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Given the constantly improving cost and speed of genome sequencing, it is reasonable to expect that personal genomes will soon be known for many millions of humans. This stands in stark contrast with our limited ability to interpret the sequence variants which we find. Although it is, perhaps, easiest to interpret variants in coding regions, knowledge of functional impact is unknown for the vast majority of missense variants. While many computational approaches can predict the impact of coding variants, they are given a little weight in the current guidelines for interpreting clinical variants. Laboratory assays produce comparatively more trustworthy results, but until recently did not scale to the space of all possible mutations. The development of deep mutational scanning and other multiplexed assays of variant effect has now brought feasibility of this endeavour within view. Here, we review progress in this field over the last decade, break down the different approaches into their components, and compare methodological differences. [ABSTRACT FROM AUTHOR]

Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on nontarget cell types that cause toxic side effects. Here, we examine a model system of antimicrobial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity--enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors. [ABSTRACT FROM AUTHOR]

Genome-wide association (GWA) studies have linked thousands of loci to human diseases, but the causal genes and variants at these loci generally remain unknown. Although investigators typically focus on genes closest to the associated polymorphisms, the causal gene is often more distal. Reliance on published work to prioritize candidates is biased toward well-characterized genes. We describe a 'prix fixe' strategy and software that uses genome-scale shared-function networks to identify sets of mutually functionally related genes spanning multiple GWA loci. Using associations from ∼100 GWA studies covering ten cancer types, our approach outperformed the common alternative strategy in ranking known cancer genes. As more GWA loci are discovered, the strategy will have increased power to elucidate the causes of human disease. [ABSTRACT FROM AUTHOR]

Phenotypes that might otherwise reveal a gene's function can be obscured by genes with overlapping function. This phenomenon is best known within gene families, in which an important shared function may only be revealed by mutating all family members. Here we describe the 'green monster' technology that enables precise deletion of many genes. In this method, a population of deletion strains with each deletion marked by an inducible green fluorescent protein reporter gene, is subjected to repeated rounds of mating, meiosis and flow-cytometric enrichment. This results in the aggregation of multiple deletion loci in single cells. The green monster strategy is potentially applicable to assembling other engineered alterations in any species with sex or alternative means of allelic assortment. To test the technology, we generated a single broadly drug-sensitive strain of Saccharomyces cerevisiae bearing precise deletions of all 16 ATP-binding cassette transporters within clades associated with multidrug resistance. [ABSTRACT FROM AUTHOR]

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks. [ABSTRACT FROM AUTHOR]

High-quality datasets are needed to understand how global and local properties of protein-protein interaction, or 'interactome', networks relate to biological mechanisms, and to guide research on individual proteins. In an evaluation of existing curation of protein interaction experiments reported in the literature, we found that curation can be error-prone and possibly of lower quality than commonly assumed. [ABSTRACT FROM AUTHOR]

Describing the 'ORFeome' of an organism, including all major isoforms, is essential for a system-level understanding of any species; however, conventional cloning and sequencing approaches are prohibitively costly and labor-intensive. We describe a potentially genome-wide methodology for efficiently capturing new coding isoforms using reverse transcriptase (RT)-PCR recombinational cloning, 'deep-well' pooling and a next-generation sequencing platform. This ORFeome discovery pipeline will be applicable to any eukaryotic species with a sequenced genome. [ABSTRACT FROM AUTHOR]

Differential regulation of gene expression is essential for cell fate specification in metazoans. Characterizing the transcriptional activity of gene promoters, in time and in space, is therefore a critical step toward understanding complex biological systems. Here we present an in vivo spatiotemporal analysis for ∼900 predicted C. elegans promoters (∼5% of the predicted protein-coding genes), each driving the expression of green fluorescent protein (GFP). Using a flow-cytometer adapted for nematode profiling, we generated 'chronograms', two-dimensional representations of fluorescence intensity along the body axis and throughout development from early larvae to adults. Automated comparison and clustering of the obtained in vivo expression patterns show that genes coexpressed in space and time tend to belong to common functional categories. Moreover, integration of this data set with C. elegans protein-protein interactome data sets enables prediction of anatomical and temporal interaction territories between protein partners. [ABSTRACT FROM AUTHOR]

