A comparison of four pair-wise sequence alignment methods

Protein sequence alignment has become an essential task in modern molecular biology research. A number of alignment techniques have been documented in literature and their corresponding tools are made available as freeware and commercial software. The choice and use of these tools for sequence alignment through the complete interpretation of alignment results is often considered non-trivial by end-users with limited skill in Bioinformatics algorithm development. Here, we discuss the comparison of sequence alignment techniques based on dynamic programming (N-W, S-W) and heuristics (LFASTA, BL2SEQ) for four sets of sequence data towards an educational purpose. The analysis suggests that heuristics based methods are faster than dynamic programming methods in alignment speed.     

Shift from morphological to recent advanced molecular approaches for the identification of nematodes

Nematodes are the most diverse but most minor studied microorganisms found in soil, water, animals, or plants. Either beneficial or pathogenic, they significantly affect human and animal health, plant production and ultimately affect the environmental equilibrium. Knowledge of their taxonomy and biology are the main issues to answer the different challenges associated with these microorganisms. The classical morphology-based nematode taxonomy and biodiversity studies have proved insufficient to identify closely related taxa and have challenged most biologists. Several molecular approaches have been used to supplement morphological methods and solve these problems with markable success. The molecular techniques range from enzyme analysis, protein-based information to DNA sequence analysis. For several decades, efforts have been made to integrate molecular approaches with digital 3D image-capturing technology to improve the identification accuracy of such a taxonomically challenging group and communicate morphological data. This review presents various molecular techniques and provides examples of recent advances in these methods to identify free-living and plant-parasitic nematodes.     

Extracting haplotypes from diploid organisms

Each diploid organism has two alleles at every gene locus. In sexual organisms such as most plants, animals and fungi, the two alleles in an individual may be genetically very different from each other. DNA sequence data from individual alleles (called a haplotype) can provide powerful information to address a variety of biological questions and guide many practical applications. The advancement in molecular technology and computational tools in the last decade has made obtaining large-scale haplotypes feasible. This review summarizes the two basic approaches for obtaining haplotypes and discusses the associated techniques and methods. The first approach is to experimentally obtain diploid sequence information and then use computer algorithms to infer haplotypes. The second approach is to obtain haplotype sequences directly through experimentation. The advantages and disadvantages of each approach are discussed. I then discussed a specific example on how the direct approach was used to obtain haplotype information to address several fundamental biological questions of a pathogenic yeast. With increasing sophistication in both bioinformatics tools and high-throughput molecular techniques, haplotype analysis is becoming an integrated component in biomedical research.     

Rapid identification of Escherichia coli microcin J25 producing strains using polymerase chain reaction and colony blot hybridization.

To screen, isolate, and characterize bacterial populations producing microcin J25, we report here two rapid, reliable, and sensitive methods, using polymerase chain reaction and colony blot hybridization with a digoxigenin-labelled probe. A sample of 26 Escherichia coli strains isolated from poultry intestinal contents was evaluated to detect the sequence of mcjA, the gene encoding the MccJ25 precursor. The two molecular techniques were compared with the commonly used cross-immunity tests. They generate accurate data with no obvious cross-reactions with other microcins. The results display that the producers of MccJ25 were widely distributed in the poultry intestinal habitat. The applications of these molecular methods will be useful in future studies of microcinogenic populations, and thus contribute to understand the relationships within the complex intestinal microbial ecosystem.     

DNA在鸟类分子系统发育研究中的应用

鸟类分子系统发育研究中常用的DNA技术有DNA杂交、RFLP和DNA序列分析等。DNA杂交技术曾在鸟类中有过大规模的应用,并由此诞生了一套新的鸟类分类系统。在鸟类的RFLP分析中,用的最多的靶序列是线粒体DNA。DNA序列分析技术被认为是进行分子系统发育研究最有效、最可靠的方法。在DNA序列分析中,线粒体基因应用最广泛,但由于其自身的一些不足,近年来,不少学者把目光投向了核基因,将线粒体基因和核基因结合起来进行系统发育研究。目前在鸟类分子系统发育中,应用较多的核基因是scnDNA,其内含子可以用于中等阶元水平的系统研究,而外显子主要用于高等阶元的系统研究。除了分子标记自身的问题之外,鸟类分子系统发育研究中还存在着方法上的问题,包括分子标记的选择,样本数量以及数据处理等。今后鸟类分子系统发育研究应该更加注重方法的标准化。     

