Sequence Comparison Methods Research Articles

TACO: Taxonomic prediction of unknown OTUs through OTU co‐abundance networks

BackgroundA main goal of metagenomics is taxonomic characterization of microbial communities. Although sequence comparison has been the main method for the taxonomic classification, there is not a clear agreement on similarity calculation and similarity thresholds, especially at higher taxonomic levels such as phylum and class. Thus taxonomic classification of novel metagenomic sequences without close homologs in the biological databases poses a challenge.MethodsIn this study, we propose to use the co‐abundant associations between taxa/operational taxonomic units (OTU) across complex and diverse communities to assist taxonomic classification. We developed a Markov Random Field model to predict taxa of unknown microorganisms using co‐abundant associations.ResultsAlthough such associations are intrinsically functional associations, we demonstrate that they are strongly correlated with taxonomic associations and can be combined with sequence comparison methods to predict taxonomic origins of unknown microorganisms at phylum and class levels.ConclusionsWith the ever‐increasing accumulation of sequence data from microbial communities, we now take the first step to explore these associations for taxonomic identification beyond sequence similarity.Availability and ImplementationSource codes of TACO are freely available at the following URL: https://github.com/baharvand/OTU‐Taxonomy‐Identification implemented in C++, supported on Linux and MS Windows.

Thermodynamically optimal whole-genome tiling microarray design and validation.

BackgroundMicroarray is an efficient apparatus to interrogate the whole transcriptome of species. Microarray can be designed according to annotated gene sets, but the resulted microarrays cannot be used to identify novel transcripts and this design method is not applicable to unannotated species. Alternatively, a whole-genome tiling microarray can be designed using only genomic sequences without gene annotations, and it can be used to detect novel RNA transcripts as well as known genes. The difficulty with tiling microarray design lies in the tradeoff between probe-specificity and coverage of the genome. Sequence comparison methods based on BLAST or similar software are commonly employed in microarray design, but they cannot precisely determine the subtle thermodynamic competition between probe targets and partially matched probe nontargets during hybridizations.FindingsUsing the whole-genome thermodynamic analysis software PICKY to design tiling microarrays, we can achieve maximum whole-genome coverage allowable under the thermodynamic constraints of each target genome. The resulted tiling microarrays are thermodynamically optimal in the sense that all selected probes share the same melting temperature separation range between their targets and closest nontargets, and no additional probes can be added without violating the specificity of the microarray to the target genome.ConclusionsThis new design method was used to create two whole-genome tiling microarrays for Escherichia coli MG1655 and Agrobacterium tumefaciens C58 and the experiment results validated the design.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-016-2113-4) contains supplementary material, which is available to authorized users.

Diversity and Evolution of Type IV pili Systems in Archaea.

Many surface structures in archaea including various types of pili and the archaellum (archaeal flagellum) are homologous to bacterial type IV pili systems (T4P). The T4P consist of multiple proteins, often with poorly conserved sequences, complicating their identification in sequenced genomes. Here we report a comprehensive census of T4P encoded in archaeal genomes using sensitive methods for protein sequence comparison. This analysis confidently identifies as T4P components about 5000 archaeal gene products, 56% of which are currently annotated as hypothetical in public databases. Combining results of this analysis with a comprehensive comparison of genomic neighborhoods of the T4P, we present models of organization of 10 most abundant variants of archaeal T4P. In addition to the differentiation between major and minor pilins, these models include extra components, such as S-layer proteins, adhesins and other membrane and intracellular proteins. For most of these systems, dedicated major pilin families are identified including numerous stand alone major pilin genes of the PilA family. Evidence is presented that secretion ATPases of the T4P and cognate TadC proteins can interact with different pilin sets. Modular evolution of T4P results in combinatorial variability of these systems. Potential regulatory or modulating proteins for the T4P are identified including KaiC family ATPases, vWA domain-containing proteins and the associated MoxR/GvpN ATPase, TFIIB homologs and multiple unrelated transcription regulators some of which are associated specific T4P. Phylogenomic analysis suggests that at least one T4P system was present in the last common ancestor of the extant archaea. Multiple cases of horizontal transfer and lineage-specific duplication of T4P loci were detected. Generally, the T4P of the archaeal TACK superphylum are more diverse and evolve notably faster than those of euryarchaea. The abundance and enormous diversity of T4P in hyperthermophilic archaea present a major enigma. Apparently, fundamental aspects of the biology of hyperthermophiles remain to be elucidated.

