8,806 research outputs found
Integrating functional genomics data using maximum likelihood based simultaneous component analysis
<p>Abstract</p> <p>Background</p> <p>In contemporary biology, complex biological processes are increasingly studied by collecting and analyzing measurements of the same entities that are collected with different analytical platforms. Such data comprise a number of data blocks that are coupled via a common mode. The goal of collecting this type of data is to discover biological mechanisms that underlie the behavior of the variables in the different data blocks. The simultaneous component analysis (SCA) family of data analysis methods is suited for this task. However, a SCA may be hampered by the data blocks being subjected to different amounts of measurement error, or noise. To unveil the true mechanisms underlying the data, it could be fruitful to take noise heterogeneity into consideration in the data analysis. Maximum likelihood based SCA (MxLSCA-P) was developed for this purpose. In a previous simulation study it outperformed normal SCA-P. This previous study, however, did not mimic in many respects typical functional genomics data sets, such as, data blocks coupled via the experimental mode, more variables than experimental units, and medium to high correlations between variables. Here, we present a new simulation study in which the usefulness of MxLSCA-P compared to ordinary SCA-P is evaluated within a typical functional genomics setting. Subsequently, the performance of the two methods is evaluated by analysis of a real life <it>Escherichia coli </it>metabolomics data set.</p> <p>Results</p> <p>In the simulation study, MxLSCA-P outperforms SCA-P in terms of recovery of the true underlying scores of the common mode and of the true values underlying the data entries. MxLSCA-P further performed especially better when the simulated data blocks were subject to different noise levels. In the analysis of an <it>E. coli </it>metabolomics data set, MxLSCA-P provided a slightly better and more consistent interpretation.</p> <p>Conclusion</p> <p>MxLSCA-P is a promising addition to the SCA family. The analysis of coupled functional genomics data blocks could benefit from its ability to take different noise levels per data block into consideration and improve the recovery of the true patterns underlying the data. Moreover, the maximum likelihood based approach underlying MxLSCA-P could be extended to custom-made solutions to specific problems encountered.</p
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
Understanding protein-protein interactions is central to our understanding of
almost all complex biological processes. Computational tools exploiting rapidly
growing genomic databases to characterize protein-protein interactions are
urgently needed. Such methods should connect multiple scales from evolutionary
conserved interactions between families of homologous proteins, over the
identification of specifically interacting proteins in the case of multiple
paralogs inside a species, down to the prediction of residues being in physical
contact across interaction interfaces. Statistical inference methods detecting
residue-residue coevolution have recently triggered considerable progress in
using sequence data for quaternary protein structure prediction; they require,
however, large joint alignments of homologous protein pairs known to interact.
The generation of such alignments is a complex computational task on its own;
application of coevolutionary modeling has in turn been restricted to proteins
without paralogs, or to bacterial systems with the corresponding coding genes
being co-localized in operons. Here we show that the Direct-Coupling Analysis
of residue coevolution can be extended to connect the different scales, and
simultaneously to match interacting paralogs, to identify inter-protein
residue-residue contacts and to discriminate interacting from noninteracting
families in a multiprotein system. Our results extend the potential
applications of coevolutionary analysis far beyond cases treatable so far.Comment: Main Text 19 pages Supp. Inf. 16 page
The inference of gene trees with species trees
Molecular phylogeny has focused mainly on improving models for the
reconstruction of gene trees based on sequence alignments. Yet, most
phylogeneticists seek to reveal the history of species. Although the histories
of genes and species are tightly linked, they are seldom identical, because
genes duplicate, are lost or horizontally transferred, and because alleles can
co-exist in populations for periods that may span several speciation events.
Building models describing the relationship between gene and species trees can
thus improve the reconstruction of gene trees when a species tree is known, and
vice-versa. Several approaches have been proposed to solve the problem in one
direction or the other, but in general neither gene trees nor species trees are
known. Only a few studies have attempted to jointly infer gene trees and
species trees. In this article we review the various models that have been used
to describe the relationship between gene trees and species trees. These models
account for gene duplication and loss, transfer or incomplete lineage sorting.
