Comparative evaluation of <Emphasis Type="Italic">Populus</Emphasis> variants total sugar release and structural features following pretreatment and digestion by two distinct biological systems

Background

Populus natural variants have been shown to realize a broad range of sugar yields during saccharification, however, the structural features responsible for higher sugar release from natural variants are not clear. In addition, the sugar release patterns resulting from digestion with two distinct biological systems, fungal enzymes and Clostridium thermocellum, have yet to be evaluated and compared. This study evaluates the effect of structural features of three natural variant Populus lines, which includes the line BESC standard, with respect to the overall process of sugar release for two different biological systems.

Results

Populus natural variants, SKWE 24-2 and BESC 876, showed higher sugar release from hydrothermal pretreatment combined with either enzymatic hydrolysis or Clostridium thermocellum fermentation compared to the Populus natural variant, BESC standard. However, C. thermocellum outperformed the fungal cellulases yielding 96.0, 95.5, and 85.9% glucan plus xylan release from SKWE 24-2, BESC 876, and BESC standard, respectively. Among the feedstock properties evaluated, cellulose accessibility and glycome profiling provided insights into factors that govern differences in sugar release between the low recalcitrant lines and the BESC standard line. However, because this distinction was more apparent in the solids after pretreatment than in the untreated biomass, pretreatment was necessary to differentiate recalcitrance among Populus lines. Glycome profiling analysis showed that SKWE 24-2 contained the most loosely bound cell wall glycans, followed by BESC 876, and BESC standard. Additionally, lower molecular weight lignin may be favorable for effective hydrolysis, since C. thermocellum reduced lignin molecular weight more than fungal enzymes across all Populus lines.

Conclusions

Low recalcitrant Populus natural variants, SKWE 24-2 and BESC 876, showed higher sugar yields than BESC standard when hydrothermal pretreatment was combined with biological digestion. However, C. thermocellum was determined to be a more robust and effective biological catalyst than a commercial fungal cellulase cocktail. As anticipated, recalcitrance was not readily predicted through analytical methods that determined structural properties alone. However, combining structural analysis with pretreatment enabled the identification of attributes that govern recalcitrance, namely cellulose accessibility, xylan content in the pretreated solids, and non-cellulosic glycan extractability.

     

Influence of Soil Type,Cultivar and Verticillium dahliae on the Structure of the Root and Rhizosphere Soil Fungal Microbiome of Strawberry

Sustainable management of crop productivity and health necessitates improved understanding of the ways in which rhizosphere microbial populations interact with each other, with plant roots and their abiotic environment. In this study we examined the effects of different soils and cultivars, and the presence of a soil-borne fungal pathogen, Verticillium dahliae, on the fungal microbiome of the rhizosphere soil and roots of strawberry plants, using high-throughput pyrosequencing. Fungal communities of the roots of two cultivars, Honeoye and Florence, were statistically distinct from those in the rhizosphere soil of the same plants, with little overlap. Roots of plants growing in two contrasting field soils had high relative abundance of Leptodontidium sp. C2 BESC 319 g whereas rhizosphere soil was characterised by high relative abundance of Trichosporon dulcitum or Cryptococcus terreus, depending upon the soil type. Differences between different cultivars were not as clear. Inoculation with the pathogen V. dahliae had a significant influence on community structure, generally decreasing the number of rhizosphere soil- and root-inhabiting fungi. Leptodontidium sp. C2 BESC 319 g was the dominant fungus responding positively to inoculation with V. dahliae. The results suggest that 1) plant roots select microorganisms from the wider rhizosphere pool, 2) that both rhizosphere soil and root inhabiting fungal communities are influenced by V. dahliae and 3) that soil type has a stronger influence on both of these communities than cultivar.     

