Multiple Alignment Of DNA Sequences Research Articles

Rprimer: an R/bioconductor package for design of degenerate oligos for sequence variable viruses

BackgroundThis paper presents a new R/Bioconductor package, rprimer, for design of degenerate oligos and PCR assays for sequence variable viruses. A multiple DNA sequence alignment is used as input data, while the outputs consist of comprehensive tables (data frames) and dashboard-like plots. The workflow can be run directly from the R console or through a graphical user interface (Shiny application). Here, rprimer is demonstrated and evaluated by using it to design two norovirus genogroup I (GI) assays: one RT-qPCR assay for quantitative detection and one RT‑PCR assay for Sanger sequencing and polymerase-capsid based genotyping.ResultsThe assays generated were evaluated using stool samples testing positive for norovirus GI. The RT-qPCR assay accurately amplified and quantified all samples and showed comparable performance to a widely-used standardised assay, while the RT-PCR assay resulted in successful sequencing and genotyping of all samples. Merits and limitations of the package were identified through comparison with three similar freely available software packages. Several features were comparable across the different tools, but important advantages of rprimer were its speed, flexibility in oligo design and capacity for visualisation.ConclusionsAn R/Bioconductor package, rprimer, was developed and shown to be successful in designing primers and probes for quantitative detection and genotyping of a sequence-variable virus. The package provides an efficient, flexible and visual approach to degenerate oligo design, and can therefore assist in virus research and method development.

Parallel multiple DNA sequence alignment using genetic algorithm and asynchronous advantage actor critic model

Multiple sequence alignment plays a crucial role in bioinformatics field. In this process, sequences are compared by establishing residue-residue correspondence among related sequences. In this paper, we present a novel framework that uses genetic algorithm and asynchronous advantage actor critic model to look for the optimal multiple DNA sequence alignment. In the first step, we generate initial alignments by applying the T-COFFEE method. Then, we recursively use a genetic algorithm to find the optimal fragmentation of the pre-aligned sequences. At this stage, the obtained short sub alignments are introduced to the A3C model for parallel alignments. So, the optimal multiple alignment of the original sequences represents the fusion of sub-alignments. To evaluate the performances of the proposed approach, we used the Balibase 3.0 benchmark and compared to TCOFFEE, MAFFT and RLALIGN methods. The obtained experimental results show that the proposed approach can achieve solutions of competitive quality.

Multiple DNA Sequence Alignment Based on Genetic Algorithms and Divide-and-Conquer Techniques

Multiple DNA sequence alignment is one of the important research topics of bioin- formatics. Because of the huge length of DNA sequences of advanced organisms, some re- searchers used divide-and-conquer techniques to cut the sequences for decreasing the space complexity for sequence alignment. Because the cutting points of sequences of the existing methods are fixed at the middle or the near-middle points, the performance of sequence align- ment of the existing methods is not good enough. In this paper, we present a new method for multiple DNA sequence alignment using genetic algorithms and divide-and-conquer techniques to choose optimal cut points of multiple DNA sequences. The experimental results show that the proposed method is better than the existing method for dealing with multiple DNA sequence alignment.

Benchmark of algorithms for multiple DNA sequence alignment across livestock species

