BioNetBuilder2.0: bringing systems biology to chicken and other model organisms

Background

Systems Biology research tools, such as Cytoscape, have greatly extended the reach of genomic research. By providing platforms to integrate data with molecular interaction networks, researchers can more rapidly begin interpretation of large data sets collected for a system of interest. BioNetBuilder is an open-source client-server Cytoscape plugin that automatically integrates molecular interactions from all major public interaction databases and serves them directly to the user's Cytoscape environment. Until recently however, chicken and other eukaryotic model systems had little interaction data available.

Results

Version 2.0 of BioNetBuilder includes a redesigned synonyms resolution engine that enables transfer and integration of interactions across species; this engine translates between alternate gene names as well as between orthologs in multiple species. Additionally, BioNetBuilder is now implemented to be part of the Gaggle, thereby allowing seamless communication of interaction data to any software implementing the widely used Gaggle software. Using BioNetBuilder, we constructed a chicken interactome possessing 72,000 interactions among 8,140 genes directly in the Cytoscape environment. In this paper, we present a tutorial on how to do so and analysis of a specific use case.

Conclusion

BioNetBuilder 2.0 provides numerous user-friendly systems biology tools that were otherwise inaccessible to researchers in chicken genomics, as well as other model systems. We provide a detailed tutorial spanning all required steps in the analysis. BioNetBuilder 2.0, the tools for maintaining its data bases, standard operating procedures for creating local copies of its back-end data bases, as well as all of the Gaggle and Cytoscape codes required, are open-source and freely available at http://err.bio.nyu.edu/cytoscape/bionetbuilder/.

     

NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics

Introduction

Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.

Objectives

The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.

Methods

NMRProcFlow was developed to be a graphical and interactive 1D NMR (¹H & ¹³C) spectra processing tool.

Results

NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.

Conclusion

Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.

     

rDolphin: a GUI R package for proficient automatic profiling of 1D <Superscript>1</Superscript>H-NMR spectra of study datasets

Introduction

Adoption of automatic profiling tools for ¹H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.

Objectives

To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.

Methods

rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.

Results

The information and quality achieved in two public datasets of complex matrices are maximized.

Conclusion

rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.

     

Accumulation of Fascin<Superscript>+</Superscript> cells during experimental autoimmune neuritis

Background

Experimental autoimmune neuritis (EAN) is a well-known animal model of human demyelinating polyneuropathies and is characterized by inflammation and demyelination in the peripheral nervous system. Fascin is an evolutionarily highly conserved cytoskeletal protein of 55 kDa containing two actin binding domains that cross-link filamentous actin to hexagonal bundles.

Methods

Here we have studied by immunohistochemistry the spatiotemporal accumulation of Fascin?+?cells in sciatic nerves of EAN rats.

Results

A robust accumulation of Fascin?+?cell was observed in the peripheral nervous system of EAN which was correlated with the severity of neurological signs in EAN.

Conclusion

Our results suggest a pathological role of Fascin in EAN.

Virtual slides

The virtual slides for this article can be found here: http://www.diagnosticphatology.diagnomx.eu/vs/6734593451114811

     

popRange: a highly flexible spatially and temporally explicit Wright-Fisher simulator

Background

Sequencing and genotyping technology advancements have led to massive, growing repositories of spatially explicit genetic data and increasing quantities of temporal data (i.e., ancient DNA). These data will allow more complex and fine-scale inferences about population history than ever before; however, new methods are needed to test complex hypotheses.

Results

This article presents popRange, a forward genetic simulator, which incorporates large-scale genetic data with stochastic spatially and temporally explicit demographic and selective models. Features such as spatially and temporally variable selection coefficients and demography are incorporated in a highly flexible manner. popRange is implemented as an R package and presented with an example simulation exploring a selected allele’s trajectory in multiple subpopulations.

Conclusions

popRange allows researchers to evaluate and test complex scenarios by simulating large-scale data with complicated demographic and selective features. popRange is available for download at http://cran.r-project.org/web/packages/popRange/index.html.

     

NormalizeMets: assessing,selecting and implementing statistical methods for normalizing metabolomics data

Introduction

In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented.

