Bjørn-Helge Mevik

AIR: A batch-oriented web program package for construction of supermatrices ready for phylogenomic analyses

BackgroundLarge multigene sequence alignments have over recent years been increasingly employed for phylogenomic reconstruction of the eukaryote tree of life. Such supermatrices of sequence data are preferred over single gene alignments as they contain vastly more information about ancient sequence characteristics, and are thus more suitable for resolving deeply diverging relationships. However, as alignments are expanded, increasingly numbers of sites with misleading phylogenetic information are also added. Therefore, a major goal in phylogenomic analyses is to maximize the ratio of information to noise; this can be achieved by the reduction of fast evolving sites.ResultsHere we present a batch-oriented web-based program package, named AIR that allows 1) transformation of several single genes to one multigene alignment, 2) identification of evolutionary rates in multigene alignments and 3) removal of fast evolving sites. These three processes can be done with the programs AIR-A ppender, AIR-I dentifier, and AIR-R emover (AIR), which can be used independently or in a semi-automated pipeline. AIR produces user-friendly output files with filtered and non-filtered alignments where residues are colored according to their evolutionary rates. Other bioinformatics applications linked to the AIR package are available at the Bioportal http://www.bioportal.uio.no, University of Oslo; together these greatly improve the flexibility, efficiency and quality of phylogenomic analyses.ConclusionThe AIR program package allows for efficient creation of multigene alignments and better assessment of evolutionary rates in sequence alignments. Removing fast evolving sites with the AIR programs has been employed in several recent phylogenomic analyses resulting in improved phylogenetic resolution and increased statistical support for branching patterns among the early diverging eukaryotes.

Effect of contextual factors on liking for wine—use of robust design methodology

Abstract This research investigated the effects of context on the acceptability of Chardonnay wines using the robust design methodology. Robust design methods distinguish between two types of design variables: control factors and noise factors. The control factors in this study were enological variables used to make the wines. The noise factors were the contexts in which the wines were evaluated. Eight Chardonnay wines were produced according to an experimental design with or without (1) malolactic fermentation, (2) oak contact, and (3) sugar addition to the finished wine. The wines were served in a laboratory and in a reception room with or without food, and rated for degree of liking on the nine-point hedonic scale by 55 wine consumers. Analyses of variance showed that the control factors and the noise factors had significant, and similar in size, effects on liking. The robust design methodology affords the product designer the ability to better understand the effects of product variation and context variation on product acceptability.

Optimal Choice of Baseline Correction for Multivariate Calibration of Spectra

Baselines are often chosen by visual inspection of their effect on selected spectra. A more objective procedure for choosing baseline correction algorithms and their parameter values for use in statistical analysis is presented. When the goal of the baseline correction is spectra with a pleasing appearance, visual inspection can be a satisfactory approach. If the spectra are to be used in a statistical analysis, objectivity and reproducibility are essential for good prediction. Variations in baselines from dataset to dataset means we have no guarantee that the best-performing algorithm from one analysis will be the best when applied to a new dataset. This paper focuses on choosing baseline correction algorithms and optimizing their parameter values based on the performance of the quality measure from the given analysis. Results presented in this paper illustrate the potential benefits of the optimization and points out some of the possible pitfalls of baseline correction.

CLOTU: An online pipeline for processing and clustering of 454 amplicon reads into OTUs followed by taxonomic annotation

BackgroundThe implementation of high throughput sequencing for exploring biodiversity poses high demands on bioinformatics applications for automated data processing. Here we introduce CLOTU, an online and open access pipeline for processing 454 amplicon reads. CLOTU has been constructed to be highly user-friendly and flexible, since different types of analyses are needed for different datasets.ResultsIn CLOTU, the user can filter out low quality sequences, trim tags, primers, adaptors, perform clustering of sequence reads, and run BLAST against NCBInr or a customized database in a high performance computing environment. The resulting data may be browsed in a user-friendly manner and easily forwarded to downstream analyses. Although CLOTU is specifically designed for analyzing 454 amplicon reads, other types of DNA sequence data can also be processed. A fungal ITS sequence dataset generated by 454 sequencing of environmental samples is used to demonstrate the utility of CLOTU.ConclusionsCLOTU is a flexible and easy to use bioinformatics pipeline that includes different options for filtering, trimming, clustering and taxonomic annotation of high throughput sequence reads. Some of these options are not included in comparable pipelines. CLOTU is implemented in a Linux computer cluster and is freely accessible to academic users through the Bioportal web-based bioinformatics service (http://www.bioportal.uio.no).

