Cloning and molecular characterization of a novel x-type glutenin gene Glu-D(t)1 from Aegilops tauschii

A novel gene encoding an x-type high molecular weight glutenin subunit (HMW-GS), designated 1Dx1.1 ^t , was isolated from Aegilops tauschii. It is the largest HMW-GS gene reported so far in this species and its product has a slower mobility than that of subunit 1Ax1 in SDS-PAGE. The open reading frame (ORF) of the gene was 2,628 bp, encoding a protein of 874 amino acid residues. Comparisons of amino acid sequences showed that subunit 1Dx1.1^t had high similarity with other 1Dx subunits but also had two unique characteristics. Firstly, a tripeptide of consensus LQE present in the N-terminal domains of other 1Dx subunits was absent from subunit Dx1.1^t. Secondly, three copies of tandem duplications of the tripeptide motif GQQ and a novel tripeptide sequence (GQL) were present in its central repetitive domain. Phylogenetic analysis showed that subunit 1Dx1.1^t clustered with other known 1Dx subunits.     

Unexpected crucial role of residue 272 in substrate specificity of fibroblast collagenase

Degradation of type I collagen by collagenases is an important part of extracellular remodeling. To understand the role of the hinge region of fibroblast collagenase in its collagenolytic activity, we individually substituted the 10 conserved amino acid residues at positions 264, 266, 268, 296, 272, 277, 284, 289, 307, and 313 in this region of the enzyme by their corresponding residues in MMP-3, a noncollagenolytic matrix metalloproteinase. The general proteolytic and triple helicase activities of all of the enzymes were determined, and their abilities to bind to type I collagen were assessed. Among the mutants, only G272D mutant enzyme exhibited a significant change in type I collagenolysis. The alteration of the Gly(272) to Asp reduced the collagenolytic activity of the enzyme to 13% without affecting its general proteolytic activity, substrate specificity, or the collagen binding ability. The catalytic efficiency of the G272D mutant for the triple helical peptide substrate [C(6)-(GP- Hyp)(4)GPL(Mca)GPQGLRGQL(DPN)GVR(GP-HYP)(4)-NH(2)](3) and the peptide substrate Mca-PLGL(Dpa)AR-NH(2) and its dissociation constant for the triple helical collagen were similar to that of the wild type enzyme, indicating that the presence of this residue in fibroblast collagenase is particularly important for the efficient cleavage of type I collagen. Gly(272) is evidently responsible for the hinge-bending motion that is essential for allowing the COOH-terminal domain to present the collagen to the active site.     

A mixture of alpha-helical and 3(10)-helical conformations for involucrin in the human epidermal corneocyte envelope provides a scaffold for the attachment of both lipids and proteins

Involucrin plays an important role in the lipid and protein compound envelopes of mammalian epidermal corneocytes. In the present study, model peptides containing the consensus repeating units PEQQEGQLEL and LEQQEGQLEH, found in the central region of human involucrin, were studied by circular dichroism spectroscopy, molecular modeling, and energy minimization. These peptides have intrinsic alpha-helix-forming properties as indicated by their circular dichroic spectra obtained in the presence of 2,2,2-trifluoroethanol. Peptide (LEQQEGQLEH)(3) had an alpha-helix content of 100% in 100% 2, 2,2-trifluoroethanol at 0 degrees C. The energy-minimized alpha-helix showed that only 50% of the glutamate side chains may be available for the attachment of lipids. However, when a 3(10)-helix was assumed for the GQL or GQLE residues in LEQQEGQLEH, all of the glutamate side chains were arrayed on one face of the helix, and all of the glutamine side chains were arrayed on the opposite face. A similar result was obtained when the nonhelical part of PEQQEGQLEL was assumed to contain a beta-turn III, which is equivalent to a short portion of 3(10)-helix. The results of this study suggest that when the central segment of human involucrin is predominantly alpha-helical, accompanied by short 3(10)-helical segments, the protein can function as a scaffold for the attachment of both lipids and proteins.     