GenePath: a system for automated construction of genetic networks from mutant data

MOTIVATION: Genetic networks are often used in the analysis of biological phenomena. In classical genetics, they are constructed manually from experimental data on mutants. The field lacks formalism to guide such analysis, and accounting for all the data becomes complicated when large amounts of data are considered. RESULTS: We have developed GenePath, an intelligent assistant that automates the analysis of genetic data. GenePath employs expert-defined patterns to uncover gene relations from the data, and uses these relations as constraints in the search for a plausible genetic network. GenePath formalizes genetic data analysis, facilitates the consideration of all the available data in a consistent manner, and the examination of the large number of possible consequences of planned experiments. It also provides an explanation mechanism that traces every finding to the pertinent data. AVAILABILITY: GenePath can be accessed at http://genepath.org. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://genepath.org/bi-.supp.     

A case study of high-throughput biological data processing on parallel platforms

MOTIVATION: Analysis of large biological data sets using a variety of parallel processor computer architectures is a common task in bioinformatics. The efficiency of the analysis can be significantly improved by properly handling redundancy present in these data combined with taking advantage of the unique features of these compute architectures. RESULTS: We describe a generalized approach to this analysis, but present specific results using the program CEPAR, an efficient implementation of the Combinatorial Extension algorithm in a massively parallel (PAR) mode for finding pairwise protein structure similarities and aligning protein structures from the Protein Data Bank. CEPAR design and implementation are described and results provided for the efficiency of the algorithm when run on a large number of processors. AVAILABILITY: Source code is available by contacting one of the authors.     

An associative analysis of gene expression array data

MOTIVATION: We face the absence of optimized standards to guide normalization, comparative analysis, and interpretation of data sets. One aspect of this is that current methods of statistical analysis do not adequately utilize the information inherent in the large data sets generated in a microarray experiment and require a tradeoff between detection sensitivity and specificity. RESULTS: We present a multistep procedure for analysis of mRNA expression data obtained from cDNA array methods. To identify and classify differentially expressed genes, results from standard paired t-test of normalized data are compared with those from a novel method, denoted an associative analysis. This method associates experimental gene expressions presented as residuals in regression analysis against control averaged expressions to a common standard-the family of similarly computed residuals for low variability genes derived from control experiments. By associating changes in expression of a given gene to a large family of equally expressed genes of the control group, this method utilizes the large data sets inherent in microarray experiments to increase both specificity and sensitivity. The overall procedure is illustrated by tabulation of genes whose expression differs significantly between Snell dwarf mice (dw/dw) and their phenotypically normal littermates (dw/+, +/+). Of the 2,352 genes examined only 450-500 were expressed above the background levels observed in nonexpressed genes and of these 120 were established as differentially expressed in dwarf mice at a significance level that excludes appearance of false positive determinations.     

NGS QC Toolkit: a toolkit for quality control of next generation sequencing data

Next generation sequencing (NGS) technologies provide a high-throughput means to generate large amount of sequence data. However, quality control (QC) of sequence data generated from these technologies is extremely important for meaningful downstream analysis. Further, highly efficient and fast processing tools are required to handle the large volume of datasets. Here, we have developed an application, NGS QC Toolkit, for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at http://www.nipgr.res.in/ngsqctoolkit.html. All the tools in the application have been implemented in Perl programming language. The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis.     

Processing and quality control of DNA array hybridization data

MOTIVATION: The technology of hybridization to DNA arrays is used to obtain the expression levels of many different genes simultaneously. It enables searching for genes that are expressed specifically under certain conditions. However, the technology produces large amounts of data demanding computational methods for their analysis. It is necessary to find ways to compare data from different experiments and to consider the quality and reproducibility of the data. RESULTS: Data analyzed in this paper have been generated by hybridization of radioactively labeled targets to DNA arrays spotted on nylon membranes. We introduce methods to compare the intensity values of several hybridization experiments. This is essential to find differentially expressed genes or to do pattern analysis. We also discuss possibilities for quality control of the acquired data. AVAILABILITY: http://www.dkfz.de/tbi CONTACT: M.Vingron@dkfz-heidelberg.de     

Unsupervised segmentation of continuous genomic data

The advent of high-density, high-volume genomic data has created the need for tools to summarize large datasets at multiple scales. HMMSeg is a command-line utility for the scale-specific segmentation of continuous genomic data using hidden Markov models (HMMs). Scale specificity is achieved by an optional wavelet-based smoothing operation. HMMSeg is capable of handling multiple datasets simultaneously, rendering it ideal for integrative analysis of expression, phylogenetic and functional genomic data. AVAILABILITY: http://noble.gs.washington.edu/proj/hmmseg     

