Statistical Alignment Research Articles

Class of Multiple Sequence Alignment Algorithm Affects Genomic Analysis

Multiple sequence alignment (MSA) is the heart of comparative sequence analysis. Recent studies demonstrate that MSA algorithms can produce different outcomes when analyzing genomes, including phylogenetic tree inference and the detection of adaptive evolution. These studies also suggest that the difference between MSA algorithms is of a similar order to the uncertainty within an algorithm and suggest integrating across this uncertainty. In this study, we examine further the problem of disagreements between MSA algorithms and how they affect downstream analyses. We also investigate whether integrating across alignment uncertainty affects downstream analyses. We address these questions by analyzing 200 chordate gene families, with properties reflecting those used in large-scale genomic analyses. We find that newly developed distance metrics reveal two significantly different classes of MSA methods (MSAMs). The similarity-based class includes progressive aligners and consistency aligners, representing many methodological innovations for sequence alignment, whereas the evolution-based class includes phylogenetically aware alignment and statistical alignment. We proceed to show that the class of an MSAM has a substantial impact on downstream analyses. For phylogenetic inference, tree estimates and their branch lengths appear highly dependent on the class of aligner used. The number of families, and the sites within those families, inferred to have undergone adaptive evolution depend on the class of aligner used. Similarity-based aligners tend to identify more adaptive evolution. We also develop and test methods for incorporating MSA uncertainty when detecting adaptive evolution but find that although accounting for MSA uncertainty does affect downstream analyses, it appears less important than the class of aligner chosen. Our results demonstrate the critical role that MSA methodology has on downstream analysis, highlighting that the class of aligner chosen in an analysis has a demonstrable effect on its outcome.

A Context Dependent Pair Hidden Markov Model for Statistical Alignment

This article proposes a novel approach to statistical alignment of nucleotide sequences by introducing a context dependent structure on the substitution process in the underlying evolutionary model. We propose to estimate alignments and context dependent mutation rates relying on the observation of two homologous sequences. The procedure is based on a generalized pair-hidden Markov structure, where conditional on the alignment path, the nucleotide sequences follow a Markov distribution. We use a stochastic approximation expectation maximization (saem) algorithm to give accurate estimators of parameters and alignments. We provide results both on simulated data and vertebrate genomes, which are known to have a high mutation rate from CG dinucleotide. In particular, we establish that the method improves the accuracy of the alignment of a human pseudogene and its functional gene.

Two-Word Collocation Extraction Using Monolingual Word Alignment Method

Statistical bilingual word alignment has been well studied in the field of machine translation. This article adapts the bilingual word alignment algorithm into a monolingual scenario to extract collocations from monolingual corpus, based on the fact that the words in a collocation tend to co-occur in similar contexts as in bilingual word alignment. First, the monolingual corpus is replicated to generate a parallel corpus, in which each sentence pair consists of two identical sentences. Next, the monolingual word alignment algorithm is employed to align potentially collocated words. Finally, the aligned word pairs are ranked according to the alignment scores and candidates with higher scores are extracted as collocations. We conducted experiments on Chinese and English corpora respectively. Compared to previous approaches that use association measures to extract collocations from co-occurrence word pairs within a given window, our method achieves higher precision and recall. According to human evaluation, our method achieves precisions of 62% on a Chinese corpus and 64% on an English corpus. In particular, we can extract collocations with longer spans, achieving a higher precision of 83% on the long-span (> 6 words) Chinese collocations.

TransSearch: from a bilingual concordancer to a translation finder

As basic as bilingual concordancers may appear, they are some of the most widely used computer-assisted translation tools among professional translators. Nevertheless, they still do not benefit from recent breakthroughs in machine translation. This paper describes the improvement of the commercial bilingual concordancer TransSearch in order to embed a word alignment feature. The use of statistical word alignment methods allows the system to spot user query translations, and thus the tool is transformed into a translation search engine. We describe several translation identification and postprocessing algorithms that enhance the application. The excellent results obtained using a large translation memory consisting of 8.3 million sentence pairs are confirmed via human evaluation.

