| 1. |
- Andersson, David C., 1978-, et al.
(författare)
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A multivariate approach to investigate docking parameters' effects on docking performance
- 2007
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Ingår i: Journal of chemical information and modeling. - : American Chemical Society Publications. - 1549-9596 .- 1549-960X. ; 47:4, s. 1673-1687
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Tidskriftsartikel (refereegranskat)abstract
- Increasingly powerful docking programs for analyzing and estimating the strength of protein-ligand interactions have been developed in recent decades, and they are now valuable tools in drug discovery. Software used to perform dockings relies on a number of parameters that affect various steps in the docking procedure. However, identifying the best choices of the settings for these parameters is often challenging. Therefore, the settings of the parameters are quite often left at their default values, even though scientists with long experience with a specific docking tool know that modifying certain parameters can improve the results. In the study presented here, we have used statistical experimental design and subsequent regression based on root-mean-square deviation values using partial least-square projections to latent structures (PLS) to scrutinize the effects of different parameters on the docking performance of two software packages: FRED and GOLD. Protein-ligand complexes with a high level of ligand diversity were selected from the PDBbind database for the study, using principal component analysis based on 1D and 2D descriptors, and space-filling design. The PLS models showed quantitative relationships between the docking parameters and the ability of the programs to reproduce the ligand crystallographic conformation. The PLS models also revealed which of the parameters and what parameter settings were important for the docking performance of the two programs. Furthermore, the variation in docking results obtained with specific parameter settings for different protein-ligand complexes in the diverse set examined indicates that there is great potential for optimizing the parameter settings for selected sets of proteins.
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| 2. |
- Bylesjö, Max, et al.
(författare)
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Data integration in plant biology the O2PLS method for combined modeling of transcript and metabolite data
- 2007
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Ingår i: The Plant Journal. - 0960-7412 .- 1365-313X. ; 52:6, s. 1181-1191
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Tidskriftsartikel (refereegranskat)abstract
- The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This 'systems biology' approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining 'omics' types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform-specific systematic variation. A study of Populus tremula x Populus tremuloides, investigating short-day-induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.
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| 3. |
- Bylesjö, Max, et al.
(författare)
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Integrated analysis of transcript, protein and metabolite data to study lignin biosynthesis in hybrid aspen
- 2009
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 8:1, s. 199-210
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Tidskriftsartikel (refereegranskat)abstract
- Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber, energy and wood products. We are now ready to make the transition from identifying candidate genes, controlling important biological processes, to discovering the detailed molecular function of these genes on a broader, more holistic, systems biology level. In this paper, a strategy is outlined for informative data generation and integrated modeling of systematic changes in transcript, protein and metabolite profiles measured from hybrid aspen samples. The aim is to study characteristics of common changes in relation to genotype-specific perturbations affecting the lignin biosynthesis and growth. We show that a considerable part of the systematic effects in the system can be tracked across all platforms and that the approach has a high potential value in functional characterization of candidate genes.
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| 4. |
- Bylesjö, Max, et al.
(författare)
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K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space
- 2008
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Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 9, s. 1-7
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Tidskriftsartikel (refereegranskat)abstract
- Background: Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.Results: We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at http://www.sourceforge.net/projects/kopls/. The package includes essential functionality and documentation for model evaluation (using cross-validation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and plot functions to simplify the visualisation of data, e.g. for detecting trends or for identification of outlying samples. The utility of the software package is demonstrated by means of a metabolic profiling data set from a biological study of hybrid aspen.Conclusion: The properties of the K-OPLS method are well suited for analysis of biological data, which in conjunction with the availability of the outlined open-source package provides a comprehensive solution for kernel-based analysis in bioinformatics applications.
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| 5. |
- Bylesjö, Max, et al.
(författare)
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LAMINA : a tool for rapid quantification of leaf size and shape parameters
- 2008
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Ingår i: BMC Plant Biology. - : BMC. - 1471-2229. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Background: An increased understanding of leaf area development is important in a number of fields: in food and non-food crops, for example short rotation forestry as a biofuels feedstock, leaf area is intricately linked to biomass productivity; in paleontology leaf shape characteristics are used to reconstruct paleoclimate history. Such fields require measurement of large collections of leaves, with resulting conclusions being highly influenced by the accuracy of the phenotypic measurement process.Results: We have developed LAMINA (Leaf shApe deterMINAtion), a new tool for the automated analysis of images of leaves. LAMINA has been designed to provide classical indicators of leaf shape (blade dimensions) and size (area), which are typically required for correlation analysis to biomass productivity, as well as measures that indicate asymmetry in leaf shape, leaf serration traits, and measures of herbivory damage (missing leaf area). In order to allow Principal Component Analysis (PCA) to be performed, the location of a chosen number of equally spaced boundary coordinates can optionally be returned.Conclusion: We demonstrate the use of the software on a set of 500 scanned images, each containing multiple leaves, collected from a common garden experiment containing 116 clones of Populus tremula (European trembling aspen) that are being used for association mapping, as well as examples of leaves from other species. We show that the software provides an efficient and accurate means of analysing leaf area in large datasets in an automated or semi-automated work flow.
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| 6. |
- Bylesjö, Max, 1978-
(författare)
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Latent variable based computational methods for applications in life sciences : Analysis and integration of omics data sets
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With the increasing availability of high-throughput systems for parallel monitoring of multiple variables, e.g. levels of large numbers of transcripts in functional genomics experiments, massive amounts of data are being collected even from single experiments. Extracting useful information from such systems is a non-trivial task that requires powerful computational methods to identify common trends and to help detect the underlying biological patterns. This thesis deals with the general computational problems of classifying and integrating high-dimensional empirical data using a latent variable based modeling approach. The underlying principle of this approach is that a complex system can be characterized by a few independent components that characterize the systematic properties of the system. Such a strategy is well suited for handling noisy, multivariate data sets with strong multicollinearity structures, such as those typically encountered in many biological and chemical applications. The main foci of the studies this thesis is based upon are applications and extensions of the orthogonal projections to latent structures (OPLS) method in life science contexts. OPLS is a latent variable based regression method that separately describes systematic sources of variation that are related and unrelated to the modeling aim (for instance, classifying two different categories of samples). This separation of sources of variation can be used to pre-process data, but also has distinct advantages for model interpretation, as exemplified throughout the work. For classification cases, a probabilistic framework for OPLS has been developed that allows the incorporation of both variance and covariance into classification decisions. This can be seen as a unification of two historical classification paradigms based on either variance or covariance. In addition, a non-linear reformulation of the OPLS algorithm is outlined, which is useful for particularly complex regression or classification tasks. The general trend in functional genomics studies in the post-genomics era is to perform increasingly comprehensive characterizations of organisms in order to study the associations between their molecular and cellular components in greater detail. Frequently, abundances of all transcripts, proteins and metabolites are measured simultaneously in an organism at a current state or over time. In this work, a generalization of OPLS is described for the analysis of multiple data sets. It is shown that this method can be used to integrate data in functional genomics experiments by separating the systematic variation that is common to all data sets considered from sources of variation that are specific to each data set.
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| 7. |
- Bylesjö, Max, et al.
(författare)
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MASQOT : a method for cDNA microarray spot quality control.
- 2005
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 6, s. 250-
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundcDNA microarray technology has emerged as a major player in the parallel detection of biomolecules, but still suffers from fundamental technical problems. Identifying and removing unreliable data is crucial to prevent the risk of receiving illusive analysis results. Visual assessment of spot quality is still a common procedure, despite the time-consuming work of manually inspecting spots in the range of hundreds of thousands or more.ResultsA novel methodology for cDNA microarray spot quality control is outlined. Multivariate discriminant analysis was used to assess spot quality based on existing and novel descriptors. The presented methodology displays high reproducibility and was found superior in identifying unreliable data compared to other evaluated methodologies.ConclusionThe proposed methodology for cDNA microarray spot quality control generates non-discrete values of spot quality which can be utilized as weights in subsequent analysis procedures as well as to discard spots of undesired quality using the suggested threshold values. The MASQOT approach provides a consistent assessment of spot quality and can be considered an alternative to the labor-intensive manual quality assessment process.
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| 8. |
- Bylesjö, Max, et al.
(författare)
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MASQOT-GUI : spot quality assessment for the two-channel microarray platform
- 2006
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 22:20, s. 2554-2555
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Tidskriftsartikel (refereegranskat)abstract
- MASQOT-GUI provides an open-source, platform-independent software pipeline for two-channel microarray spot quality control. This includes gridding, segmentation, quantification, quality assessment and data visualization. It hosts a set of independent applications, with interactions between the tools as well as import and export support for external software. The implementation of automated multivariate quality control assessment, which is a unique feature of MASQOT-GUI, is based on the previously documented and evaluated MASQOT methodology. Further abilities of the application are outlined and illustrated. AVAILABILITY: MASQOT-GUI is Java-based and licensed under the GNU LGPL. Source code and installation files are available for download at http://masqot-gui.sourceforge.net/
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| 9. |
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| 10. |
- Bylesjö, Max, et al.
(författare)
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Normalization and Closure
- 2009
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Ingår i: Comprehensive Chemometrics. - AMSTERDAM : Elsevier. - 9780444527028 ; , s. A109-A127
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 11. |
- Bylesjö, Max, et al.
(författare)
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OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification
- 2006
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 341-351
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Tidskriftsartikel (refereegranskat)abstract
- The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results.
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| 12. |
- Bylesjö, Max, et al.
(författare)
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Orthogonal projections to latent structures as a strategy for microarray data normalization
- 2007
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 8:207
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundDuring generation of microarray data, various forms of systematic biases are frequently introduced which limits accuracy and precision of the results. In order to properly estimate biological effects, these biases must be identified and discarded.ResultsWe introduce a normalization strategy for multi-channel microarray data based on orthogonal projections to latent structures (OPLS); a multivariate regression method. The effect of applying the normalization methodology on single-channel Affymetrix data as well as dual-channel cDNA data is illustrated. We provide a parallel comparison to a wide range of commonly employed normalization methods with diverse properties and strengths based on sensitivity and specificity from external (spike-in) controls. On the illustrated data sets, the OPLS normalization strategy exhibits leading average true negative and true positive rates in comparison to other evaluated methods.ConclusionsThe OPLS methodology identifies joint variation within biological samples to enable the removal of sources of variation that are non-correlated (orthogonal) to the within-sample variation. This ensures that structured variation related to the underlying biological samples is separated from the remaining, bias-related sources of systematic variation. As a consequence, the methodology does not require any explicit knowledge regarding the presence or characteristics of certain biases. Furthermore, there is no underlying assumption that the majority of elements should be non-differentially expressed, making it applicable to specialized boutique arrays.
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| 13. |
- Bylesjö, Max
(författare)
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Predictive modelling and integration of functional genomics data for applications in plant biology
- 2008
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Ingår i: Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology. - : Elsevier. - 1095-6433. ; , s. S50-
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber and wood products. Through technological advancements, it may be possible to introduce trees that are optimized for faster growth or more efficient wood formation to grow more wood in managed plantation forests. To study and ultimately understand the underlying regulatory mechanisms of e.g. wood formation in trees, parallel profiling of molecular events are frequently utilized in functional genomics contexts. In practice, this implies that massive amounts of data are being generated from biological samples, e.g. the simultaneous measurement of tens of thousands of transcripts, proteins and metabolites at a current state or over time. This is not only creating logistical problems but also makes data integration and data mining one of the main challenges in post-genomics functional genomics studies. This presentation covers the integration of omics data sets using a predictive modeling approach. Based on the bi-directional, multivariate O2PLS method, it is possible to separate sources of variation that are overlapping across multiple analytical platforms as well as systematic sources of variation that are unique to each platform. This property can be highly beneficial for the biological interpretation of the results. Characteristics of the method as well as a conceptual comparison to related methods will be discussed. The main focus will be on the utility of the approach for studying simultaneous regulatory events in plant biology, exemplified by a study to reveal potential targets for optimization of the lignin biosynthesis.
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| 14. |
- Fahlén, Jessica, 1973-, et al.
(författare)
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Bioinformatics strategies for cDNA-microarray data processing
- 2009. - 1
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Ingår i: Batch effects and noise in microarray experiments. - : Wiley and Sons. - 9780470741382 ; , s. 61-74
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Pre-processing plays a vital role in cDNA-microarray data analysis. Without proper pre-processing it is likely that the biological conclusions will be misleading. However, there are many alternatives and in order to choose a proper pre-processing procedure it is necessary to understand the effect of different methods. This chapter discusses several pre-processing steps, including image analysis, background correction, normalization, and filtering. Spike-in data are used to illustrate how different procedures affect the analytical ability to detect differentially expressed genes and estimate their regulation. The result shows that pre-processing has a major impact on both the experiment’s sensitivity andits bias. However, general recommendations are hard to give, since pre-processing consists of several actions that are highly dependent on each other. Furthermore, it is likely that pre-processing have a major impact on downstream analysis, such as clustering and classification, and pre-processing methods should be developed and evaluated with this in mind.
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| 15. |
- Hoffman, Daniel E., et al.
(författare)
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Changes in diurnal patterns within the Populus transcriptome and metabolome in response to photoperiod variation
- 2010
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Ingår i: Plant, Cell and Environment. - : Wiley. - 0140-7791 .- 1365-3040. ; 33:8, s. 1298-1313
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Tidskriftsartikel (refereegranskat)abstract
- Changes in seasonal photoperiod provides an important environmental signal that affects the timing of winter dormancy in perennial, deciduous, temperate tree species, such as hybrid aspen (Populus tremula x Populus tremuloides). In this species, growth cessation, cold acclimation and dormancy are induced in the autumn by the detection of day-length shortening that occurs at a given critical day length. Important components in the detection of such day-length changes are photoreceptors and the circadian clock, and many plant responses at both the gene regulation and metabolite levels are expected to be diurnal. To directly examine this expectation and study components in these events, here we report transcriptomic and metabolomic responses to a change in photoperiod from long to short days in hybrid aspen. We found about 16% of genes represented on the arrays to be diurnally regulated, as assessed by our pre-defined criteria. Furthermore, several of these genes were involved in circadian-associated processes, including photosynthesis and primary and secondary metabolism. Metabolites affected by the change in photoperiod were mostly involved in carbon metabolism. Taken together, we have thus established a molecular catalog of events that precede a response to winter.
