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.     

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data

Background  

Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models.     

Impact of T-RFLP data analysis choices on assessments of microbial community structure and dynamics

Background

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a common DNA-fingerprinting technique used for comparisons of complex microbial communities. Although the technique is well established there is no consensus on how to treat T-RFLP data to achieve the highest possible accuracy and reproducibility. This study focused on two critical steps in the T-RFLP data treatment: the alignment of the terminal restriction fragments (T-RFs), which enables comparisons of samples, and the normalization of T-RF profiles, which adjusts for differences in signal strength, total fluorescence, between samples.

Results

Variations in the estimation of T-RF sizes were observed and these variations were found to affect the alignment of the T-RFs. A novel method was developed which improved the alignment by adjusting for systematic shifts in the T-RF size estimations between the T-RF profiles. Differences in total fluorescence were shown to be caused by differences in sample concentration and by the gel loading. Five normalization methods were evaluated and the total fluorescence normalization procedure based on peak height data was found to increase the similarity between replicate profiles the most. A high peak detection threshold, alignment correction, normalization and the use of consensus profiles instead of single profiles increased the similarity of replicate T-RF profiles, i.e. lead to an increased reproducibility. The impact of different treatment methods on the outcome of subsequent analyses of T-RFLP data was evaluated using a dataset from a longitudinal study of the bacterial community in an activated sludge wastewater treatment plant. Whether the alignment was corrected or not and if and how the T-RF profiles were normalized had a substantial impact on ordination analyses, assessments of bacterial dynamics and analyses of correlations with environmental parameters.

Conclusions

A novel method for the evaluation and correction of the alignment of T-RF profiles was shown to reduce the uncertainty and ambiguity in alignments of T-RF profiles. Large differences in the outcome of assessments of bacterial community structure and dynamics were observed between different alignment and normalization methods. The results of this study can therefore be of value when considering what methods to use in the analysis of T-RFLP data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0360-8) contains supplementary material, which is available to authorized users.     

NERVE: New Enhanced Reverse Vaccinology Environment

Background  

Since a milestone work on Neisseria meningitidis B, Reverse Vaccinology has strongly enhanced the identification of vaccine candidates by replacing several experimental tasks using in silico prediction steps. These steps have allowed scientists to face the selection of antigens from the predicted proteome of pathogens, for which cell culture is difficult or impossible, saving time and money. However, this good example of bioinformatics-driven immunology can be further developed by improving in silico steps and implementing biologist-friendly tools.     

Initiation of male sperm-transfer behavior in Caenorhabditis elegans requires input from the ventral nerve cord

Background  

The Caenorhabditis elegans male exhibits a stereotypic behavioral pattern when attempting to mate. This behavior has been divided into the following steps: response, backing, turning, vulva location, spicule insertion, and sperm transfer. We and others have begun in-depth analyses of all these steps in order to understand how complex behaviors are generated. Here we extend our understanding of the sperm-transfer step of male mating behavior.     

LOSITAN: A workbench to detect molecular adaptation based on a <Emphasis Type="Italic">F</Emphasis><Subscript><Emphasis Type="Italic">st</Emphasis></Subscript>-outlier method

Background  

Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user.     

The malate synthase of Paracoccidioides brasiliensis is a linked surface protein that behaves as an anchorless adhesin

Background  

The pathogenic fungus Paracoccidioides brasiliensis is the agent of paracoccidioidomycosis (PCM). This is a pulmonary mycosis acquired by inhalation of fungal airborne propagules that can disseminate to several organs and tissues leading to a severe form of the disease. Adhesion and invasion to host cells are essential steps involved in the internalization and dissemination of pathogens. Inside the host, P. brasiliensis may use the glyoxylate cycle for intracellular survival.     

IdentiCS – Identification of coding sequence and <Emphasis Type="Italic">in silico</Emphasis> reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence

Background  

A necessary step for a genome level analysis of the cellular metabolism is the in silico reconstruction of the metabolic network from genome sequences. The available methods are mainly based on the annotation of genome sequences including two successive steps, the prediction of coding sequences (CDS) and their function assignment. The annotation process takes time. The available methods often encounter difficulties when dealing with unfinished error-containing genomic sequence.     

The <Emphasis Type="Italic">lethal giant larvae</Emphasis>tumour suppressor mutation requires dMyc oncoprotein to promote clonal malignancy

Background  

Neoplastic overgrowth depends on the cooperation of several mutations ultimately leading to major rearrangements in cellular behaviour. Precancerous cells are often removed by cell death from normal tissues in the early steps of the tumourigenic process, but the molecules responsible for such a fundamental safeguard process remain in part elusive. With the aim to investigate the molecular crosstalk occurring between precancerous and normal cells in vivo, we took advantage of the clonal analysis methods that are available in Drosophila for studying the phenotypes due to lethal giant larvae (lgl) neoplastic mutation induced in different backgrounds and tissues.     

Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method

Background  

Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple methods have been suggested to date, but it is not clear which is the best. It is therefore important to further study the different normalization methods in detail and the nature of microarray data in general.     

Structural genomics of human proteins – target selection and generation of a public catalogue of expression clones

Background  

The availability of suitable recombinant protein is still a major bottleneck in protein structure analysis. The Protein Structure Factory, part of the international structural genomics initiative, targets human proteins for structure determination. It has implemented high throughput procedures for all steps from cloning to structure calculation. This article describes the selection of human target proteins for structure analysis, our high throughput cloning strategy, and the expression of human proteins in Escherichia colihost cells.     