New and mild method for the synthesis of alprazolam and diazepam and computational study of their binding mode to GABAA receptor

A new method for the synthesis of 8-chloro-1-methyl-6-phenyl-4H-benzo[f][1,2,4]triazolo[4,3-a][1,4]diazepine (alprazolam) and 7-chloro-1-methyl-5-phenyl-1H-benzo[e][1,4]diazepin-2(3H)-one (diazepam) from 2-amino-5-chloro benzophenone was described under mild conditions. Most of the synthetic steps were carried out under solvent-free conditions, and the products were obtained in high yield and purity. The products were characterized by comparison of physical properties with authentic samples and also by IR, 1H NMR and 13C NMR. Three-dimensional (3D) model of GABAA was constructed using X-ray crystal structure of homopentameric caenorhabditis elegans glutamate-gated chloride channel (GluCl) (3RHW) at 3.3 A as the template based on sequence comparison and homology modeling method. The homology modeling and MD simulation studies predicted the 3D structure of receptor in a water environment. The resulted conformation of the receptor was used for docking of the alprazolam and diazepam. Docking studies indicated many important interactions of the drugs with the receptor. Furthermore, the complex of GABA with drugs was used in MD simulation to realize the conformation changes of the complex.

A Provably Efficient Algorithm for the k-Mismatch Average Common Substring Problem.

Alignment-free sequence comparison methods are attracting persistent interest, driven by data-intensive applications in genome-wide molecular taxonomy and phylogenetic reconstruction. Among all the methods based on substring composition, the average common substring (ACS) measure admits a straightforward linear time sequence comparison algorithm, while yielding impressive results in multiple applications. An important direction of this research is to extend the approach to permit a bounded edit/hamming distance between substrings, so as to reflect more accurately the evolutionary process. To date, however, algorithms designed to incorporate k ≥ 1 mismatches have O(n(2)) worst-case time complexity, where n is the total length of the input sequences. On the other hand, accounting for mismatches has shown to lead to much improved classification, while heuristics can improve practical performance. In this article, we close the gap by presenting the first provably efficient algorithm for the k-mismatch average common string (ACSk) problem that takes O(n) space and O(n log(k) n) time in the worst case for any constant k. Our method extends the generalized suffix tree model to incorporate a carefully selected bounded set of perturbed suffixes, and can be applied to other complex approximate sequence matching problems.

Statistical and Alignment Based Methods for Comparison of Non-Coding DNA Sequences

Statistical and Alignment Based Methods for Comparison of Non-Coding DNA Sequences

Whole genome/proteome based phylogeny reconstruction for prokaryotes using higher order Markov model and chaos game representation

Traditional methods for sequence comparison and phylogeny reconstruction rely on pair wise and multiple sequence alignments. But alignment could not be directly applied to whole genome/proteome comparison and phylogenomic studies due to their high computational complexity. Hence alignment-free methods became popular in recent years. Here we propose a fast alignment-free method for whole genome/proteome comparison and phylogeny reconstruction using higher order Markov model and chaos game representation. In the present method, we use the transition matrices of higher order Markov models to characterize amino acid or DNA sequences for their comparison. The order of the Markov model is uniquely identified by maximizing the average Shannon entropy of conditional probability distributions. Using one-dimensional chaos game representation and linked list, this method can reduce large memory and time consumption which is due to the large-scale conditional probability distributions. To illustrate the effectiveness of our method, we employ it for fast phylogeny reconstruction based on genome/proteome sequences of two species data sets used in previous published papers. Our results demonstrate that the present method is useful and efficient.Availability and implementation: The source codes for our algorithm to get the distance matrix and genome/proteome sequences can be downloaded from ftp://121.199.20.25/. The software Phylip and EvolView we used to construct phylogenetic trees can be referred from their websites.