Systematic genetic interaction studies have illuminated many cellular processes. Here we quantitatively examine genetic interactions among 26 Saccharomyces cerevisiae genes conferring resistance to the DNA-damaging agent methyl methanesulfonate (MMS), as determined by chemogenomic fitness profiling of pooled deletion strains. We constructed 650 double-deletion strains, corresponding to all pairings of these 26 deletions. The fitness of single- and double-deletion strains were measured in the presence and absence of MMS. Genetic interactions were defined by combining principles from both statistical and classical genetics. The resulting network predicts that the Mph1 helicase has a role in resolving homologous recombination–derived DNA intermediates that is similar to (but distinct from) that of the Sgs1 helicase. Our results emphasize the utility of small molecules and multifactorial deletion mutants in uncovering functional relationships and pathway order. [ABSTRACT FROM AUTHOR]

An earlier search in the human, mouse and rat genomes for sequences that are 100% conserved in orthologous segments and ≥200 bp in length identified 481 distinct sequences. These human-mouse-rat sequences, which represent ultraconserved elements (UCEs), are believed to be important for functions involving DNA binding, RNA processing and the regulation of transcription and development. In vivo and additional computational studies of UCEs and other highly conserved sequences are consistent with these functional associations, with some observations indicating enhancer-like activity for these elements. Here, we show that UCEs are significantly depleted among segmental duplications and copy number variants. Notably, of the UCEs that are found in segmental duplications or copy number variants, the majority overlap exons, indicating, along with other findings presented, that UCEs overlapping exons represent a distinct subset. [ABSTRACT FROM AUTHOR]

Although numerous fundamental aspects of development have been uncovered through the study of individual genes and proteins, system-level models are still missing for most developmental processes. The first two cell divisions of Caenorhabditis elegans embryogenesis constitute an ideal test bed for a system-level approach. Early embryogenesis, including processes such as cell division and establishment of cellular polarity, is readily amenable to large-scale functional analysis. A first step toward a system-level understanding is to provide ‘first-draft’ models both of the molecular assemblies involved and of the functional connections between them. Here we show that such models can be derived from an integrated gene/protein network generated from three different types of functional relationship: protein interaction, expression profiling similarity and phenotypic profiling similarity, as estimated from detailed early embryonic RNA interference phenotypes systematically recorded for hundreds of early embryogenesis genes. The topology of the integrated network suggests that C. elegans early embryogenesis is achieved through coordination of a limited set of molecular machines. We assessed the overall predictive value of such molecular machine models by dynamic localization of ten previously uncharacterized proteins within the living embryo. [ABSTRACT FROM AUTHOR]

In apparently scale-free protein-protein interaction networks, or ‘interactome’ networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the ‘hubs’, interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: ‘party’ hubs, which interact with most of their partners simultaneously, and ‘date’ hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes-or modules -to each other, whereas party hubs function inside modules. [ABSTRACT FROM AUTHOR]

Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomics owing in part to robust spectral interpretation algorithms. Widely used algorithms do not fully exploit the intensity patterns present in mass spectra. Here, we demonstrate that intensity pattern modeling improves peptide and protein identification from MS/MS spectra. We modeled fragment ion intensities using a machine-learning approach that estimates the likelihood of observed intensities given peptide and fragment attributes. From 1,000,000 spectra, we chose 27,000 with high-quality, nonredundant matches as training data. Using the same 27,000 spectra, intensity was similarly modeled with mismatched peptides. We used these two probabilistic models to compute the relative likelihood of an observed spectrum given that a candidate peptide is matched or mismatched. We used a 'decoy' proteome approach to estimate incorrect match frequency, and demonstrated that an intensity-based method reduces peptide identification error by 50-96% without any loss in sensitivity. [ABSTRACT FROM AUTHOR]

Next-generation sequencing has not been applied to protein-protein interactome network mapping so far because the association between the members of each interacting pair would not be maintained in en masse sequencing. We describe a massively parallel interactome-mapping pipeline, Stitch-seq, that combines PCR stitching with next-generation sequencing and used it to generate a new human interactome dataset. Stitch-seq is applicable to various interaction assays and should help expand interactome network mapping. [ABSTRACT FROM AUTHOR]

An addition to an article on literature-curated protein-protein interaction (PPI) datasets that was published in the December 2009 issue is presented.

Presents a corrected reprint of an article published in the periodical "Nature."