Background: Due to the growing amount of biological data, it is often necessary to select the most optimal estimation method for DNA sequence alignment across livestock species. One of the most important benches of genomics is to modelling homology between considered DNA sequences. A multiple sequence alignment is a potent tool for molecular and evolutionary biology, and there are several programs and algorithms applicable for this purpose. The purpose of this paper was to study the most commonly used DNA alignment algorithms to select the optimal tool dedicated for short sequences.Methods: Four steps of bioinformatics pipelines were considered to benchmark the algorithms for multiple DNA sequence alignment across livestock species: 1) selection of reference genome sequences of ARS1.2 for cattle, EquCab3.0 for horse and vicPac2 for alpaca with a low E-value using TBLASTn 2) removing gaps for these sequences 3) alignment of obtained sequences using examined algorithms 4) matching the quality of aligned sequences with sequences of reference genomes by more software. The time of computation was archived for the whole analysis. The seven programs were utilized, each based on different alignment algorithms, namely: ClustalO, ClustalW, Kalign, MAFFT, MUSCLE, Probcons and T-Coffee.Results: The result obtained in this study showed that the fastest is progressive algorithms such as Kalign or MUSCLE-FAST. Moreover, the iterative algorithms like MAFFT and MUSCLE revealed a higher quality of the alignment. The T-Coffee and Probcons programs were computational cost-effective; simultaneously, they were generating a medium-quality calculation in a relatively long time. The best quality of alignment was shown by iterative variants of the MAFFT program; however, the speed of the calculations was relatively low. The fastest algorithm was Kalign, making alignment much faster than the competitors, but achieving average results in the quality of the alignment. The average speed ratio concerning the quality of the analyzed algorithms was obtained by the progressive version of MAFFT, NS1.Conclusions: We conclude that the results of this study can be used to re-alignment of variant primers in new livestock genome releases.

Полиморфизм генов ферментов антиоксидантной системы у культурных видов пшеницы и дикорастущих сородичей

Abstract. The purpose of the study. Investigation of the polymorphism of genes of superoxide dismutase (SOD) and amylase in cultivated wheat species and wild relatives, the possibility of using them as molecular genetic markers to assess the genetic diversity of wheat varieties. Methods. The objects of research were wheat varieties cultivated in different periods in Kazakhstan, distant relatives and wild wheat species. The material was kindly provided by the staff of the laboratory of the gene pool of the SPC ZH im. A.I. Baraev, and also obtained from the USDA genetic resource collections (http://wheat.pw.usda.gov/GG3), John Innes Center (Germplasm Resources Unit, BBSRC) (http://data.jic.bbsrc.ac.uk/cgi-bin/germplasm/cereals.asp), All-Russian Institute of Plant Industry named after N.I. Vavilov (VIR) (http://91.151.189.38/virdb). Isolation of total DNA from etiolated seedlings was performed using the CTAB method. Selection of primers for PCR amplification, in silico PCR, analysis of oligonucleotides, and multiple alignment of DNA sequences were performed using the FastPCR program (http://primerdigital.com/fastpcr.html). Bioinformatic analysis of candidate genes was carried out using the NCBI database (http://www.ncbi.nlm.nih.gov/gene), UniProt http://www.uniprot.org/uniprot), Ensembl Plants (http://plants.ensembl.org). Results. Analysis of genetic diversity based on the spectra of superoxide dismutase and amylase isoenzymes in cultivated wheat species and wild relatives cultivated in different breeding periods showed that the level of differentiation between the studied groups was quite high, most of the genetic diversity was revealed within the groups. The genetic diversity of modern wheat varieties has a lower number of polymorphic loci and effective alleles of storage proteins, SOD, and amylases genes in comparison with distant relatives and ancient varieties from the world collection. Practical significance. The study of the polymorphism of the gene families of superoxide dismutase and amylase will increase the efficiency of identification of varieties and hybrids, study of their heterogeneity and targeted selection of parental pairs for crosses.

Association of a novel polymorphism in the bovine PPARGC1A gene with growth, slaughter and meat quality traits in Brangus steers

The PPARGC1A gene (peroxysome proliferator-activated receptor-γ coactivator 1α gene) controls muscle fiber type and brown adipocyte differentiation; therefore, it is a candidate gene for beef quality traits (tenderness and fat content). Two SNPs ( Single Nucleotide Polymorphisms) were identified within exon 8 by multiple alignment of DNA sequences obtained from 24 bulls: a transition G/A (SNP 1181) and a transversion A/T (SNP 1299). The SNP 1181 is a novel SNP, corresponding to a non-conservative substitution (A GT/A AT) that could be the cause of amino acid substitution ( 364Serine/ 364Asparagine). A Mismatch PCR method was designed to determine genotypes of 73 bulls and 268 steers for SNP 1181. Growth, slaughter and meat quality information were available for the group of steers. Allele A of SNP 1181 was not found in Angus. In 243 steers, no significant differences ( P > 0.05) were found for either final live body weight, gain in backfat thickness in Spring, kidney fat weight, kidney fat percentage, Warner–Bratzler shear force at 7 days postmortem, intramuscular fat percentage or meat colour between genotype GG and AG. This SNP could be included in breed composition and population admixture analyses because there are marked differences in allelic frequencies between Bos taurus and Bos indicus breeds.

