Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “probabilistic modelling”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31Linked to original sources
PubMed2005

Analysis of evolution of exon-intron structure of eukaryotic genes.

The availability of multiple, complete eukaryotic genome sequences allows one to address many fundamental evolutionary questions on genome scale. One such important, long-standing problem is evolution of exon-intron structure of eukaryotic genes. Analysis of orthologous genes from completely sequenced genomes revealed numerous shared intron positions in orthologous genes from animals and plants and even between animals, plants and protists. The data on shared and lineage-specific intron positions were used as the starting point for evolutionary reconstruction with parsimony and maximum-likelihood approaches. Parsimony methods produce reconstructions with intron-rich ancestors but also infer lineage-specific, in many cases, high levels of intron loss and gain. Different probabilistic models gave opposite results, apparently depending on model parameters and assumptions, from domination of intron loss, with extremely intron-rich ancestors, to dramatic excess of gains, to the point of denying any true conservation of intron positions among deep eukaryotic lineages. Development of models with adequate, realistic parameters and assumptions seems to be crucial for obtaining more definitive estimates of intron gain and loss in different eukaryotic lineages. Many shared intron positions were detected in ancestral eukaryotic paralogues which evolved by duplication prior to the divergence of extant eukaryotic lineages. These findings indicate that numerous introns were present in eukaryotic genes already at the earliest stages of evolution of eukaryotes and are compatible with the hypothesis that the original, catastrophic intron invasion accompanied the emergence of the eukaryotic cells. Comparison of various features of old and younger introns starts shedding light on probable mechanisms of intron insertion, indicating that propagation of old introns is unlikely to be a major mechanism for origin of new ones. The existence and structure of ancestral protosplice sites were addressed by examining the context of introns inserted within codons that encode amino acids conserved in all eukaryotes and, accordingly, are not subject to selection for splicing efficiency. It was shown that introns indeed predominantly insert into or are fixed in specific protosplice sites which have the consensus sequence (A/C)AG|Gt.

Animals↗
PubMed2006

Statistical significance in biological sequence analysis.

One of the major goals of computational sequence analysis is to find sequence similarities, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Since the degree of similarity is usually assessed by the sequence alignment score, it is necessary to know if a score is high enough to indicate a biologically interesting alignment. A powerful approach to defining score cutoffs is based on the evaluation of the statistical significance of alignments. The statistical significance of an alignment score is frequently assessed by its P-value, which is the probability that this score or a higher one can occur simply by chance, given the probabilistic models for the sequences. In this review we discuss the general role of P-value estimation in sequence analysis, and give a description of theoretical methods and computational approaches to the estimation of statistical signifiance for important classes of sequence analysis problems. In particular, we concentrate on the P-value estimation techniques for single sequence studies (both score-based and score-free), global and local pairwise sequence alignments, multiple alignments, sequence-to-profile alignments and alignments built with hidden Markov models. We anticipate that the review will be useful both to researchers professionally working in bioinformatics as well as to biomedical scientists interested in using contemporary methods of DNA and protein sequence analysis.

Computational Biology↗
PubMed1998

Rose: generating sequence families.

MOTIVATION: We present a new probabilistic model of the evolution of RNA-, DNA-, or protein-like sequences and a software tool, Rose, that implements this model. Guided by an evolutionary tree, a family of related sequences is created from a common ancestor sequence by insertion, deletion and substitution of characters. During this artificial evolutionary process, the 'true' history is logged and the 'correct' multiple sequence alignment is created simultaneously. The model also allows for varying rates of mutation within the sequences, making it possible to establish so-called sequence motifs. RESULTS: The data created by Rose are suitable for the evaluation of methods in multiple sequence alignment computation and the prediction of phylogenetic relationships. It can also be useful when teaching courses in or developing models of sequence evolution and in the study of evolutionary processes. AVAILABILITY: Rose is available on the Bielefeld Bioinformatics WebServer under the following URL: http://bibiserv.TechFak.Uni-Bielefeld.DE/rose/ The source code is available upon request. CONTACT: folker@TechFak.Uni-Bielefeld.DE

Algorithms↗
PubMed2000

The language of RNA: a formal grammar that includes pseudoknots.