Fixed Character States and the Optimization of Molecular Sequence Data

A method is proposed to optimize molecular sequence data that does not employ multiple sequence alignment. This method treats entire homologous contiguous stretches of sequence data as individual characters. This sequence is treated as the homologous unit employed in phylogeny reconstruction. The sets of specific sequences exhibited by the terminal taxa constitute the character states. The number of states is then less than or equal to the number of unique sequences (or homologous fragments) exhibited by the data. A matrix of transformation costs is created to relate the states to one another. The cells of this matrix are defined as the minimum transformation cost between each pair of states based on insertion–deletion and base substitution costs. The diagnosis of a topology then follows existing dynamic programming techniques, with the number of states greatly expanded. Since the possible sequences reconstructed at nodes are limited to those exhibited by the terminals, cladograms constructed in this way may be longer than those of other methods in that they require a greater number of weighted evolutionary events. Example data, the effects of missing data, restricted ancestors, and putative long-branch attraction are discussed.     

Waiting for Sequences: Morris Goodman,Immunodiffusion Experiments,and the Origins of Molecular Anthropology

During the early 1960s, Morris Goodman used a variety of immunological tests to demonstrate the very close genetic relationships among humans, chimpanzees, and gorillas. Molecular anthropologists often point to this early research as a critical step in establishing their new specialty. Based on his molecular results, Goodman challenged the widely accepted taxonomic classification that separated humans from chimpanzees and gorillas in two separate families. His claim that chimpanzees and gorillas should join humans in family Hominidae sparked a well-known conflict with George Gaylord Simpson, Ernst Mayr, and other prominent evolutionary biologists. Less well known, but equally significant, were a series of disagreements between Goodman and other prominent molecular evolutionists concerning both methodological and theoretical issues. These included qualitative versus quantitative data, the role of natural selection, rates of evolution, and the reality of molecular clocks. These controversies continued throughout Goodman’s career, even as he moved from immunological techniques to protein and DNA sequence analysis. This episode highlights the diversity of methods used by molecular evolutionists and the conflicting conclusions drawn from the data that these methods generated.     

Calculating bootstrap probabilities of phylogeny using multilocus sequence data

Phylogeny estimation is extremely crucial in the study of molecular evolution. The increase in the amount of available genomic data facilitates phylogeny estimation from multilocus sequence data. Although maximum likelihood and Bayesian methods are available for phylogeny reconstruction using multilocus sequence data, these methods require heavy computation, and their application is limited to the analysis of a moderate number of genes and taxa. Distance matrix methods present suitable alternatives for analyzing huge amounts of sequence data. However, the manner in which distance methods can be applied to multilocus sequence data remains unknown. Here, we suggest new procedures to estimate molecular phylogeny using multilocus sequence data and evaluate its significance in the framework of the distance method. We found that concatenation of the multilocus sequence data may result in incorrect phylogeny estimation with an extremely high bootstrap probability (BP), which is due to incorrect estimation of the distances and intentional ignorance of the intergene variations. Therefore, we suggest that the distance matrices for multilocus sequence data be estimated separately and these matrices be subsequently combined to reconstruct phylogeny instead of phylogeny reconstruction using concatenated sequence data. To calculate the BPs of the reconstructed phylogeny, we suggest that 2-stage bootstrap procedures be adopted; in this, genes are resampled followed by resampling of the sequence columns within the resampled genes. By resampling the genes during calculation of BPs, intergene variations are properly considered. Via simulation studies and empirical data analysis, we demonstrate that our 2-stage bootstrap procedures are more suitable than the conventional bootstrap procedure that is adopted after sequence concatenation.     

Molecular sequence accuracy: analysing imperfect data.

Molecular sequences are experimentally derived data that can be expected to contain errors as a result of diverse phenomena such as biological variation, molecular cloning artifacts, imperfect sequence determination, and data handling during contig assembly. Errors will affect the reliability of database searches and sequence alignments, but their impact may be minimized by the use of analytical techniques that anticipate that the data will be imperfect.     

Gene regulatory network inference: Data integration in dynamic models—A review

Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein–DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.     

PNA for rapid microbiology.

