The Application of Gaussian Mixture Models for Signal Quantification in MALDI-ToF Mass Spectrometry of Peptides

Matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) coupled with stable isotope standards (SIS) has been used to quantify native peptides. This peptide quantification by MALDI-TOF approach has difficulties quantifying samples containing peptides with ion currents in overlapping spectra. In these overlapping spectra the currents sum together, which modify the peak heights and make normal SIS estimation problematic. An approach using Gaussian mixtures based on known physical constants to model the isotopic cluster of a known compound is proposed here. The characteristics of this approach are examined for single and overlapping compounds. The approach is compared to two commonly used SIS quantification methods for single compound, namely Peak Intensity method and Riemann sum area under the curve (AUC) method. For studying the characteristics of the Gaussian mixture method, Angiotensin II, Angiotensin-2-10, and Angiotenisn-1-9 and their associated SIS peptides were used. The findings suggest, Gaussian mixture method has similar characteristics as the two methods compared for estimating the quantity of isolated isotopic clusters for single compounds. All three methods were tested using MALDI-TOF mass spectra collected for peptides of the renin-angiotensin system. The Gaussian mixture method accurately estimated the native to labeled ratio of several isolated angiotensin peptides (5.2% error in ratio estimation) with similar estimation errors to those calculated using peak intensity and Riemann sum AUC methods (5.9% and 7.7%, respectively). For overlapping angiotensin peptides, (where the other two methods are not applicable) the estimation error of the Gaussian mixture was 6.8%, which is within the acceptable range. In summary, for single compounds the Gaussian mixture method is equivalent or marginally superior compared to the existing methods of peptide quantification and is capable of quantifying overlapping (convolved) peptides within the acceptable margin of error.     

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.     

Discriminant analysis: methods and program.

Because of the disparity in the results obtained using different methods of discriminant analysis, we have written and used a unique program to test them. These methods can be divided into two groups corresponding to a classical probabilistic approach (i.e., Bayesian methods) or a topological approach (i.e., Sebestyen method).     

Is multiple-sequence alignment required for accurate inference of phylogeny?

The process of inferring phylogenetic trees from molecular sequences almost always starts with a multiple alignment of these sequences but can also be based on methods that do not involve multiple sequence alignment. Very little is known about the accuracy with which such alignment-free methods recover the correct phylogeny or about the potential for increasing their accuracy. We conducted a large-scale comparison of ten alignment-free methods, among them one new approach that does not calculate distances and a faster variant of our pattern-based approach; all distance-based alignment-free methods are freely available from http://www.bioinformatics.org.au (as Python package decaf+py). We show that most methods exhibit a higher overall reconstruction accuracy in the presence of high among-site rate variation. Under all conditions that we considered, variants of the pattern-based approach were significantly better than the other alignment-free methods. The new pattern-based variant achieved a speed-up of an order of magnitude in the distance calculation step, accompanied by a small loss of tree reconstruction accuracy. A method of Bayesian inference from k-mers did not improve on classical alignment-free (and distance-based) methods but may still offer other advantages due to its Bayesian nature. We found the optimal word length k of word-based methods to be stable across various data sets, and we provide parameter ranges for two different alphabets. The influence of these alphabets was analyzed to reveal a trade-off in reconstruction accuracy between long and short branches. We have mapped the phylogenetic accuracy for many alignment-free methods, among them several recently introduced ones, and increased our understanding of their behavior in response to biologically important parameters. In all experiments, the pattern-based approach emerged as superior, at the expense of higher resource consumption. Nonetheless, no alignment-free method that we examined recovers the correct phylogeny as accurately as does an approach based on maximum-likelihood distance estimates of multiply aligned sequences.     

