Towards the identification of essential genes using targeted genome sequencing and comparative analysis

Gustafson, Adam M.; Snitkin, Evan S.; Parker, Stephen C. J.; DeLisi, Charles; Kasif, Simon

doi:10.1186/1471-2164-7-265

Cited by 124 publications

(122 citation statements)

References 38 publications

(45 reference statements)

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“…While the clustering coefficient is often a good measure of local density of interactions, it may not be a good measure for SFL networks because of the enrichment of bipartite motifs. Factors such as the codon adaptation index, number of protein-protein interaction partners, phyletic retention (Gustafson et al 2006), and essentiality Davierwala et al 2005) were not retained in the model, possibly owing to correlations with retained factors. The adjusted R 2 for the final model was 0.69.…”

Section: Graph Diffusion Kernels For Genetic Interactionsmentioning

confidence: 99%

Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions

Qi¹,

Suhail²,

Yang³

et al. 2008

Genome Res.

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The yeast synthetic lethal genetic interaction network contains rich information about underlying pathways and protein complexes as well as new genetic interactions yet to be discovered. We have developed a graph diffusion kernel as a unified framework for inferring complex/pathway membership analogous to "friends" and genetic interactions analogous to "enemies" from the genetic interaction network. When applied to the Saccharomyces cerevisiae synthetic lethal genetic interaction network, we can achieve a precision around 50% with 20% to 50% recall in the genome-wide prediction of new genetic interactions, supported by experimental validation. The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data.[Supplemental material is available online at www.genome.org.]Genetics establishes links between genotype and phenotype. Many genes are pleiotropic, carrying out multiple functions in different pathways under different environmental conditions and can have partially redundant function with other genes. Genetic buffering can be evolutionarily stable (Nowak et al. 1997). While individual gene perturbations may have little or no effect, combined perturbations can generate a phenotype. This is the rationale of genetic interaction screens, which test for phenotypes from two-gene perturbations that differ from the singlegene effects. Pairwise genetic interaction screens have been valuable in understanding functional relationships between genes and assigning functions to genes in a pathway-dependent manner.With the completion of the Yeast Knockout deletion collection (Giaever et al. 2002), high-throughput studies of pairwise lethal or growth defect interactions between null or hypomorph alleles of Saccharomyces cerevisiae (budding yeast) have made vast progress in the past few years. "Synthetic growth defect" or "synthetic sickness" describes a genetic interaction between two genes whose individual deletion mutants have minimal growth defects, while the double knockout results in a significant growth defect under a given condition. A subset of those pairs whose double knockouts lead to diminished growth or death are called "synthetic lethal" (Dobzhansky 1946). We will refer to the union of "synthetic sickness" and "synthetic lethal" as a "synthetic fitness or lethal interaction," or SFL. Multiple studies have screened a subset of deletions or hypomorph alleles against the entire set of viable yeast deletion mutants using methods including synthetic genetic array (SGA) (Tong et al. 2001, synthetic lethality analyzed by microarray (SLAM), and diploid-based SLAM (dSLAM) (Ooi et al. 2003;Pan et al. 2004Pan et al. , 2006. A second approach, termed an epistatic miniarray profile, searches for both positive and negative interactions among a subset of genes (Collins et al. 2007).Large . To achieve these goals, especially prediction of pathway membership, algorithms have progressed from counting the number of shared neighbo...

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Section: Graph Diffusion Kernels For Genetic Interactionsmentioning

confidence: 99%

Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions

Qi¹,

Suhail²,

Yang³

et al. 2008

Genome Res.

View full text Add to dashboard Cite

show abstract

“…Similarly, Fig. 6 in the report of Gustafson et al [57] shows little gain associated with the addition of PPI data for S. cerevisiae. The fact that high-throughput PPI data in particular suffer from high rates of false positive and false negative information indicates that care needs to be taken in selecting which particular source of data to examine [61].…”

mentioning

confidence: 74%

“…Measures of codon usage bias were also repeatedly shown to be predictive of essentiality. Subcellular localization proved useful in the two studies that examined this attribute (Seringhaus et al [56], Gustafson et al [57]). This was true regardless of whether the information was derived based on sequence analysis as by Seringhaus et al, or based on previous large scale experimental results as by Gustafson et al The latter group also found a measure of gene regulatory complexity, upstream size, to be an important contributor to predicting essentiality.…”

mentioning

confidence: 97%

“…This result is despite the fact that the two studies measured phyletic retention across different sets of organisms. Gustafson et al [57] were apparently the first to systematically assess how the predictive power of this attribute varies with the specific subset of organisms used to measure phyletic retention. They draw the intriguing conclusion that this measure works best when assessed across a set of host-associated organisms with small genomes that are closely related to the study organism.…”

mentioning

confidence: 99%

“…Gustafson et al [57] sought to construct an effective classifier of gene essentiality based on attributes that can be generated from sequence and comparative genomics data, although they included PPI data to determine if additional improvement could be achieved. One attribute of particular interest to these investigators was phyletic retention, i.e., the number of organisms that possess an ortholog of a gene of interest.…”

mentioning

confidence: 99%

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