NvAssign: protein NMR spectral assignment with NMRView

MOTIVATION: Nuclear magnetic resonance (NMR) protein studies rely on the accurate assignment of resonances. The general procedure is to (1) pick peaks, (2) cluster data from various experiments or spectra, (3) assign peaks to the sequence and (4) verify the assignments with the spectra. Many algorithms already exist for automating the assignment process (step 3). What is lacking is a flexible interface to help a spectroscopist easily move from clustering (step 2) to assignment algorithms (step 3) and back to verification of the algorithm output with spectral analysis (step 4). RESULTS: A software module, NvAssign, was written for use with NMRView. It is a significant extension of the previous CBCA module. The module provides a flexible interface to cluster data and interact with the existing assignment algorithms. Further, the software module is able to read the results of other algorithms so that the data can be easily verified by spectral analysis. The generalized interface is demonstrated by connecting the clustered data with the assignment algorithms PACES and MONTE using previously assigned data for the lyase domain of DNA polymerase lambda. The spectral analysis program NMRView is now able to read the output of these programs for simplified analysis and verification. AVAILABILITY: NvAssign is available from http://dir.niehs.nih.gov/dirnmr/nvassign     

MAD: a suite of tools for microarray data management and processing

SUMMARY: Microarray data management and processing (MAD) is a set of Windows integrated software for microarray analysis. It consists of a relational database for data storage with many user-interfaces for data manipulation, several text file parsers and Microsoft Excel macros for automation of data processing, and a generator to produce text files that are ready for cluster analysis. AVAILABILITY: Executable is available free of charge on http://pompous.swmed.edu. The source code is also available upon request.     

Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data

MOTIVATION: The major difficulties relating to mathematical modelling of spectroscopic data are inconsistencies in spectral reproducibility and the black box nature of the modelling techniques. For the analysis of biological samples the first problem is due to biological, experimental and machine variability which can lead to sample size differences and unavoidable baseline shifts. Consequently, there is often a requirement for mathematical correction(s) to be made to the raw data if the best possible model is to be formed. The second problem prevents interpretation of the results since the variables that most contribute to the analysis are not easily revealed; as a result, the opportunity to obtain new knowledge from such data is lost. METHODS: We used genetic algorithms (GAs) to select spectral pre-processing steps for Fourier transform infrared (FT-IR) spectroscopic data. We demonstrate a novel approach for the selection of important discriminatory variables by GA from FT-IR spectra for multi-class identification by discriminant function analysis (DFA). RESULTS: The GA selects sensible pre-processing steps from a total of approximately 10(10) possible mathematical transformations. Application of these algorithms results in a 16% reduction in the model error when compared against the raw data model. GA-DFA recovers six variables from the full set of 882 spectral variables against which a satisfactory DFA model can be formed; thus inferences can be made as to the biochemical differences that are reflected by these spectral bands.     

Computing the maximum similarity bi-clusters of gene expression data

MOTIVATIONS: Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions. RESULTS: We develop a polynomial time algorithm to find an optimal bi-cluster with the maximum similarity score. To our knowledge, this is the first formulation for bi-cluster problems that admits a polynomial time algorithm for optimal solutions. The algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experiments on simulation data and real data show that the new algorithms outperform most of the existing methods in many cases. Our new algorithms have the following advantages: (1) no discretization procedure is required, (2) performs well for overlapping bi-clusters and (3) works well for additive bi-clusters. AVAILABILITY: The software is available at http://www.cs.cityu.edu.hk/~liuxw/msbe/help.html.     

Statistical error in isothermal titration calorimetry: variance function estimation from generalized least squares

The method of generalized least squares (GLS) is used to assess the variance function for isothermal titration calorimetry (ITC) data collected for the 1:1 complexation of Ba(2+) with 18-crown-6 ether. In the GLS method, the least squares (LS) residuals from the data fit are themselves fitted to a variance function, with iterative adjustment of the weighting function in the data analysis to produce consistency. The data are treated in a pooled fashion, providing 321 fitted residuals from 35 data sets in the final analysis. Heteroscedasticity (nonconstant variance) is clearly indicated. Data error terms proportional to q(i) and q(i)/v are well defined statistically, where q(i) is the heat from the ith injection of titrant and v is the injected volume. The statistical significance of the variance function parameters is confirmed through Monte Carlo calculations that mimic the actual data set. For the data in question, which fall mostly in the range of q(i)=100-2000 microcal, the contributions to the data variance from the terms in q(i)(2) typically exceed the background constant term for q(i)>300 microcal and v<10 microl. Conversely, this means that in reactions with q(i) much less than this, heteroscedasticity is not a significant problem. Accordingly, in such cases the standard unweighted fitting procedures provide reliable results for the key parameters, K and DeltaH(degrees) and their statistical errors. These results also support an important earlier finding: in most ITC work on 1:1 binding processes, the optimal number of injections is 7-10, which is a factor of 3 smaller than the current norm. For high-q reactions, where weighting is needed for optimal LS analysis, tips are given for using the weighting option in the commercial software commonly employed to process ITC data.     