Gating of a G protein-sensitive Mammalian Kir3.1 Prokaryotic Kir Channel Chimera in Planar Lipid Bilayers

Kir3 channels control heart rate and neuronal excitability through GTP-binding (G) protein and phosphoinositide signaling pathways. These channels were the first characterized effectors of the βγ subunits of G proteins. Because we currently lack structures of complexes between G proteins and Kir3 channels, their interactions leading to modulation of channel function are not well understood. The recent crystal structure of a chimera between the cytosolic domain of a mammalian Kir3.1 and the transmembrane region of a prokaryotic KirBac1.3 (Kir3.1 chimera) has provided invaluable structural insight. However, it was not known whether this chimera could form functional K(+) channels. Here, we achieved the functional reconstitution of purified Kir3.1 chimera in planar lipid bilayers. The chimera behaved like a bona fide Kir channel displaying an absolute requirement for PIP(2) and Mg(2+)-dependent inward rectification. The channel could also be blocked by external tertiapin Q. The three-dimensional reconstruction of the chimera by single particle electron microscopy revealed a structure consistent with the crystal structure. Channel activity could be stimulated by ethanol and activated G proteins. Remarkably, the presence of both activated Gα and Gβγ subunits was required for gating of the channel. These results confirm the Kir3.1 chimera as a valid structural and functional model of Kir3 channels.

Constraining a Generative Word Alignment Model with Discriminative Output

We present a method to constrain a statistical generative word alignment model with the output from a discriminative model. The discriminative model is trained using a small set of hand-aligned data that ensures higher precision in alignment. On the other hand, the generative model improves the recall of alignment. By combining these two models, the alignment output becomes more suitable for use in developing a translation model for a phrase-based statistical machine translation (SMT) system. Our experimental results show that the joint alignment model improves the translation performance. The improvement in average of BLEU and METEOR scores is around 1.0-3.9 points.

On the role of unsteady forcing of tracer gradient in local stirring

Local stirring properties in two basic mixing flows – namely, the blinking vortex and the sine flow – are studied through the tracer gradient approach. The velocity gradient tensor and related quantities such as the strain persistence parameter are derived from the analytical velocity fields. Numerical Lagrangian tracking of the gradient of a tracer shows how local stirring is affected by forcing experienced through strain persistence. In both flows Lagrangian variations of strain persistence occuring on a time scale shorter than the response time scale of the tracer gradient lead the latter to align close to the direction determined by the mean strain persistence. It is the special alternating behaviour of strain persistence resulting from flow operation that makes this direction coincide with the local compressional strain direction for both the sine flow and the clockwise/counterclockwise blinking vortex. The rise of the tracer gradient and thus local stirring are in turn promoted by this statistical alignment.

Radio and optical orientations of galaxies

We investigate the correlations between optical and radio isophotal position angles for 14302 SDSS galaxies with $r$ magnitudes brighter than 18 and which have been associated with extended FIRST radio sources. We identify two separate populations of galaxies using the colour, concentration and their principal components. Surprisingly strong statistical alignments are found: late-type galaxies are overwhelmingly biased towards a position angle differences of $0^{\circ}$ and early-type galaxies to $90^{\circ}$. The late-type alignment can be easily understood in terms of the standard picture in which the radio emission is intimately related to areas of recent star-formation. In early-type galaxies the radio emission is expected to be driven by accretion on to a nuclear black hole. We argue that the observed correlation of the radio axis with the minor axis of the large-scale stellar distribution gives a fundamental insight into the structure of elliptical galaxies, for example, whether or not the nuclear kinematics are decoupled form the rest of the galaxy. Our results imply that the galaxies are oblate spheroids with their radio emission aligned with the minor axis. Remarkably the strength of the correlation of the radio major axis with the optical minor axis depends on radio loudness. Those objects with a low ratio of FIRST radio flux density to total stellar light show a strong minor axis correlation while the stronger radio sources do not. This may reflect different formation histories for the different objects and we suggest we may be seeing the different behaviour of rationally supported and non-rotationally supported ellipticals.

Fast Statistical Alignment

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment—previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches—yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/.

