TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders

We describe two new Generalized Hidden Markov Model implementations for ab initio eukaryotic gene prediction. The C/C++ source code for both is available as open source and is highly reusable due to their modular and extensible architectures. Unlike most of the currently available gene-finders, the programs are re-trainable by the end user. They are also re-configurable and include several types of probabilistic submodels which can be independently combined, such as Maximal Dependence Decomposition trees and interpolated Markov models. Both programs have been used at TIGR for the annotation of the Aspergillus fumigatus and Toxoplasma gondii genomes. AVAILABILITY: Source code and documentation are available under the open source Artistic License from http://www.tigr.org/software/pirate     

Computational approaches to gene prediction

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.     

An evaluation of contemporary hidden Markov model genefinders with a predicted exon taxonomy

We present an independent evaluation of six recent hidden Markov model (HMM) genefinders. Each was tested on the new dataset (FSH298), the results of which showed no dramatic improvement over the genefinders tested five years ago. In addition, we introduce a comprehensive taxonomy of predicted exons and classify each resulting exon accordingly. These results are useful in measuring (with finer granularity) the effects of changes in a genefinder. We present an analysis of these results and identify four patterns of inaccuracy common in all HMM-based results.     

A response to Yu et al. "A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array", BMC Bioinformatics 2007, 8: 145

Background  

Yu et al. (BMC Bioinformatics 2007,8: 145+) have recently compared the performance of several methods for the detection of genomic amplification and deletion breakpoints using data from high-density single nucleotide polymorphism arrays. One of the methods compared is our non-homogenous Hidden Markov Model approach. Our approach uses Markov Chain Monte Carlo for inference, but Yu et al. ran the sampler for a severely insufficient number of iterations for a Markov Chain Monte Carlo-based method. Moreover, they did not use the appropriate reference level for the non-altered state.     

Hidden Markov Models and their Applications in Biological Sequence Analysis

Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. We show how these HMMs can be used to solve various sequence analysis problems, such as pairwise and multiple sequence alignments, gene annotation, classification, similarity search, and many others.Key Words: Hidden Markov model (HMM), pair-HMM, profile-HMM, context-sensitive HMM (csHMM), profile-csHMM, sequence analysis.     

Comparison of glycosyltransferase families using the profile hidden Markov model

In order to investigate the relationship between glycosyltransferase families and the motif for them, we classified 47 glycosyltransferase families in the CAZy database into four superfamilies, GTS-A, -B, -C, and -D, using a profile Hidden Markov Model method. On the basis of the classification and the similarity between GTS-A and nucleotidylyltransferase family catalyzing the synthesis of nucleotide-sugar, we proposed that ancient oligosaccharide might have been synthesized by the origin of GTS-B whereas the origin of GTS-A might be the gene encoding for synthesis of nucleotide-sugar as the donor and have evolved to glycosyltransferases to catalyze the synthesis of divergent carbohydrates. We also suggested that the divergent evolution of each superfamily in the corresponding subcellular component has increased the complexities of eukaryotic carbohydrate structure.     

A Hidden Markov Model approach to variation among sites in rate of evolution

The method of Hidden Markov Models is used to allow for unequal and unknown evolutionary rates at different sites in molecular sequences. Rates of evolution at different sites are assumed to be drawn from a set of possible rates, with a finite number of possibilities. The overall likelihood of phylogeny is calculated as a sum of terms, each term being the probability of the data given a particular assignment of rates to sites, times the prior probability of that particular combination of rates. The probabilities of different rate combinations are specified by a stationary Markov chain that assigns rate categories to sites. While there will be a very large number of possible ways of assigning rates to sites, a simple recursive algorithm allows the contributions to the likelihood from all possible combinations of rates to be summed, in a time proportional to the number of different rates at a single site. Thus with three rates, the effort involved is no greater than three times that for a single rate. This "Hidden Markov Model" method allows for rates to differ between sites and for correlations between the rates of neighboring sites. By summing over all possibilities it does not require us to know the rates at individual sites. However, it does not allow for correlation of rates at nonadjacent sites, nor does it allow for a continuous distribution of rates over sites. It is shown how to use the Newton-Raphson method to estimate branch lengths of a phylogeny and to infer from a phylogeny what assignment of rates to sites has the largest posterior probability. An example is given using beta-hemoglobin DNA sequences in eight mammal species; the regions of high and low evolutionary rates are inferred and also the average length of patches of similar rates.      

Using hidden Markov models to analyze gene expression time course data

MOTIVATION: Cellular processes cause changes over time. Observing and measuring those changes over time allows insights into the how and why of regulation. The experimental platform for doing the appropriate large-scale experiments to obtain time-courses of expression levels is provided by microarray technology. However, the proper way of analyzing the resulting time course data is still very much an issue under investigation. The inherent time dependencies in the data suggest that clustering techniques which reflect those dependencies yield improved performance. RESULTS: We propose to use Hidden Markov Models (HMMs) to account for the horizontal dependencies along the time axis in time course data and to cope with the prevalent errors and missing values. The HMMs are used within a model-based clustering framework. We are given a number of clusters, each represented by one Hidden Markov Model from a finite collection encompassing typical qualitative behavior. Then, our method finds in an iterative procedure cluster models and an assignment of data points to these models that maximizes the joint likelihood of clustering and models. Partially supervised learning--adding groups of labeled data to the initial collection of clusters--is supported. A graphical user interface allows querying an expression profile dataset for time course similar to a prototype graphically defined as a sequence of levels and durations. We also propose a heuristic approach to automate determination of the number of clusters. We evaluate the method on published yeast cell cycle and fibroblasts serum response datasets, and compare them, with favorable results, to the autoregressive curves method.     

