Nmrglue: an open source Python package for the analysis of multidimensional NMR data

Nmrglue, an open source Python package for working with multidimensional NMR data, is described. When used in combination with other Python scientific libraries, nmrglue provides a highly flexible and robust environment for spectral processing, analysis and visualization and includes a number of common utilities such as linear prediction, peak picking and lineshape fitting. The package also enables existing NMR software programs to be readily tied together, currently facilitating the reading, writing and conversion of data stored in Bruker, Agilent/Varian, NMRPipe, Sparky, SIMPSON, and Rowland NMR Toolkit file formats. In addition to standard applications, the versatility offered by nmrglue makes the package particularly suitable for tasks that include manipulating raw spectrometer data files, automated quantitative analysis of multidimensional NMR spectra with irregular lineshapes such as those frequently encountered in the context of biomacromolecular solid-state NMR, and rapid implementation and development of unconventional data processing methods such as covariance NMR and other non-Fourier approaches. Detailed documentation, install files and source code for nmrglue are freely available at http://nmrglue.com. The source code can be redistributed and modified under the New BSD license.     

KEGGanim: pathway animations for high-throughput data

MOTIVATION: Gene expression analysis with microarrays has become one of the most widely used high-throughput methods for gathering genome-wide functional data. Emerging -omics fields such as proteomics and interactomics introduce new information sources. With the rise of systems biology, researchers need to concentrate on entire complex pathways that guide individual genes and related processes. Bioinformatics methods are needed to link the existing knowledge about pathways with the growing amounts of experimental data. RESULTS: We present KEGGanim, a novel web-based tool for visualizing experimental data in biological pathways. KEGGanim produces animations and images of KEGG pathways using public or user uploaded high-throughput data. Pathway members are coloured according to experimental measurements, and animated over experimental conditions. KEGGanim visualization highlights dynamic changes over conditions and allows the user to observe important modules and key genes that influence the pathway. The simple user interface of KEGGanim provides options for filtering genes and experimental conditions. KEGGanim may be used with public or private data for 14 organisms with a large collection of public microarray data readily available. Most common gene and protein identifiers and microarray probesets are accepted for visualization input. AVAILABILITY: http://biit.cs.ut.ee/KEGGanim/.     

KUJIRA,a package of integrated modules for systematic and interactive analysis of NMR data directed to high-throughput NMR structure studies

The recent expansion of structural genomics has increased the demands for quick and accurate protein structure determination by NMR spectroscopy. The conventional strategy without an automated protocol can no longer satisfy the needs of high-throughput application to a large number of proteins, with each data set including many NMR spectra, chemical shifts, NOE assignments, and calculated structures. We have developed the new software KUJIRA, a package of integrated modules for the systematic and interactive analysis of NMR data, which is designed to reduce the tediousness of organizing and manipulating a large number of NMR data sets. In combination with CYANA, the program for automated NOE assignment and structure determination, we have established a robust and highly optimized strategy for comprehensive protein structure analysis. An application of KUJIRA in accordance with our new strategy was carried out by a non-expert in NMR structure analysis, demonstrating that the accurate assignment of the chemical shifts and a high-quality structure of a small protein can be completed in a few weeks. The high completeness of the chemical shift assignment and the NOE assignment achieved by the systematic analysis using KUJIRA and CYANA led, in practice, to increased reliability of the determined structure.     

Microspectroscopic fluorescence analysis with prism-based imaging spectrometers: review and current studies.

BACKGROUND: Fluorescence imaging spectroscopy is a powerful but under-utilized tool. This article gives perspective on the use of imaging spectroscopy, and provides two examples of imaging spectroscopy done with a prism-based system. The intent is to give insight into the power of imaging spectroscopy when used in combination with other imaging techniques. In particular, studies of intact coral photobleaching and beads designed to show energy transfer are reported. In the bead study, spectroscopic lifetime imaging was performed at each photobleaching step. RESULTS: Spectroscopic photobleaching of the hard coral, Montastrea annularis, revealed two spectral regions. A region in the red portion of the spectrum bleached rapidly while progressively increasing fluorescence was observed over a wide portion of the spectrum. This behavior is consistent with current theories for the role of fluorescent proteins in corals.Following a photobleaching study of beads designed to exhibit energy transfer with imaging spectroscopic fluorescence lifetime imaging microscopy (ISFLIM) allowed unambiguous assignment of fluorescence resonance energy transfer (FRET). The data in this experiment indicated that most of the commonly used markers of FRET would have been inconclusive. The ability of the ISFLIM to look at all regions of the spectrum, particularly the acceptor region, allowed FRET to be assigned. CONCLUSIONS: Fluorescence imaging spectroscopy is a rapidly advancing technology, uniquely suited to the flexible detection of dyes over a wide range of wavelengths.     

OLAV: towards high-throughput tandem mass spectrometry data identification

Mass spectrometry combined with database searching has become the preferred method for identifying proteins in proteomics projects. Proteins are digested by one or several enzymes to obtain peptides, which are analyzed by mass spectrometry. We introduce a new family of scoring schemes, named OLAV, aimed at identifying peptides in a database from their tandem mass spectra. OLAV scoring schemes are based on signal detection theory, and exploit mass spectrometry information more extensively than previously existing schemes. We also introduce a new concept of structural matching that uses pattern detection methods to better separate true from false positives. We show the superiority of OLAV scoring schemes compared to MASCOT, a widely used identification program. We believe that this work introduces a new way of designing scoring schemes that are especially adapted to high-throughput projects such as GeneProt large-scale human plasma project, where it is impractical to check all identifications manually.     

