Michael K. Winson

Systematic functional analysis of the yeast genome.

The genome sequence of the yeast Saccharomyces cerevisiae has provided the first complete inventory of the working parts of a eukaryotic cell. The challenge is now to discover what each of the gene products does and how they interact in a living yeast cell. Systematic and comprehensive approaches to the elucidation of yeast gene function are discussed and the prospects for the functional genomics of eukaryotic organisms evaluated.

Wavelet Denoising of Infrared Spectra

The application of wavelet denoising to infrared spectra was investigated. Six different wavelet denoising methods (SURE, VISU, HYBRID, MINMAX, MAD and WAVELET PACKETS) were applied to pure infrared spectra with various added levels of homo- and heteroscedastic noise. The performances of the wavelet denoising methods were compared with the standard Fourier and moving mean filtering in terms of root mean square errors between the pure and denoised spectra and visual quality of the denoised spectrum. The use of predictive ability as a possible objective criterion for denoising performance was also investigated. The main conclusion is that for very low signal-to-noise ratios (S/N) the standard denoising methods (Fourier and moving mean) are comparable to the more sophisticated methods. At higher S/N levels the wavelet denoising methods, in particular the HYBRID and VISU methods, are better. Wavelet methods are also better in restoring the visual quality of the denoised infrared spectra.

Variable selection in wavelet regression models

Variable selection and compression are often used to produce more parsimonious regression models. But when they are applied directly to the original spectrum domain, it is not easy to determine the type of feature the selected variables represent. By performing variable selection in the wavelet domain we show that it is possible to identify important variables as being part of short- or large-scale features. Therefore, the suggested method is to extract information about the selected variables that otherwise would have been inaccessible. We are also able to obtain information about the location of these features in the original domain. In this article we demonstrate three types of variable selection methods applied to the wavelet domain: selection of optimal combination of scales, thresholding based on mutual information and truncation of weight vectors in the partial least squares (PLS) regression algorithm. We found that truncation of weight vectors in PLS was the most effective method for selecting variables. For the two experimental data sets tested we obtained approximately the same prediction error using less than 1% (for Data set 1) and 10% (for Data set 2) of the original variables. We also discovered that the selected variables were restricted to a limited number of wavelet scales. This information can be used to suggest whether the underlying features may be dominated by narrow (selective) peaks (indicated by variables in short wavelet scale regions) or by broader regions (indicated by variables in long wavelet scale regions). Thus, wavelet regression is here used as an extension of the more traditional Fourier regression (where the modelling is performed in the frequency domain without taking into consideration any of the information in the time domain).

Diffuse reflectance absorbance spectroscopy taking in chemometrics (DRASTIC). A hyperspectral FT-IR-based approach to rapid screening for metabolite overproduction

We introduce diffuse-reflectance absorbance spectroscopy in the mid-infrared as a novel method of chemical imaging for the rapid screening of biological samples for metabolite overproduction, using mixtures of ampicillin with Escherichia coli and Staphylococcus aureus as model systems. Deconvolution of the hyperspectral information provided by the raw diffuse reflectance-absorbance mid-infrared spectra was achieved using a combination of principal components analysis (PCA), artificial neural networks (ANNs) and partial least squares regression (PLS). Whereas a univariate approach necessitates appropriate data selection to remove any interferences, the chemometrics/hyperspectral approach could be employed to permit filtering of undesired components to give accurate quantification by PLS and ANNs without any preprocessing. The use of PCs as inputs to the ANNs decreased the training time from some 12 h to ca. 5 min. Equivalent concentrations of ampicillin between 0.05 and 20 mM in an E. coli or S. aureus background were quantified with >95% accuracy using this approach.

Explanatory Analysis of the Metabolome Using Genetic Programming of Simple, Interpretable Rules

Genetic programming, in conjunction with advanced analytical instruments, is a novel tool for the investigation of complex biological systems at the whole-tissue level. In this study, samples from tomato fruit grown hydroponically under both high- and low-salt conditions were analysed using Fourier-transform infrared spectroscopy (FTIR), with the aim of identifying spectral and biochemical features linked to salinity in the growth environment. FTIR spectra of whole tissue extracts are not amenable to direct visual analysis, so numerical modelling methods were used to generate models capable of classifying the samples based on their spectral characteristics. Genetic programming (GP) provided models with a better prediction accuracy to the conventional data modelling methods used, whilst being much easier to interpret in terms of the variables used. Examination of the GP-derived models showed that there were a small number of spectral regions that were consistently being used. In particular, the spectral region containing absorbances potentially due to a cyanide/nitrile functional group was identified as discriminatory. The explanatory power of the GP models enabled a chemical interpretation of the biochemical differences to be proposed. The combination of FTIR and GP is therefore a powerful and novel analytical tool that, in this study, improves our understanding of the biochemistry of salt tolerance in tomato plants.

Rapid Analysis of High-Dimensional Bioprocesses Using Multivariate Spectroscopies and Advanced Chemometrics

There are an increasing number of instrumental methods for obtaining data from biochemical processes, many of which now provide information on many (indeed many hundreds) of variables simultaneously. The wealth of data that these methods provide, however, is useless without the means to extract the required information. As instruments advance, and the quantity of data produced increases, the fields of bioinformatics and chemometrics have consequently grown greatly in importance. The chemometric methods nowadays available are both powerful and dangerous, and there are many issues to be considered when using statistical analyses on data for which there are numerous measurements (which often exceed the number of samples). It is not difficult to carry out statistical analysis on multivariate data in such a way that the results appear much more impressive than they really are. The authors present some of the methods that we have developed and exploited in Aberystwyth for gathering highly multivariate data from bioprocesses, and some techniques of sound multivariate statistical analyses (and of related methods based on neural and evolutionary computing) which can ensure that the results will stand up to the most rigorous scrutiny.

