| 1. |
- Björkholm, Patrik, et al.
(author)
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Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts
- 2009
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 25:10, s. 1264-1270
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Journal article (peer-reviewed)abstract
- Motivation: Correct prediction of residue-residue contacts in proteins that lack good templates with known structure would take ab initio protein structure prediction a large step forward. The lack of correct contacts, and in particular long-range contacts, is considered the main reason why these methods often fail. Results: We propose a novel hidden Markov model (HMM)based method for predicting residue-residue contacts from protein sequences using as training data homologous sequences, predicted secondary structure and a library of local neighborhoods (local descriptors of protein structure). The library consists of recurring structural entities incorporating short-, medium- and long-range interactions and is general enough to reassemble the cores of nearly all proteins in the PDB. The method is tested on an external test set of 606 domains with no significant sequence similarity to the training set as well as 151 domains with SCOP folds not present in the training set. Considering the top 0.2 . L predictions (L = sequence length), our HMMs obtained an accuracy of 22.8% for long-range interactions in new fold targets, and an average accuracy of 28.6% for long-, medium- and short- range contacts. This is a significant performance increase over currently available methods when comparing against results published in the literature.
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| 2. |
- Bylesjö, Max, et al.
(author)
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MASQOT-GUI : spot quality assessment for the two-channel microarray platform
- 2006
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 22:20, s. 2554-2555
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Journal article (peer-reviewed)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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| 3. |
- Climer, Sharlee, et al.
(author)
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How frugal is mother nature with haplotypes?
- 2009
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In: Bioinformatics. - Oxford : Oxford University Press. - 1367-4803 .- 1367-4811. ; 25:1, s. 68-74
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Journal article (peer-reviewed)abstract
- Motivation: Inference of haplotypes from genotype data is crucial and challenging for many vitally important studies. The first, and most critical step, is the ascertainment of a biologically sound model to be optimized. Many models that have been proposed rely partially or entirely on reducing the number of unique haplotypes in the solution.Results: This article examines the parsimony of haplotypes using known haplotypes as well as genotypes from the HapMap project. Our study reveals that there are relatively few unique haplotypes, but not always the least possible, for the datasets with known solutions. Furthermore, we show that there are frequently very large numbers of parsimonious solutions, and the number increases exponentially with increasing cardinality. Moreover, these solutions are quite varied, most of which are not consistent with the true solutions. These results quantify the limitations of the Pure Parsimony model and demonstrate the imperative need to consider additional properties for haplotype inference models. At a higher level, and with broad applicability, this article illustrates the power of combinatorial methods to tease out imperfections in a given biological model.
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| 4. |
- Delhomme, Nicolas, et al.
(author)
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easyRNASeq : a bioconductor package for processing RNA-Seq data.
- 2012
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 28:19
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Journal article (peer-reviewed)abstract
- MOTIVATION: RNA sequencing is becoming a standard for expression profiling experiments and many tools have been developed in the past few years to analyze RNA-Seq data. Numerous 'Bioconductor' packages are available for next-generation sequencing data loading in R, e.g. ShortRead and Rsamtools as well as to perform differential gene expression analyses, e.g. DESeq and edgeR. However, the processing tasks lying in between these require the precise interplay of many Bioconductor packages, e.g. Biostrings, IRanges or external solutions are to be sought.RESULTS: We developed 'easyRNASeq', an R package that simplifies the processing of RNA sequencing data, hiding the complex interplay of the required packages behind a single functionality.AVAILABILITY: The package is implemented in R (as of version 2.15) and is available from Bioconductor (as of version 2.10) at the URL: http://bioconductor.org/packages/release/bioc/html/easyRNASeq.html, where installation and usage instructions can be found.CONTACT: delhomme@embl.de.
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| 5. |
- Dickinson, Q., et al.
(author)
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Multi-omic integration by machine learning (MIMaL)
- 2022
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 38:21, s. 4908-4918
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Journal article (peer-reviewed)abstract
- Motivation: Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow for measuring the abundances of RNA, proteins, lipids and metabolites. These highly complex datasets reflect the states of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through the integration of these data remains challenging. Results: Connections between molecules in different omic layers were discovered through a combination of machine learning and model interpretation. Discovered connections reflected protein control (ProC) over metabolites. Proteins discovered to control citrate were mapped onto known genetic and metabolic networks, revealing that these protein regulators are novel. Further, clustering the magnitudes of ProC over all metabolites enabled the prediction of five gene functions, each of which was validated experimentally. Two uncharacterized genes, YJR120W and YDL157C, were accurately predicted to modulate mitochondrial translation. Functions for three incompletely characterized genes were also predicted and validated, including SDH9, ISC1 and FMP52. A website enables results exploration and also MIMaL analysis of user-supplied multi-omic data.
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| 6. |
- Holme, Petter, et al.
