A method for estimating rates of nucleotide substitution using DNA sequence data

An estimate of the average number of evolutionarily acceptable substitutions per nucleotide since the most recent common ancestor of a pair of homologous sequences is found which uses nucleotide sequence data. The estimate is derived assuming a Poisson-like model for the evolutionary process. A method is also suggested for analyzing nucleotide sequence data in M homologous sequences (M 3). A simulation study is reported showing that the estimates are satisfactory providing there is sufficient homology between the sequences. To demonstrate the methods a numerical example using some β-globin data is presented.     

Inference of selection and recombination from nucleotide sequence data*

Two techniques for obtaining information about population structure from nucleotide sequences in DNA are summarized. The first focuses on the selection or neutrality of enzyme polymorphisms, the second on the detection of recombination. Neither method requires phylogeny estimation.     

A method for molecular phylogeny construction by direct use of nucleotide sequence data

Summary A method for molecular phylogeny construction is newly developed. The method, called the stepwise ancestral sequence method, estimates molecular phylogenetic trees and ancestral sequences simultaneously on the basis of parsimony and sequence homology. For simplicity the emphasis is placed more on parsiomony than on sequence homology in the present study, though both are certainly important. Because parsimony alone will sometimes generate plural candidate trees, the method retains not one but five candidates from which one can then single out the final tree taking other criteria into account.The properties and performance of the method are then examined by simulating an evolving gene along a model phylogenetic tree. The estimated trees are found to lie in a narrow range of the parsimony criteria used in the present study. Thus, other criteria such as biological evidence and likelihood are necessary to single out the correct tree among them, with biological evidence taking precedence over any other criterion. The computer simulation also reveals that the method satisfactorily estimates both tree topology and ancestral sequences, at least for the evolutionary model used in the present study.     

A new method based on entropy theory for genomic sequence analysis

We have refined entropy theory to explore the meaning of the increasing sequence data on nucleic acids and proteins more conveniently. The concept of selection constraint was not introduced, only the analyzed sequences themselves were considered. The refined theory serves as a basis for deriving a method to analyze non-coding regions (NCRs) as well as coding regions. Positions with maximal entropy might play the most important role in genome functions as opposed to positions with minimal entropy. This method was tested in the well-characterized coding regions of 12 strains of Classical Swine Fever Virus (CSFV) and non-coding regions of 20 strains of CSFV. It is suitable to analyze nucleic acid sequences of a complete genome and to detect sensitive positions for mutagenesis. As such, the method serves to formulate the basis for elucidating the functional mechanism.     

Multilocus markers for mouse genome analysis: PCR amplification based on single primers of arbitrary nucleotide sequence

Polymerase chain reaction (PCR) based on single primers of arbitrary nucleotide sequence provides a powerful marker system for genome analysis because each primer amplifies multiple products, and cloning, sequencing, and hybridization are not required. We have evaluated this typing system for the mouse by identifying optimal PCR conditions; characterizing effects of GC content, primer length, and multiplexed primers; demonstrating considerable variation among a panel of inbred strains; and establishing linkage for several products. Mg²⁺, primer, template, and annealing conditions were identified that optimized the number and resolution of amplified products. Primers with 40% GC content failed to amplify products readily, primers with 50% GC content resulted in reasonable amplification, and primers with 60% GC content gave the largest number of well-resolved products. Longer primers did not necessarily amplify more products than shorter primers of the same proportional GC content. Multiplexed primers yielded more products than either primer alone and usually revealed novel variants. A strain survey showed that most strains could be readily distinguished with a modest number of primers. Finally, linkage for seven products was established on five chromosomes. These characteristics establish single primer PCR as a powerful method for mouse genome analysis.     