草坪草总RNA几种提取方法的比较

分别采用DEPC水法、TRIzol法、CTAB-LiCl沉淀法、SDS-酚法、SDS提取液抽提法以及异硫氰酸胍法等6种方法提取草坪草总RNA,通过检测对各种方法的提取效果进行比较分析。结果表明,DEPC水法是最经济、操作最简单的一种方法,并且提取的RNA质量较好、可靠性高、易于大量制备,是提取草坪草RNA比较理想的方法之一,建议使用该方法。利用TRIzol法提取到的28S rRNA亮度约为18S rRNA的两倍,效果较好,操作简单,符合分子生物学实验要求。CTAB-LiCl沉淀法、SDS-酚法、SDS提取液抽提法也获得较高质量的RNA,但存在不同程度的DNA污染。异硫氰酸胍法提取RNA有明显的蛋白质污染,且RNA部分降解,此种方法不予采用。     

Comparison of two equivalency factor approaches with simplified risk assessment for lcia of toxicity impact potential

Three approaches recommended for characterization of toxicity impact potential in a life cycle impact assessment (LCIA) are tested on a case study and compared. The two equivalency factor methods are the Persistence, Bioaccumulation, and Toxicity (PBT) method and the Multimedia Fate Modeling (MFM) method using a Mackay Level III model with state-specific environmental data. The simplified risk assessment (SRA) method involved dispersion modeling using site-specific environmental data. The life cycle inventory information evaluated by all three methods was limited to manufacturing of the RDX-based explosive in Kingsport, Tennessee. The effort to collect site-specific environmental data and conduct air dispersion modeling for the SRA method required about 24 times more effort than the PBT method and about 4 times more effort than the MFM method. Direct comparison of impact potential scores for the three approaches were limited to inhalation toxicity scores for nine air pollutants modeled by SRA. Correlations were made on the rank order of the impact potential scores for the nine air emissions evaluated for all three LCIA methods. Although the number of chemicals compared is very limited, the best correlation coefficient (0.96) was between the rank orders for the MFM and the SRA methods. The minimal effort and reduced accuracy of the PBT approach make it best suited for screening large numbers of chemicals for further evaluation of the highest ranked chemicals. The intermediate effort and reasonable accuracy (includes transfers to other media) of the MFM approach make it well suited for LCIAs involving comparative assertions or governmental policy decisions. The maximum effort and assumption of highest accuracy make the SRA approach suitable only after limiting the locations of interest to a few sites by screening with the other two approaches.     

Comparing a template approach and complex bandpass filtering for single-trial analysis of auditory evoked M100.

Two methods for single-trial analysis were compared, an established parametric template approach and a recently proposed non-parametric method based on complex bandpass filtering. The comparison was carried out by means of pseudo-real simulations based on magnetoencephalography measurements of cortical responses to auditory signals. The comparison focused on amplitude and latency estimation of the M100 response. The results show that both methods are well suited for single-trial analysis of the auditory evoked M100. While both methods performed similarly with respect to latency estimation, the non-parametric approach was observed to be more robust for amplitude estimation. The non-parametric approach can thus be recommended as an additional valuable tool for single-trial analysis.     

Application of String Kernels in Protein Sequence Classification

INTRODUCTION: The production of biological information has become much greater than its consumption. The key issue now is how to organise and manage the huge amount of novel information to facilitate access to this useful and important biological information. One core problem in classifying biological information is the annotation of new protein sequences with structural and functional features. METHOD: This article introduces the application of string kernels in classifying protein sequences into homogeneous families. A string kernel approach used in conjunction with support vector machines has been shown to achieve good performance in text categorisation tasks. We evaluated and analysed the performance of this approach, and we present experimental results on three selected families from the SCOP (Structural Classification of Proteins) database. We then compared the overall performance of this method with the existing protein classification methods on benchmark SCOP datasets. RESULTS: According to the F1 performance measure and the rate of false positive (RFP) measure, the string kernel method performs well in classifying protein sequences. The method outperformed all the generative-based methods and is comparable with the SVM-Fisher method. DISCUSSION: Although the string kernel approach makes no use of prior biological knowledge, it still captures sufficient biological information to enable it to outperform some of the state-of-the-art methods.     

Theoretical note: tests of synergy in sweetener mixtures

Some methods for examining the additivity of sweeteners, and their synergy in mixtures depend upon setting component concentrations on the basis of sweetness equivalence, usually to a sucrose reference. These methods may under- or over-predict the sweetness of a mixture, leading to spurious claims of synergy or mixture suppression. This paper points out one problem with one such popular method, in that the method can lead to a conclusion that a substance would synergize with itself. To the extent that self-synergy is an illogical conclusion for a mixture comparison, such a method should be avoided in tests of synergy. The sweetness equivalence approach is contrasted with a simpler approach based on concentrations that does not lead to conclusions of self- synergy.      

