DiffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data.
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ABSTRACT:
SUBMITTER: Marangio P
PROVIDER: S-EPMC8157727 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Structure probing coupled with high-throughput sequencing holds the potential to revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite major technological advances, intrinsic noise and high coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline which accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome-wide. We demonstrate on two yeast data sets that our method has greatly increased sensitivity, enabling the identification of modified regions on many more transcripts compared with existing pipelines. It also provides confident predictions at much lower coverage levels than previously reported. Our results show that statistical modeling greatly extends the scope and potential of transcriptome-wide structure probing experiments.
2016-11-01 | GSE78208 | GEO
Project description:The identification of binding sites of transcription factors (TF) and other regulatory regions, referred to as motifs, located in a set of molecular sequences is of fundamental importance in genomic research. Many computational and experimental approaches have been developed to locate motifs. The set of sequences of interest can be concatenated to form a long sequence of length n. One of the successful approaches for motif discovery is to identify statistically over- or under-represented patterns in this long sequence. A pattern refers to a fixed word W over the alphabet. In the example of interest, W is a word in the set of patterns of the motif. Despite extensive studies on motif discovery, no studies have been carried out on the power of detecting statistically over- or under-represented patterns Here we address the issue of how the known presence of random instances of a known motif affects the power of detecting patterns, such as patterns within the motif. Let N(W)(n) be the number of possibly overlapping occurrences of a pattern W in the sequence that contains instances of a known motif; such a sequence is modeled here by a Hidden Markov Model (HMM). First, efficient computational methods for calculating the mean and variance of N(W)(n) are developed. Second, efficient computational methods for calculating parameters involved in the normal approximation of N(W)(n) for frequent patterns and compound Poisson approximation of N(W)(n) for rare patterns are developed. Third, an easy to use web program is developed to calculate the power of detecting patterns and the program is used to study the power of detection in several interesting biological examples.
| S-EPMC3203519 | biostudies-literature
Project description:We have developed a statistical regression modeling approach to discover genes that are differentially expressed between two predefined sample groups in DNA microarray experiments. Our model is based on well-defined assumptions, uses rigorous and well-characterized statistical measures, and accounts for the heterogeneity and genomic complexity of the data. In contrast to cluster analysis, which attempts to define groups of genes and/or samples that share common overall expression profiles, our modeling approach uses known sample group membership to focus on expression profiles of individual genes in a sensitive and robust manner. Further, this approach can be used to test statistical hypotheses about gene expression. To demonstrate this methodology, we compared the expression profiles of 11 acute myeloid leukemia (AML) and 27 acute lymphoblastic leukemia (ALL) samples from a previous study (Golub et al. 1999) and found 141 genes differentially expressed between AML and ALL with a 1% significance at the genomic level. Using this modeling approach to compare different sample groups within the AML samples, we identified a group of genes whose expression profiles correlated with that of thrombopoietin and found that genes whose expression associated with AML treatment outcome lie in recurrent chromosomal locations. Our results are compared with those obtained using t-tests or Wilcoxon rank sum statistics.
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Project description:BackgroundAn important goal of comparative genomics is the identification of functional elements through conservation analysis. Phylo-HMM was recently introduced to detect conserved elements based on multiple genome alignments, but the method has not been rigorously evaluated.ResultsWe report here a simulation study to investigate the power of phylo-HMM. We show that the power of the phylo-HMM approach depends on many factors, the most important being the number of species-specific genomes used and evolutionary distances between pairs of species. This finding is consistent with results reported by other groups for simpler comparative genomics models. In addition, the conservation ratio of conserved elements and the expected length of the conserved elements are also major factors. In contrast, the influence of the topology and the nucleotide substitution model are relatively minor factors.ConclusionOur results provide for general guidelines on how to select the number of genomes and their evolutionary distance in comparative genomics studies, as well as the level of power we can expect under different parameter settings.
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Project description:Protein conformational changes and dynamic behavior are fundamental for such processes as catalysis, regulation, and substrate recognition. Although protein dynamics have been successfully explored in computer simulation, there is an intermediate-scale of motions that has proven difficult to simulate - the motion of individual segments or domains that move independently of the body the protein. Here, we introduce a molecular-dynamics perturbation method, the Rotamerically Induced Perturbation (RIP), which can generate large, coherent motions of structural elements in picoseconds by applying large torsional perturbations to individual sidechains. Despite the large-scale motions, secondary structure elements remain intact without the need for applying backbone positional restraints. Owing to its computational efficiency, RIP can be applied to every residue in a protein, producing a global map of deformability. This map is remarkably sparse, with the dominant sites of deformation generally found on the protein surface. The global map can be used to identify loops and helices that are less tightly bound to the protein and thus are likely sites of dynamic modulation that may have important functional consequences. Additionally, they identify individual residues that have the potential to drive large-scale coherent conformational change. Applying RIP to two well-studied proteins, Dihdydrofolate Reductase and Triosephosphate Isomerase, which possess functionally-relevant mobile loops that fluctuate on the microsecond/millisecond timescale, the RIP deformation map identifies and recapitulates the flexibility of these elements. In contrast, the RIP deformation map of alpha-lytic protease, a kinetically stable protein, results in a map with no significant deformations. In the N-terminal domain of HSP90, the RIP deformation map clearly identifies the ligand-binding lid as a highly flexible region capable of large conformational changes. In the Estrogen Receptor ligand-binding domain, the RIP deformation map is quite sparse except for one large conformational change involving Helix-12, which is the structural element that allosterically links ligand binding to receptor activation. RIP analysis has the potential to discover sites of functional conformational changes and the linchpin residues critical in determining these conformational states.