Novel DNA Sequence Comparison Method Based on Markov Chain and Information Entropy

Novel DNA Sequence Comparison Method Based on Markov Chain and Information Entropy

PR2ALIGN: a stand-alone software program and a web-server for protein sequence alignment using weighted biochemical properties of amino acids.

BackgroundAlignment of amino acid sequences is the main sequence comparison method used in computational molecular biology. The selection of the amino acid substitution matrix best suitable for a given alignment problem is one of the most important decisions the user has to make. In a conventional amino acid substitution matrix all elements are fixed and their values cannot be easily adjusted. Moreover, most existing amino acid substitution matrices account for the average (dis)similarities between amino acid types and do not distinguish the contribution of a specific biochemical property to these (dis)similarities.FindingsPR2ALIGN is a stand-alone software program and a web-server that provide the functionality for implementing flexible user-specified alignment scoring functions and aligning pairs of amino acid sequences based on the comparison of the profiles of biochemical properties of these sequences. Unlike the conventional sequence alignment methods that use 20x20 fixed amino acid substitution matrices, PR2ALIGN uses a set of weighted biochemical properties of amino acids to measure the distance between pairs of aligned residues and to find an optimal minimal distance global alignment. The user can provide any number of amino acid properties and specify a weight for each property. The higher the weight for a given property, the more this property affects the final alignment. We show that in many cases the approach implemented in PR2ALIGN produces better quality pair-wise alignments than the conventional matrix-based approach.ConclusionsPR2ALIGN will be helpful for researchers who wish to align amino acid sequences by using flexible user-specified alignment scoring functions based on the biochemical properties of amino acids instead of the amino acid substitution matrix. To the best of the authors’ knowledge, there are no existing stand-alone software programs or web-servers analogous to PR2ALIGN. The software is freely available from http://pr2align.rit.albany.edu.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-015-1152-6) contains supplementary material, which is available to authorized users.

遠縁タンパク質検索に適した新規アミノ酸置換行列

Amino acid similarity search methods are fundamental tools for inferring protein structure and function. Optimization of amino acid substitution matrices is indispensable to improve search performance. We explored matrices that are ideal for detecting distantly related proteins in the PCA subspace that represents a set of typical existing matrices, and derived a novel sensitive matrix: MIQS. Test results show that the MIQS detection performance is superior to those of existing matrices and other sequence comparison methods. This refinement of the amino acid substitution matrix might influence many fields of sequence analysis. MIQS is available at http://csas.cbrc.jp/Ssearch/.

Measurement of word frequencies in genomic DNA sequences based on partial alignment and fuzzy set.

Accompanied with the rapid increase of the amount of data registered in the databases of biological sequences, the need for a fast method of sequence comparison applicable to sequences of large size is also increasing. In general, alignment is used for sequence comparison. However, the alignment may not be appropriate for comparison of sequences of large size such as whole genome sequences due to its large time complexity. In this article, we propose a semi alignment-free method of sequence comparison based on word frequency distributions, in which we partially use the alignment to measure word frequencies along with the idea of fuzzy set theory. Experiments with ten bacterial genome sequences demonstrated that the fuzzy measurements has the effect that facilitates discrimination between close relatives and distant relatives.

Spaced words and kmacs: fast alignment-free sequence comparison based on inexact word matches.

