Pattern statistics on Markov chains and sensitivity to parameter estimation

Background:  

In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...).     

A linear memory algorithm for Baum-Welch training

Background:  

Baum-Welch training is an expectation-maximisation algorithm for training the emission and transition probabilities of hidden Markov models in a fully automated way. It can be employed as long as a training set of annotated sequences is known, and provides a rigorous way to derive parameter values which are guaranteed to be at least locally optimal. For complex hidden Markov models such as pair hidden Markov models and very long training sequences, even the most efficient algorithms for Baum-Welch training are currently too memory-consuming. This has so far effectively prevented the automatic parameter training of hidden Markov models that are currently used for biological sequence analyses.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.     

An evaluation of minimal cellular functions to sustain a bacterial cell

Background  

Both computational and experimental approaches have been used to determine the minimal gene set required to sustain a bacterial cell. Such studies have provided clues to the minimal cellular-function set needed for life. We evaluate a minimal cellular-function set directly, instead of a geneset.     

Pitfalls of the most commonly used models of context dependent substitution

Background  

Neighboring nucleotides exert a striking influence on mutation, with the hypermutability of CpG dinucleotides in many genomes being an exemplar. Among the approaches employed to measure the relative importance of sequence neighbors on molecular evolution have been continuous-time Markov process models for substitutions that treat sequences as a series of independent tuples. The most widely used examples are the codon substitution models. We evaluated the suitability of derivatives of the nucleotide frequency weighted (hereafter NF) and tuple frequency weighted (hereafter TF) models for measuring sequence context dependent substitution. Critical properties we address are their relationships to an independent nucleotide process and the robustness of parameter estimation to changes in sequence composition. We then consider the impact on inference concerning dinucleotide substitution processes from application of these two forms to intron sequence alignments from primates.     

Identification of protein functions using a machine-learning approach based on sequence-derived properties

Background  

Predicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.     

pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree

Background  

Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets.     

Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures

Background  

Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction. Thus far, however, they have been confined to representing 1D sequence (or the aspects of structure that could be represented by character strings).     

Ultra-fast sequence clustering from similarity networks with <Emphasis FontCategory="NonProportional">SiLiX</Emphasis>

Background  

The number of gene sequences that are available for comparative genomics approaches is increasing extremely quickly. A current challenge is to be able to handle this huge amount of sequences in order to build families of homologous sequences in a reasonable time.     

PyEvolve: a toolkit for statistical modelling of molecular evolution

Background  

Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences – ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation.     

ICRPfinder: a fast pattern design algorithm for coding sequences and its application in finding potential restriction enzyme recognition sites

Background  

Restriction enzymes can produce easily definable segments from DNA sequences by using a variety of cut patterns. There are, however, no software tools that can aid in gene building -- that is, modifying wild-type DNA sequences to express the same wild-type amino acid sequences but with enhanced codons, specific cut sites, unique post-translational modifications, and other engineered-in components for recombinant applications. A fast DNA pattern design algorithm, ICRPfinder, is provided in this paper and applied to find or create potential recognition sites in target coding sequences.     

MISHIMA - a new method for high speed multiple alignment of nucleotide sequences of bacterial genome scale data

Background  

Large nucleotide sequence datasets are becoming increasingly common objects of comparison. Complete bacterial genomes are reported almost everyday. This creates challenges for developing new multiple sequence alignment methods. Conventional multiple alignment methods are based on pairwise alignment and/or progressive alignment techniques. These approaches have performance problems when the number of sequences is large and when dealing with genome scale sequences.     

RIO: Analyzing proteomes by automated phylogenomics using resampled inference of orthologs

Background  

When analyzing protein sequences using sequence similarity searches, orthologous sequences (that diverged by speciation) are more reliable predictors of a new protein's function than paralogous sequences (that diverged by gene duplication). The utility of phylogenetic information in high-throughput genome annotation ("phylogenomics") is widely recognized, but existing approaches are either manual or not explicitly based on phylogenetic trees.     

Evolutionary models for insertions and deletions in a probabilistic modeling framework

Background  

Probabilistic models for sequence comparison (such as hidden Markov models and pair hidden Markov models for proteins and mRNAs, or their context-free grammar counterparts for structural RNAs) often assume a fixed degree of divergence. Ideally we would like these models to be conditional on evolutionary divergence time.     

Lentiviral vectors encoding tetracycline-dependent repressors and transactivators for reversible knockdown of gene expression: a comparative study

Background  

RNA interference (RNAi)-mediated by the expression of short hairpin RNAs (shRNAs) has emerged as a powerful experimental tool for reverse genetic studies in mammalian cells. A number of recent reports have described approaches allowing regulated production of shRNAs based on modified RNA polymerase II (Pol II) or RNA polymerase III (Pol III) promoters, controlled by drug-responsive transactivators or repressors such as tetracycline (Tet)-dependent transactivators and repressors. However, the usefulness of these approaches is often times limited, caused by inefficient delivery and/or expression of shRNA-encoding sequences in target cells and/or poor design of shRNAs sequences. With a view toward optimizing Tet-regulated shRNA expression in mammalian cells, we compared the capacity of a variety of hybrid Pol III promoters to express short shRNAs in target cells following lentivirus-mediated delivery of shRNA-encoding cassettes.     

Word correlation matrices for protein sequence analysis and remote homology detection

Background  

Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive.     

Optimal choice of word length when comparing two Markov sequences using a <Emphasis Type="Italic">χ</Emphasis><Superscript>2</Superscript>-statistic

Background

Alignment-free sequence comparison using counts of word patterns (grams, k-tuples) has become an active research topic due to the large amount of sequence data from the new sequencing technologies. Genome sequences are frequently modelled by Markov chains and the likelihood ratio test or the corresponding approximate χ ²-statistic has been suggested to compare two sequences. However, it is not known how to best choose the word length k in such studies.

Results

We develop an optimal strategy to choose k by maximizing the statistical power of detecting differences between two sequences. Let the orders of the Markov chains for the two sequences be r ₁ and r ₂, respectively. We show through both simulations and theoretical studies that the optimal k= max(r ₁,r ₂)+1 for both long sequences and next generation sequencing (NGS) read data. The orders of the Markov chains may be unknown and several methods have been developed to estimate the orders of Markov chains based on both long sequences and NGS reads. We study the power loss of the statistics when the estimated orders are used. It is shown that the power loss is minimal for some of the estimators of the orders of Markov chains.

Conclusion

Our studies provide guidelines on choosing the optimal word length for the comparison of Markov sequences.