Estimation of the size of an open population using local estimating equations II: a partially parametric approach

Kernel smoothing methods are applied to extend a modification of the closed population approach of Lloyd and Yip (1991, in Estimating Equations, 65-88) to open populations with frequent capture occasions. The method complements previous nonparametric methods and, when the parametric assumptions are met, simulations show the new method has a smaller integrated mean squared error than the previous fully nonparametric method. The method is applied to capture-recapture data on short-tailed shearwaters collected annually for 48 years.     

On parameter estimation in population models II: Multi-dimensional processes and transient dynamics

Recently, a computationally-efficient method was presented for calibrating a wide-class of Markov processes from discrete-sampled abundance data. The method was illustrated with respect to one-dimensional processes and required the assumption of stationarity. Here we demonstrate that the approach may be directly extended to multi-dimensional processes, and two analogous computationally-efficient methods for non-stationary processes are developed. These methods are illustrated with respect to disease and population models, including application to infectious count data from an outbreak of “Russian influenza” (A/USSR/1977 H1N1) in an educational institution. The methodology is also shown to provide an efficient, simple and yet rigorous approach to calibrating disease processes with gamma-distributed infectious period.     

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.     

Comparison of longitudinal variance components and regression-based approaches for linkage detection on chromosome 17 for systolic blood pressure

We compare two methods to detect genetic linkage by using serial observations of systolic blood pressure in pedigree data from the Framingham Heart Study focusing on chromosome 17. The first method is a variance components (VC) approach that incorporates longitudinal pedigree data, and the second method is a regression-based approach that summarizes all longitudinal measures in one single measure. No evidence of linkage was found either using the VC longitudinal approach or the regression-based approach, except when all time points were used from Cohorts 1 and 2 and only subjects aged 25 and 75 years were included.     

Focal plane array infrared imaging: a new way to analyse leaf tissue

* Here, a new approach to macromolecular imaging of leaf tissue using a multichannel focal plane array (FPA) infrared detector was compared with the proven method of infrared mapping with a synchrotron source, using transverse sections of leaves from a species of Eucalyptus. * A new histological method was developed, ideally suited to infrared spectroscopic analysis of leaf tissue. Spatial resolution and the signal-to-noise ratio of the FPA imaging and synchrotron mapping methods were compared. * An area of tissue 350 microm(2) required approx. 8 h to map using the synchrotron technique and approx. 2 min to image using the FPA. The two methods produced similar infrared images, which differentiated all tissue types in the leaves according to their macromolecular chemistry. * The synchrotron and FPA methods produced similar results, with the synchrotron method having superior signal-to-noise ratio and potentially better spatial resolution, whereas the FPA method had the advantage in terms of data acquisition time, expense and ease of use. FPA imaging offers a convenient, laboratory-based approach to microscopic chemical imaging of leaves.     

An Escherichia coli expression-based method for heme substitution

Heme reconstitution with porphyrin analogs is a powerful approach toward understanding the molecular function of heme proteins; present methods, however, have not proven to be generally useful. Here we describe the development and application of an expression-based method for introducing modified porphyrins. The approach allows efficient incorporation of heme analogs using a widely available bacterial strain and offers an attractive alternative to present reconstitution methods that subject proteins to harsh, denaturing conditions.     

A new approach for estimating the efficiencies of the nucleotide substitution models

In this article, a new approach is presented for estimating the efficiencies of the nucleotide substitution models in a four-taxon case and then this approach is used to estimate the relative efficiencies of six substitution models under a wide variety of conditions. In this approach, efficiencies of the models are estimated by using a simple probability distribution theory. To assess the accuracy of the new approach, efficiencies of the models are also estimated by using the direct estimation method. Simulation results from the direct estimation method confirmed that the new approach is highly accurate. The success of the new approach opens a unique opportunity to develop analytical methods for estimating the relative efficiencies of the substitution models in a straightforward way.     

