Browse
Submit Data
Databases
API
Help

Dataset Information

13 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Quantifying polymorphism and divergence from epigenetic data: a framework for inferring the action of selection.

ABSTRACT: Epigenetic modifications are alterations that regulate gene expression without modifying the underlying DNA sequence. DNA methylation and histone modifications, for example, are capable of spatial and temporal regulation of expression-with several studies demonstrating that these epigenetic marks are heritable. Thus, like DNA sequence, epigenetic marks are capable of storing information and passing it from one generation to the next. Because the epigenome is dynamic and epigenetic modifications can respond to external environmental stimuli, such changes may play an important role in adaptive evolution. While recent studies provide strong evidence for species-specific signatures of epigenetic marks, little is known about the mechanisms by which such modifications evolve. In order to address this question, we analyze the genome wide distribution of an epigenetic histone mark (H3K4me3) in prefrontal cortex neurons of humans, chimps and rhesus macaques. We develop a novel statistical framework to quantify within- and between-species variation in histone methylation patterns, using an ANOVA-based method and defining an FST -like measure for epigenetics (termed epi- FST), in order to develop a deeper understanding of the evolutionary pressures acting on epigenetic variation. Results demonstrate that genes with high epigenetic FST values are indeed significantly overrepresented among genes that are differentially expressed between species, and we observe only a weak correlation with SNP density.

SUBMITTER: Mahajan S

PROVIDER: S-EPMC4446996 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Inferring weak selection from patterns of polymorphism and divergence at "silent" sites in Drosophila DNA.

Project description:Patterns of codon usage and "silent" DNA divergence suggest that natural selection discriminates among synonymous codons in Drosophila. "Preferred" codons are consistently found in higher frequencies within their synonymous families in Drosophila melanogaster genes. This suggests a simple model of silent DNA evolution where natural selection favors mutations from unpreferred to preferred codons (preferred changes). Changes in the opposite direction, from preferred to unpreferred synonymous codons (unpreferred changes), are selected against. Here, selection on synonymous DNA mutations is investigated by comparing the evolutionary dynamics of these two categories of silent DNA changes. Sequences from outgroups are used to determine the direction of synonymous DNA changes within and between D. melanogaster and Drosophila simulans for five genes. Population genetics theory shows that differences in the fitness effect of mutations can be inferred from the comparison of ratios of polymorphism to divergence. Unpreferred changes show a significantly higher ratio of polymorphism to divergence than preferred changes in the D. simulans lineage, confirming the action of selection at silent sites. An excess of unpreferred fixations in 28 genes suggests a relaxation of selection on synonymous mutations in D. melanogaster. Estimates of selection coefficients for synonymous mutations (3.6 < magnitude of Nes < 1.3) in D. simulans are consistent with the reduced efficacy of natural selection (magnitude of Nes < 1) in the three- to sixfold smaller effective population size of D. melanogaster. Synonymous DNA changes appear to be a prevalent class of weakly selected mutations in Drosophila.

| S-EPMC1206357 | biostudies-other

Detecting and Quantifying Changing Selection Intensities from Time-Sampled Polymorphism Data.

Project description:During his well-known debate with Fisher regarding the phenotypic dataset of Panaxia dominula, Wright suggested fluctuating selection as a potential explanation for the observed change in allele frequencies. This model has since been invoked in a number of analyses, with the focus of discussion centering mainly on random or oscillatory fluctuations of selection intensities. Here, we present a novel method to consider nonrandom changes in selection intensities using Wright-Fisher approximate Bayesian (ABC)-based approaches, in order to detect and evaluate a change in selection strength from time-sampled data. This novel method jointly estimates the position of a change point as well as the strength of both corresponding selection coefficients (and dominance for diploid cases) from the allele trajectory. The simulation studies of this method reveal the combinations of parameter ranges and input values that optimize performance, thus indicating optimal experimental design strategies. We apply this approach to both the historical dataset of P. dominula in order to shed light on this historical debate, as well as to whole-genome time-serial data from influenza virus in order to identify sites with changing selection intensities in response to drug treatment.

| S-EPMC4825659 | biostudies-literature

Complex Selection on Human Polyadenylation Signals Revealed by Polymorphism and Divergence Data.

Project description:Polyadenylation is a step of mRNA processing which is crucial for its expression and stability. The major polyadenylation signal (PAS) represents a nucleotide hexamer that adheres to the AATAAA consensus sequence. Over a half of human genes have multiple cleavage and polyadenylation sites, resulting in a great diversity of transcripts differing in function, stability, and translational activity. Here, we use available whole-genome human polymorphism data together with data on interspecies divergence to study the patterns of selection acting on PAS hexamers. Common variants of PAS hexamers are depleted of single nucleotide polymorphisms (SNPs), and SNPs within PAS hexamers have a reduced derived allele frequency (DAF) and increased conservation, indicating prevalent negative selection; at the same time, the SNPs that "improve" the PAS (i.e., those leading to higher cleavage efficiency) have increased DAF, compared to those that "impair" it. SNPs are rarer at PAS of "unique" polyadenylation sites (one site per gene); among alternative polyadenylation sites, at the distal PAS and at exonic PAS. Similar trends were observed in DAFs and divergence between species of placental mammals. Thus, selection permits PAS mutations mainly at redundant and/or weakly functional PAS. Nevertheless, a fraction of the SNPs at PAS hexamers likely affect gene functions; in particular, some of the observed SNPs are associated with disease.

| S-EPMC4943204 | biostudies-literature

Quantifying Selection with Pool-Seq Time Series Data.

