Network-Based Prediction and Analysis of HIV Dependency Factors
| dc.contributor | Virginia Tech | en |
| dc.contributor.author | Murali, T. M. | en |
| dc.contributor.author | Dyer, Matthew D. | en |
| dc.contributor.author | Badger, David | en |
| dc.contributor.author | Tyler, Brett M. | en |
| dc.contributor.author | Katze, Michael G. | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessed | 2014-07-02 | en |
| dc.date.accessioned | 2014-07-03T17:00:45Z | en |
| dc.date.available | 2014-07-03T17:00:45Z | en |
| dc.date.issued | 2011-09-22 | en |
| dc.description.abstract | HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other. | en |
| dc.description.sponsorship | Public Health Service grants P30DA015625, P51RR000166, and R24RR016354 from the National Institutes of Health to MGK, grants from the Virginia Bioinformatics Institute Fellows program to TMM and BMT, and a grant from the ASPIRES program at the Virginia Polytechnic Institute and State University to TMM supported this research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Murali TM, Dyer MD, Badger D, Tyler BM, Katze MG (2011) Network-Based Prediction and Analysis of HIV Dependency Factors. PLoS Comput Biol 7(9): e1002164. doi:10.1371/journal.pcbi.1002164 | en |
| dc.identifier.doi | https://doi.org/10.1371/journal.pcbi.1002164 | en |
| dc.identifier.issn | 1553-734X | en |
| dc.identifier.uri | http://hdl.handle.net/10919/49308 | en |
| dc.identifier.url | http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002164 | en |
| dc.language.iso | en_US | en |
| dc.publisher | Public Library of Science | en |
| dc.rights | Creative Commons CC0 1.0 Universal Public Domain Dedication | en |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | en |
| dc.subject | AIDS | en |
| dc.subject | Algorithms | en |
| dc.subject | Brass | en |
| dc.subject | Forecasting | en |
| dc.subject | HIV | en |
| dc.subject | Lymph nodes | en |
| dc.subject | Protein interaction networks | en |
| dc.subject | Protein interactions | en |
| dc.title | Network-Based Prediction and Analysis of HIV Dependency Factors | en |
| dc.title.serial | Plos Computational Biology | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |