| 21. |
- Margulies, Elliott H, et al.
(författare)
-
Analyses of deep mammalian sequence alignments and constraint predictions for 1% of the human genome
- 2007
-
Ingår i: Genome Research. - : Cold Spring Harbor Laboratory. - 1088-9051 .- 1549-5469. ; 17:6, s. 760-774
-
Tidskriftsartikel (refereegranskat)abstract
- A key component of the ongoing ENCODE project involves rigorous comparative sequence analyses for the initially targeted 1% of the human genome. Here, we present orthologous sequence generation, alignment, and evolutionary constraint analyses of 23 mammalian species for all ENCODE targets. Alignments were generated using four different methods; comparisons of these methods reveal large-scale consistency but substantial differences in terms of small genomic rearrangements, sensitivity (sequence coverage), and specificity (alignment accuracy). We describe the quantitative and qualitative trade-offs concomitant with alignment method choice and the levels of technical error that need to be accounted for in applications that require multisequence alignments. Using the generated alignments, we identified constrained regions using three different methods. While the different constraint-detecting methods are in general agreement, there are important discrepancies relating to both the underlying alignments and the specific algorithms. However, by integrating the results across the alignments and constraint-detecting methods, we produced constraint annotations that were found to be robust based on multiple independent measures. Analyses of these annotations illustrate that most classes of experimentally annotated functional elements are enriched for constrained sequences; however, large portions of each class (with the exception of protein-coding sequences) do not overlap constrained regions. The latter elements might not be under primary sequence constraint, might not be constrained across all mammals, or might have expendable molecular functions. Conversely, 40% of the constrained sequences do not overlap any of the functional elements that have been experimentally identified. Together, these findings demonstrate and quantify how many genomic functional elements await basic molecular characterization.
|
|
| 22. |
- Rantalainen, Mattias, et al.
(författare)
-
Kernel-based orthogonal projections to latent structures (K-OPLS)
- 2007
-
Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 21:7-9, s. 379-385
-
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.
|
|
| 23. |
- Rantalainen, Mattias, et al.
(författare)
-
Piecewise multivariate modelling of sequential metabolic profiling data
- 2008
-
Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints.Results: A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted.Conclusion: The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.
|
|
| 24. |
- Rantalainen, Mattias, et al.
(författare)
-
Piecewise multivariate modelling of sequential metabolic profiling data
- 2008
-
Ingår i: BMC Bioinformatics. - : EMBO. - 1471-2105. ; 9, s. 105-
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. Results: A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. Conclusion: The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.
|
|
| 25. |
- Rantalainen, Mattias, et al.
(författare)
-
Statistically Integrated Metabonomic-Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice
- 2006
-
Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 10, s. 2642-55
-
Tidskriftsartikel (refereegranskat)abstract
- A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies
|
|
| 26. |
- Trygg, Johan, et al.
(författare)
-
Chemometrics in metabonomics
- 2007
-
Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 6:2, s. 469-479
-
Forskningsöversikt (refereegranskat)abstract
- We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data. This includes the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS (OPLS), and dynamic extensions thereof. This is illustrated by examples from the literature.
|
|
| 27. |
- Vorkas, Panagiotis A., et al.
(författare)
-
Perturbations in fatty acid metabolism and apoptosis are manifested in calcific coronary artery disease : An exploratory lipidomic study
- 2015
-
Ingår i: International Journal of Cardiology. - : Elsevier. - 0167-5273 .- 1874-1754. ; 197, s. 192-199
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Controversy exists concerning the beneficial or harmful effects of the presence of ectopic calcification in the coronary arteries. Additionally, further elucidation of the exact pathophysiological mechanism is needed. In this study, we sought to identify metabolic markers of vascular calcification that could assist in understanding the disease, monitoring its progress and generating hypotheses describing its pathophysiology. Methods: Untargeted lipid profiling and complementary modeling strategies were employed to compare serum samples from patients with different levels of calcific coronary artery disease (CCAD) based on their calcium score (CS). Subsequently, patients were divided into three groups: no calcification (NC; CS = 0; n = 26), mild calcification (MC; CS: 1-250; n = 27) and severe (SC; CS > 250; n = 17). Results: Phosphatidylcholine levels were found to be significantly altered in the disease states (p = 0.001-0.04). Specifically, 18-carbon fatty acyl chain (FAC) phosphatidylcholines were detected in lower levels in the SC group, while 20:4 FAC lipid species were detected in higher concentrations. A statistical trend was observed with phosphatidylcholine lipids in the MC group, showing the same tendency as with the SC group. We also observed several sphingomyelin signals present at lower intensities in SC when compared with NC or MC groups (p = 0.000001-0.01). Conclusions: This is the first lipid profiling study reported in CCAD. Our data demonstrate dysregulations of phosphatidylcholine lipid species, which suggest perturbations in fatty acid elongation/desaturation. The altered levels of the 18-carbon and 20:4 FAC lipids may be indicative of disturbed inflammation homeostasis. The marked sphingomyelin dysregulation in SC is consistent with profound apoptosis as a potential mechanism of CCAD. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
|
|
| 28. |
- Zuntini, Alexandre R., et al.
(författare)
-
Phylogenomics and the rise of the angiosperms
- 2024
-
Ingår i: NATURE. - 0028-0836 .- 1476-4687. ; 629, s. 843-850
-
Tidskriftsartikel (refereegranskat)abstract
- Angiosperms are the cornerstone of most terrestrial ecosystems and human livelihoods(1,2). A robust understanding of angiosperm evolution is required to explain their rise to ecological dominance. So far, the angiosperm tree of life has been determined primarily by means of analyses of the plastid genome(3,4). Many studies have drawn on this foundational work, such as classification and first insights into angiosperm diversification since their Mesozoic origins(5-7). However, the limited and biased sampling of both taxa and genomes undermines confidence in the tree and its implications. Here, we build the tree of life for almost 8,000 (about 60%) angiosperm genera using a standardized set of 353 nuclear genes(8). This 15-fold increase in genus-level sampling relative to comparable nuclear studies(9) provides a critical test of earlier results and brings notable change to key groups, especially in rosids, while substantiating many previously predicted relationships. Scaling this tree to time using 200 fossils, we discovered that early angiosperm evolution was characterized by high gene tree conflict and explosive diversification, giving rise to more than 80% of extant angiosperm orders. Steady diversification ensued through the remaining Mesozoic Era until rates resurged in the Cenozoic Era, concurrent with decreasing global temperatures and tightly linked with gene tree conflict. Taken together, our extensive sampling combined with advanced phylogenomic methods shows the deep history and full complexity in the evolution of a megadiverse clade.
|
|