Objectives

We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers.

Methods

We present NormalizeMets—a joint graphical user interface within the familiar Microsoft Excel and freely-available R software for comparative evaluation of different normalization methods. The NormalizeMets R package along with the vignette describing the workflow can be downloaded from https://cran.r-project.org/web/packages/NormalizeMets/. The Excel Interface and the Excel user guide are available on https://metabolomicstats.github.io/ExNormalizeMets.

Results

NormalizeMets allows for comparative evaluation of normalization methods using criteria that depend on the given dataset and the ultimate research question. Hence it guides researchers to assess, select and implement a suitable normalization method using either the familiar Microsoft Excel and/or freely-available R software. In addition, the package can be used for visualisation of metabolomics data using interactive graphical displays and to obtain end statistical results for clustering, classification, biomarker identification adjusting for confounding variables, and correlation analysis.

Conclusion

NormalizeMets is designed for comparative evaluation of normalization methods, and can also be used to obtain end statistical results. The use of freely-available R software offers an attractive proposition for programming-oriented researchers, and the Excel interface offers a familiar alternative to most biological researchers. The package handles the data locally in the user’s own computer allowing for reproducible code to be stored locally.

     

GeneXplorer: an interactive web application for microarray data visualization and analysis

Background

When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data.

Results

We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields.

Conclusions

The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://search.cpan.org/dist/Microarray-GeneXplorer/.

     

Reconstruction of ancestral RNA sequences under multiple structural constraints

Background

Secondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA) families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors.

Methods

In this paper, we introduce achARNement, a maximum parsimony approach that, given two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree, simultaneously calculates ancestral RNA sequences for these two families.

Results

We test our methodology on simulated data sets, and show that achARNement outperforms classical maximum parsimony approaches in terms of accuracy, but also reduces by several orders of magnitude the number of candidate sequences. To conclude this study, we apply our algorithms on the Glm clan and the FinP-traJ clan from the Rfam database.

Conclusions

Our results show that our methods reconstruct small sets of high-quality candidate ancestors with better agreement to the two target structures than with classical approaches. Our program is freely available at: http://csb.cs.mcgill.ca/acharnement.

     

Factors influencing adherence to an app-based exercise program in adolescents with painful hyperkyphosis

Background

Software applications (apps) could potentially promote exercise adherence. However, it is unclear whether adolescents with painful hyperkyphosis will use an app designed for a home exercise program. The purpose of this study is to assess factors regarding adherence to an app-based home exercise program in adolescents with hyperkyphosis and back pain who were provided a one-time exercise treatment.

Methods

Twenty-one participants were instructed in a one-time exercise treatment and asked to complete a home exercise program 3 times a week for 6 months using an app called PT PAL. At a 6-month follow-up, 14 participants completed a survey assessing factors related to their experiences using the app and their treatment engagement.

Results

Although most participants did not use the app, they reported performing their exercises a few times per week. The adolescent participants considered the app to be more of a barrier than a supportive measure for promoting exercise adherence. Most participants still reported bothersome back pain.

Conclusions

Although adherence to the 6-month app-based home exercise program was not successful, adolescents still viewed technology support such as text reminders as a potential solution.

Trial registration

ClinicalTrials.gov Identifier: NCT03212664. Registered 11 July 2017. Retrospectively registered.

     

FLIM-FRET analyzer: open source software for automation of lifetime-based FRET analysis

Background

Despite the broad use of FRET techniques, available methods for analyzing protein-protein interaction are subject to high labor and lack of systematic analysis. We propose an open source software allowing the quantitative analysis of fluorescence lifetime imaging (FLIM) while integrating the steady-state fluorescence intensity information for protein-protein interaction studies.

Findings

Our developed open source software is dedicated to fluorescence lifetime imaging microscopy (FLIM) data obtained from Becker & Hickl SPC-830. FLIM-FRET analyzer includes: a user-friendly interface enabling automated intensity-based segmentation into single cells, time-resolved fluorescence data fitting to lifetime value for each segmented objects, batch capability, and data representation with donor lifetime versus acceptor/donor intensity quantification as a measure of protein-protein interactions.