Expansion of a chromosomal repeat in Escherichia coli: roles of replication, repair, and recombination functions

Background  

Previous studies of gene amplification in Escherichia coli have suggested that it occurs in two steps: duplication and expansion. Expansion is thought to result from homologous recombination between the repeated segments created by duplication. To explore the mechanism of expansion, a 7 kbp duplication in the chromosome containing a leaky mutant version of the lac operon was constructed, and its expansion into an amplified array was studied.     

ParaHaplo: A program package for haplotype-based whole-genome association study using parallel computing

Background  

Fungi from environmental samples are typically identified to species level through DNA sequencing of the nuclear ribosomal internal transcribed spacer (ITS) region for use in BLAST-based similarity searches in the International Nucleotide Sequence Databases. These searches are time-consuming and regularly require a significant amount of manual intervention and complementary analyses. We here present software – in the form of an identification pipeline for large sets of fungal ITS sequences – developed to automate the BLAST process and several additional analysis steps. The performance of the pipeline was evaluated on a dataset of 350 ITS sequences from fungi growing as epiphytes on building material.     

The Annotation,Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology

Background  

High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently.     

BBP:Brucellagenome annotation with literature mining and curation

Background  

Brucellaspecies are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and animals. Sequences of fourBrucellagenomes have been published, and variousBrucellagene and genome data and analysis resources exist. A web gateway to integrate these resources will greatly facilitateBrucellaresearch.Brucellagenome data in current databases is largely derived from computational analysis without experimental validation typically found in peer-reviewed publications. It is partially due to the lack of a literature mining and curation system able to efficiently incorporate the large amount of literature data into genome annotation. It is further hypothesized that literature-basedBrucellagene annotation would increase understanding of complicatedBrucellapathogenesis mechanisms.     

Cost-effective HRMA pre-sequence typing of clone libraries; application to phage display selection

Background  

Methodologies like phage display selection, in vitro mutagenesis and the determination of allelic expression differences include steps where large numbers of clones need to be compared and characterised. In the current study we show that high-resolution melt curve analysis (HRMA) is a simple, cost-saving tool to quickly study clonal variation without prior nucleotide sequence knowledge.     

Optimizing transformations for automated,high throughput analysis of flow cytometry data

Background  

In a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.     

Phylogenetic diversity, host-specificity and community profiling of sponge-associated bacteria in the northern Gulf of Mexico

Background

Marine sponges can associate with abundant and diverse consortia of microbial symbionts. However, associated bacteria remain unexamined for the majority of host sponges and few studies use phylogenetic metrics to quantify symbiont community diversity. DNA fingerprinting techniques, such as terminal restriction fragment length polymorphisms (T-RFLP), might provide rapid profiling of these communities, but have not been explicitly compared to traditional methods.

Methodology/Principal Findings

We investigated the bacterial communities associated with the marine sponges Hymeniacidon heliophila and Haliclona tubifera, a sympatric tunicate, Didemnum sp., and ambient seawater from the northern Gulf of Mexico by combining replicated clone libraries with T-RFLP analyses of 16S rRNA gene sequences. Clone libraries revealed that bacterial communities associated with the two sponges exhibited lower species richness and lower species diversity than seawater and tunicate assemblages, with differences in species composition among all four source groups. T-RFLP profiles clustered microbial communities by source; individual T-RFs were matched to the majority (80.6%) of clone library sequences, indicating that T-RFLP analysis can be used to rapidly profile these communities. Phylogenetic metrics of community diversity indicated that the two sponge-associated bacterial communities include dominant and host-specific bacterial lineages that are distinct from bacteria recovered from seawater, tunicates, and unrelated sponge hosts. In addition, a large proportion of the symbionts associated with H. heliophila were shared with distant, conspecific host populations in the southwestern Atlantic (Brazil).

Conclusions/Significance

The low diversity and species-specific nature of bacterial communities associated with H. heliophila and H. tubifera represent a distinctly different pattern from other, reportedly universal, sponge-associated bacterial communities. Our replicated sampling strategy, which included samples that reflect the ambient environment, allowed us to differentiate resident symbionts from potentially transient or prey bacteria. Pairing replicated clone library construction with rapid community profiling via T-RFLP analyses will greatly facilitate future studies of sponge-microbe symbioses.     

Elucidation of the outer membrane proteome of <Emphasis Type="Italic">Salmonella enterica</Emphasis> serovar Typhimurium utilising a lipid-based protein immobilization technique

Background  

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a major cause of human gastroenteritis worldwide. The outer membrane proteins expressed by S. Typhimurium mediate the process of adhesion and internalisation within the intestinal epithelium of the host thus influencing the progression of disease. Since the outer membrane proteins are surface-exposed, they provide attractive targets for the development of improved antimicrobial agents and vaccines. Various techniques have been developed for their characterisation, but issues such as carryover of cytosolic proteins still remain a problem. In this study we attempted to characterise the surface proteome of S. Typhimurium using Lipid-based Protein Immobilisation technology in the form of LPI™ FlowCells. No detergents are required and no sample clean up is needed prior to downstream analysis. The immobilised proteins can be digested with proteases in multiple steps to increase sequence coverage, and the peptides eluted can be characterised directly by liquid chromatography - tandem mass spectrometry (LC-MS/MS) and identified from mass spectral database searches.