In this article, we present a user-friendly web interface for two alignment-free sequence-comparison methods that we recently developed. Most alignment-free methods rely on exact word matches to estimate pairwise similarities or distances between the input sequences. By contrast, our new algorithms are based on inexact word matches. The first of these approaches uses the relative frequencies of so-called spaced words in the input sequences, i.e. words containing ‘don't care’ or ‘wildcard’ symbols at certain pre-defined positions. Various distance measures can then be defined on sequences based on their different spaced-word composition. Our second approach defines the distance between two sequences by estimating for each position in the first sequence the length of the longest substring at this position that also occurs in the second sequence with up to k mismatches. Both approaches take a set of deoxyribonucleic acid (DNA) or protein sequences as input and return a matrix of pairwise distance values that can be used as a starting point for clustering algorithms or distance-based phylogeny reconstruction. The two alignment-free programmes are accessible through a web interface at ‘Göttingen Bioinformatics Compute Server (GOBICS)’: http://spaced.gobics.de http://kmacs.gobics.de and the source codes can be downloaded.

Fast alignment-free sequence comparison using spaced-word frequencies

Motivation: Alignment-free methods for sequence comparison are increasingly used for genome analysis and phylogeny reconstruction; they circumvent various difficulties of traditional alignment-based approaches. In particular, alignment-free methods are much faster than pairwise or multiple alignments. They are, however, less accurate than methods based on sequence alignment. Most alignment-free approaches work by comparing the word composition of sequences. A well-known problem with these methods is that neighbouring word matches are far from independent.Results: To reduce the statistical dependency between adjacent word matches, we propose to use ‘spaced words’, defined by patterns of ‘match’ and ‘don’t care’ positions, for alignment-free sequence comparison. We describe a fast implementation of this approach using recursive hashing and bit operations, and we show that further improvements can be achieved by using multiple patterns instead of single patterns. To evaluate our approach, we use spaced-word frequencies as a basis for fast phylogeny reconstruction. Using real-world and simulated sequence data, we demonstrate that our multiple-pattern approach produces better phylogenies than approaches relying on contiguous words.Availability and implementation: Our program is freely available at http://spaced.gobics.de/.Contact: chris.leimeister@stud.uni-goettingen.deSupplementary information: Supplementary data are available at Bioinformatics online.

Comparison of Metatranscriptomic Samples Based on k-Tuple Frequencies

BackgroundThe comparison of samples, or beta diversity, is one of the essential problems in ecological studies. Next generation sequencing (NGS) technologies make it possible to obtain large amounts of metagenomic and metatranscriptomic short read sequences across many microbial communities. De novo assembly of the short reads can be especially challenging because the number of genomes and their sequences are generally unknown and the coverage of each genome can be very low, where the traditional alignment-based sequence comparison methods cannot be used. Alignment-free approaches based on k-tuple frequencies, on the other hand, have yielded promising results for the comparison of metagenomic samples. However, it is not known if these approaches can be used for the comparison of metatranscriptome datasets and which dissimilarity measures perform the best.ResultsWe applied several beta diversity measures based on k-tuple frequencies to real metatranscriptomic datasets from pyrosequencing 454 and Illumina sequencing platforms to evaluate their effectiveness for the clustering of metatranscriptomic samples, including three dissimilarity measures, one dissimilarity measure in CVTree, one relative entropy based measure S2 and three classical distances. Results showed that the measure can achieve superior performance on clustering metatranscriptomic samples into different groups under different sequencing depths for both 454 and Illumina datasets, recovering environmental gradients affecting microbial samples, classifying coexisting metagenomic and metatranscriptomic datasets, and being robust to sequencing errors. We also investigated the effects of tuple size and order of the background Markov model. A software pipeline to implement all the steps of analysis is built and is available at http://code.google.com/p/d2-tools/.ConclusionsThe k-tuple based sequence signature measures can effectively reveal major groups and gradient variation among metatranscriptomic samples from NGS reads. The dissimilarity measure performs well in all application scenarios and its performance is robust with respect to tuple size and order of the Markov model.