Development of Porphyromonas gingivalis-specific quantitative real-time PCR primers based on the nucleotide sequence of rpoB

Species-specific quantitative real-time PCR (qPCR) primers were developed for the detection of Porphyromonas gingivalis. These primers, Pg-F/Pg-R, were designed based on the nucleotide sequences of RNA polymerase β-subunit gene (rpoB). Species-specific amplicons were obtained from the tested P. gingivalis strains but not in any of the other strains (46 strains of 46 species). The qPCR primers could detect as little as 4 fg of P. gingivalis chromosomal DNA. These findings suggest that these qPCR primers are suitable for applications in epidemiological studies.     

Role of mutational bias and natural selection on genome-wide nucleotide bias in prokaryotic organisms

Correlations between genomic GC contents and amino acid frequencies were studied in the homologous sequences of 12 eubacterial genomes. Results show that amino acids encoded by GC-rich codons increases significantly with genomic GC contents, whereas opposite trend was observed in case of amino acids encoded by GC-poor codons. Further studies show all the amino acids do not change in the predicted direction according to their genomic GC pressure, suggesting that protein evolution is not entirely dictated by their nucleotide frequencies. Amino acid substitution matrix calculated among hydrophobic, amphipathic and hydrophilic amino acid groups' shows that amphipathic and hydrophilic amino acids are more frequently substituted by hydrophobic amino acids than from hydrophobic to hydrophilic or amphipathic amino acids. This indicates that nucleotide bias induces a directional changes in proteome composition in such a way that underwent strong changes in hydropathy values. In fact, significant increases in hydrophobicity values have also been observed with the increase of genomic GC contents. Correlations between GC contents and amino acid compositions in three different predicted protein secondary structures show that hydropathy values increases significantly with GC contents in aperiodic and helix structures whereas strand structure remains insensitive with the genomic GC levels. The relative importance of mutation and selection on the evolution of proteins have been discussed on the basis of these results.     

Automated constraint-based nucleotide sequence selection for DNA computation

We present techniques for automating the design of computational systems built using DNA, given a set of high-level constraints on the desired behavior and performance of the system. We have developed a program called SCAN that exploits a previously implemented computational melting temperature primitive to search a 'nucleotide space' for sequences satisfying a pre-specified set of constraints, including hybridization discrimination, primer 5' end and 3' end stability, secondary structure reduction, and prevention of oligonucleotide dimer formation. The first version of SCAN utilized 24 h of computer time to search a space of over 7.5 billion unary counter designs and found only nine designs satisfying all of the pre-specified constraints. One of SCAN's designs has been implemented in the laboratory and has shown a marked improvement in performance over the products of previous attempts at manual design. We conclude with some novel ideas for improving the overall speed of the program that offer the promise of an efficient method for selecting optimal nucleotide sequences in an automated fashion.     

CoVrimer: A tool for aligning SARS-CoV-2 primer sequences and selection of conserved/degenerate primers

As mutations in SARS-CoV-2 virus accumulate rapidly, novel primers that amplify this virus sensitively and specifically are in demand. We have developed a webserver named CoVrimer by which users can search for and align existing or newly designed conserved/degenerate primer pair sequences against the viral genome and assess the mutation load of both primers and amplicons. CoVrimer uses mutation data obtained from an online platform established by NGDC-CNCB (12 May 2021) to identify genomic regions, either conserved or with low levels of mutations, from which potential primer pairs are designed and provided to the user for filtering based on generalized and SARS-CoV-2 specific parameters. Alignments of primers and probes can be visualized with respect to the reference genome, indicating variant details and the level of conservation. Consequently, CoVrimer is likely to help researchers with the challenges posed by viral evolution and is freely available at http://konulabapps.bilkent.edu.tr:3838/CoVrimer/.     