New modifications and applications of fuzzy C-means methodology

The fuzzy C-means (FCM) algorithm and various modifications of it with focus on practical applications in both industry and science are discussed. The general methodology is presented, as well as some well-known and also some less known modifications. It is demonstrated that the simple structure of the FCM algorithm allows for cluster analysis with non-typical and implicitly defined distance measures. Examples are residual distance for regression purposes, prediction sorting and penalised clustering criteria. Specialised applications of fuzzy clustering to be used for a sequential clustering strategy and for semi-supervised clustering are also discussed.

Prediction and classification of tenderness in beef from non-invasive diode array detected NIR spectra

NIR absorbance spectra of 48 beef samples were recorded 2, 9 and 21 days post mortem in the wavelength range 950–1700 nm with a Zeiss MCS 511 instrument equipped with diode array detector. These spectra were used to predict tenderness of the meat samples when Warner–Bratzler (WB) shear force was used as the reference method. Two types of prediction models were made. The models were either based on NIR spectra alone or NIR spectra in combination with information about post slaughter treatments. Prediction models from NIR spectra alone gave correlation coefficients in the range 0.52–0.83, but when variables for post slaughter treatments were included in the models the correlation coefficients were in the range 0.71–0.85. The additional variables had no effect on the prediction results when tenderness was predicted at the same time as NIR spectra were acquired, but improvements were found when tenderness was forecast later than the spectral acquisition. Based on these prediction models the beef samples were classified into two or three tenderness groups. When the beef samples were classified into two groups, 73–98% of the samples were correctly classified, while there were 63–75% correct classified samples when they were allocated into three groups.

Low-Cost Approaches to Robust Temperature Compensation in Near-Infrared Calibration and Prediction Situations

The traditional way of handling temperature shifts and other perturbations in calibration situations is to incorporate the non-relevant spectral variation in the calibration set by measuring the samples at various conditions. The present paper proposes two low-cost approaches based on simulation and prior knowledge about the perturbations, and these are compared to traditional methods. The first approach is based on augmentation of the calibration matrix through adding simulated noise on the spectra. The second approach is a correction method that removes the non-relevant variation from new spectra. Neither method demands exact knowledge of the perturbation levels. Using the augmentation method it was found that a few, in this case four, selected samples run under different conditions gave approximately the same robustness as running all the calibration samples under different conditions. For the carbohydrate data set, all robustification methods investigated worked well, including the use of pure water spectra for temperature compensation. For the more complex meat data set, only the augmentation method gave comparable results to the full global model.

Path modelling by sequential PLS regression

This paper presents a new approach to path modelling, based on a sequential multi‐block modelling in latent variables. The approach is explorative and focused on interpretation. The method breaks with standard traditions of estimating all paths using one single modelling. Instead, one separate model is estimated for each endogenous block. Each separate model is constructed by stepwise use of the standard PLS regression on matrices that are orthogonalised with respect to each other. The advantages of the approach are that it can allow for different dimensionality within each block, it is invariant to relative weighting of the blocks and it is based on simple and standard methodology allowing for simple outlier detection, validation and interpretation. No convergence problems are involved and the method can be used for situations with many more variables than samples. An application based on sensory analysis of wines will be used to illustrate the method. Copyright

The flexibility of fuzzy clustering illustrated by examples.

This paper presents a discussion of the versatility and flexibility of fuzzy clustering. Three examples of very different applications are presented. The focus is on flexibility with respect to distance measure used and with respect to the possibility of utilizing known membership values for some of the samples. Copyright

Using raw material measurements in robust process optimization

Abstract Unwanted variation due to variable raw material quality is often a problem in production processes. Robust process optimization seeks to reduce the effects of such variation by identifying settings of the adjustable factors that makes the process less sensitive to the variations. This paper develops a unified framework for studying and developing robust process optimization and process control techniques. We divide the factors of the process into groups based on characterizations of their properties. We also develop a robust process optimization technique for batch-wise processes, called batch-wise robust process optimization, which utilizes all available measurements of raw material qualities at the start of each production batch. The technique achieves a reduction of variability due to variation in raw material qualities, compared to ordinary robust process optimization. Two examples taken from baking of hearth bread illustrate the technique.