GenoProfiler: batch processing of high-throughput capillary fingerprinting data

High-throughput content fingerprinting techniques employing capillary electrophoresis place new demands on the editing of fingerprint files for the downstream contig assembly program, FPC. A cross-platform software application, GenoProfiler, was developed for automated editing of sized fingerprinting profiles generated by the ABI Genetic Analyzers. The batch-processing module extracts the sized fragment information directly from the ABI raw trace files, or from data files exported from GeneMapper or other size calling software, removes the background noise and undesired fragments, and generates fragment size files compatible with the FPC software. AVAILABILITY: http://wheat.pw.usda.gov/PhysicalMapping/     

PRISM: a data management system for high-throughput proteomics

Advanced proteomic research efforts involving areas such as systems biology or biomarker discovery are enabled by the use of high level informatics tools that allow the effective analysis of large quantities of differing types of data originating from various studies. Performing such analyses on a large scale is not feasible without a computational platform that performs data processing and management tasks. Such a platform must be able to provide high-throughput operation while having sufficient flexibility to accommodate evolving data analysis tools and methodologies. The Proteomics Research Information Storage and Management system (PRISM) provides a platform that serves the needs of the accurate mass and time tag approach developed at Pacific Northwest National Laboratory. PRISM incorporates a diverse set of analysis tools and allows a wide range of operations to be incorporated by using a state machine that is accessible to independent, distributed computational nodes. The system has scaled well as data volume has increased over several years, while allowing adaptability for incorporating new and improved data analysis tools for more effective proteomics research.     

NMR analysis of in vitro-synthesized proteins without purification: a high-throughput approach

The gene (agu) encoding p-nitrophenyl alpha-D-glucuronopyranoside (pNP-GUA) hydrolyzing alpha-glucuronidase of the hyperthermophilic bacterium Thermotoga maritima was cloned and expressed in Escherichia coli. The recombinant enzyme was purified and characterized. The gene previously designated as putative alpha-glucosidase was found to code for a protein that had no alpha-glucosidase activity. It showed a rare activity profile with its ability to hydrolyze pNP-GUA, an activity not known in the alpha-glucuronidases from microbial sources. This is the first report on the occurrence of an alpha-glucuronidase which belongs to the family 4 of glycosyl hydrolases.     

Integration and cross-validation of high-throughput gene expression data: comparing heterogeneous data sets

Data analysis--not data production--is becoming the bottleneck in gene expression research. Data integration is necessary to cope with an ever increasing amount of data, to cross-validate noisy data sets, and to gain broad interdisciplinary views of large biological data sets. New Internet resources may help researchers to combine data sets across different gene expression platforms. However, noise and disparities in experimental protocols strongly limit data integration. A detailed review of four selected studies reveals how some of these limitations may be circumvented and illustrates what can be achieved through data integration.     

ROCS: receiver operating characteristic surface for class-skewed high-throughput data

The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the curve (AUC) insufficient for the purpose of judging classifier performance. Here we define an ROC surface (ROCS) using true positive rate (TPR), false positive rate (FPR), and true discovery rate (TDR). The ROC surface, together with the associated quantities, volume under the surface (VUS) and FDR-controlled area under the ROC curve (FCAUC), provide a useful approach for gauging classifier performance on class-skewed high-throughput data. The implementation as an R package is available at http://userwww.service.emory.edu/~tyu8/ROCS/.     

miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression

Background  

MicroRNAs (miRNAs), small non-coding RNAs of 19 to 25 nt, play important roles in gene regulation in both animals and plants. In the last few years, the oligonucleotide microarray is one high-throughput and robust method for detecting miRNA expression. However, the approach is restricted to detecting the expression of known miRNAs. Second-generation sequencing is an inexpensive and high-throughput sequencing method. This new method is a promising tool with high sensitivity and specificity and can be used to measure the abundance of small-RNA sequences in a sample. Hence, the expression profiling of miRNAs can involve use of sequencing rather than an oligonucleotide array. Additionally, this method can be adopted to discover novel miRNAs.     

WholePathwayScope: a comprehensive pathway-based analysis tool for high-throughput data

Background  

Analysis of High Throughput (HTP) Data such as microarray and proteomics data has provided a powerful methodology to study patterns of gene regulation at genome scale. A major unresolved problem in the post-genomic era is to assemble the large amounts of data generated into a meaningful biological context. We have developed a comprehensive software tool, WholePathwayScope (WPS), for deriving biological insights from analysis of HTP data.     

Molecular recording: transcriptional data collection into the genome

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NGC: lossless and lossy compression of aligned high-throughput sequencing data

A major challenge of current high-throughput sequencing experiments is not only the generation of the sequencing data itself but also their processing, storage and transmission. The enormous size of these data motivates the development of data compression algorithms usable for the implementation of the various storage policies that are applied to the produced intermediate and final result files. In this article, we present NGC, a tool for the compression of mapped short read data stored in the wide-spread SAM format. NGC enables lossless and lossy compression and introduces the following two novel ideas: first, we present a way to reduce the number of required code words by exploiting common features of reads mapped to the same genomic positions; second, we present a highly configurable way for the quantization of per-base quality values, which takes their influence on downstream analyses into account. NGC, evaluated with several real-world data sets, saves 33–66% of disc space using lossless and up to 98% disc space using lossy compression. By applying two popular variant and genotype prediction tools to the decompressed data, we could show that the lossy compression modes preserve >99% of all called variants while outperforming comparable methods in some configurations.