Some of them consider several types of events together, but none exists
currently that considers the full repertoire of processes that generate gene
trees along the species tree. Simulations as well as empirical studies on
genomic data show that combining gene tree-species tree models with models of
sequence evolution improves gene tree reconstruction. In turn, these better
gene trees provide a better basis for studying genome evolution or
reconstructing ancestral chromosomes and ancestral gene sequences. We predict
that gene tree-species tree methods that can deal with genomic data sets will
be instrumental to advancing our understanding of genomic evolution.Comment: Review article in relation to the "Mathematical and Computational
Evolutionary Biology" conference, Montpellier, 201
From principal component to direct coupling analysis of coevolution in proteins: Low-eigenvalue modes are needed for structure prediction
Various approaches have explored the covariation of residues in
multiple-sequence alignments of homologous proteins to extract functional and
structural information. Among those are principal component analysis (PCA),
which identifies the most correlated groups of residues, and direct coupling
analysis (DCA), a global inference method based on the maximum entropy
principle, which aims at predicting residue-residue contacts. In this paper,
inspired by the statistical physics of disordered systems, we introduce the
Hopfield-Potts model to naturally interpolate between these two approaches. The
Hopfield-Potts model allows us to identify relevant 'patterns' of residues from
the knowledge of the eigenmodes and eigenvalues of the residue-residue
correlation matrix. We show how the computation of such statistical patterns
makes it possible to accurately predict residue-residue contacts with a much
smaller number of parameters than DCA. This dimensional reduction allows us to
avoid overfitting and to extract contact information from multiple-sequence
alignments of reduced size. In addition, we show that low-eigenvalue
correlation modes, discarded by PCA, are important to recover structural
information: the corresponding patterns are highly localized, that is, they are
concentrated in few sites, which we find to be in close contact in the
three-dimensional protein fold.Comment: Supporting information can be downloaded from:
http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.100317
Recombination and its impact on the genome of the haplodiploid parasitoid wasp Nasonia
Homologous meiotic recombination occurs in most sexually reproducing organisms, yet its evolutionary advantages are elusive. Previous research explored recombination in the honeybee, a eusocial hymenopteran with an exceptionally high genome-wide recombination rate. A comparable study in a non-social member of the Hymenoptera that would disentangle the impact of sociality from Hymenoptera-specific features such as haplodiploidy on the evolution of the high genome-wide recombination rate in social Hymenoptera is missing. Utilizing single-nucleotide polymorphisms (SNPs) between two Nasonia parasitoid wasp genomes, we developed a SNP genotyping microarray to infer a high-density linkage map for Nasonia. The map comprises 1,255 markers with an average distance of 0.3 cM. The mapped markers enabled us to arrange 265 scaffolds of the Nasonia genome assembly 1.0 on the linkage map, representing 63.6% of the assembled N. vitripennis genome. We estimated a genome-wide recombination rate of 1.4-1.5 cM/Mb for Nasonia, which is less than one tenth of the rate reported for the honeybee. The local recombination rate in Nasonia is positively correlated with the distance to the center of the linkage groups, GC content, and the proportion of simple repeats. In contrast to the honeybee genome, gene density in the parasitoid wasp genome is positively associated with the recombination rate; regions of low recombination are characterized by fewer genes with larger introns and by a greater distance between genes. Finally, we found that genes in regions of the genome with a low recombination frequency tend to have a higher ratio of non-synonymous to synonymous substitutions, likely due to the accumulation of slightly deleterious non-synonymous substitutions. These findings are consistent with the hypothesis that recombination reduces interference between linked sites and thereby facilitates adaptive evolution and the purging of deleterious mutations. Our results imply that the genomes of haplodiploid and of diploid higher eukaryotes do not differ systematically in their recombination rates and associated parameters.Publisher PDFPeer reviewe
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