Conclusions

The FLIM-FRET analyzer software is a flexible application for lifetime-based FRET analysis. The application, the C#. NET source code, and detailed documentation are freely available at the following URL: http://FLIM-analyzer.ip-korea.org.

     

Histological subtypes and characteristic structures of HPV-associated oropharyngeal carcinoma; study with Japanese cases

Background

Human papillomavirus-associated oropharyngeal carcinoma (HPV-OPC) is clinicopathologically distinct entity from the HPV-unassociated one (nHPV-OPC). This study aimed to determine the relationship between histological subtypes of OPC and HPV status for Japanese cases and to identify histological structures of HPV-OPC.

Methods

66 OPC cases were categorized into conventional squamous cell carcinoma (SCC) and the variants. Conventional SCC was subcategorized into keratinizing (KSCC), non-keratinizing (NKSCC), and hybrid SCC (HSCC). HPV status of all cases was determined using p16-immunohistochemistry and HPV-DNA ISH.

Results

Two histological subtypes, NKSCC and HSCC, tended to be HPV-OPC and KSCC tended to be nHPV-OPC with statistical significance. Two histological structures, abrupt keratinization, defined in the text, and comedo-necrosis among non-maturing tumor island, were observed for 58.1% and 38.7% of HPV-OPC, and tended to exist for HPV-OPC with statistical significance.

Conclusions

This study showed the association of NKSCC/HSCC with HPV-OPC in Japanese cases, and two histological structures, abrupt keratinization and comedo-necrosis among non-maturing island, were considered characteristic histological features of HPV-OPC.

Virtual slides

The virtual slide(s) for this article can be found here:http://www.diagnosticpathology.diagnomx.eu/vs/1816432541113073.

     

Log::ProgramInfo: A Perl module to collect and log data for bioinformatics pipelines

Background

To reproduce and report a bioinformatics analysis, it is important to be able to determine the environment in which a program was run. It can also be valuable when trying to debug why different executions are giving unexpectedly different results.

Results

Log::ProgramInfo is a Perl module that writes a log file at the termination of execution of the enclosing program, to document useful execution characteristics. This log file can be used to re-create the environment in order to reproduce an earlier execution. It can also be used to compare the environments of two executions to determine whether there were any differences that might affect (or explain) their operation.

Availability

The source is available on CPAN (Macdonald and Boutros, Log-ProgramInfo. http://search.cpan.org/~boutroslb/Log-ProgramInfo/).

Conclusion

Using Log::ProgramInfo in programs creating result data for publishable research, and including the Log::ProgramInfo output log as part of the publication of that research is a valuable method to assist others to duplicate the programming environment as a precursor to validating and/or extending that research.

     

Hot spot prediction in protein-protein interactions by an ensemble system

Background

Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features.

Results

This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance.

Conclusion

The experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction.

Availability

http://deeplearner.ahu.edu.cn/web/HotspotEL.htm.

     

Evaluation of a large healthy lifestyle program: informing program implementation and scale-up in the prevention of obesity

Background

The Healthy Lifestyle Program for women (HeLP-her) is a low-intensity, self-management program which has demonstrated efficacy in preventing excess weight gain in women. However, little is known about the implementation, reach, and sustainability of low-intensity prevention programs in rural settings, where risk for obesity in women is higher than urban settings. We aimed to evaluate a low-intensity healthy lifestyle program delivered to women in a rural setting to inform development of effective community prevention programs.

Methods

A mixed method hybrid implementation and evaluation study, guided by the RE-AIM framework (addressing the Reach, Effectiveness, Adoption, Implementation, and Maintenance), was undertaken. Data collection tools included anthropometric measures, program checklists, questionnaires, and semi-structured interviews with participants and local stakeholders. The RE-AIM self-audit tool was applied to assess evaluation rigor.

Results

Six hundred and forty-nine women from 41 relatively socio-economic disadvantaged communities in Australia participated: mean age 39.6 years (±SD 6.7) and body mass index of 28.8 kg/m² (±SD 6.9). A between-group weight difference of ?0.92 kg (95% CI ?1.67 to ?0.16) showed program effectiveness. Reach was broad across 41 towns with 62% of participants reporting influencing some of the health behaviors of their families. Strong implementation fidelity was achieved with good retention rates at 1 year (76%) and high participant satisfaction (82% of participants willing to recommend this program). Over 300 multi-level community partnerships were established supporting high adoption. Stakeholders reported potential capacity to implement and sustain the prevention program in resource poor rural settings, due to the low-intensity design and minimal resources required.

