Synthetic lethality prediction software tools | Protein interaction data analysis
Two genes are said to be in a synthetic lethality (SL) relationship if a perturbation of either gene alone is not lethal but perturbations of both genes lead to cell death or a dramatic decrease in cell viability (Boone et al., 2007). Source text: Guo et al., 2015.
Proposes a targeted enumeration procedure for identification of synthetic lethal (SL) genes or reactions using genome-scale metabolic models. SL Finder relies on the solution of a bilevel optimization framework that utilizes flux balance analysis to identify all multi-reaction/gene lethals. The user needs to first specify a parameter n, indicating the order of synthetic lethals. This bilevel formulation then identifies the set of n gene/reaction deletions that minimizes the maximum biomass formation potential of the network. If the minimal value of the maximum biomass is found to be below a pre-specified viability threshold (e.g., one percent of maximum biomass) then the corresponding combination of n gene/reaction deletions forms a SL. All alternative SL gene/reaction sets of size n are successively obtained by excluding the previously identified SLs using integer cuts and resolving the bilevel formulation.
A computational approach to infer synthetic lethality interactions directly from frequently altered genes in human cancers. MutExSL is based on the observation that pairs of genes that are altered in a (significantly) mutually exclusive manner in cancers are likely to constitute lethal combinations.
An approach for predicting yeast synthetic lethality, which integrates 17 genomic and proteomic features and the outputs of 10 classification methods. MetaSL thus combines the strengths of existing methods and achieves the highest area under the receiver operating characteristics (ROC) curve (AUC) of 87.1% among all competitors on yeast data.
Allows users to automatically extract median time of death from “death fluorescence” DF curves. LFASS enables new insights about stress resistance and to detect ageing. The software runs without a strict sample size and added reagents. It is compatible with transgenic, frail or immobile worms and is applicable to modern screening platforms employing transparent mate.
Dr. Yashwanth Subbannayya obtained his M.Sc. degree in Medical Biochemistry from Manipal University. He qualified the competitive CSIR-UGC National Eligibility Test and joined the Institute of Bioinformatics, Bangalore as a UGC Junior Research Fellow. As part of his Ph.D. work, he studied the molecular mechanisms of gastric cancer in clinical specimens using quantitative proteomic technologies. This study, the results of which were published in Cancer Biology and Therapy, yielded a novel therapeutic target for gastric cancer- CAMKK2. Further, he also studied the serum proteome of gastric cancer patients and developed assays for potential markers using the revolutionary multiple reaction monitoring approach. The results of this study were published in Journal of Proteomics. In addition to his research work, he also trained extensively in sample preparation for mass spectrometry, fractionation techniques and gained expertise in quantitative proteomic techniques and data analysis. In addition, he also trained extensively in various validation platforms including immunohistochemsitry, multiple reaction monitoring and Western blot. He has also worked as a curator for several biological databases including NetPath, Human Protein Reference Database (HPRD) and Breast cancer database. His work in various research projects have yielded him 23 publications either as lead author or co-author in peer reviewed journals. He is a reviewer for the journal Proteomics.
Dr. Yashwanth Subbannayya joined the YU-IOB Center for Systems Biology and Molecular Medicine in June, 2015. During the initial period, his job consisted of assisting other personnel of the university in the establishment of YU-IOB Center for Systems Biology and Molecular Medicine. He was also involved in training of Ph.D. students in biological aspects. After the establishment of the center, he trained in cell culture techniques and metabolomics analysis. At YU-IOB CSBMM, he is studying the molecular mechanisms in various cancers including oral cancer. In addition, he is studying the molecular mechanisms as well as the metabolic constituents of traditional medicine formulations using mass spectrometry technologies. In June 2016, he convened the national symposium “Genomics in clinical practice: Future of precision medicine” held at Yenepoya University on June 1 and 2, 2016. The resource persons included 16 individuals from various academic organizations as well as industry. The symposium was attended by 218 participants from 24 institutions around India. He is a member of the Scientific Review Board of Yenepoya Research Centre where he facilitates timely scientific review of research projects.