DNA sequence analysis linguistic tools: contrast vocabularies, compositional spectra and linguistic complexity

This is a review of the methods based on counting oligomers in nucleotide and amino acid sequences. Such methods are analogous to the formal linguistic analysis of human texts. This review includes methods based on the calculation of observed occurrences (frequencies) of oligomers and their distribution, as well as those based on deviations between the observed and the expected occurrences (contrast words, genome signatures) in biological sequences. Both types of methods have a wide range of sensitivity and can identify homologous as well as functionally and taxonomically related sequences.     

Hypothetical Ancestors and Rooting in Cladistic Analysis

Most hypothetical ancestors that are used to root trees in cladistic analyses summarize character-state information in one or more outgroup taxa. Nonetheless, hypothetical ancestors also provide a means of rooting trees using the ontogenetic and paleontological methods of polarizing character transformations, and for incorporating the inferences of more than one of these methods into a single analysis. However, the use of one hypothetical ancestor that combines inferences based on outgroup comparison with those based on other methods of polarizing character transformations to root a cladogram is invalid. Inferences regarding plesiomorphic character states based on outgroup comparison apply to the outgroup node, whereas inferences based on either the ontogenetic or paleontological method apply to the ingroup node. These inferences cannot be combined into a single hypothetical construct. A hypothetical ancestor based on outgroup information is included in the data matrix and used to root the resulting network; however, because this ancestor places potentially problematic constraints on the analysis, the use of actual outgroup taxa is preferable in most instances. Correct use of a hypothetical ancestor inferred with the ontogenetic and paleontological methods involves the Lundberg method in which the shortest ingroup network is rooted at the internode to which the hypothetical ancestor attaches most parsimoniously. Because inferences of polarity based on outgroup comparison cannot be combined directly with those based on other polarization methods, the synthesis of information from all three methods in a single tree must involve taxonomic congruence.     

A Benchmark of Parametric Methods for Horizontal Transfers Detection

Horizontal gene transfer (HGT) has appeared to be of importance for prokaryotic species evolution. As a consequence numerous parametric methods, using only the information embedded in the genomes, have been designed to detect HGTs. Numerous reports of incongruencies in results of the different methods applied to the same genomes were published. The use of artificial genomes in which all HGT parameters are controlled allows testing different methods in the same conditions. The results of this benchmark concerning 16 representative parametric methods showed a great variety of efficiencies. Some methods work very poorly whatever the type of HGTs and some depend on the conditions or on the metrics used. The best methods in terms of total errors were those using tetranucleotides as criterion for the window methods or those using codon usage for gene based methods and the Kullback-Leibler divergence metric. Window methods are very sensitive but less specific and detect badly lone isolated gene. On the other hand gene based methods are often very specific but lack of sensitivity. We propose using two methods in combination to get the best of each category, a gene based one for specificity and a window based one for sensitivity.     

A Coarse-Grained Elastic Network Atom Contact Model and Its Use in the Simulation of Protein Dynamics and the Prediction of the Effect of Mutations

Normal mode analysis (NMA) methods are widely used to study dynamic aspects of protein structures. Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form. There is a trade-off between speed and accuracy in different choices. In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials. On the other extreme, fast elastic network model (ENM) methods with C_α−only representations and simplified potentials that based on geometry alone, thus oblivious to protein sequence. Here we present ENCoM, an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA. ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles. Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations. We compare ENCoM to a large number of methods in terms of accuracy and self-consistency. We show that the accuracy of ENCoM is comparable to that of the best existing methods. We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations.     