MOTIVATION: In a previous paper, we presented a polynomial time dynamic programming algorithm for predicting optimal RNA secondary structure including pseudoknots. However, a formal grammatical representation for RNA secondary structure with pseudoknots was still lacking. RESULTS: Here we show a one-to-one correspondence between that algorithm and a formal transformational grammar. This grammar class encompasses the context-free grammars and goes beyond to generate pseudoknotted structures. The pseudoknot grammar avoids the use of general context-sensitive rules by introducing a small number of auxiliary symbols used to reorder the strings generated by an otherwise context-free grammar. This formal representation of the residue correlations in RNA structure is important because it means we can build full probabilistic models of RNA secondary structure, including pseudoknots, and use them to optimally parse sequences in polynomial time.

Algorithms↗
PubMed2000

Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs.

MOTIVATION: Several results in the literature suggest that biologically interesting RNAs have secondary structures that are more stable than expected by chance. Based on these observations, we developed a scanning algorithm for detecting noncoding RNA genes in genome sequences, using a fully probabilistic version of the Zuker minimum-energy folding algorithm. RESULTS: Preliminary results were encouraging, but certain anomalies led us to do a carefully controlled investigation of this class of methods. Ultimately, our results argue that for the probabilistic model there is indeed a statistical effect, but it comes mostly from local base-composition bias and not from RNA secondary structure. For the thermodynamic implementation (which evaluates statistical significance by doing Monte Carlo shuffling in fixed-length sequence windows, thus eliminating the base-composition effect) the signals for noncoding RNAs are still usually indistinguishable from noise, especially when certain statistical artifacts resulting from local base-composition inhomogeneity are taken into account. We conclude that although a distinct, stable secondary structure is undoubtedly important in most noncoding RNAs, the stability of most noncoding RNA secondary structures is not sufficiently different from the predicted stability of a random sequence to be useful as a general genefinding approach.

Algorithms↗
PubMed2025

Bayesian inference of fitness landscapes via tree-structured branching processes.

MOTIVATION: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. RESULTS: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Bayes Theorem↗
PubMed2025

PhyClone: accurate Bayesian reconstruction of cancer phylogenies from bulk sequencing.

MOTIVATION: Cancer is driven by somatic mutations that result in the expansion of genomically distinct sub-populations of cells called clones. Identifying the clonal composition of tumours and understanding the evolutionary relationships between clones is a crucial task in cancer genomics. Bulk DNA sequencing is commonly used for studying the clonal composition of tumours, but it is challenging to infer the genetic relationship between different clones due to the mixture of different cell populations. RESULTS: In this work, we introduce a new probabilistic model called PhyClone that can infer clonal phylogenies from bulk-sequencing data. We demonstrate the performance of PhyClone on simulated and real-world datasets and show that it outperforms previous methods in terms of accuracy and sample scalability. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at: https://github.com/Roth-Lab/PhyClone under the GPL v3.0 license.

Neoplasms↗
PubMed2003

Methods for optimizing antiviral combination therapies.

MOTIVATION: Despite some progress with antiretroviral combination therapies, therapeutic success in the management of HIV-infected patients is limited. The evolution of drug-resistant genetic variants in response to therapy plays a key role in treatment failure and finding a new potent drug combination after therapy failure is considered challenging. RESULTS: To estimate the activity of a drug combination against a particular viral strain, we develop a scoring function whose independent variables describe a set of antiviral agents and viral DNA sequences coding for the molecular targets of the respective drugs. The construction of this activity score involves (1) predicting phenotypic drug resistance from genotypes for each drug individually, (2) probabilistic modeling of predicted resistance values and integration into a score for drug combinations, and (3) searching through the mutational neighborhood of the considered strain in order to estimate activity on nearby mutants. For a clinical data set, we determine the optimal search depth and show that the scoring scheme is predictive of therapeutic outcome. Properties of the activity score and applications are discussed.

Algorithms↗
PubMed2003

Discovering molecular pathways from protein interaction and gene expression data.

In this paper, we describe an approach for identifying 'pathways' from gene expression and protein interaction data. Our approach is based on the assumption that many pathways exhibit two properties: their genes exhibit a similar gene expression profile, and the protein products of the genes often interact. Our approach is based on a unified probabilistic model, which is learned from the data using the EM algorithm. We present results on two Saccharomyces cerevisiae gene expression data sets, combined with a binary protein interaction data set. Our results show that our approach is much more successful than other approaches at discovering both coherent functional groups and entire protein complexes.