Use of earthworm casts to validate FT-IR spectroscopy as a 'sentinel' technology for high-throughput monitoring of global changes in microbial ecology

Summary This study aimed to evaluate metabolic fingerprinting by Fourier transform infrared (FT-IR) spectroscopy as a technique for investigating microbial communities and their activities in soil. FT-IR spectra from earthworm casts, and other ‘biosamples’, were compared using multivariate cluster analyses. The work formed part of a wider study to quantify the risk of horizontal gene flow and to assess ecological impacts associated with the release of GM crops or recombinant micro-organisms. A range of samples, including pure cultures of similar soil bacteria, plant materials and earthworm casts of various ages and feeding regimes were analysed. A subset of the cast FT-IR data was compared with DGGE analysis of extracted DNA/RNA. Cluster analysis of FT-IR spectra was capable of differentiating between different bacterial, litter and cast samples. There was congruence between FT-IR and DGGE clustering for food type but not for cast age. Further detailed work on the microbial populations will be needed to investigate relationships between microbial and spectroscopy data.

Improving the interpretation of multivariate and rule induction models by using a peak parameter representation

This paper demonstrates that the interpretation of multivariate calibration and rule induction classification models can be significantly improved by adopting a new representation of data profiles (e.g., spectra and chromatograms) containing identifiable peaks. The new representation is based on estimating Gaussian or Lorentzian curve parameters of data profiles by non-linear curve fitting. All modelling is performed on these peak parameters rather than using the traditional approach where each variable is assigned a sampling point in the data profile. Loading weight plots from the multivariate methods and decision trees obtained from rule induction algorithms become more parsimonious and easier to interpret in terms of the new representation.

Treating landfill leachate using passive aeration trickling filters; effects of leachate characteristics and temperature on rates and process dynamics

Biological ammoniacal-nitrogen (NH(4)(+)-N) and organic carbon (TOC) treatment was investigated in replicated mesoscale attached microbial film trickling filters, treating strong and weak strength landfill leachates in batch mode at temperatures of 3, 10, 15 and 30 degrees C. Comparing leachates, rates of NH(4)(+)-N reduction (0.126-0.159 g m(-2) d(-1)) were predominantly unaffected by leachate characteristics; there were significant differences in TOC rates (0.072-0.194 g m(-2) d(-1)) but no trend relating to leachate strength. Rates of total oxidised nitrogen (TON) accumulation (0.012-0.144 g m(-2) d(-1)) were slower for strong leachates. Comparing temperatures, treatment rates varied between 0.029-0.319 g NH(4)(+)-N m(-2) d(-1) and 0.033-0.251 g C m(-2) d(-1) generally increasing with rising temperatures; rates at 3 degrees C were 9 and 13% of those at 30 degrees C for NH(4)(+)-N and TOC respectively. For the weak leachates (NH(4)(+)-N<140 mg l(-1)) complete oxidation of NH(4)(+)-N was achieved. For the strong leachates (NH(4)(+)-N 883-1150 mg l(-1)) a biphasic treatment response resulted in NH(4)(+)-N removal efficiencies of between 68 and 88% and for one leachate no direct transformation of NH(4)(+)-N to TON in bulk leachate. The temporal decoupling of NH(4)(+)-N oxidation and TON accumulation in this leachate could not be fully explained by denitrification, volatilisation or anammox, suggesting temporary storage of N within the treatment system. This study demonstrates that passive aeration trickling filters can treat well-buffered high NH(4)(+)-N strength landfill leachates under a range of temperatures and that leachate strength has no effect on initial NH(4)(+)-N treatment rates. Whether this approach is a practicable option depends on a range of site specific factors.

A DRASTIC (Diffuse Reflectance Absorbance Spectroscopy Taking in Chemometrics) approach for the rapid analysis of microbial fermentation products: Quantification of aristeromycin and neplanocin A in Streptomyces citricolor broths

Microbial cultures can provide metabolites which are useful as structural templates for rational drug design. Increasing the titre of the metabolite is an important part of this process and is often achieved by random mutagenesis. As titre improved mutants derived by this method are extremely rare, many thousands need to be screened. screening mutants for increased metabolite production relies on methods such as assessing binding via the scintilation proximity assay or identifying an increase in concentration using chromatography. Such methods are typically restricted by the necessity to perform solvent extractions and, in the case of HPLC analysis, to optimise separation of the components of interest. Although the routine procedures can be automated, such multi-step screening processes are far from ideal. Diffuse reflectance absorbance infra-red spectroscopy provides an alternative rapid, automated, quantitative approach which yields more detailed information about chemical characteristics than, for example, the UV absorbance spectrum typically used in HPLC analysis. The method can also be employed non-invasively on unprocessed fermentation samples. We demonstrate the use of this spectroscopictechnique in combination with chemometrics for determining the concentrations of aristeromycin and neplanocin A in Streptomyces citricolor fermentations. The fermentation broths of a range of mutants previously obtained during a titre improvement programme were analysed by standard HPLC techniques and by automated diffuse reflectance absorbance infra-red spectroscopy. Chemometric processing of the infra-red spectra was performed using supervised and unsupervised multivariate calibration methods. DRASTIC proved to be a rapid and reliable method for the estimation of metabolite overproduction in cultures of biotechnological interest, and it was possible to discriminate cultures overproducing closely related molecules.