Synchrotron radiation for direct analysis of metalloproteins on electrophoresis gels

Metalloproteomics requires analytical techniques able to assess and quantify the inorganic species in metalloproteins. The most widely used methods are hyphenated techniques, based on the coupling of a high resolution chromatographic method with a high sensitivity method for metal analysis in solution. An alternative approach is the use of methods for solid sample analysis, combining metalloprotein separation by gel electrophoresis and direct analysis of the gels. Direct methods are based on beam analysis, such as lasers, ion beams or synchrotron radiation beams. The aim of this review article is to present the main features of synchrotron radiation based methods and their applications for metalloprotein analysis directly on electrophoresis gels. Synchrotron radiation X-ray fluorescence has been successfully employed for sensitive metal identification, and X-ray absorption spectroscopy for metal local structure speciation in proteins. Synchrotron based methods will be compared to ion beam and mass spectrometry for direct analysis of metalloproteins in electrophoresis gels.     

Analyzing gene expression data in terms of gene sets: methodological issues

MOTIVATION: Many statistical tests have been proposed in recent years for analyzing gene expression data in terms of gene sets, usually from Gene Ontology. These methods are based on widely different methodological assumptions. Some approaches test differential expression of each gene set against differential expression of the rest of the genes, whereas others test each gene set on its own. Also, some methods are based on a model in which the genes are the sampling units, whereas others treat the subjects as the sampling units. This article aims to clarify the assumptions behind different approaches and to indicate a preferential methodology of gene set testing. RESULTS: We identify some crucial assumptions which are needed by the majority of methods. P-values derived from methods that use a model which takes the genes as the sampling unit are easily misinterpreted, as they are based on a statistical model that does not resemble the biological experiment actually performed. Furthermore, because these models are based on a crucial and unrealistic independence assumption between genes, the P-values derived from such methods can be wildly anti-conservative, as a simulation experiment shows. We also argue that methods that competitively test each gene set against the rest of the genes create an unnecessary rift between single gene testing and gene set testing.     

Comparison of a culture‐based and a PCR‐based methods for estimating bacterial abundance on eggshells,with comments on statistical analyses

Field ornithologists have used traditional culture‐based techniques to determine the presence and abundance of microbes on surfaces such as eggshells, but culture‐independent PCR‐based methods have recently been introduced. We compared the traditional culture‐based and the real‐time PCR‐based methods for detecting and quantifying Escherichia coli on the eggshells of Eurasian Magpies (Pica pica). PCR estimates of bacterial abundance were ～10 times higher than culture‐based estimates, and the culture‐based technique failed to detect bacteria at lower densities. When both methods detected bacteria, bacterial densities determined by the two methods were positively correlated, indicating that both methods can be used to study factors affecting bacterial densities. The difference between the two methods is consistent with generally acknowledged higher sensitivity of the PCR method, but the extent of the difference in our study (10×) may have been influenced by both a PCR‐based overestimation and culture‐based underestimation of bacterial densities. Our results also illustrate that bacterial counts may sometimes produce left‐censored data (i.e., we did not detect E. coli in 62% of our samples using the culture‐based method). Specific statistical methods have been developed for analyzed left‐censored data, but, to our knowledge, have not been used by ornithologists. In future studies, investigators studying bacterial loads should provide information about the possible degree of left censoring and should justify their choice of statistical methods from the broad set of available methods, including those explicitly designed for censored data.     

MC-Net: a method for the construction of phylogenetic networks based on the Monte-Carlo method

Background  

A phylogenetic network is a generalization of phylogenetic trees that allows the representation of conflicting signals or alternative evolutionary histories in a single diagram. There are several methods for constructing these networks. Some of these methods are based on distances among taxa. In practice, the methods which are based on distance perform faster in comparison with other methods. The Neighbor-Net (N-Net) is a distance-based method. The N-Net produces a circular ordering from a distance matrix, then constructs a collection of weighted splits using circular ordering. The SplitsTree which is a program using these weighted splits makes a phylogenetic network. In general, finding an optimal circular ordering is an NP-hard problem. The N-Net is a heuristic algorithm to find the optimal circular ordering which is based on neighbor-joining algorithm.     