OCPAT: an online codon-preserved alignment tool for evolutionary genomic analysis of protein coding sequences.

BackgroundRapidly accumulating genome sequence data from multiple species offer powerful opportunities for the detection of DNA sequence evolution. Phylogenetic tree construction and codon-based tests for natural selection are the prevailing tools used to detect functionally important evolutionary change in protein coding sequences. These analyses often require multiple DNA sequence alignments that maintain the correct reading frame for each collection of putative orthologous sequences. Since this feature is not available in most alignment tools, codon reading frames often must be checked manually before evolutionary analyses can commence.ResultsHere we report an online codon-preserved alignment tool (OCPAT) that generates multiple sequence alignments automatically from the coding sequences of any list of human gene IDs and their putative orthologs from genomes of other vertebrate tetrapods. OCPAT is programmed to extract putative orthologous genes from genomes and to align the orthologs with the reading frame maintained in all species. OCPAT also optimizes the alignment by trimming the most variable alignment regions at the 5' and 3' ends of each gene. The resulting output of alignments is returned in several formats, which facilitates further molecular evolutionary analyses by appropriate available software. Alignments are generally robust and reliable, retaining the correct reading frame. The tool can serve as the first step for comparative genomic analyses of protein-coding gene sequences including phylogenetic tree reconstruction and detection of natural selection. We aligned 20,658 human RefSeq mRNAs using OCPAT. Most alignments are missing sequence(s) from at least one species; however, functional annotation clustering of the ~1700 transcripts that were alignable to all species shows that genes involved in multi-subunit protein complexes are highly conserved.ConclusionThe OCPAT program facilitates large-scale evolutionary and phylogenetic analyses of entire biological processes, pathways, and diseases.

DNA reference alignment benchmarks based on tertiary structure of encoded proteins

Multiple sequence alignments (MSAs) are at the heart of bioinformatics analysis. Recently, a number of multiple protein sequence alignment benchmarks (i.e. BAliBASE, OXBench, PREFAB and SMART) have been released to evaluate new and existing MSA applications. These databases have been well received by researchers and help to quantitatively evaluate MSA programs on protein sequences. Unfortunately, analogous DNA benchmarks are not available, making evaluation of MSA programs difficult for DNA sequences. This work presents the first known multiple DNA sequence alignment benchmarks that are (1) comprised of protein-coding portions of DNA (2) based on biological features such as the tertiary structure of encoded proteins. These reference DNA databases contain a total of 3545 alignments, comprising of 68 581 sequences. Two versions of the database are available: mdsa_100s and mdsa_all. The mdsa_100s version contains the alignments of the data sets that TBLASTN found 100% sequence identity for each sequence. The mdsa_all version includes all hits with an E-value score above the threshold of 0.001. A primary use of these databases is to benchmark the performance of MSA applications on DNA data sets. The first such case study is included in the Supplementary Material.