The acceptance of rRNA sequence diversity as a criterion for phylogenetic discrimination heralds the transition from microbiological identification methods based on phenotypic markers to assays employing molecular techniques. Robust amplification assays and sensitive direct detection methods are rapidly becoming the standard protocols of microbiology laboratories. The emergence of peptide nucleic acid (PNA) from its status as an academic curiosity to that of a promising and powerful molecular tool, coincides with, and complements, the transition to rapid molecular tests. The unique properties of PNA enable the development of assay formats, which go above and beyond the possibilities of DNA probes. PNA probes targeting specific rRNA sequences of yeast and bacteria with clinical, environmental, and industrial value have recently been developed and applied to a variety of rapid assay formats. Some simply incorporate the sensitivity and specificity of PNA probes into traditional methods, such as membrane filtration and microscopic analysis; others involve recent techniques such as real-time and end-point analysis of amplification reactions.     

Potentials and limitations of molecular diagnostic methods in food safety

Molecular methods allow the detection of pathogen nucleic acids (DNA and RNA) and, therefore, the detection of contamination in food is carried out with high selectivity and rapidity. In the last 2 decades molecular methods have accompanied traditional diagnostic methods in routine pathogen detection, and might replace them in the upcoming future. In this review the implementation in diagnostics of four of the most used molecular techniques (PCR, NASBA, microarray, LDR) are described and compared, highlighting advantages and limitations of each of them. Drawbacks of molecular methods with regard to traditional ones and the difficulties encountered in pathogen detection from food or clinical specimen are also discussed. Moreover, criteria for the choice of the target sequence for a secure detection and classification of pathogens and possible developments in molecular diagnostics are also proposed.     

Intercontinental and intracontinental biogeography patterns and methods

The study of biogeography has benefited from the exponential increase of DNA sequence data from recent molecular systematic studies, the development of analytical methods in the last decade concerning divergence time estimation and geographic area analyses, and the availability of large-scale distributiofi data of species in many groups of organisms. The underlying principle of divergence time estimation from DNA and protein data is that sequence divergence depends on the product of evolutionary rate and time. With their molecular clock hypothesis, Zuckerkandl and Pauling （1965） separated rates of molecular evolution from time by incorporating fossil evidence. Originally,     

Within- and between-species DNA sequence variation and the 'footprint' of natural selection

Extensive DNA data emerging from genome-sequencing projects have revitalized interest in the mechanisms of molecular evolution. Although the contribution of natural selection at the molecular level has been debated for over 30 years, the relevant data and appropriate statistical methods to address this issue have only begun to emerge. This paper will first present the predominant models of neutral, nearly neutral, and adaptive molecular evolution. Then, a method to identify the role of natural selection in molecular evolution by comparing within- and between-species DNA sequence variation will be presented. Computer simulations show that such methods are powerful for detecting even very weak selection. Examination of DNA variation data within and between Drosophila species suggests that 'silent' sites evolve under a balance between weak selection and genetic drift. Simulated data also show that sequence comparisons are a powerful method to detect adaptive protein evolution, even when selection is weak or affects a small fraction of nucleotide sites. In the Drosophila data examined, positive selection appears to be a predominant force in protein evolution.     

Nucleotide sequence of an arginine transfer ribonucleic acid from bacteriophage T4.

The nucleotide sequence of a phage T4-coded low molecular weight RNA, previously designated polyacrylamide gel band epsilon, has been determined. This RNA can be arranged in the cloverleaf configuration common to tRNAs, with an anticodon sequence, U-C-U, which corresponds to the arginine-specific codons A-G-A and A-G-G; it is therefore assumed to be an arginine tRNA. The complete nucleotide sequence of this RNA species is: pG-U-C-C-C-G-C-U-G-G-U-G-U-A-A-U-Gm2'-G-A-D-A-G-C-A-U-A-C-G-A-U-C-C-U-U-C-U-A-A-G-psi-U-U-G-C-G-G-U-C-C-U-G-G-T-psi-C-G-A-U-C-C-C-A-G-G-G-C-G-G-G-A-U-A-C-C-AOH. The nucleotide sequence was determined by analysis of RNA, uniformly labeled in vivo, according to the conventional techniques. In addition, RNA synthesized in vitro in the presence of alpha-32P-labeled nucleoside triphosphates was analyzed through the use of nearest neighbor sequencing techniques. Although a unique sequence could not be determined by this latter analysis, restrictions on the sequence imposed by nearest neighbor data and secondary structure common to tRNA molecules allowed prediction of the correct nucleotide sequence.     