Comment on analyzing flow cytometric data for comparison of mean values of the coefficient of variation of the G1 peak.

BACKGROUND: The coefficient of variation (CV) is often used to characterize and summarize the flow cytometry analysis of nuclear DNA of the Go/G1 peak in a cell population within an individual organism. CV values are frequently used in subsequent statistical analysis to compare experimental groups of individuals. METHODS: We explain why the conventional analysis of variance, linear comparisons and regressions that employ the F and t-tests are not appropriate for analyzing CV data sets. The weighted least squares procedure which utilizes the chi-square test is presented as an adequate method. We further explain why this type of data needs to be analyzed by this procedure. RESULTS: To illustrate the application of the weighted least squares procedure, we analyzed a real data set that had been previously analyzed by conventional methods. We found that a non-significant result (p = 1) using the latter was significant when re-analyzed with the weighted least squares procedure (p = 0.032). CONCLUSIONS: Significant differences between treatments established by the weighted least squares often go unidentified by the conventional analysis. Use of the weighted least squares procedure is recommended for analyzing CV data sets.     

Outlier Detection using Projection Quantile Regression for Mass Spectrometry Data with Low Replication

ABSTRACT: BACKGROUND: Mass spectrometry (MS) data are often generated from various biological or chemical experiments and there may exist outlying observations, which are extreme due to technical reasons. The determination of outlying observations is important in the analysis of replicated MS data because elaborate pre-processing is essential for successful analysis with reliable results and manual outlier detection as one of pre-processing steps is time-consuming. The heterogeneity of variability and low replication are often obstacles to successful analysis, including outlier detection. Existing approaches, which assume constant variability, can generate many false positives (outliers) and/or false negatives non-outliers). Thus, a more powerful and accurate approach is needed to account for the heterogeneity of variability and low replication. FINDINGS: We proposed an outlier detection algorithm using projection and quantile regression in MS data from multiple experiments. The performance of the algorithm and program was demonstrated by using both simulated and real-life data. The projection approach with linear, nonlinear, or nonparametric quantile regression was appropriate in heterogeneous high-throughput data with low replication. CONCLUSION: Various quantile regression approaches combined with projection were proposed for detecting outliers. The choice among linear, nonlinear, and nonparametric regressions is dependent on the degree of heterogeneity of the data. The proposed approach was illustrated with MS data with two or more replicates.     

A friendly statistics package for microarray analysis

SUMMARY: The friendly statistics package for microarray analysis (FSPMA) is a tool that aims to fill the gap between simple to use and powerful analysis. FSPMA is a platform-independent R-package that allows efficient exploration of microarray data without the need for computer programming. Analysis is based on a mixed model ANOVA library (YASMA) that was extended to allow more flexible comparisons and other useful operations like k nearest neighbour imputing and spike-based normalization. Processing is controlled by a definition file that specifies all the steps necessary to derive analysis results from quantified microarray data. In addition to providing analysis without programming, the definition file also serves as exact documentation of all the analysis steps. AVAILABILITY: The library is available under GPL 2 license and, together with additional information, provided at http://www.ccbi.cam.ac.uk/software/psyk/software.html#fspma     

SIMPLEX: Cloud-Enabled Pipeline for the Comprehensive Analysis of Exome Sequencing Data

In recent studies, exome sequencing has proven to be a successful screening tool for the identification of candidate genes causing rare genetic diseases. Although underlying targeted sequencing methods are well established, necessary data handling and focused, structured analysis still remain demanding tasks. Here, we present a cloud-enabled autonomous analysis pipeline, which comprises the complete exome analysis workflow. The pipeline combines several in-house developed and published applications to perform the following steps: (a) initial quality control, (b) intelligent data filtering and pre-processing, (c) sequence alignment to a reference genome, (d) SNP and DIP detection, (e) functional annotation of variants using different approaches, and (f) detailed report generation during various stages of the workflow. The pipeline connects the selected analysis steps, exposes all available parameters for customized usage, performs required data handling, and distributes computationally expensive tasks either on a dedicated high-performance computing infrastructure or on the Amazon cloud environment (EC2). The presented application has already been used in several research projects including studies to elucidate the role of rare genetic diseases. The pipeline is continuously tested and is publicly available under the GPL as a VirtualBox or Cloud image at http://simplex.i-med.ac.at; additional supplementary data is provided at http://www.icbi.at/exome.     

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.     