Whole-genome linkage disequilibrium screening for complex traits in horses

The identification of candidate genes for significant traits is crucial. In this study, we developed and tested effective and systematic methods based on linkage disequilibrium (LD) for the identification of candidate regions for genes with Mendelian inheritance and those associated with complex traits. Our approach entailed the combination of primary screening using pooled DNA samples based on ΔTAC, secondary screening using an individual typing method and tertiary screening using a permutation test based on the differences in the haplotype frequency between two neighbouring microsatellites. This series of methods was evaluated using horse coat colour traits (chestnut/non-chestnut) as a simple Mendelian inheritance model. In addition, the methods were evaluated using a complex trait model constructed by mixing samples from chestnut and non-chestnut horses. Using both models, the methods could detect the expected regions for the horse coat colour trait. The results revealed that LD extends up to several centimorgans in horses, indicating that whole-genome LD screening in horses could be performed systematically and efficiently by combining the above-mentioned methods. Since genetic maps based on microsatellites have been constructed for many other species, the approaches present here could have wide applicability.     

系统发育基因组学研究进展

系统发育基因组学是利用全基因组数据构建系统发育树的新领域。全基因组数据能有效消除横向基因转移和类群间基因进化速率差异等因素对系统发育树的影响。根据所使用的全基因组数据的类型, 可以将系统发育基因组学方法分为以下5类：多基因联合建树方法, 基于基因含量的方法, 基于基因排列信息的方法, 基于序列短串含量特征信息的方法及基于代谢途径的方法。文章系统地总结了每一类方法的原理、速度、准确性、适用范围及在各个生物类群中的应用, 并对系统发育基因组学的前景及面临的挑战进行了概述。     

Methods for the isolation and identification of Listeria spp. and Listeria monocytogenes: a review

Listeria monocytogenes is an important food-borne pathogen and is widely tested for in food, environmental and clinical samples. Identification traditionally involved culture methods based on selective enrichment and plating followed by the characterization of Listeria spp. based on colony morphology, sugar fermentation and haemolytic properties. These methods are the gold standard; but they are lengthy and may not be suitable for testing of foods with short shelf lives. As a result more rapid tests were developed based on antibodies (ELISA) or molecular techniques (PCR or DNA hybridization). While these tests possess equal sensitivity, they are rapid and allow testing to be completed within 48 h. More recently, molecular methods were developed that target RNA rather than DNA, such as RT-PCR, real time PCR or nucleic acid based sequence amplification (NASBA). These tests not only provide a measure of cell viability but they can also be used for quantitative analysis. In addition, a variety of tests are available for sub-species characterization, which are particularly useful in epidemiological investigations. Early typing methods differentiated isolates based on phenotypic markers, such as multilocus enzyme electrophoresis, phage typing and serotyping. These phenotypic typing methods are being replaced by molecular tests, which reflect genetic relationships between isolates and are more accurate. These new methods are currently mainly used in research but their considerable potential for routine testing in the future cannot be overlooked.     

Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller

Background: An open challenge of P300-based BCI systems focuses on recognizing ERP signals using a reduced number of trials with enough classification rate.Methods: Three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials. The proposed methods were evaluated with two freely available EEG datasets based on 6x6 speller and were compared with five standard methods: Mean-Amplitude, Step-Wise, Principal Component Analysis, Peak, and CCA.Results: The proposed methods outperform significantly standard algorithms for P300 identification with a maximum AUC of 0.93 and 0.98, and an average of 0.73 and 0.76, using a single trial.Conclusion: Proposed methods based on Filter Bank are robust for the identification of P300 using a reduced number of trials, which could be used in real-time BCI spellers for rehabilitation engineering.     

磷酸化蛋白质组学研究中磷酸化肽段富集与分离方法研究进展

对磷酸化蛋白质组（phosphoproteome）进行系统深入的研究依赖于高重复性和特异性的磷酸化肽段富集与分离方法。目前发展了多种不同原理的磷酸化肽段富集方法,它们往往具有不同的选择性和特异性,因此,根据不同的研究目的选择最适合的富集方法显得尤为重要。本文综述了基于亲和色谱法（affinity chromatography）、免疫沉淀法（immunoprecipitation）、化学衍生法（chemical derivatization）、色谱法（chromatography）和其他新发展方法的磷酸化肽段富集方法,详细介绍了各自的优缺点及相关的优化与改进策略。此外,还简单介绍了磷酸化肽段富集与预分方法的不同组合的研究进展。     

An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature

The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1) Protein–protein interactions extraction, and (2) Gene–suicide association extraction. The evaluation of task (1) on the benchmark dataset (AImed corpus) showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene–suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.     