SNAP: Combine and Map modules for multilocus population genetic analysis

We have added two software tools to our Suite of Nucleotide Analysis Programs (SNAP) for working with DNA sequences sampled from populations. SNAP Map collapses DNA sequence data into unique haplotypes, extracts variable sites and manipulates output into multiple formats for input into existing software packages for evolutionary analyses. Map collapses DNA sequence data into unique haplotypes, extracts variable sites and manipulates output into multiple formats for input into existing software packages for evolutionary analyses. Map includes novel features such as recoding insertions or deletions, including or excluding variable sites that violate an infinite-sites model and the option of collapsing sequences with corresponding phenotypic information, important in testing for significant haplotype-phenotype associations. SNAP Combine merges multiple DNA sequence alignments into a single multiple alignment file. The resulting file can be the union or intersection of the input files. SNAP Combine currently reads from and writes to several sequence alignment file formats including both sequential and interleaved formats. Combine also keeps track of the start and end positions of each separate alignment file allowing the user to exclude variable sites or taxa, important in creating input files for multilocus analyses.

A Heuristic Bayesian Method for Segmenting DNA Sequence Alignments and Detecting Evidence for Recombination and Gene Conversion

We propose a heuristic approach to the detection of evidence for recombination and gene conversion in multiple DNA sequence alignments. The proposed method consists of two stages. In the first stage, a sliding window is moved along the DNA sequence alignment, and phylogenetic trees are sampled from the conditional posterior distribution with MCMC. To reduce the noise intrinsic to inference from the limited amount of data available in the typically short sliding window, a clustering algorithm based on the Robinson-Foulds distance is applied to the trees thus sampled, and the posterior distribution over tree clusters is obtained for each window position. While changes in this posterior distribution are indicative of recombination or gene conversion events, it is difficult to decide when such a change is statistically significant. This problem is addressed in the second stage of the proposed algorithm, where the distributions obtained in the first stage are post-processed with a Bayesian hidden Markov model (HMM). The emission states of the HMM are associated with posterior distributions over phylogenetic tree topology clusters. The hidden states of the HMM indicate putative recombinant segments. Inference is done in a Bayesian sense, sampling parameters from the posterior distribution with MCMC. Of particular interest is the determination of the number of hidden states as an indication of the number of putative recombinant regions. To this end, we apply reversible jump MCMC, and sample the number of hidden states from the respective posterior distribution.

Culture-independent analysis of fecal enterobacteria in environmental samples by single-cell mRNA profiling.

A culture-independent method called mRNA profiling has been developed for the analysis of fecal enterobacteria and their physiological status in environmental samples. This taxon-specific approach determines the single-cell content of selected gene transcripts whose abundance is either directly or inversely proportional to growth state. Fluorescence in situ hybridization using fluorochrome-labeled oligonucleotide probes was used to measure the cellular concentration of fis and dps mRNA. Relative levels of these transcripts provided a measure of cell growth state and the ability to enumerate fecal enterobacterial cell number. Orthologs were cloned by inverse PCR from several major enterobacterial genera, and probes specific for fecal enterobacteria were designed using multiple DNA sequence alignments. Probe specificity was determined experimentally using pure and mixed cultures of the major enterobacterial genera as well as secondary treated wastewater samples seeded with pure culture inocula. Analysis of the fecal enterobacterial community resident in unseeded secondary treated wastewater detected fluctuations in transcript abundance that were commensurate with incubation time and nutrient availability and demonstrated the utility of the method using environmental samples. mRNA profiling provides a new strategy to improve wastewater disinfection efficiency by accelerating water quality analysis.

An Eulerian path approach to global multiple alignment for DNA sequences.

With the rapid increase in the dataset of genome sequences, the multiple sequence alignment problem is increasingly important and frequently involves the alignment of a large number of sequences. Many heuristic algorithms have been proposed to improve the speed of computation and the quality of alignment. We introduce a novel approach that is fundamentally different from all currently available methods. Our motivation comes from the Eulerian method for fragment assembly in DNA sequencing that transforms all DNA fragments into a de Bruijn graph and then reduces sequence assembly to a Eulerian path problem. The paper focuses on global multiple alignment of DNA sequences, where entire sequences are aligned into one configuration. Our main result is an algorithm with almost linear computational speed with respect to the total size (number of letters) of sequences to be aligned. Five hundred simulated sequences (averaging 500 bases per sequence and as low as 70% pairwise identity) have been aligned within three minutes on a personal computer, and the quality of alignment is satisfactory. As a result, accurate and simultaneous alignment of thousands of long sequences within a reasonable amount of time becomes possible. Data from an Arabidopsis sequencing project is used to demonstrate the performance.