Algorithms↗
PubMed2003

Inferring strengths of protein-protein interactions from experimental data using linear programming.

MOTIVATION: Several computational methods have been proposed for inference of protein-protein interactions. Most of the existing methods assume that protein-protein interaction data are given as binary data (i.e. whether or not each protein pair interacts). However, multiple biological experiments are performed for the same protein pairs and thus the ratio (strength) of the number of observed interactions to the number of experiments is available for each protein pair. RESULTS: We propose a new method for inference of protein-protein interactions from such experimental data. This method tries to minimize the errors between the ratios of observed interactions and the predicted probabilities in training data, where this problem is formalized as a linear program based on a probabilistic model. We compared the proposed method with the association method, the EM method and the SVM-based method using real interaction data. It is shown that a variant of the method is comparable to existing methods for binary data. It is also shown that the method outperforms existing methods for numerical data. AVAILABILITY: Programs transforming input data into LP format files are available upon request.

Algorithms↗
PubMed2003

A Bayesian missing value estimation method for gene expression profile data.

MOTIVATION: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the treatment of missing values, this problem has received little attention. There are many options for dealing with missing values, each of which reaches drastically different results. Ignoring missing values is the simplest method and is frequently applied. This approach, however, has its flaws. In this article, we propose an estimation method for missing values, which is based on Bayesian principal component analysis (BPCA). Although the methodology that a probabilistic model and latent variables are estimated simultaneously within the framework of Bayes inference is not new in principle, actual BPCA implementation that makes it possible to estimate arbitrary missing variables is new in terms of statistical methodology. RESULTS: When applied to DNA microarray data from various experimental conditions, the BPCA method exhibited markedly better estimation ability than other recently proposed methods, such as singular value decomposition and K-nearest neighbors. While the estimation performance of existing methods depends on model parameters whose determination is difficult, our BPCA method is free from this difficulty. Accordingly, the BPCA method provides accurate and convenient estimation for missing values. AVAILABILITY: The software is available at http://hawaii.aist-nara.ac.jp/~shige-o/tools/.

Algorithms↗
PubMed2004

Probabilistic inference of molecular networks from noisy data sources.

Information on molecular networks, such as networks of interacting proteins, comes from diverse sources that contain remarkable differences in distribution and quantity of errors. Here, we introduce a probabilistic model useful for predicting protein interactions from heterogeneous data sources. The model describes stochastic generation of protein-protein interaction networks with real-world properties, as well as generation of two heterogeneous sources of protein-interaction information: research results automatically extracted from the literature and yeast two-hybrid experiments. Based on the domain composition of proteins, we use the model to predict protein interactions for pairs of proteins for which no experimental data are available. We further explore the prediction limits, given experimental data that cover only part of the underlying protein networks. This approach can be extended naturally to include other types of biological data sources.

Algorithms↗
PubMed2004

Automatic prediction of protein domains from sequence information using a hybrid learning system.

MOTIVATION: We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence and are combined into a single predictor using a neural network. The output is further smoothed and post-processed using a probabilistic model to predict the most likely transition positions between domains. RESULTS: The method was assessed using the domain definitions in SCOP and CATH for proteins of known structure and was compared with several other existing methods. Our method performs well both in terms of accuracy and sensitivity. It improves significantly over the best methods available, even some of the semi-manual ones, while being fully automatic. Our method can also be used to suggest and verify domain partitions based on structural data. A few examples of predicted domain definitions and alternative partitions, as suggested by our method, are also discussed. AVAILABILITY: An online domain-prediction server is available at http://biozon.org/tools/domains/

Algorithms↗
PubMed2004

Predicting protein-peptide interactions via a network-based motif sampler.