AMarge

AMarge is a web tool for the automatic quality assessment of Affymetrix GeneChip data. It is essential to have a trustworthy set of chips in order to derive gene expression data for phenotypic analysis, and AMarge provides a complete and rigorous web-accessible tool to fulfill this need. The quality assessment steps include image plots of weights derived from a robust linear model fit of the data, a 3'/5' RNA digestion plot, and Affymetrix Microarray Suite version 5.0 (MAS 5.0) quality standard procedures. Furthermore, robust multi-array average expression values are generated in order to have a start-up expression set for the subsequent analysis. The results of the complete analysis are summarised and returned as an HTML report. AVAILABILITY: The AMarge web interface is accessible at http://nin.crg.es/cgi-binf/AMargeWeb.cgi. A mirror server is also available at http://bioinformatics.istge.it/AMarge-bin/AMargeWeb.cgi. The software implementing all these methods is part of the Bioconductor project (http://www.bioconductor.org).     

AVA: visual analysis of gene expression microarray data

SUMMARY: AVA (Array Visual Analyzer) is a Java program that provides a graphical environment for visualization and analysis of gene expression microarray data. Together with its interactive visualization tools and a variety of built-in data analysis and filtration methods, AVA effectively integrates microarray data normalization, quality assessment, and data mining into one application. AVAILABILITY: The software is freely available for academic users on request from the authors.     

Integrating data from biological experiments into metabolic networks with the DBE information system

Modern 'omics'-technologies result in huge amounts of data about life processes. For analysis and data mining purposes this data has to be considered in the context of the underlying biological networks. This work presents an approach for integrating data from biological experiments into metabolic networks by mapping the data onto network elements and visualising the data enriched networks automatically. This methodology is implemented in DBE, an information system that supports the analysis and visualisation of experimental data in the context of metabolic networks. It consists of five parts: (1) the DBE-Database for consistent data storage, (2) the Excel-Importer application for the data import, (3) the DBE-Website as the interface for the system, (4) the DBE-Pictures application for the up- and download of binary (e. g. image) files, and (5) DBE-Gravisto, a network analysis and graph visualisation system. The usability of this approach is demonstrated in two examples.     

USAGE: a web-based approach towards the analysis of SAGE data. Serial Analysis of Gene Expression

MOTIVATION: SAGE enables the determination of genome-wide mRNA expression profiles. A comprehensive analysis of SAGE data requires software, which integrates (statistical) data analysis methods with a database system. Furthermore, to facilitate data sharing between users, the application should reside on a central server and be accessed via the internet. Since such an application was not available we developed the USAGE package. RESULTS: USAGE is a web-based application that comprises an integrated set of tools, which offers many functions for analysing and comparing SAGE data. Additionally, USAGE includes a statistical method for the planning of new SAGE experiments. USAGE is available in a multi-user environment giving users the option of sharing data. USAGE is interfaced to a relational database to store data and analysis results. The USAGE query editor allows the composition of queries for searching this database. Several database functions have been included which enable the selection and combination of data. USAGE provides the biologist increased functionality and flexibility for analysing SAGE data. AVAILABILITY: USAGE is freely accessible for academic institutions at http://www.cmbi.kun.nl/usage/. The source code of USAGE is freely available for academic institutions on request from the first author.     

2HAPI: a microarray data analysis system

SUMMARY: 2HAPI (version 2 of High density Array Pattern Interpreter) is a web-based, publicly-available analytical tool designed to aid researchers in microarray data analysis. 2HAPI includes tools for searching, manipulating, visualizing, and clustering the large sets of data generated by microarray experiments. Other features include association of genes with NCBI information and linkage to external data resources. Unique to 2HAPI is the ability to retrieve upstream sequences of co-regulated genes for promoter analysis using MEME (Multiple Expectation-maximization for Motif Elicitation) AVAILABILITY: 2HAPI is freely available at http://array.sdsc.edu. Users can try 2HAPI anonymously with pre-loaded data or they can register as a 2HAPI user and upload their data.     

datPAV--an online processing, analysis and visualization tool for exploratory investigation of experimental data

SUMMARY: Data processing, analysis and visualization (datPAV) is an exploratory tool that allows experimentalist to quickly assess the general characteristics of the data. This platform-independent software is designed as a generic tool to process and visualize data matrices. This tool explores organization of the data, detect errors and support basic statistical analyses. Processed data can be reused whereby different step-by-step data processing/analysis workflows can be created to carry out detailed investigation. The visualization option provides publication-ready graphics. Applications of this tool are demonstrated at the web site for three cases of metabolomics, environmental and hydrodynamic data analysis. AVAILABILITY: datPAV is available free for academic use at http://www.sdwa.nus.edu.sg/datPAV/.