Development of Prevotella nigrescens-specific PCR primers based on the nucleotide sequence of a Pn23 DNA probe

A previous study reported the cloning of a putative Prevotella nigrescens-specific DNA probe, Pn23, using random shotgun method. The present study evaluated the species-specificity of Pn23 for P. nigrescens using the clinical strains of Prevotella intermedia and P. nigrescens to develop P. nigrescens-specific polymerase chain reaction (PCR) primers. Southern blot analysis showed that the DNA probe, Pn23, detected only the genomic DNA of P. nigrescens strains. PCR showed that the two sets of PCR primers, Pn23-F1/Pn23-R1 and Pn23-F2/Pn23-R2, had species-specificity for P. nigrescens. Interestingly, the two sets of PCR primers, Pn23-F6/Pn23-R6 and Pn23-F7/Pn23-R7, had strain-specificity for P. nigrescens ATCC 33563. The detection limits of the four primer sets were 40 or 4 pg of the purified genomic DNA of P. nigrescens ATCC 33563. These results suggest that the DNA probe, Pn23, and the two sets of PCR primers, Pn23-F1/Pn23-R1 and Pn23-F2/Pn23-R2, can be useful for the detection of P. nigrescens in the molecular epidemiological studies of oral infectious diseases.     

A new method for analyzing protein sequence relationships based on Sammon maps.

Recent advances in gene sequencing and rational drug design have re-emphasized the need for new methods for protein analysis, classification, and structure and function prediction. In this article, we introduce a new method for analyzing protein sequences based on Sammon''s non-linear mapping algorithm. When applied to a family of homologous sequences, the method is able to capture the essential features of the similarity matrix, and provides a faithful representation of chemical or evolutionary distance in a simple and intuitive way. The merits of the new algorithm are demonstrated using examples from the protein kinase family.     

A new approach to primer design for the control of PCR bias in methylation studies

Background

Nerve growth factor (NGF) helps in the healing and survival of ganglion cells, photoreceptors, and optic nerve after injury and has been implicated to have a role in pathophysiology of glaucoma. So far, in animal studies, injury to iris in vitro has revealed an increase in NGF levels in aqueous. There is a great interest in investigating the levels of NGF in human aqueous in glaucomatous eyes, as suggested by animal studies, to gain a better understanding of the pathophysiology of glaucoma.

Findings

In this study, we examined the presence of NGF levels in aqueous humor collected from human eyes and the limitations in determining the NGF levels in human samples. NGF was assessed by ELISA immunoassay in undiluted aqueous samples collected from 32 consecutive patients undergoing surgery for cataract (control) or primary open angle glaucoma (POAG). Recombinant NGF was used as positive control. NGF levels were below undetectable levels in aqueous humor from eyes with POAG and controls by immunoassay. Less than 10% of samples had detectable NGF levels and these were considered outliers.

Conclusion

Our result highlights the undetectable levels of NGF in human aqueous samples.     

Characterizing and measuring bias in sequence data

Background

DNA sequencing technologies deviate from the ideal uniform distribution of reads. These biases impair scientific and medical applications. Accordingly, we have developed computational methods for discovering, describing and measuring bias.

Results

We applied these methods to the Illumina, Ion Torrent, Pacific Biosciences and Complete Genomics sequencing platforms, using data from human and from a set of microbes with diverse base compositions. As in previous work, library construction conditions significantly influence sequencing bias. Pacific Biosciences coverage levels are the least biased, followed by Illumina, although all technologies exhibit error-rate biases in high- and low-GC regions and at long homopolymer runs. The GC-rich regions prone to low coverage include a number of human promoters, so we therefore catalog 1,000 that were exceptionally resistant to sequencing. Our results indicate that combining data from two technologies can reduce coverage bias if the biases in the component technologies are complementary and of similar magnitude. Analysis of Illumina data representing 120-fold coverage of a well-studied human sample reveals that 0.20% of the autosomal genome was covered at less than 10% of the genome-wide average. Excluding locations that were similar to known bias motifs or likely due to sample-reference variations left only 0.045% of the autosomal genome with unexplained poor coverage.

Conclusions

The assays presented in this paper provide a comprehensive view of sequencing bias, which can be used to drive laboratory improvements and to monitor production processes. Development guided by these assays should result in improved genome assemblies and better coverage of biologically important loci.     