Statistical Alignment with a Sequence Evolution Model Allowing Rate Heterogeneity along the Sequence

We present a stochastic sequence evolution model to obtain alignments and estimate mutation rates between two homologous sequences. The model allows two possible evolutionary behaviors along a DNA sequence in order to determine conserved regions and take its heterogeneity into account. In our model, the sequence is divided into slow and fast evolution regions. The boundaries between these sections are not known. It is our aim to detect them. The evolution model is based on a fragment insertion and deletion process working on fast regions only and on a substitution process working on fast and slow regions with different rates. This model induces a pair hidden Markov structure at the level of alignments, thus making efficient statistical alignment algorithms possible. We propose two complementary estimation methods, namely, a Gibbs sampler for Bayesian estimation and a stochastic version of the EM algorithm for maximum likelihood estimation. Both algorithms involve the sampling of alignments. We propose a partial alignment sampler, which is computationally less expensive than the typical whole alignment sampler. We show the convergence of the two estimation algorithms when used with this partial sampler. Our algorithms provide consistent estimates for the mutation rates and plausible alignments and sequence segmentations on both simulated and real data.

Alignment and Prediction of cis-Regulatory Modules Based on a Probabilistic Model of Evolution

Cross-species comparison has emerged as a powerful paradigm for predicting cis-regulatory modules (CRMs) and understanding their evolution. The comparison requires reliable sequence alignment, which remains a challenging task for less conserved noncoding sequences. Furthermore, the existing models of DNA sequence evolution generally do not explicitly treat the special properties of CRM sequences. To address these limitations, we propose a model of CRM evolution that captures different modes of evolution of functional transcription factor binding sites (TFBSs) and the background sequences. A particularly novel aspect of our work is a probabilistic model of gains and losses of TFBSs, a process being recognized as an important part of regulatory sequence evolution. We present a computational framework that uses this model to solve the problems of CRM alignment and prediction. Our alignment method is similar to existing methods of statistical alignment but uses the conserved binding sites to improve alignment. Our CRM prediction method deals with the inherent uncertainties of binding site annotations and sequence alignment in a probabilistic framework. In simulated as well as real data, we demonstrate that our program is able to improve both alignment and prediction of CRM sequences over several state-of-the-art methods. Finally, we used alignments produced by our program to study binding site conservation in genome-wide binding data of key transcription factors in the Drosophila blastoderm, with two intriguing results: (i) the factor-bound sequences are under strong evolutionary constraints even if their neighboring genes are not expressed in the blastoderm and (ii) binding sites in distal bound sequences (relative to transcription start sites) tend to be more conserved than those in proximal regions. Our approach is implemented as software, EMMA (Evolutionary Model-based cis-regulatory Module Analysis), ready to be applied in a broad biological context.

Stochastic models of sequence evolution including insertion—deletion events

Comparison of sequences that have descended from a common ancestor based on an explicit stochastic model of substitutions, insertions and deletions has risen to prominence in the last decade. Making statements about the positions of insertions-deletions (abbr. indels) is central in sequence and genome analysis and is called alignment. This statistical approach is harder conceptually and computationally, than competing approaches based on choosing an alignment according to some optimality criteria. But it has major practical advantages in terms of testing evolutionary hypotheses and parameter estimation. Basic dynamic approaches can allow the analysis of up to 4-5 sequences. MCMC techniques can bring this to about 10-15 sequences. Beyond this, different or heuristic approaches must be used. Besides the computational challenges, increasing realism in the underlying models is presently being addressed. A recent development that has been especially fruitful is combining statistical alignment with the problem of sequence annotation, making statements about the function of each nucleotide/amino acid. So far gene finding, protein secondary structure prediction and regulatory signal detection has been tackled within this framework. Much progress can be reported, but clearly major challenges remain if this approach is to be central in the analyses of large incoming sequence data sets.

PostCAT - Posterior Constrained Alignment Toolkit

In this paper we present a new open-source toolkit for statistical word alignments - Posterior Constrained Alignment Toolkit (PostCAT). e toolkit implements three well known word alignment algorithms (IBM M1, IBM M2, HMM) as well as six new models. In addition to the usual Viterbi decoding scheme, the toolkit provides posterior decoding with several flavors for tuning the threshold. e toolkit also provides an implementation of alignment symmetrization heuristics and a set of utilities for analyzing and pretty printing alignments. e new models have already been shown to improve intrinsic alignment metrics and also to lead to better translations when integrated into a state of the art machine translation system. e toolkit is developed in Java and available in source at its website ¹. We encourage other researchers to build on our work by modifying the toolkit and using it for their research.

BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC.

BackgroundWe have previously combined statistical alignment and phylogenetic footprinting to detect conserved functional elements without assuming a fixed alignment. Considering a probability-weighted distribution of alignments removes sensitivity to alignment errors, properly accommodates regions of alignment uncertainty, and increases the accuracy of functional element prediction. Our method utilized standard dynamic programming hidden markov model algorithms to analyze up to four sequences.ResultsWe present a novel approach, implemented in the software package BigFoot, for performing phylogenetic footprinting on greater numbers of sequences. We have developed a Markov chain Monte Carlo (MCMC) approach which samples both sequence alignments and locations of slowly evolving regions. We implement our method as an extension of the existing StatAlign software package and test it on well-annotated regions controlling the expression of the even-skipped gene in Drosophila and the α-globin gene in vertebrates. The results exhibit how adding additional sequences to the analysis has the potential to improve the accuracy of functional predictions, and demonstrate how BigFoot outperforms existing alignment-based phylogenetic footprinting techniques.ConclusionBigFoot extends a combined alignment and phylogenetic footprinting approach to analyze larger amounts of sequence data using MCMC. Our approach is robust to alignment error and uncertainty and can be applied to a variety of biological datasets. The source code and documentation are publicly available for download from

System Combination for Machine Translation of Spoken and Written Language

This paper describes an approach for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The consensus translation is computed by weighted majority voting on a confusion network, similarly to the well-established ROVER approach of Fiscus for combining speech recognition hypotheses. To create the confusion network, pairwise word alignments of the original MT hypotheses are learned using an enhanced statistical alignment algorithm that explicitly models word reordering. The context of a whole corpus of automatic translations rather than a single sentence is taken into account in order to achieve high alignment quality. The confusion network is rescored with a special language model, and the consensus translation is extracted as the best path. The proposed system combination approach was evaluated in the framework of the TC-STAR speech translation project. Up to six state-of-the-art statistical phrase-based translation systems from different project partners were combined in the experiments. Significant improvements in translation quality from Spanish to English and from English to Spanish in comparison with the best of the individual MT systems were achieved under official evaluation conditions.

Model-based prediction of sequence alignment quality

Multiple sequence alignment (MSA) is an essential prerequisite for many sequence analysis methods and valuable tool itself for describing relationships between protein sequences. Since the success of the sequence analysis is highly dependent on the reliability of alignments, measures for assessing the quality of alignments are highly requisite. We present a statistical model-based alignment quality score. Unlike other quality scores, it does not require several parallel alignments for the same set of sequences or additional structural information. Our quality score is based on measuring the conservation level of reference alignments in Homstrad. Reference sequences were realigned with the Mafft, Muscle and Probcons alignment programs, and a sum-of-pairs (SP) score was used to measure the quality of the realignments. Statistical modelling of the SP score as a function of conservation level and other alignment characteristics makes it possible to predict the SP score for any global MSA. The predicted SP scores are highly correlated with the correct SP scores, when tested on the Homstrad and SABmark databases. The results are comparable to that of multiple overlap score (MOS) and better than those of normalized mean distance (NorMD) and normalized iRMSD (NiRMSD) alignment quality criteria. Furthermore, the predicted SP score is able to detect alignments with badly aligned or unrelated sequences. The method is freely available at http://www.mtt.fi/AlignmentQuality/.

Investigating selection on viruses: a statistical alignment approach

BackgroundTwo problems complicate the study of selection in viral genomes: Firstly, the presence of genes in overlapping reading frames implies that selection in one reading frame can bias our estimates of neutral mutation rates in another reading frame. Secondly, the high mutation rates we are likely to encounter complicate the inference of a reliable alignment of genomes. To address these issues, we develop a model that explicitly models selection in overlapping reading frames. We then integrate this model into a statistical alignment framework, enabling us to estimate selection while explicitly dealing with the uncertainty of individual alignments. We show that in this way we obtain un-biased selection parameters for different genomic regions of interest, and can improve in accuracy compared to using a fixed alignment.ResultsWe run a series of simulation studies to gauge how well we do in selection estimation, especially in comparison to the use of a fixed alignment. We show that the standard practice of using a ClustalW alignment can lead to considerable biases and that estimation accuracy increases substantially when explicitly integrating over the uncertainty in inferred alignments. We even manage to compete favourably for general evolutionary distances with an alignment produced by GenAl. We subsequently run our method on HIV2 and Hepatitis B sequences.ConclusionWe propose that marginalizing over all alignments, as opposed to using a fixed one, should be considered in any parametric inference from divergent sequence data for which the alignments are not known with certainty. Moreover, we discover in HIV2 that double coding regions appear to be under less stringent selection than single coding ones. Additionally, there appears to be evidence for differential selection, where one overlapping reading frame is under positive and the other under negative selection.