Alignment-free distance measure based on return time distribution for sequence analysis: Applications to clustering, molecular phylogeny and subtyping

The data deluge in post-genomic era demands development of novel data mining tools. Existing molecular phylogeny analyses (MPAs) developed for individual gene/protein sequences are alignment-based. However, the size of genomic data and uncertainties associated with alignments, necessitate development of alignment-free methods for MPA. Derivation of distances between sequences is an important step in both, alignment-dependant and alignment-free methods. Various alignment-free distance measures based on oligo-nucleotide frequencies, information content, compression techniques, etc. have been proposed. However, these distance measures do not account for relative order of components viz. nucleotides or amino acids. A new distance measure, based on the concept of 'return time distribution' (RTD) of k-mers is proposed, which accounts for the sequence composition and their relative orders. Statistical parameters of RTDs are used to derive a distance function. The resultant distance matrix is used for clustering and phylogeny using Neighbor-joining. Its performance for MPA and subtyping was evaluated using simulated data generated by block-bootstrap, receiver operating characteristics and leave-one-out cross validation methods. The proposed method was successfully applied for MPA of family Flaviviridae and subtyping of Dengue viruses. It is observed that method retains resolution for classification and subtyping of viruses at varying levels of sequence similarity and taxonomic hierarchy.     

Methods used for the detection and subtyping of Listeria monocytogenes

Listeria monocytogenes is an important foodborne pathogen responsible for non-invasive and invasive diseases in the elderly, pregnant women, neonates and immunocompromised populations. This bacterium has many similarities with other non-pathogenic Listeria species which makes its detection from food and environmental samples challenging. Subtyping of L. monocytogenes strains can prove to be crucial in epidemiological investigations, source tracking contamination from food processing plants and determining evolutionary relationships between different strains. In recent years there has been a shift towards the use of molecular subtyping. This has led to the development of new subtyping techniques such as multi-locus variable number tandem repeat analysis (MLVA) and multi-locus sequence based typing (MLST). This review focuses on the available methods for Listeria detection including immuno-based techniques and the more recently developed molecular methods and analytical techniques such as matrix-assisted laser desorption/ionisation time-of-flight based mass spectrometry (MALDI-TOF MS). It also includes a comparison and critical analysis of the available phenotypic and genotypic subtyping techniques that have been investigated for L. monocytogenes.     

现代分子生物学技术在瘤胃微生态系统研究中的应用

瘤胃中栖息着大量的微生物,由于这些微生物组成复杂且有些细菌在体外无法培养,目前对这些微生物的了解仍然很少。现代分子生物学技术的发展为研究瘤胃微生物提供了有效的方法,利用核酸探针、基因序列分析、遗传指纹技术、全细胞杂交和实时定量PCR等技术可以对瘤胃微生物的分类及进化关系、区系结构图、重要酶的表达以及目的微生物的准确定量进行更为深入和透彻的研究。发展和利用这些技术不仅可以研究微生物之间的关系以及微生物与饲料颗粒之间时间与空间的关系,还能直接在细菌自然生长的环境中对其各种特征进行研究。     

Spectroscopic methods for analysis of protein secondary structure

Several methods for determination of the secondary structure of proteins by spectroscopic measurements are reviewed. Circular dichroism (CD) spectroscopy provides rapid determinations of protein secondary structure with dilute solutions and a way to rapidly assess conformational changes resulting from addition of ligands. Both CD and Raman spectroscopies are particularly useful for measurements over a range of temperatures. Infrared (IR) and Raman spectroscopy require only small volumes of protein solution. The frequencies of amide bands are analyzed to determine the distribution of secondary structures in proteins. NMR chemical shifts may also be used to determine the positions of secondary structure within the primary sequence of a protein. However, the chemical shifts must first be assigned to particular residues, making the technique considerably slower than the optical methods. These data, together with sophisticated molecular modeling techniques, allow for refinement of protein structural models as well as rapid assessment of conformational changes resulting from ligand binding or macromolecular interactions. A selected number of examples are given to illustrate the power of the techniques in applications of biological interest.