Phylogenetic analysis of DNA sequences based on [formula omitted]-word and rough set theory

Among alignment-free methods for sequence comparison, the model of k-word frequencies is a well-developed one. However, most existing word-based methods neglect relationships among k-word frequencies, while a few others focus on the correlation of k-words but ignore the word frequency itself. In this paper, we propose a new k-word method which succeeds in conquering the two problems.By means of characteristic sequences of a DNA sequence, we construct a 3×2k dimensional complete word-based vector. Then we present a feature selection scheme based on rough set theory (RST) to extract the most informative k-words and use only these selected features to represent the DNA sequence. To evaluate the effectiveness of our method, we test it by phylogenetic analysis on three datasets. The first one is used as a training set, by which 869 top ranked k-words are selected. The other two are used as the testing set. The results demonstrate that the proposed method can capture more important information and is more efficient for molecular phylogenetic analysis.

Biological Intuition in Alignment-Free Methods: Response to Posada

A recent editorial in Journal of Molecular Evolution highlights opportunities and challenges facing molecular evolution in the era of next-generation sequencing. Abundant sequence data should allow more-complex models to be fit at higher confidence, making phylogenetic inference more reliable and improving our understanding of evolution at the molecular level. However, concern that approaches based on multiple sequence alignment may be computationally infeasible for large datasets is driving the development of so-called alignment-free methods for sequence comparison and phylogenetic inference. The recent editorial characterized these approaches as model-free, not based on the concept of homology, and lacking in biological intuition. We argue here that alignment-free methods have not abandoned models or homology, and can be biologically intuitive.

Study of LZ-word distribution and its application for sequence comparison

Lempel–Ziv complexity has been widely used for sequence comparison and achieved promising results, but until now components' distribution in exhaustive history has not been studied. This paper investigated the whole distribution of LZ-words and presented a novel statistical method for sequence comparison. With the components' length in mind, we revised Lempel–Ziv complexity and obtained various sets of LZ-words. Instead of calculating the LZ-words' contents, we defined a series of set operations on LZ-word set to compare biological sequences. In order to assess the effectiveness of the proposed method, we performed two sets of experiments and compared it with alignment-based methods.

Rapid similarity search of proteins using alignments of domain arrangements

Homology search methods are dominated by the central paradigm that sequence similarity is a proxy for common ancestry and, by extension, functional similarity. For determining sequence similarity in proteins, most widely used methods use models of sequence evolution and compare amino-acid strings in search for conserved linear stretches. Probabilistic models or sequence profiles capture the position-specific variation in an alignment of homologous sequences and can identify conserved motifs or domains. While profile-based search methods are generally more accurate than simple sequence comparison methods, they tend to be computationally more demanding. In recent years, several methods have emerged that perform protein similarity searches based on domain composition. However, few methods have considered the linear arrangements of domains when conducting similarity searches, despite strong evidence that domain order can harbour considerable functional and evolutionary signal. Here, we introduce an alignment scheme that uses a classical dynamic programming approach to the global alignment of domains. We illustrate that representing proteins as strings of domains (domain arrangements) and comparing these strings globally allows for a both fast and sensitive homology search. Further, we demonstrate that the presented methods complement existing methods by finding similar proteins missed by popular amino-acid-based comparison methods. An implementation of the presented algorithms, a web-based interface as well as a command-line program for batch searching against the UniProt database can be found at http://rads.uni-muenster.de. Furthermore, we provide a JAVA API for programmatic access to domain-string–based search methods.