ZFIQ: a software package for zebrafish biology

Rapid development, transparency and small size are the outstanding features of zebrafish that make it as an increasingly important vertebrate system for developmental biology, functional genomics, disease modeling and drug discovery. Zebrafish has been regarded as ideal animal specie for studying the relationship between genotype and phenotype, for pathway analysis and systems biology. However, the tremendous amount of data generated from large numbers of embryos has led to the bottleneck of data analysis and modeling. The zebrafish image quantitator (ZFIQ) software provides streamlined data processing and analysis capability for developmental biology and disease modeling using zebrafish model. AVAILABILITY: ZFIQ is available for download at http://www.cbi-platform.net.     

STENCIL: A web templating engine for visualizing and sharing life science datasets

The ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil     

MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data

SUMMARY: New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis. AVAILABILITY: MZmine is available under GNU Public license from http://mzmine.sourceforge.net/.     

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.     

Tools for T-RFLP data analysis using Excel

Background

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a DNA-fingerprinting method that can be used for comparisons of the microbial community composition in a large number of samples. There is no consensus on how T-RFLP data should be treated and analyzed before comparisons between samples are made, and several different approaches have been proposed in the literature. The analysis of T-RFLP data can be cumbersome and time-consuming, and for large datasets manual data analysis is not feasible. The currently available tools for automated T-RFLP analysis, although valuable, offer little flexibility, and few, if any, options regarding what methods to use. To enable comparisons and combinations of different data treatment methods an analysis template and an extensive collection of macros for T-RFLP data analysis using Microsoft Excel were developed.

Results

The Tools for T-RFLP data analysis template provides procedures for the analysis of large T-RFLP datasets including application of a noise baseline threshold and setting of the analysis range, normalization and alignment of replicate profiles, generation of consensus profiles, normalization and alignment of consensus profiles and final analysis of the samples including calculation of association coefficients and diversity index. The procedures are designed so that in all analysis steps, from the initial preparation of the data to the final comparison of the samples, there are various different options available. The parameters regarding analysis range, noise baseline, T-RF alignment and generation of consensus profiles are all given by the user and several different methods are available for normalization of the T-RF profiles. In each step, the user can also choose to base the calculations on either peak height data or peak area data.

Conclusions

The Tools for T-RFLP data analysis template enables an objective and flexible analysis of large T-RFLP datasets in a widely used spreadsheet application.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0361-7) contains supplementary material, which is available to authorized users.     

Analysis of longitudinal metabolomics data

MOTIVATION: Metabolomics datasets are generally large and complex. Using principal component analysis (PCA), a simplified view of the variation in the data is obtained. The PCA model can be interpreted and the processes underlying the variation in the data can be analysed. In metabolomics, often a priori information is present about the data. Various forms of this information can be used in an unsupervised data analysis with weighted PCA (WPCA). A WPCA model will give a view on the data that is different from the view obtained using PCA, and it will add to the interpretation of the information in a metabolomics dataset. RESULTS: A method is presented to translate spectra of repeated measurements into weights describing the experimental error. These weights are used in the data analysis with WPCA. The WPCA model will give a view on the data where the non-uniform experimental error is accounted for. Therefore, the WPCA model will focus more on the natural variation in the data. AVAILABILITY: M-files for MATLAB for the algorithm used in this research are available at http://www-its.chem.uva.nl/research/pac/Software/pcaw.zip.     