MOTIVATION: Many protein-protein interactions are mediated by peptide recognition modules (PRMs), compact domains that bind to short peptides, and play a critical role in a wide array of biological processes. Recent experimental protein interaction data provide us with an opportunity to examine whether we may explain, or even predict their interactions by computational sequence analysis. Such a question was recently posed by the use of random peptide screens to characterize the ligands of one such PRM, the SH3 domain. RESULTS: We describe a general computational procedure for identifying the ligand peptides of PRMs by combining protein sequence information and observed physical interactions into a simple probabilistic model and from it derive an interaction-mediated de novo motif-finding framework. Using a recent all-versus-all yeast two-hybrid SH3 domain interaction network, we demonstrate that our technique can be used to derive independent predictions of interactions mediated by SH3 domains. We show that only when sequence information is combined with such all versus all protein interaction datasets, are we capable of identifying motifs with sufficient sensitivity and specificity for predicting interactions. The algorithm is general so that it may be applied to other PRM domains (e.g. SH2, WW, PDZ). AVAILABILITY: The Netmotsa software and source code, as part of a general Gibbs motif sampling library, are available at http://sf.net/projects/netmotsa

Algorithms↗
PubMed2004

Improving genome annotations using phylogenetic profile anomaly detection.

MOTIVATION: A promising strategy for refining genome annotations is to detect features that conflict with known functional or evolutionary relationships between groups of genes. Previous work in this area has been focused on investigating the absence of 'housekeeping' genes or components of well-studied pathways. We have sought to develop a method for improving new annotations that can automatically synthesize and use the information available in a database of other annotated genomes. RESULTS: We show that a probabilistic model of phylogenetic profiles, trained from a database of curated genome annotations, can be used to reliably detect errors in new annotations. We use our method to identify 22 genes that were missed in previously published annotations of prokaryotic genomes. AVAILABILITY: The method was evaluated using MATLAB and open source software referenced in this work. Scripts and datasets are available from the authors upon request. CONTACT: tarjei@broad.mit.edu.

Algorithms↗
PubMed2005

A motif-based framework for recognizing sequence families.

MOTIVATION: Many signals in biological sequences are based on the presence or absence of base signals and their spatial combinations. One of the best known examples of this is the signal identifying a core promoter--the site at which the basal transcription machinery starts the transcription of a gene. Our goal is a fully automatic pattern recognition system for a family of sequences, which simultaneously discovers the base signals, their spatial relationships and a classifier based upon them. RESULTS: In this paper we present a general method for characterizing a set of sequences by their recurrent motifs. Our approach relies on novel probabilistic models for DNA binding sites and modules of binding sites, on algorithms to study them from the data and on a support vector machine that uses the models studied to classify a set of sequences. We demonstrate the applicability of our approach to diverse instances, ranging from families of promoter sequences to a dataset of intronic sequences flanking alternatively spliced exons. On a core promoter dataset our results are comparable with the state-of-the-art McPromoter. On a dataset of alternatively spliced exons we outperform a previous approach. We also achieve high success rates in recognizing cell cycle regulated genes. These results demonstrate that a fully automatic pattern recognition algorithm can meet or exceed the performance of hand-crafted approaches. AVAILABILITY: The software and datasets are available from the authors upon request.

Algorithms↗
PubMed2005

Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data.

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.

Algorithms↗
PubMed2006

Interpreting anonymous DNA samples from mass disasters--probabilistic forensic inference using genetic markers.

MOTIVATION: The problem of identifying victims in a mass disaster using DNA fingerprints involves a scale of computation that requires efficient and accurate algorithms. In a typical scenario there are hundreds of samples taken from remains that must be matched to the pedigrees of the alleged victim's surviving relatives. Moreover the samples are often degraded due to heat and exposure. To develop a competent method for this type of forensic inference problem, the complicated quality issues of DNA typing need to be handled appropriately, the matches between every sample and every family must be considered, and the confidence of matches need to be provided. RESULTS: We present a unified probabilistic framework that efficiently clusters samples, conservatively eliminates implausible sample-pedigree pairings, and handles both degraded samples (missing values) and experimental errors in producing and/or reading a genotype. We present a method that confidently exclude forensically unambiguous sample-family matches from the large hypothesis space of candidate matches, based on posterior probabilistic inference. Due to the high confidentiality of disaster DNA data, simulation experiments are commonly performed and used here for validation. Our framework is shown to be robust to these errors at levels typical in real applications. Furthermore, the flexibility in the probabilistic models makes it possible to extend this framework to include other biological factors such as interdependent markers, mitochondrial sequences, and blood type. AVAILABILITY: The software and data sets are available from the authors upon request.

Algorithms↗