Comprehensive analysis of the HEPN superfamily: identification of novel roles in intra-genomic conflicts, defense, pathogenesis and RNA processing

BackgroundThe major role of enzymatic toxins that target nucleic acids in biological conflicts at all levels has become increasingly apparent thanks in large part to the advances of comparative genomics. Typically, toxins evolve rapidly hampering the identification of these proteins by sequence analysis. Here we analyze an unexpectedly widespread superfamily of toxin domains most of which possess RNase activity.ResultsThe HEPN superfamily is comprised of all α-helical domains that were first identified as being associated with DNA polymerase β-type nucleotidyltransferases in prokaryotes and animal Sacsin proteins. Using sensitive sequence and structure comparison methods, we vastly extend the HEPN superfamily by identifying numerous novel families and by detecting diverged HEPN domains in several known protein families. The new HEPN families include the RNase LS and LsoA catalytic domains, KEN domains (e.g. RNaseL and Ire1) and the RNase domains of RloC and PrrC. The majority of HEPN domains contain conserved motifs that constitute a metal-independent endoRNase active site. Some HEPN domains lacking this motif probably function as non-catalytic RNA-binding domains, such as in the case of the mannitol repressor MtlR. Our analysis shows that HEPN domains function as toxins that are shared by numerous systems implicated in intra-genomic, inter-genomic and intra-organismal conflicts across the three domains of cellular life. In prokaryotes HEPN domains are essential components of numerous toxin-antitoxin (TA) and abortive infection (Abi) systems and in addition are tightly associated with many restriction-modification (R-M) and CRISPR-Cas systems, and occasionally with other defense systems such as Pgl and Ter. We present evidence of multiple modes of action of HEPN domains in these systems, which include direct attack on viral RNAs (e.g. LsoA and RNase LS) in conjunction with other RNase domains (e.g. a novel RNase H fold domain, NamA), suicidal or dormancy-inducing attack on self RNAs (RM systems and possibly CRISPR-Cas systems), and suicidal attack coupled with direct interaction with phage components (Abi systems). These findings are compatible with the hypothesis on coupling of pathogen-targeting (immunity) and self-directed (programmed cell death and dormancy induction) responses in the evolution of robust antiviral strategies. We propose that altruistic cell suicide mediated by HEPN domains and other functionally similar RNases was essential for the evolution of kin and group selection and cell cooperation. HEPN domains were repeatedly acquired by eukaryotes and incorporated into several core functions such as endonucleolytic processing of the 5.8S-25S/28S rRNA precursor (Las1), a novel ER membrane-associated RNA degradation system (C6orf70), sensing of unprocessed transcripts at the nuclear periphery (Swt1). Multiple lines of evidence suggest that, similar to prokaryotes, HEPN proteins were recruited to antiviral, antitransposon, apoptotic systems or RNA-level response to unfolded proteins (Sacsin and KEN domains) in several groups of eukaryotes.ConclusionsExtensive sequence and structure comparisons reveal unexpectedly broad presence of the HEPN domain in an enormous variety of defense and stress response systems across the tree of life. In addition, HEPN domains have been recruited to perform essential functions, in particular in eukaryotic rRNA processing. These findings are expected to stimulate experiments that could shed light on diverse cellular processes across the three domains of life.ReviewersThis article was reviewed by Martijn Huynen, Igor Zhulin and Nick Grishin

Information entropy based methods for genome comparison

A plethora of biologically useful information lies obscured in the genomes of organisms. Encoded within the genome of an organism is the information about its evolutionary history. Evolutionary signals are scattered throughout the genome. Bioinformatics approaches are frequently invoked to deconstruct the evolutionary patterns underlying genomes, which are difficult to decipher using traditional laboratory experiments. However, interpreting constantly evolving genomes is a non-trivial task for bioinformaticians. Processes such as mutations, recombinations, insertions and deletions make genomes not only heterogeneous and difficult to decipher but also renders direct sequence comparison less effective. Here we present a brief overview of the sequence comparison methods with a focus on recently proposed alignment-free sequence comparison methods based on Shannon information entropy. Many of these sequence comparison methods have been adapted to construct phylogenetic trees to infer relationships among organisms.