NAMIHEI: A Novel Algorithm for Genomic Polymorphism Detection from DNA Sequence

Potential genomic polymorphisms such as single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) have been discovered from a multiple alignment of DNA sequences. However, mostof conventional alignment algorithms are originally designed for assembling fragment sequences intocontigs, and usually are not suitable for detecting genomic polymorphisms. For example, it is almostimpossible to detect polymorphisms on the traces are much noisy and the downstream region of aheterozygous indel where maternal or paternal traces shift in some distance.In our previous work, we reported a unique computer algorithm called Jessica for detecting in-dels from DNA sequences [1]. In this work, we developed another algorithm named NAMIHEI fordiscovering and genotyping of both SNPs and indels, making use of some features hidden in tracesand a public database sequence as a standard reference. Furthermore, we also present an applicationprogram equipped with an ecient user interface for supporting the check of detected polymorphismcandidates, which requires time and e orts most.

Simulated Evolutionary Optimization and Local Search: Introduction and Application to Tree Search

Tree search and its more complicated variant, tree search and simultaneous multiple DNA sequence alignment, are difficult NP-complete optimization problems, which require the application of advanced computational techniques, if large data sets are to be solved within reasonable computation times. Traditionally tree search has been attacked with a search strategy that is best described as multistart hill-climbing; local search by branch swapping has been performed on several different starting trees. Recently a different tree search strategy was tested in the Parsigal parsimony program, which used a combination of evolutionary optimization and local search. Evolutionary optimization algorithms use principles adopted from biological evolution to solve technical optimization tasks. Evolutionary optimization is a stochastic global search method, which means that the method is able to escape local optima, and is in principle able to produce any solution in the search space (although this may take a long time). Local search techniques, such as branch swapping, employ a completely different search strategy; they exploit local information maximally in order to achieve quick improvement in the value of the objective function. However, local search algorithms lack the ability to escape from local optima, which is a fundamental requirement for any search algorithm that aims to be able to discover the global optimum of a multimodal optimization problem. Hence it seems that an optimization strategy combining the good properties of both evolutionary algorithms and local search would be ideal. In this study, aspects of global optimization and local search are discussed, and the method of simulated evolutionary optimization is reviewed in detail. The application of simulated evolutionary optimization to tree search in Parsigal is then reviewed briefly.

Simulated Evolutionary Optimization and Local Search: Introduction and Application to Tree Search

Tree search and its more complicated variant, tree search and simultaneous multiple DNA sequence alignment, are difficult NP‐complete optimization problems, which require the application of advanced computational techniques, if large data sets are to be solved within reasonable computation times. Traditionally tree search has been attacked with a search strategy that is best described as multistart hill‐climbing; local search by branch swapping has been performed on several different starting trees. Recently a different tree search strategy was tested in the Parsigal parsimony program, which used a combination of evolutionary optimization and local search. Evolutionary optimization algorithms use principles adopted from biological evolution to solve technical optimization tasks. Evolutionary optimization is a stochastic global search method, which means that the method is able to escape local optima, and is in principle able to produce any solution in the search space (although this may take a long time). Local search techniques, such as branch swapping, employ a completely different search strategy; they exploit local information maximally in order to achieve quick improvement in the value of the objective function. However, local search algorithms lack the ability to escape from local optima, which is a fundamental requirement for any search algorithm that aims to be able to discover the global optimum of a multimodal optimization problem. Hence it seems that an optimization strategy combining the good properties of both evolutionary algorithms and local search would be ideal. In this study, aspects of global optimization and local search are discussed, and the method of simulated evolutionary optimization is reviewed in detail. The application of simulated evolutionary optimization to tree search in Parsigal is then reviewed briefly.