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| 16. |
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| 17. |
- Rantalainen, Mattias, et al.
(författare)
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Kernel-based orthogonal projections to latent structures (K-OPLS)
- 2007
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 21:7-9, s. 379-385
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Tidskriftsartikel (refereegranskat)abstract
- The orthogonal projections to latent structures (OPLS) method has been successfully applied in various chemical and biological systems for modeling and interpretation of linear relationships between a descriptor matrix and response matrix. A kernel-based reformulation of the original OPLS algorithm is presented where the kernel Gram matrix is utilized as a replacement for the descriptor matrix. This enables usage of the kernel trick to efficiently transform the data into a higher-dimensional feature space where predictive and response-orthogonal components are calculated. This strategy has the capacity to improve predictive performance considerably in situations where strong non-linear relationships exist between descriptor and response variables while retaining the OPLS model framework. We put particular focus on describing properties of the rearranged algorithm in relation to the original OPLS algorithm. Four separate problems, two simulated and two real spectroscopic data sets, are employed to illustrate how the algorithm enables separate modeling of predictive and response-orthogonal variation in the feature space. This separation can be highly beneficial for model interpretation purposes while providing a flexible framework for supervised regression.
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| 18. |
- Sjödin, Andreas, et al.
(författare)
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Global expression profiling in leaves of free-growing aspen
- 2008
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Ingår i: BMC Plant Biology. - : Springer Science and Business Media LLC. - 1471-2229. ; 8, s. 61-
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundGenomic studies are routinely performed on young plants in controlled environments which is very different from natural conditions. In reality plants in temperate countries are exposed to large fluctuations in environmental conditions, in the case of perennials over several years. We have studied gene expression in leaves of a free-growing aspen (Populus tremula) throughout multiple growing seasons.ResultsWe show that gene expression during the first month of leaf development was largely determined by a developmental program although leaf expansion, chlorophyll accumulation and the speed of progression through this program was regulated by the temperature. We were also able to define "transcriptional signatures" for four different substages of leaf development. In mature leaves, weather factors were important for gene regulation.ConclusionsThis study shows that multivariate methods together with high throughput transcriptional methods in the field can provide additional, novel information as to plant status under changing environmental conditions that is impossible to mimic in laboratory conditions. We have generated a dataset that could be used to e.g. identify marker genes for certain developmental stages or treatments, as well as to assess natural variation in gene expression.
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| 19. |
- Street, Nathaniel Robert, 1979-, et al.
(författare)
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A cross-species transcriptomics approach to identify genes involved in leaf development
- 2008
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 9:1, s. 539-
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- BackgroundWe have made use of publicly available gene expression data to identify transcription factors and transcriptional modules (regulons) associated with leaf development in Populus. Different tissue types were compared to identify genes informative in the discrimination of leaf and non-leaf tissues. Transcriptional modules within this set of genes were identified in a much wider set of microarray data collected from leaves in a number of developmental, biotic, abiotic and transgenic experiments.ResultsTranscription factors that were over represented in leaf EST libraries and that were useful for discriminating leaves from other tissues were identified, revealing that the C2C2-YABBY, CCAAT-HAP3 and 5, MYB, and ZF-HD families are particularly important in leaves. The expression of transcriptional modules and transcription factors was examined across a number of experiments to select those that were particularly active during the early stages of leaf development. Two transcription factors were found to collocate to previously published Quantitative Trait Loci (QTL) for leaf length. We also found that miRNA family 396 may be important in the control of leaf development, with three members of the family collocating with clusters of leaf development QTL.ConclusionThis work provides a set of candidate genes involved in the control and processes of leaf development. This resource can be used for a wide variety of purposes such as informing the selection of candidate genes for association mapping or for the selection of targets for reverse genetics studies to further understanding of the genetic control of leaf size and shape.
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| 20. |
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The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
- 2010
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Ingår i: Nature Biotechnology. - : Nature Publishing Group. - 1087-0156 .- 1546-1696. ; 28:8, s. 827-838
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Tidskriftsartikel (refereegranskat)abstract
- Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
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