A hybrid on-line ECG segmenting system for long-term monitoring.

This paper introduces a new hybrid ECG beat segmenting system, which can be applied in the processing unit of single-channel, long-term ECG monitors for the on-line segmentation of the ECG signal. Numerous ECG segmentation techniques are already existing and applied, however sufficiently robust and reliable methods currently require more than one ECG signal channel and quite complex computations, which are practically not feasible in stand-alone, low-cost monitors. Our new system approach presents a time domain segmentation technique based on a priori physiological and morphological information of the ECG beat. The segmentation is carried out after classifying the ECG beat, using the linear approximation of the filtered ECG signal and considering the pathophysiological properties as well. The proposed algorithms require moderate computational power, allowing the practical realization in battery powered stand-alone long-term cardiac monitors or small-sized cardiac defibrillators. The prototype version of the system was implemented in Matlab. The test and evaluation of the system was carried out with the help of reference signal databases.     

A general modular framework for gene set enrichment analysis

Background  

Analysis of microarray and other high-throughput data on the basis of gene sets, rather than individual genes, is becoming more important in genomic studies. Correspondingly, a large number of statistical approaches for detecting gene set enrichment have been proposed, but both the interrelations and the relative performance of the various methods are still very much unclear.     

A set of modular binary vectors for transformation of cereals

Genetic transformation of crop plants offers the possibility of testing hypotheses about the function of individual genes as well as the exploitation of transgenes for targeted trait improvement. However, in most cereals, this option has long been compromised by tedious and low-efficiency transformation protocols, as well as by the lack of versatile vector systems. After having adopted and further improved the protocols for Agrobacterium-mediated stable transformation of barley (Hordeum vulgare) and wheat (Triticum aestivum), we now present a versatile set of binary vectors for transgene overexpression, as well as for gene silencing by double-stranded RNA interference. The vector set is offered with a series of functionally validated promoters and allows for rapid integration of the desired genes or gene fragments by GATEWAY-based recombination. Additional in-built flexibility lies in the choice of plant selectable markers, cassette orientation, and simple integration of further promoters to drive specific expression of genes of interest. Functionality of the cereal vector set has been demonstrated by transient as well as stable transformation experiments for transgene overexpression, as well as for targeted gene silencing in barley.     

A clustering-based preprocessing method for the elimination of unwanted residuals in metabolomic data

Introduction

The metabolome of a biological system is affected by multiple factors including factor of interest (e.g. metabolic perturbation due to disease) and unwanted factors or factors which are not primarily the focus of the study (e.g. batch effect, gender, and level of physical activity). Removal of these unwanted data variations is advantageous, as the unwanted variations may complicate biological interpretation of the data.

Objectives

We aim to develop a new unwanted variations elimination (UVE) method called clustering-based unwanted residuals elimination (CURE) to reduce metabolic variation caused by unwanted/hidden factors in metabolomic data.

Methods

A mean-centered metabolomic dataset can be viewed as a combination of a studied factor matrix and a residual matrix. The CURE method assumes that the residual should be normally distributed if it only contains inter-individual variation. However, if the residual forms multiple clusters in feature subspace of principal components analysis or partial least squares discriminant analysis, the residual may contain variation due to unwanted factors. This unwanted variation is removed by doing K-means data clustering and removal of means for each cluster from the residuals. The process is iterated until the residual no longer forms multiple clusters in feature subspace.

Results

Three simulated datasets and a human metabolomic dataset were used to demonstrate the performance of the proposed CURE method. CURE was found able to remove most of the variations caused by unwanted factors, while preserving inter-individual variation between samples.

Conclusion

The CURE method can effectively remove unwanted data variation, and can serve as an alternative UVE method for metabolomic data.

     

A modular set of prokaryotic and eukaryotic expression vectors

A modular series of versatile expression vectors is described for improved affinity purification of recombinant fusion proteins. Special features of these vectors include (i) serial affinity tags (hexahistidine-GST) to yield extremely pure protein even with very low expression rates, (ii) highly efficient proteolytic cleavage of affinity tags under a variety of conditions by hexahistidine-tagged tobacco etch virus (TEV) protease, (iii) PCR cloning design that results in a product of proteolytic cleavage with only one (a single glycine) or two (gly-ala) amino acids at the N-terminus of the protein, and (iv) expression in either Escherichia coli or Saccharomyces cerevisiae. In addition, singly hexahistidine-tagged proteins can be produced for purification under denaturing conditions and some vectors allow addition of five amino acid kinase recognition sites for easy radiolabeling of proteins. To illustrate the use of these vectors, all regulatory components of the yeast GAL regulon, rather than abundant highly soluble proteins, were produced and purified under native or denaturing conditions, and their biological activity was confirmed.     