Conclusions

Our comprehensive RE-AIM evaluation demonstrates that an evidence-based obesity prevention program can be successfully implemented in real-world settings. The program achieved broad reach, effectiveness, and satisfaction at the community and stakeholder level, revealing potential for program sustainability. The evaluation addressed implementation knowledge gaps to support future obesity prevention program scale-up.

Trial registration

Australian and New Zealand Clinical Trial Registry ACTRN 12612000115831 [http://www.anzctr.org.au/].

     

Nasal gene expression differentiates COPD from controls and overlaps bronchial gene expression

Background

Nasal gene expression profiling is a promising method to characterize COPD non-invasively. We aimed to identify a nasal gene expression profile to distinguish COPD patients from healthy controls. We investigated whether this COPD-associated gene expression profile in nasal epithelium is comparable with the profile observed in bronchial epithelium.

Methods

Genome wide gene expression analysis was performed on nasal epithelial brushes of 31 severe COPD patients and 22 controls, all current smokers, using Affymetrix Human Gene 1.0 ST Arrays. We repeated the gene expression analysis on bronchial epithelial brushes in 2 independent cohorts of mild-to-moderate COPD patients and controls.

Results

In nasal epithelium, 135 genes were significantly differentially expressed between severe COPD patients and controls, 21 being up- and 114 downregulated in COPD (false discovery rate?<?0.01). Gene Set Enrichment Analysis (GSEA) showed significant concordant enrichment of COPD-associated nasal and bronchial gene expression in both independent cohorts (FDR_GSEA <?0.001).

Conclusion

We identified a nasal gene expression profile that differentiates severe COPD patients from controls. Of interest, part of the nasal gene expression changes in COPD mimics differentially expressed genes in the bronchus. These findings indicate that nasal gene expression profiling is potentially useful as a non-invasive biomarker in COPD.

Trial registration

ClinicalTrials.gov registration number NCT01351792 (registration date May 10, 2011), ClinicalTrials.gov registration number NCT00848406 (registration date February 19, 2009), ClinicalTrials.gov registration number NCT00807469 (registration date December 11, 2008).

     

Low-dose lymphocyte immunotherapy rebalances the peripheral blood Th1/Th2/Treg paradigm in patients with unexplained recurrent miscarriage

Background

The published results regarding lymphocytes immunotherapy for unexplained recurrent miscarriage (uRM) patients are conflicting due to different screening criteria and therapeutic protocols. The objective of the present study is to evaluate the effectiveness of immunotherapy using low-dose lymphocytes in patients with uRM and Th1/Th2/Treg paradigm disorders.

Methods

Sixty-four uRM patients who received low-dose lymphocytes immunotherapy served as the immunotherapy group, while the other 35 women who did not receive the treatment served as the control group. The proportions of peripheral blood Th1 cells, Th2 cells and Treg cells; and the concentration of TGF-β1 in serum were detected by flow cytometry and enzyme-linked immunosorbent assay (ELISA), respectively, before and after the immunotherapy.

Results

The proportion of Th1 cells was significantly decreased while the proportions of Th2 cells and Treg cells were significantly increased in immunotherapy group patients after treatment. In addition, the concentration of TGF-β1 in serum was significantly higher after immunotherapy than before. Forty-three uRM patients achieved pregnancy after receiving immunotherapy and 5 patients underwent miscarriages in the immunotherapy group (11.6%, 5/43), while 8 of the 23 pregnant patients experienced a miscarriage in the control group (34.8%, 8/23; p?<?0.05).

Conclusions

Low-dose lymphocyte immunotherapy is beneficial for restoring balance in the Th1/Th2/Treg paradigm and improving pregnancy outcome in uRM patients.

Trial registration

NCT03081325. ClinicalTrials.gov. Retrospectively registered July 2015.