(author)
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Subnetwork hierarchies of biochemical pathways
- 2003
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 19:4, s. 532-538
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Journal article (peer-reviewed)abstract
- Motivation: The vastness and complexity of the biochemical networks that have been mapped out by modern genomics calls for decomposition into subnetworks. Such networks can have inherent non-local features that require the global structure to be taken into account in the decomposition procedure. Furthermore, basic questions such as to what extent the network (graph theoretically) can be said to be built by distinct subnetworks are little studied. Results: We present a method to decompose biochemical networks into subnetworks based on the global geometry of the network. This method enables us to analyze the full hierarchical organization of biochemical networks and is applied to 43 organisms from the WIT database. Two types of biochemical networks are considered: metabolic networks and whole-cellular networks (also including for example information processes). Conceptual and quantitative ways of describing the hierarchical ordering are discussed. The general picture of the metabolic networks arising from our study is that of a few core-clusters centred around the most highly connected substances enclosed by other substances in outer shells, and a few other well-defined subnetworks.
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| 7. |
- Johansson, Daniel, et al.
(author)
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A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcription
- 2003
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4811 .- 1367-4803. ; 19:4, s. 467-73
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Journal article (peer-reviewed)abstract
- We have analyzed microarray data using a modeling approach based on the multivariate statistical method partial least squares (PLS) regression to identify genes with periodic fluctuations in expression levels coupled to the cell cycle in the budding yeast, Saccharomyces cerevisiae. PLS has major advantages for analyzing microarray data since it can model data sets with large numbers of variables and with few observations.A response model was derived describing the expression profile over time expected for periodically transcribed genes, and was used to identify budding yeast transcripts with similar profiles. PLS was then used to interpret the importance of the variables (genes) for the model, yielding a ranking list of how well the genes fitted the generated model. Application of an appropriate cutoff value, calculated from randomized data, allows the identification of genes whose expression appears to be synchronized with cell cycling. Our approach also provides information about the stage in the cell cycle where their transcription peaks.Three synchronized yeast cell microarray data sets were analyzed, both separately and combined. Cell cycle-coupled periodicity was suggested for 455 of the 6,178 transcripts monitored in the combined data set, at a significance level of 0.5%. Among the candidates, 85% of the known periodic transcripts were included. Analysis of the three data sets separately yielded similar ranking lists, showing that the method is robust.
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| 8. |
- Kim, Dae-Kyum, et al.
(author)
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EVpedia: A Community Web Portal for Extracellular Vesicles Research
- 2015
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 31:6, s. 933-939
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Journal article (peer-reviewed)abstract
- Motivation: Extracellular vesicles (EVs) are spherical bilayered proteolipids, harboring various bioactive molecules. Due to the complexity of the vesicular nomenclatures and components, online searches for EV-related publications and vesicular components are currently challenging. Results: We present an improved version of EVpedia, a public database for EVs research. This community web portal contains a database of publications and vesicular components, identification of orthologous vesicular components, bioinformatic tools and a personalized function. EVpedia includes 6879 publications, 172 080 vesicular components from 263 high-throughput datasets, and has been accessed more than 65 000 times from more than 750 cities. In addition, about 350 members from 73 international research groups have participated in developing EVpedia. This free web-based database might serve as a useful resource to stimulate the emerging field of EV research.
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| 9. |
- Loo, Ruey Leng, et al.
(author)
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Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS)
- 2020
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In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:21, s. 5229-5236
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Journal article (peer-reviewed)abstract
- Motivation: Large-scale population omics data can provide insight into associations between gene-environment interactions and disease. However, existing dimension reduction modelling techniques are often inefficient for extracting detailed information from these complex datasets.Results: Here, we present an interactive software pipeline for exploratory analyses of population-based nuclear magnetic resonance spectral data using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS) within the R-library hastaLaVista framework. Principal component analysis models are generated for a sequential series of spectral regions (blocks) to provide more granular detail defining sub-populations within the dataset. Molecular identification of key differentiating signals is subsequently achieved by implementing Statistical TOtal Correlation SpectroscopY on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset are used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS method thus provides an efficient semi-automated approach for screening population datasets.
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| 10. |
- Lärkeryd, Adrian, et al.
(author)
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CanSNPer : a hierarchical genotype classifier of clonal pathogens
- 2014
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In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 30:12, s. 1762-1764
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Journal article (peer-reviewed)abstract
- Advances in typing methodologies have recently reformed the field of molecular epidemiology of pathogens. The falling cost of sequencing technologies is creating a deluge of whole genome sequencing data that burdens bioinformatics resources and tool development. In particular, single nucleotide polymorphisms in core genomes of pathogens are recognized as the most important markers for inferring genetic relationships because they are evolutionarily stable and amenable to high-throughput detection methods. Sequence data will provide an excellent opportunity to extend our understanding of infectious disease when the challenge of extracting knowledge from available sequence resources is met. Here, we present an efficient and user-friendly genotype classification pipeline, CanSNPer, based on an easily expandable database of predefined canonical single nucleotide polymorphisms.
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