Combining statistical alignment and phylogenetic footprinting to detect regulatory elements

Traditional alignment-based phylogenetic footprinting approaches make predictions on the basis of a single assumed alignment. The predictions are therefore highly sensitive to alignment errors or regions of alignment uncertainty. Alternatively, statistical alignment methods provide a framework for performing phylogenetic analyses by examining a distribution of alignments. We developed a novel algorithm for predicting functional elements by combining statistical alignment and phylogenetic footprinting (SAPF). SAPF simultaneously performs both alignment and annotation by combining phylogenetic footprinting techniques with an hidden Markov model (HMM) transducer-based multiple alignment model, and can analyze sequence data from multiple sequences. We assessed SAPF's predictive performance on two simulated datasets and three well-annotated cis-regulatory modules from newly sequenced Drosophila genomes. The results demonstrate that removing the traditional dependence on a single alignment can significantly augment the predictive performance, especially when there is uncertainty in the alignment of functional regions. SAPF is freely available to download online at http://www.stats.ox.ac.uk/~satija/SAPF/

Automatic Acquisition of Paraphrases Using Bilingual Dependency Relations

ABSTRACT ⎯ This letter introduces a new method to automatically acquire paraphrases using bilingual corpora. It utilizes the bilingual dependency relations obtained by projecting a monolingual dependency parse onto the other language’s sentence based on statistical alignment techniques. Since the proposed paraphrasing method can clearly disambiguate the sense of the original phrases using the bilingual context of dependency relations, it would be possible to obtain interchangeable paraphrases under a given context. Through experiments with parallel corpora of Korean and English language pairs, we demonstrate that our method effectively extracts paraphrases with high precision, achieving success rates of 94.3% and 84.6%, respectively, for Korean and English. Keywords ⎯ Paraphrase, bilingual dependency parsing, alignment, sense disambiguation, dependency relation. I. Introduction Approaches based on bilingual corpora are promising for the automatic acquisition of translation knowledge. Phrase-based statistical machine translation (SMT) models have advanced the state of the art in machine translation by expanding the basic unit from words to phrases [1], [2]. However, phrase-based SMT techniques suffer from data sparseness problems, such as unreliable translation probabilities of low-frequency phrases and low coverage, in that many phrases encountered at run-time are not observed in the training data. An alternative to these problems is to use paraphrases. In this study, we introduce a method of automatically acquiring paraphrases to smooth the translation parameters and to increase the coverage of translation knowledge. One previous approach

ON THE STATISTICAL ESTIMATION OF STOCHASTIC FINITE-STATE TRANSDUCERS IN MACHINE TRANSLATION

The inference of finite-state transducers from bilingual training data plays an important role in many natural-language tasks and mainly in machine translation. However, there are only a few techniques to infer such models. One of these techniques is the grammatical inference and alignments for transducer inference (GIATI) technique that has proven to be very adequate for speech translation, text-input machine translation, or computer-assisted translation. GIATI is a heuristic technique that requires segmented training data (i.e., the input sentences and the output sentences must be segmented with the restriction that the input segments and the output segments must be monotone aligned). For the purpose of obtaining segmented training data, pure statistical word-alignment models are used. This technique is revisited in this article. The main goal is to formally derive the complete GIATI technique using classical expectation-maximization statistical estimation procedure. This new approach allows us to avoid a hard dependence on heuristic “external” statistical techniques (statistical alignments and n-grams). A first set of experimental results obtained in a machine-translation task are also reported to initially validate this new version of the inference technique of finite-state transducers.