcriptional networks |
| url | https://doi.org/10.1186/s12920-019-0582-8 |
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