A genetic algorithm for maximum-likelihood phylogeny inference using nucleotide sequence data

Phylogeny reconstruction is a difficult computational problem, because the number of possible solutions increases with the number of included taxa. For example, for only 14 taxa, there are more than seven trillion possible unrooted phylogenetic trees. For this reason, phylogenetic inference methods commonly use clustering algorithms (e.g., the neighbor-joining method) or heuristic search strategies to minimize the amount of time spent evaluating nonoptimal trees. Even heuristic searches can be painfully slow, especially when computationally intensive optimality criteria such as maximum likelihood are used. I describe here a different approach to heuristic searching (using a genetic algorithm) that can tremendously reduce the time required for maximum-likelihood phylogenetic inference, especially for data sets involving large numbers of taxa. Genetic algorithms are simulations of natural selection in which individuals are encoded solutions to the problem of interest. Here, labeled phylogenetic trees are the individuals, and differential reproduction is effected by allowing the number of offspring produced by each individual to be proportional to that individual's rank likelihood score. Natural selection increases the average likelihood in the evolving population of phylogenetic trees, and the genetic algorithm is allowed to proceed until the likelihood of the best individual ceases to improve over time. An example is presented involving rbcL sequence data for 55 taxa of green plants. The genetic algorithm described here required only 6% of the computational effort required by a conventional heuristic search using tree bisection/reconnection (TBR) branch swapping to obtain the same maximum-likelihood topology.      

A method for estimating nucleotide diversity from AFLP data

A method for estimating the nucleotide diversity from AFLP data is developed by using the relationship between the number of nucleotide changes and the proportion of shared bands. The estimation equation is based on the assumption that GC-content is 0.5. Computer simulations, however, show that this method gives a reasonably accurate estimate even when GC-content deviates from 0.5, as long as the number of nucleotide changes per site (nucleotide diversity) is small. As an example, the nucleotide diversity of the wild yam, Dioscorea tokoro, was estimated. The estimated nucleotide diversity is 0.0055, which is larger than estimations from nucleotide sequence data for Adh and Pgi.     

A new method for analyzing gene expression based on frequency analysis of RT-PCR products obtained with degenerate primers

This paper presents a new method of gene expression analysis: frequency analysis of RT-PCR products obtained with degenerate primers (FAPP). The main advantage of the new approach compared to the present methods of gene expression analysis is that it is applicable to non-model plant objects whose gene sequences are unknown. The advantages and disadvantages of FAPP are described in detail using data on calcium-dependent protein kinase, stilbene synthase, and phenylalanine ammonia-lyase gene expression in two cell cultures of Vitis amurensis. We compared the expression profiles obtained by FAPP to those obtained by real-time PCR and expressed sequence tags.     

Assessment of a method for cultivar selection based on regional trial data

Summary In this study a method for analyzing regional trial data is investigated for its effectiveness in cultivar selection. The method is a synthesis of three procedures: (1) regression analysis for genotype × environment (GE) interaction, and subsequent cluster analysis for grouping cultivars for similarity of response; (2) superiority measure analysis of cultivar performance based on the distance mean square between the test cultivar and the maximum response; (3) type 4 stability analysis for three-way classification data (cultivar × location × year), to measure a cultivar's stability. Each of these three procedures is aimed at different aspects of the selection problem: the first obtains an overview of the types of cultivar response; the second measures a cultivar's general adaptability within the region; the third assesses a cultivar's ability to withstand unpredictable variation, namely that caused by year effects. Four sets of published data, each originally analyzed by a univariate or a multivariate approach, were used as examples. The results suggest that the present method provides direct and useful information for selection purposes. The superiority measure, which is the core of the method, can be used even if the data do not fit the linear model for GE interaction. Plotting the data with a fixed standard represented by the maximum response provides a simple visual tool for identifying environments where a cultivar performs well.Contribution No. C-035 from Research Program Service, Research Branch, Agriculture Canada, Ottawa, Ontario, K1A 0C6