Iain M. Wallace

Clustal W and Clustal X version 2.0

SUMMARY The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems. AVAILABILITY The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site ftp://ftp.ebi.ac.uk/pub/software/clustalw2/

The Genetic Landscape of a Cell

Making Connections Genetic interaction profiles highlight cross-connections between bioprocesses, providing a global view of cellular pleiotropy, and enable the prediction of genetic network hubs. Costanzo et al. (p. 425) performed a pairwise fitness screen covering approximately one-third of all potential genetic interactions in yeast, examining 5.4 million gene-gene pairs and generating quantitative profiles for ∼75% of the genome. Of the pairwise interactions tested, about 3% of the genes investigated interact under the conditions tested. On the basis of these data, a reference map for the yeast genetic network was created. A genome-wide interaction map of yeast identifies genetic interactions, networks, and function. A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

M-Coffee: combining multiple sequence alignment methods with T-Coffee

We introduce M-Coffee, a meta-method for assembling multiple sequence alignments (MSA) by combining the output of several individual methods into one single MSA. M-Coffee is an extension of T-Coffee and uses consistency to estimate a consensus alignment. We show that the procedure is robust to variations in the choice of constituent methods and reasonably tolerant to duplicate MSAs. We also show that performances can be improved by carefully selecting the constituent methods. M-Coffee outperforms all the individual methods on three major reference datasets: HOMSTRAD, Prefab and Balibase. We also show that on a case-by-case basis, M-Coffee is twice as likely to deliver the best alignment than any individual method. Given a collection of pre-computed MSAs, M-Coffee has similar CPU requirements to the original T-Coffee. M-Coffee is a freeware open-source package available from .

Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples

Next-generation sequencing has proven an extremely effective technology for molecular counting applications where the number of sequence reads provides a digital readout for RNA-seq, ChIP-seq, Tn-seq and other applications. The extremely large number of sequence reads that can be obtained per run permits the analysis of increasingly complex samples. For lower complexity samples, however, a point of diminishing returns is reached when the number of counts per sequence results in oversampling with no increase in data quality. A solution to making next-generation sequencing as efficient and affordable as possible involves assaying multiple samples in a single run. Here, we report the successful 96-plexing of complex pools of DNA barcoded yeast mutants and show that such ‘Bar-seq’ assessment of these samples is comparable with data provided by barcode microarrays, the current benchmark for this application. The cost reduction and increased throughput permitted by highly multiplexed sequencing will greatly expand the scope of chemogenomics assays and, equally importantly, the approach is suitable for other sequence counting applications that could benefit from massive parallelization.

An integrated platform of genomic assays reveals small-molecule bioactivities

Bioactive compounds are widely used to modulate protein function and can serve as important leads for drug development. Identifying the in vivo targets of these compounds remains a challenge. Using yeast, we integrated three genome-wide gene-dosage assays to measure the effect of small molecules in vivo. A single TAG microarray was used to resolve the fitness of strains derived from pools of (i) homozygous deletion mutants, (ii) heterozygous deletion mutants and (iii) genomic library transformants. We demonstrated, with eight diverse reference compounds, that integration of these three chemogenomic profiles improves the sensitivity and specificity of small-molecule target identification. We further dissected the mechanism of action of two protein phosphatase inhibitors and in the process developed a framework for the rational design of multidrug combinations to sensitize cells with specific genotypes more effectively. Finally, we applied this platform to 188 novel synthetic chemical compounds and identified both potential targets and structure-activity relationships.

Mapping the Cellular Response to Small Molecules Using Chemogenomic Fitness Signatures

Yeasty HIPHOP In order to identify how chemical compounds target genes and affect the physiology of the cell, tests of the perturbations that occur when treated with a range of pharmacological chemicals are required. By examining the haploinsufficiency profiling (HIP) and homozygous profiling (HOP) chemogenomic platforms, Lee et al. (p. 208) analyzed the response of yeast to thousands of different small molecules, with genetic, proteomic, and bioinformatic analyses. Over 300 compounds were identified that targeted 121 genes within 45 cellular response signature networks. These networks were used to extrapolate the likely effects of related chemicals, their impact upon genetic pathways, and to identify putative gene functions. Guilt by association helps identify the chemogenomic signatures of compounds targeting yeast genes. Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes.

A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans

The resistance of Caenorhabditis elegans to pharmacological perturbation limits its use as a screening tool for novel small bioactive molecules. One strategy to improve the hit rate of small-molecule screens is to preselect molecules that have an increased likelihood of reaching their target in the worm. To learn which structures evade the worms defenses, we performed the first survey of the accumulation and metabolism of over 1,000 commercially available drug-like small molecules in the worm. We discovered that fewer than 10% of these molecules accumulate to concentrations greater than 50% of that present in the worms environment. Using our dataset, we developed a structure-based accumulation model that identifies compounds with an increased likelihood of bioavailability and bioactivity, and we describe structural features that facilitate small-molecule accumulation in the worm. Preselecting molecules that are more likely to reach a target by first applying our model to the tens of millions of commercially available compounds will undoubtedly increase the success of future small-molecule screens with C. elegans.

Evaluation of iterative alignment algorithms for multiple alignment

MOTIVATION Iteration has been used a number of times as an optimization method to produce multiple alignments, either alone or in combination with other methods. Iteration has a great advantage in that it is often very simple both in terms of coding the algorithms and the complexity of the time and memory requirements. In this paper, we systematically test several different iteration strategies by comparing the results on sets of alignment test cases. RESULTS We tested three schemes where iteration is used to improve an existing alignment. This was found to be remarkably effective and could induce a significant improvement in the accuracy of alignments from most packages. For example the average accuracy of ClustalW was improved by over 6% on the hardest test cases. Iteration was found to be even more powerful when it was directly incorporated into a progressive alignment scheme. Here, iteration was used to improve subalignments at each step of progressive alignment. The beneficial effects of iteration come, in part, from the ability to get round the usual local minimum problem with progressive alignment. This ability can also be used to help reduce the complexity of T-Coffee, without losing accuracy. Alignments can be generated, using T-Coffee, to align subgroups of sequences, which can then be iteratively improved and merged. AVAILABILITY All of the scripts are freely available on the web at http://www.bioinf.ucd.ie/people/iain/iteration.html CONTACT iain.wallace@ucd.ie.

Target Prediction for an Open Access Set of Compounds Active against Mycobacterium tuberculosis

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), infects an estimated two billion people worldwide and is the leading cause of mortality due to infectious disease. The development of new anti-TB therapeutics is required, because of the emergence of multi-drug resistance strains as well as co-infection with other pathogens, especially HIV. Recently, the pharmaceutical company GlaxoSmithKline published the results of a high-throughput screen (HTS) of their two million compound library for anti-mycobacterial phenotypes. The screen revealed 776 compounds with significant activity against the M. tuberculosis H37Rv strain, including a subset of 177 prioritized compounds with high potency and low in vitro cytotoxicity. The next major challenge is the identification of the target proteins. Here, we use a computational approach that integrates historical bioassay data, chemical properties and structural comparisons of selected compounds to propose their potential targets in M. tuberculosis. We predicted 139 target - compound links, providing a necessary basis for further studies to characterize the mode of action of these compounds. The results from our analysis, including the predicted structural models, are available to the wider scientific community in the open source mode, to encourage further development of novel TB therapeutics.

Compound Prioritization Methods Increase Rates of Chemical Probe Discovery in Model Organisms

Preselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in Saccharomyces cerevisiae and identified ~7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes, we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ~7500 growth-inhibitory molecules have been made commercially available and the computational model and filter used are provided.