A modular set of lacZ fusion vectors for studying gene expression in Caenorhabditis elegans

We describe a series of plasmid vectors which contain modular features particularly useful for studying gene expression in eukaryotic systems. The vectors contain the Escherichia coli beta-galactosidase (beta Gal)-encoding region (the lacZ gene) flanked by unique polylinker segments on the 5' and 3' ends, and several combinations of a variety of modules: a selectable marker (an amber suppressor tRNA), a translational initiation region, a synthetic intron segment, the early polyadenylation signal from SV40, and 3' regions from two nematode genes. A segment encoding the nuclear localization peptide from the SV40 T antigen is incorporated into many of the constructs, leading to beta Gal accumulation in nuclei, which can facilitate identification of producing cells in complex tissues. To make functional beta Gal fusions to secreted proteins, we constructed plasmids with an alternate module encoding a synthetic transmembrane domain upstream from lacZ. This domain is designed to stop transfer of secreted proteins across the membrane during secretion, allowing the beta Gal domain of the fusion polypeptide to remain in the cytoplasm and thus function in enzymatic assays. We have used the vectors to analyze expression of several genes in the nematode Caenorhabditis elegans, and have demonstrated in these studies that lacZ can be expressed in a wide variety of different tissues and cell types. These vectors should be useful in studying gene expression both in C. elegans and in other experimental systems.     

Wave-spec: a preprocessing package for mass spectrometry data

Wave-spec is a pre-processing package for mass spectrometry (MS) data. The package includes several novel algorithms that overcome conventional difficulties with the pre-processing of such data. In this application note, we demonstrate step-by-step use of this package on a real-world MALDI dataset. AVAILABILITY: The package can be downloaded at http://www.vicc.org/biostatistics/supp.php. A shared mailbox (wave-spec@vanderbilt.edu) also is available for questions regarding application of the package.     

ASAP: an environment for automated preprocessing of sequencing data

Background

Next-generation sequencing (NGS) has yielded an unprecedented amount of data for genetics research. It is a daunting task to process the data from raw sequence reads to variant calls and manually processing this data can significantly delay downstream analysis and increase the possibility for human error. The research community has produced tools to properly prepare sequence data for analysis and established guidelines on how to apply those tools to achieve the best results, however, existing pipeline programs to automate the process through its entirety are either inaccessible to investigators, or web-based and require a certain amount of administrative expertise to set up.

Findings

Advanced Sequence Automated Pipeline (ASAP) was developed to provide a framework for automating the translation of sequencing data into annotated variant calls with the goal of minimizing user involvement without the need for dedicated hardware or administrative rights. ASAP works both on computer clusters and on standalone machines with minimal human involvement and maintains high data integrity, while allowing complete control over the configuration of its component programs. It offers an easy-to-use interface for submitting and tracking jobs as well as resuming failed jobs. It also provides tools for quality checking and for dividing jobs into pieces for maximum throughput.

Conclusions

ASAP provides an environment for building an automated pipeline for NGS data preprocessing. This environment is flexible for use and future development. It is freely available at http://biostat.mc.vanderbilt.edu/ASAP.     

Quadratic variance models for adaptively preprocessing SELDI-TOF mass spectrometry data

Background  

Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI) is a proteomics tool for biomarker discovery and other high throughput applications. Previous studies have identified various areas for improvement in preprocessing algorithms used for protein peak detection. Bottom-up approaches to preprocessing that emphasize modeling SELDI data acquisition are promising avenues of research to find the needed improvements in reproducibility.     

Gifa V. 4: A complete package for NMR data set processing

Summary The Gifa program is designed for processing, displaying and analysing 1D, 2D and 3D NMR data sets. It has been constructed in a modular fashion, based on three independent modules: a set of commands that perform all the basic processing operations such as apodisation functions, a complete set of Fourier Transforms, phasing and baseline correction, peak-picking and line fitting, linear prediction and maximum entropy processing; a set of command language primitives that permit the execution of complex macro commands; and a set of graphic commands that permit to build a complete graphic user interface, allowing the user to interact easily with the program. We have tried to create a versatile program that can be easily extended according to the user's requirements and that is adapted to a novice as well as an experienced user. The program runs on any UNIX computer, with or without graphic display, in interactive or batch mode.     