| S-EPMC2660149 | biostudies-literature
Project description:MotivationWith improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data.ResultsWe propose an empirical Bayes mixture modeling approach called EBSeq-HMM. In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate dependence in gene expression across ordered conditions. As demonstrated in simulation and case studies, the output proves useful in identifying differentially expressed genes and in specifying gene-specific expression paths. EBSeq-HMM may also be used for inference regarding isoform expression.Availability and implementationAn R package containing examples and sample datasets is available at Bioconductor.Contactkendzior@biostat.wisc.eduSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BackgroundTiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis.ResultsHere we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure.ConclusionWe illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.
| S-EPMC2536674 | biostudies-literature
Project description:One outcome of interspecific hybridization and subsequent effects of evolutionary forces is introgression, which is the integration of genetic material from one species into the genome of an individual in another species. The evolution of several groups of eukaryotic species has involved hybridization, and cases of adaptation through introgression have been already established. In this work, we report on PhyloNet-HMM-a new comparative genomic framework for detecting introgression in genomes. PhyloNet-HMM combines phylogenetic networks with hidden Markov models (HMMs) to simultaneously capture the (potentially reticulate) evolutionary history of the genomes and dependencies within genomes. A novel aspect of our work is that it also accounts for incomplete lineage sorting and dependence across loci. Application of our model to variation data from chromosome 7 in the mouse (Mus musculus domesticus) genome detected a recently reported adaptive introgression event involving the rodent poison resistance gene Vkorc1, in addition to other newly detected introgressed genomic regions. Based on our analysis, it is estimated that about 9% of all sites within chromosome 7 are of introgressive origin (these cover about 13 Mbp of chromosome 7, and over 300 genes). Further, our model detected no introgression in a negative control data set. We also found that our model accurately detected introgression and other evolutionary processes from synthetic data sets simulated under the coalescent model with recombination, isolation, and migration. Our work provides a powerful framework for systematic analysis of introgression while simultaneously accounting for dependence across sites, point mutations, recombination, and ancestral polymorphism.
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Project description:BACKGROUND:A recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data. RESULTS:SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. By implementing SLIMEr on the consensus GEM of Saccharomyces cerevisiae, we can represent accurate amounts of lipid species, analyze the flexibility of the resulting distribution, and compute the energy costs of moving from one metabolic state to another. CONCLUSIONS:The approach shows potential for better understanding lipid metabolism in yeast under different conditions. SLIMEr is freely available at https://github.com/SysBioChalmers/SLIMEr .
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Project description:Biomarker identification by differentially expressed genes (DEGs) using RNA-sequencing technology is an important task to characterize the transcriptomics data. This is possible with the advancement of next-generation sequencing technology (NGS). There are a number of statistical techniques to identify DEGs from high-dimensional RNA-seq count data with different groups or conditions such as edgeR, SAMSeq, voom-limma, etc. However, these methods produce high false positives and low accuracy in presence of outliers. We describe a robust t-statistic method to overcome these drawbacks using both simulated and real RNA-seq datasets. The model performance with 61.2%, 35.2%, 21.6%, 6.9%, 74.5%, 78.4%, 93.1%, 35.2% sensitivity, specificity, MER, FDR, AUC, ACC, PPV, and NPV, respectively at 20% outliers is reported. We identified 409 DE genes with p-values<0.05 using robust t-test in HIV viremic vs avirmeic state real dataset. There are 28 up-regulated genes and 381 down-regulated genes estimated by log2 fold change (FC) approach at threshold value 1.5. The up-regulated genes form three clusters and it is found that 11 genes are highly associated in HIV- 1/AIDS. Protein-protein interaction (PPI) of up-regulated genes using STRING database found 21 genes with strong association among themselves. Thus, the identification of potential biomarkers from RNA-seq dataset using a robust t-statistical model is demonstrated.
| S-EPMC6016759 | biostudies-literature