An integrated feature selection and classification method to select minimum number of variables on the case study of gene expression data

This paper introduces a novel generic approach for classification problems with the objective of achieving maximum classification accuracy with minimum number of features selected. The method is illustrated with several case studies of gene expression data. Our approach integrates filter and wrapper gene selection methods with an added objective of selecting a small set of non-redundant genes that are most relevant for classification with the provision of bins for genes to be swapped in the search for their biological relevance. It is capable of selecting relatively few marker genes while giving comparable or better leave-one-out cross-validation accuracy when compared with gene ranking selection approaches. Additionally, gene profiles can be extracted from the evolving connectionist system, which provides a set of rules that can be further developed into expert systems. The approach uses an integration of Pearson correlation coefficient and signal-to-noise ratio methods with an adaptive evolving classifier applied through the leave-one-out method for validation. Datasets of gene expression from four case studies are used to illustrate the method. The results show the proposed approach leads to an improved feature selection process in terms of reducing the number of variables required and an increased in classification accuracy.     

Multiparallel chiral method development screening using an 8-channel microfluidic HPLC system

The Eksigent Express 800 8-channel microfluidic HPLC system was investigated for carrying out multiparallel screening and development of fast normal phase chiral separations. In contrast to the familiar automated sequential chiral method development approaches that often afford a next day result, the multiparallel approach offers the exciting possibility of near "real time" method development, often affording an optimized method in less than 1 h. In this study, four column types (300 microm i.d.) with two different mobile phases are screened using a universal standard gradient approach. Interestingly, parallel method optimization following initial screening was shown to sometimes lead to surprising and unanticipated outcomes, emphasizing the value of the multiparallel screening approach. A variety of standard test racemates were analyzed, with optimized separation methods for most in the 1- to 2-min range. These results compare favorably with results obtained on a single channel conventional HPLC system using 4.6-mm i.d. columns. In addition, isocratic methods developed on the microbore columns are readily translated to the larger column format.     

Improved sequence tag generation method for peptide identification in tandem mass spectrometry

The sequence tag-based peptide identification methods are a promising alternative to the traditional database search approach. However, a more comprehensive analysis, optimization, and comparison with established methods are necessary before these methods can gain widespread use in the proteomics community. Using the InsPecT open source code base ( Tanner et al., Anal. Chem. 2005, 77, 4626- 39 ), we present an improved sequence tag generation method that directly incorporates multicharged fragment ion peaks present in many tandem mass spectra of higher charge states. We also investigate the performance of sequence tagging under different settings using control data sets generated on five different types of mass spectrometers, as well as using a complex phosphopeptide-enriched sample. We also demonstrate that additional modeling of InsPecT search scores using a semiparametric approach incorporating the accuracy of the precursor ion mass measurement provides additional improvement in the ability to discriminate between correct and incorrect peptide identifications. The overall superior performance of the sequence tag-based peptide identification method is demonstrated by comparison with a commonly used SEQUEST/PeptideProphet approach.     

Assessing methods for identifying pair-wise atomic contacts across binding interfaces

An essential step in understanding the molecular basis of protein-protein interactions is the accurate identification of inter-protein contacts. We evaluate a number of common methods used in analyzing protein-protein interfaces: a Voronoi polyhedra-based approach, changes in solvent accessible surface area (DeltaSASA) and various radial cutoffs (closest atom, Cbeta, and centroid). First, we compared the Voronoi polyhedra-based analysis to the DeltaSASA and show that using Voronoi polyhedra finds knob-in-hole contacts. To assess the accuracy between the Voronoi polyhedra-based approach and the various radial cutoff methods, two sets of data were used: a small set of 75 experimental mutants and a larger one of 592 structures of protein-protein interfaces. In an assessment using the small set, the Voronoi polyhedra-based methods, a solvent accessible surface area method, and the closest atom radial method identified 100% of the direct contacts defined by mutagenesis data, but only the Voronoi polyhedra-based method found no false positives. The other radial methods were not able to find all of the direct contacts even using a cutoff of 9A. With the larger set of structures, we compared the overall number contacts using the Voronoi polyhedra-based method as a standard. All the radial methods using a 6-A cutoff identified more interactions, but these putative contacts included many false positives as well as missed many false negatives. While radial cutoffs are quicker to calculate as well as to implement, this result highlights why radial cutoff methods do not have the proper resolution to detail the non-homogeneous packing within protein interfaces, and suggests an inappropriate bias in pair-wise contact potentials. Of the radial cutoff methods, using the closest atom approach exhibits the best approximation to the more intensive Voronoi calculation. Our version of the Voronoi polyhedra-based method QContacts is available at .