隐马尔科夫过程在生物信息学中的应用

隐马尔科夫过程（hidden markov model,简称HMM）是20世纪70年代提出来的一种统计方法,以前主要用于语音识别。1989年Churchill将其引入计算生物学。目前,HMM是生物信息学中应用比较广泛的一种统计方法,主要用于：线性序列分析、模型分析、基因发现等方面。对HMM进行了简明扼要的描述,并对其在上述几个方面的应用作一概略介绍。     

Hap-seqX: Expedite algorithm for haplotype phasing with imputation using sequence data

Haplotype phasing is one of the most important problems in population genetics as haplotypes can be used to estimate the relatedness of individuals and to impute genotype information which is a commonly performed analysis when searching for variants involved in disease. The problem of haplotype phasing has been well studied. Methodologies for haplotype inference from sequencing data either combine a set of reference haplotypes and collected genotypes using a Hidden Markov Model or assemble haplotypes by overlapping sequencing reads. A recent algorithm Hap-seq considers using both sequencing data and reference haplotypes and it is a hybrid of a dynamic programming algorithm and a Hidden Markov Model (HMM), which is shown to be optimal. However, the algorithm requires extremely large amount of memory which is not practical for whole genome datasets. The current algorithm requires saving intermediate results to disk and reads these results back when needed, which significantly affects the practicality of the algorithm. In this work, we proposed the expedited version of the algorithm Hap-seqX, which addressed the memory issue by using a posterior probability to select the records that should be saved in memory. We show that Hap-seqX can save all the intermediate results in memory and improves the execution time of the algorithm dramatically. Utilizing the strategy, Hap-seqX is able to predict haplotypes from whole genome sequencing data.     

A hidden Markov model for predicting protein interfaces

Protein-protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.     

Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13

We report the results of residue-residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)-based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact-map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end-to-end training and stacking, resulting in two complementary programs called TripletRes and ResTriplet, respectively. For the 31 free-modeling domains that do not have homologous templates in the PDB, TripletRes and ResTriplet generated comparable results with an average accuracy of 0.640 and 0.646, respectively, for the top L/5 long-range predictions, where 71% and 74% of the cases have an accuracy above 0.5. Detailed data analyses showed that the strength of the pipeline is due to the sensitive MSA construction and the advanced strategies for coevolutionary feature ensembling. Domain splitting was also found to help enhance the contact prediction performance. Nevertheless, contact models for tail regions, which often involve a high number of alignment gaps, and for targets with few homologous sequences are still suboptimal. Development of new approaches where the model is specifically trained on these regions and targets might help address these problems.     

Thirty-plus functional families from a single motif

It is now possible to identify over 30 functional subfamilies among the WD-repeat-containing proteins found in the completed genomes. The majority of these subfamilies have at least one member for which experimental data allow assignment to a cellular pathway or process. Half of the 63 WD-repeat-containing proteins in Saccharomyces cerevisiae, half of the 70 in Caenorhabditis elegans, and a third of the 100 plus predicted in Drosophila can be assigned to 23 of these functional subfamilies. Perhaps indicative of the future, 33 WD-repeat-containing proteins from the partial genome of Arabidopsis thaliana can now be assigned to 18 of these subfamilies. These assignments have been made possible by combining traditional sequence similarity with an implied common beta propeller structural context to obtain measures of protein-protein surface similarity. The beta propeller structural context is represented in the form of a Hidden Markov Model. The procedure is completely automated.     

广义隐Markov模型在基因识别中的应用

广义隐Markov模型(GHMM)是基因识别的一种重要模型,但是其计算量比传统的隐Markov模型大得多,以至于不能直 接在基因识别中使用。根据原核生物基因的结构特点,提出了一种高效的简化算法,其计算量是序列长度的线性函数。在此 基础上,构建了针对原核生物基因的识别程序GeneMiner,对实际数据的测试表明,此算法是有效的。     

A Segmental Semi Markov Model for protein secondary structure prediction

Hidden Markov Models (HMMs) are practical tools which provide probabilistic base for protein secondary structure prediction. In these models, usually, only the information of the left hand side of an amino acid is considered. Accordingly, these models seem to be inefficient with respect to long range correlations. In this work we discuss a Segmental Semi Markov Model (SSMM) in which the information of both sides of amino acids are considered. It is assumed and seemed reasonable that the information on both sides of an amino acid can provide a suitable tool for measuring dependencies. We consider these dependencies by dividing them into shorter dependencies. Each of these dependency models can be applied for estimating the probability of segments in structural classes. Several conditional probabilities concerning dependency of an amino acid to the residues appeared on its both sides are considered. Based on these conditional probabilities a weighted model is obtained to calculate the probability of each segment in a structure. This results in 2.27% increase in prediction accuracy in comparison with the ordinary Segmental Semi Markov Models, SSMMs. We also compare the performance of our model with that of the Segmental Semi Markov Model introduced by Schmidler et al. [C.S. Schmidler, J.S. Liu, D.L. Brutlag, Bayesian segmentation of protein secondary structure, J. Comp. Biol. 7(1/2) (2000) 233-248]. The calculations show that the overall prediction accuracy of our model is higher than the SSMM introduced by Schmidler.     

Applications of generalized pair hidden Markov models to alignment and gene finding problems.

Hidden Markov models (HMMs) have been successfully applied to a variety of problems in molecular biology, ranging from alignment problems to gene finding and annotation. Alignment problems can be solved with pair HMMs, while gene finding programs rely on generalized HMMs in order to model exon lengths. In this paper, we introduce the generalized pair HMM (GPHMM), which is an extension of both pair and generalized HMMs. We show how GPHMMs, in conjunction with approximate alignments, can be used for cross-species gene finding and describe applications to DNA-cDNA and DNA-protein alignment. GPHMMs provide a unifying and probabilistically sound theory for modeling these problems.