Genetic algorithm-based optimization of hydrophobicity tables

SUMMARY: The genomic abundance and pharmacological importance of membrane proteins have fueled efforts to identify them based solely on sequence information. Previous methods based on the physicochemical principle of a sliding window of hydrophobicity (hydropathy analysis) have been replaced by approaches based on hidden Markov models or neural networks which prevail due to their probabilistic orientation. In the current study, an optimization of the hydrophobicity tables used in hydropathy analysis is performed using a genetic algorithm. As such, the approach can be viewed as a synthesis between the physicochemically and statistically based methods. The resulting hydrophobicity tables lead to significant improvement in the prediction accuracy of hydropathy analysis. Furthermore, since hydropathy analysis is less dependent on the basis set of membrane proteins is used to hone the statistically based methods, as well as being faster, it may be valuable in the analysis of new genomes. Finally, the values obtained for each of the amino acids in the new hydrophobicity tables are discussed.     

林窗几何特征的测定方法

林窗面积、形状及边界木高是决定林窗环境异质性的3个林窗几何特征,影响林窗内植物更新。林窗几何特征的快速测量方法是林窗研究的基础,测量方法可分为2类:基于地面实际测量的地面法和基于林窗林冠照片的相片法。地面法费时费力,受人为因素影响大,可测量林冠林窗和扩展林窗的面积,但不能测量林窗形状和边界木高。相片法具有简单、客观、可重复的优点,但仅适用于林冠林窗。相片法共有5种:"平面相片法"、"航片法"、"半球面影像法"、"双半球面影像法"和"改进的半球面影像法"。前3种测量方法只能测量林冠林窗面积;"改进的半球面影像法"可测量林冠林窗面积和形状,且精度高于前3种相片法,但所需参数最多;"双半球面影像法"可测量林窗面积、形状及边界木高这3个林窗几何特征,且精度较高,但拍摄要求较高。     

Admixture estimation using skin reflectance data

Several different methods are suggested for the estimation of admixture proportions in hybrid populations based on skin reflectance data. These methods are applied to hybrid populations of known ancestry and yield results generally quite similar to those expected based on a simple genetic model. Results indicate the usefulness of these methods in hybridization studies and in the development and refinement of models of the genetics of skin color.     

What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry

Targeted proteomics has gained significant popularity in mass spectrometry‐based protein quantification as a method to detect proteins of interest with high sensitivity, quantitative accuracy and reproducibility. However, with the emergence of a wide variety of targeted proteomics methods, some of them with high‐throughput capabilities, it is easy to overlook the essence of each method and to determine what makes each of them a targeted proteomics method. In this viewpoint, we revisit the main targeted proteomics methods and classify them in four categories differentiating those methods that perform targeted data acquisition from targeted data analysis, and those methods that are based on peptide ion data (MS1 targeted methods) from those that rely on the peptide fragments (MS2 targeted methods).     

Comparison of autofocus methods for automated microscopy

Traditional autofocus methods were designed for microscopes driven by single processor computers. As computers are developed that exploit massive parallelism when acquiring and analyzing images, parallel cellular logic techniques became available to focus automatically. This paper introduces the reader to both cellular logic techniques for autofocus and a new spectral moment autofocus measure. It then compares these methods with more traditional autofocus methods. It is shown that traditional methods based on measurements of image power-give the best results when tested on one set of real images and two sets of synthetic images. The next best methods are the cellular logic and spectral moment techniques, while the worst are those based on the image probability density function or histogram.