A modular approach for integrative analysis of large-scale gene-expression and drug-response data

High-throughput technologies are now used to generate more than one type of data from the same biological samples. To properly integrate such data, we propose using co-modules, which describe coherent patterns across paired data sets, and conceive several modular methods for their identification. We first test these methods using in silico data, demonstrating that the integrative scheme of our Ping-Pong Algorithm uncovers drug-gene associations more accurately when considering noisy or complex data. Second, we provide an extensive comparative study using the gene-expression and drug-response data from the NCI-60 cell lines. Using information from the DrugBank and the Connectivity Map databases we show that the Ping-Pong Algorithm predicts drug-gene associations significantly better than other methods. Co-modules provide insights into possible mechanisms of action for a wide range of drugs and suggest new targets for therapy.     

A non-negative matrix factorization framework for identifying modular patterns in metagenomic profile data

Metagenomic studies sequence DNA directly from environmental samples to explore the structure and function of complex microbial and viral communities. Individual, short pieces of sequenced DNA (“reads”) are classified into (putative) taxonomic or metabolic groups which are analyzed for patterns across samples. Analysis of such read matrices is at the core of using metagenomic data to make inferences about ecosystem structure and function. Non-negative matrix factorization (NMF) is a numerical technique for approximating high-dimensional data points as positive linear combinations of positive components. It is thus well suited to interpretation of observed samples as combinations of different components. We develop, test and apply an NMF-based framework to analyze metagenomic read matrices. In particular, we introduce a method for choosing NMF degree in the presence of overlap, and apply spectral-reordering techniques to NMF-based similarity matrices to aid visualization. We show that our method can robustly identify the appropriate degree and disentangle overlapping contributions using synthetic data sets. We then examine and discuss the NMF decomposition of a metabolic profile matrix extracted from 39 publicly available metagenomic samples, and identify canonical sample types, including one associated with coral ecosystems, one associated with highly saline ecosystems and others. We also identify specific associations between pathways and canonical environments, and explore how alternative choices of decompositions facilitate analysis of read matrices at a finer scale.     

A set of programs for analysis of kinetic and equilibrium data

A program package that can be used for analysis of a wide rangeof kinetic and equilibrium data is described. The four programswere written in Turbo Pascal and run on PC, XT, AT and compatibles.The first of the programs allows the user to fit data with 16predefined and one user-defined function, using two differentnon-linear least-squares procedures. Two additional programsare used to test both the evaluation of model functions andthe least-squares fits. One of these programs uses two simpleprocedures to generate a Gaussian-distributed random variablethat is used to simulate the experimental error of measurements.The last program simulates kinetics described by differentialequations that cannot be solved analytically, using numericalintegration. This program helps the user to judge the validityof steady-state assumptions or treatment of kinetic measurementsas relaxations. Received on September 19, 1989; accepted on March 16, 1990     

A hybrid machine-learning approach for segmentation of protein localization data

MOTIVATION: Subcellular protein localization data are critical to the quantitative understanding of cellular function and regulation. Such data are acquired via observation and quantitative analysis of fluorescently labeled proteins in living cells. Differentiation of labeled protein from cellular artifacts remains an obstacle to accurate quantification. We have developed a novel hybrid machine-learning-based method to differentiate signal from artifact in membrane protein localization data by deriving positional information via surface fitting and combining this with fluorescence-intensity-based data to generate input for a support vector machine. RESULTS: We have employed this classifier to analyze signaling protein localization in T-cell activation. Our classifier displayed increased performance over previously available techniques, exhibiting both flexibility and adaptability: training on heterogeneous data yielded a general classifier with good overall performance; training on more specific data yielded an extremely high-performance specific classifier. We also demonstrate accurate automated learning utilizing additional experimental data.     

Radiation hybrid mapping of 70 rat genes from a data set of differentially expressed genes

The spontaneously hypertensive rat (SHR) is a model of human essential hypertension. Increased blood pressure in SHR is associated with other risk factors associated with cardiovascular disease, including insulin resistance and dyslipidemia. DNA microarray studies identified over 200 differentially expressed genes and ESTs between SHR and normotensive control rats. These clones represent candidate genes that may underlie previously detected QTLs in SHR. This study made use of the publication of two whole-genome maps to identify positional QTL candidates. Radiation hybrid (RH) mapping was used to determine the chromosomal locations of 70 rat genes and ESTs from this dataset. Most of the locations are novel, but in five cases we identified a definitive map location for genes previously mapped by somatic cell hybrids and/or linkage analysis. Genes for which the mouse genome map location was already determined mapped to syntenic segments in the rat genome map, except for two rat genes whose map locations confirmed previous findings. Where synteny comparisons could be made only with the human, 74% of the genes mapped in this study lay in a conserved syntenic segment. Chromosomal localisation of these mouse and human orthologs to syntenic segments produces a high level of confidence in the data presented in this study. The data provide new map locations for rat genes and will aid efforts to advance the rat genome map. The data may also be used to prioritize candidate QTL genes in SHR and other rat strains on the basis of their map location.