Project description:Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies.

| S-EPMC5850601 | biostudies-literature

Antagonistic selection factors induce a continuous population divergence in a polymorphism.

Project description:Understanding the relative importance of selection and stochastic factors in population divergence of adaptive traits is a classical topic in evolutionary biology. However, it is difficult to separate these factors and detect the effects of selection when two or more contrasting selective factors are simultaneously acting on a single locus. In the damselfly Ischnura senegalensis, females exhibit color dimorphism and morph frequencies change geographically. We here evaluated the role of selection and stochastic factors in population divergence of morph frequencies by comparing the divergences in color locus and neutral loci. Comparisons between population pairwise FST for neutral loci and for the color locus did not detect any stochastic factors affecting color locus. Although comparison between population divergence in color and neutral loci using all populations detected only divergent selection, we detected two antagonistic selective factors acting on the color locus, that is, balancing and divergent selection, when considering geographical distance between populations. Our results suggest that a combination of two antagonistic selective factors, rather than stochastic factors, establishes the geographic cline in morph frequency in this system.

| S-EPMC3966123 | biostudies-literature

An integer programming framework for inferring disease complexes from network data.

Project description:MotivationUnraveling the molecular mechanisms that underlie disease calls for methods that go beyond the identification of single causal genes to inferring larger protein assemblies that take part in the disease process.ResultsHere, we develop an exact, integer-programming-based method for associating protein complexes with disease. Our approach scores proteins based on their proximity in a protein-protein interaction network to a prior set that is known to be relevant for the studied disease. These scores are combined with interaction information to infer densely interacting protein complexes that are potentially disease-associated. We show that our method outperforms previous ones and leads to predictions that are well supported by current experimental data and literature knowledge.Availability and implementationThe datasets we used, the executables and the results are available at www.cs.tau.ac.il/roded/disease_complexes.zipContactroded@post.tau.ac.il.

| S-EPMC4908347 | biostudies-literature

Inference of natural selection from interspersed genomic elements based on polymorphism and divergence.

Project description:Complete genome sequences contain valuable information about natural selection, but this information is difficult to access for short, widely scattered noncoding elements such as transcription factor binding sites or small noncoding RNAs. Here, we introduce a new computational method, called Inference of Natural Selection from Interspersed Genomically coHerent elemenTs (INSIGHT), for measuring the influence of natural selection on such elements. INSIGHT uses a generative probabilistic model to contrast patterns of polymorphism and divergence in the elements of interest with those in flanking neutral sites, pooling weak information from many short elements in a manner that accounts for variation among loci in mutation rates and coalescent times. The method is able to disentangle the contributions of weak negative, strong negative, and positive selection based on their distinct effects on patterns of polymorphism and divergence. It obtains information about divergence from multiple outgroup genomes using a general statistical phylogenetic approach. The INSIGHT model is efficiently fitted to genome-wide data using an approximate expectation maximization algorithm. Using simulations, we show that the method can accurately estimate the parameters of interest even in complex demographic scenarios, and that it significantly improves on methods based on summary statistics describing polymorphism and divergence. To demonstrate the usefulness of INSIGHT, we apply it to several classes of human noncoding RNAs and to GATA2-binding sites in the human genome.

| S-EPMC3697874 | biostudies-other

Quantifying selection in high-throughput Immunoglobulin sequencing data sets.

Project description:High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B-cell cancers. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.

| S-EPMC3458526 | biostudies-other

A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks.

Project description:Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein-protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

| S-EPMC4126456 | biostudies-literature

A novel framework for inferring parameters of transmission from viral sequence data.

Project description:Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events, designed for influenza virus, but adaptable to other viral species. Our approach solves multiple shortcomings of previous methods for this purpose; for example, we consider transmission as an event involving whole viruses, rather than sets of independent alleles. We demonstrate how selection during transmission and noisy sequence data may each affect naive inferences of the population bottleneck, accounting for these in our framework so as to achieve a correct inference. We identify circumstances in which selection for increased viral transmission may or may not be identified from data. Applying our method to experimental data in which transmission occurs in the presence of strong selection, we show that our framework grants a more quantitative insight into transmission events than previous approaches, inferring the bottleneck in a manner that accounts for selection, both for within-host virulence, and for inherent viral transmissibility. Our work provides new opportunities for studying transmission processes in influenza, and by extension, in other infectious diseases.

| S-EPMC6203404 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data