GITAR: An Open Source Tool for Analysis and Visualization of Hi-C Data

Interactions between chromatin segments play a large role in functional genomic assays and developments in genomic interaction detection methods have shown interacting topological domains within the genome. Among these methods, Hi-C plays a key role. Here, we present the Genome Interaction Tools and Resources (GITAR), a software to perform a comprehensive Hi-C data analysis, including data preprocessing, normalization, and visualization, as well as analysis of topologically-associated domains (TADs). GITAR is composed of two main modules: (1) HiCtool, a Python library to process and visualize Hi-C data, including TAD analysis; and (2) processed data library, a large collection of human and mouse datasets processed using HiCtool. HiCtool leads the user step-by-step through a pipeline, which goes from the raw Hi-C data to the computation, visualization, and optimized storage of intra-chromosomal contact matrices and TAD coordinates. A large collection of standardized processed data allows the users to compare different datasets in a consistent way, while saving time to obtain data for visualization or additional analyses. More importantly, GITAR enables users without any programming or bioinformatic expertise to work with Hi-C data. GITAR is publicly available at http://genomegitar.org as an open-source software.     

MicroCore: mapping genome expression to cell pathways and networks

The MicroCore toolkit is a suite of analysis programs for microarray and proteomics data that is open source and programmed exclusively in Java. MicroCore provides a flexible and extensible environment for the interpretation of functional genomics data through visualization. The first version of the application (downloadable from the MicroCore website: http://www.ucl.ac.uk/oncology/MicroCore/microcore.htm), implements two programs-PIMs (protein interaction maps) and MicroExpress-and is soon to be followed by an extended version which will also feature a fuzzy k-means clustering application and a Java-based R plug-in for microarray analysis. PIMs and MicroExpress provide a simple yet powerful way of graphically relating large quantities of expression data from multiple experiments to cellular pathways and biological processes in a statistically meaningful way.     

Comparison and meta-analysis of microarray data: from the bench to the computer desk

The upcoming availability of public microarray repositories and of large compendia of gene expression information opens up a new realm of possibilities for microarray data analysis. An essential challenge is the efficient integration of microarray data generated by different research groups on different array platforms. This review focuses on the problems associated with this integration, which are: (1) the efficient access to and exchange of microarray data; (2) the validation and comparison of data from different platforms (cDNA and short and long oligonucleotides); and (3) the integrated statistical analysis of multiple data sets.     

Application of Bayesian decomposition for analysing microarray data

MOTIVATION: Microarray and gene chip technology provide high throughput tools for measuring gene expression levels in a variety of circumstances, including cellular response to drug treatment, cellular growth and development, tumorigenesis, among many other processes. In order to interpret the large data sets generated in experiments, data analysis techniques that consider biological knowledge during analysis will be extremely useful. We present here results showing the application of such a tool to expression data from yeast cell cycle experiments. RESULTS: Originally developed for spectroscopic analysis, Bayesian Decomposition (BD) includes two features which make it useful for microarray data analysis: the ability to assign genes to multiple coexpression groups and the ability to encode biological knowledge into the system. Here we demonstrate the ability of the algorithm to provide insight into the yeast cell cycle, including identification of five temporal patterns tied to cell cycle phases as well as the identification of a pattern tied to an approximately 40 min cell cycle oscillator. The genes are simultaneously assigned to the patterns, including partial assignment to multiple patterns when this is required to explain the expression profile. AVAILABILITY: The application is available free to academic users under a material transfer agreement. Go to http://bioinformatics.fccc.edu/ for more details.     

Statistical Application and Challenges in Global Gel-Free Proteomic Analysis by Mass Spectrometry

Global gel-free proteomic analysis by mass spectrometry has been widely used as an important tool for exploring complex biological systems at the whole genome level. Simultaneous analysis of a large number of protein species is a complicated and challenging task. The challenges exist throughout all stages of a global gel-free proteomic analysis: experimental design, peptide/protein identification, data preprocessing and normalization, and inferential analysis. In addition to various efforts to improve the analytical technologies, statistical methodologies have been applied in all stages of proteomic analyses to help extract relevant information efficiently from large proteomic datasets. In this review, we summarize current applications of statistics in several stages of global gel-free proteomic analysis by mass spectrometry. We discuss the challenges associated with the applications of various statistical tools. Whenever possible, we also propose potential solutions on how to improve the data collection and interpretation for mass-spectrometry-based global proteomic analysis using more sophisticated and/or novel statistical approaches.