Improving model construction of profile HMMs for remote homology detection through structural alignment

<p>Abstract</p> <p>Background</p> <p>Remote homology detection is a challenging problem in Bioinformatics. Arguably, profile Hidden Markov Models (pHMMs) are one of the most successful approaches in addressing this important problem. pHMM packages present a relatively small computational cost, and perform particularly well at recognizing remote homologies. This raises the question of whether structural alignments could impact the performance of pHMMs trained from proteins in the <it>Twilight Zone</it>, as structural alignments are often more accurate than sequence alignments at identifying motifs and functional residues. Next, we assess the impact of using structural alignments in pHMM performance.</p> <p>Results</p> <p>We used the SCOP database to perform our experiments. Structural alignments were obtained using the 3DCOFFEE and MAMMOTH-mult tools; sequence alignments were obtained using CLUSTALW, TCOFFEE, MAFFT and PROBCONS. We performed leave-one-family-out cross-validation over super-families. Performance was evaluated through ROC curves and paired two tailed t-test.</p> <p>Conclusion</p> <p>We observed that pHMMs derived from structural alignments performed significantly better than pHMMs derived from sequence alignment in low-identity regions, mainly below 20%. We believe this is because structural alignment tools are better at focusing on the important patterns that are more often conserved through evolution, resulting in higher quality pHMMs. On the other hand, sensitivity of these tools is still quite low for these low-identity regions. Our results suggest a number of possible directions for improvements in this area.</p

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

<p>Abstract</p> <p>Background</p> <p>Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.</p> <p>Results</p> <p>In this paper, a novel building block of proteins called Top-<it>n</it>-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-<it>n</it>-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-<it>n</it>-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-<it>n</it>-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-<it>n</it>-grams and LSA gives significantly better results compared to related methods.</p> <p>Conclusion</p> <p>The method based on Top-<it>n</it>-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-<it>n</it>-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.</p

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models

<p>Abstract</p> <p>Background</p> <p>Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM).</p> <p>Results</p> <p>We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function.</p> <p>Conclusions</p> <p>The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.</p

Probabilistic Phylogenetic Inference with Insertions and Deletions

A fundamental task in sequence analysis is to calculate the probability of a multiple alignment given a phylogenetic tree relating the sequences and an evolutionary model describing how sequences change over time. However, the most widely used phylogenetic models only account for residue substitution events. We describe a probabilistic model of a multiple sequence alignment that accounts for insertion and deletion events in addition to substitutions, given a phylogenetic tree, using a rate matrix augmented by the gap character. Starting from a continuous Markov process, we construct a non-reversible generative (birth–death) evolutionary model for insertions and deletions. The model assumes that insertion and deletion events occur one residue at a time. We apply this model to phylogenetic tree inference by extending the program dnaml in phylip. Using standard benchmarking methods on simulated data and a new “concordance test” benchmark on real ribosomal RNA alignments, we show that the extended program dnamlε improves accuracy relative to the usual approach of ignoring gaps, while retaining the computational efficiency of the Felsenstein peeling algorithm

Epitope and T-cell Reactivity Prediction Using Machine Learning Approaches

13301甲第3953号博士（工学）金沢大学博士論文本文Ful

Statistical estimation problems in phylogenomics and applications in microbial ecology

With the growing awareness of the potential for microbial communities to play a role in human health, environmental remediation and other important processes, the challenge of understanding such a complex population through the lens of high-throughput sequencing output has risen to the fore. For a de novo sequenced community, the first step to understanding the population involves comparing the sequences to a reference database in some form. In this dissertation, we consider some challenges and benefits of organizing the reference data according to evolution, with orthologous genes grouped together and stored as a multiple sequence alignment and phylogenetic tree. First we consider the related problem of estimating the population-level phylogeny of a group of species based on the alignments and phylogenies of several individual genes. Under one common model, species tree estimation is provably statistically consistent by several different methods, but those proofs rely on two separate and potentially shaky assumptions: that every species appears in the data for every gene (i.e., there is no missing data), and that since gene tree estimation is itself consistent, the gene trees used to compute the population-level tree are correct. Second, we explore some novel ways to use a Bayesian MCMC algorithm for jointly estimating alignment and phylogeny. The result is increased accuracy for large alignments, where the MCMC method alone would not be tractable. In the process, we identify a peculiar property of this Bayesian algorithm: it performs much differently on simulated sequences than on sequences from biological alignment benchmarks. No other alignment method tested showed the same divergence. Finally, we present two different practical applications a reference database containing an alignment and tree for a group of gene families in the context of microbial ecology. The first is an algorithm that uses the tree and alignment to construct an ensemble of profile hidden Markov models that improves remote homology detection. The second is a data visualization technique that generates an image of the community with a high density of data, but one that makes it naturally easy to compare many different samples at a time, potentially uncovering otherwise elusive patterns in the data