S2P: A software tool to quickly carry out reproducible biomedical research projects involving 2D-gel and MALDI-TOF MS protein data

FLEXOR: A support tool for efficient and seamless experiment data processing to evaluate musculo-articular stiffness

BACKGROUND AND OBJECTIVE

The evaluation of musculo-articular stiffness (MAS) is an increasingly demanded procedure with applications in different fields, such as sports performance and lower limbs injury prevention. However, this task is non-automated, time-consuming and error-prone due to manual handling of data streams and files across several software applications. Despite the fact that process automation of validated procedures helps to prevent errors, there is still a lack of easy-to-use tools for analysis, management and visualization of MAS trials.

METHODS

In the present work a tool called FLEXOR has been developed which applies mathematical methods and novel algorithms to automatically adjust curves of data streams for MAS analysis decreasing substantially time employed and errors. This tool permits to define different adjustment parameters, detect curve peaks and valleys, and display the results on the fly. FLEXOR has been implemented through a component-based software development (CBSD) process. All physiological fundamentals for the biomechanical measurement have been included in the tool developed. To describe the integration of all required components a 4 + 1 view model architecture has been used. The installation guide, the FLEXOR software and some data samples can be found on its GitHub repository (https://github.com/FlexorSoftware/flexor).

RESULTS

A multiplatform software tool to simplify traditional complex and manual procedures for MAS analysis is obtained. The tool turns them into a simple all-in-one procedure, reducing processing times from hours to a few minutes. The methodology was tested on multiple datasets generated by previous tools in former procedures as well as on real-time trials in the laboratory, showing identical results.

CONCLUSION

The results show that the developed tool can accomplish an unfilled essential task in the analysis, management and visualization of MAS measurement. The presented software tool empowers analysts to handle the different studies, investigate different parameters related to each experiment and even test with different output parameters in each experiment, enabling real-time trials and shared studies between different analysts.

The Reliable, Automatic Classification of Neonates in First-Tier MALDI-MS Screening for Sickle Cell Disease

Previous research has shown that a MALDI-MS technique can be used to screen for sickle cell disease (SCD), and that a system combining automated sample preparation, MALDI-MS analysis and classification software is a relevant approach for first-line, high-throughput SCD screening. In order to achieve a high-throughput "plug and play" approach while detecting "non-standard" profiles that might prompt the misclassification of a sample, we have incorporated various sets of alerts into the decision support software. These included "biological alert" indicators of a newborn's clinical status (e. g., detecting samples with no or low HbA), and "technical alerts" indicators for the most common non-standard profiles, i.e., those which might otherwise lead to sample misclassification. We evaluated these alerts by applying them to two datasets (produced by different laboratories). Despite the random generation of abnormal spectra by one-off technical faults or due to the nature and quality of the samples, the use of alerts fully secured the process of automatic sample classification. Firstly, cases of β-thalassemia were detected. Secondly, after a visual check on the tagged profiles and reanalysis of the corresponding biological samples, all the samples were correctly reclassified without prompting further alerts. All of the samples for which the results were not tagged were well classified (i.e., sensitivity and specificity = 1). The alerts were mainly designed for detecting false-negative classifications; all the FAS samples misclassified by the software as FA (a false negative) were marked with an alert. The implementation of alerts in the NeoScreening^® Laboratory Information Management System's decision support software opens up perspectives for the safe, reliable, automated classification of samples, with a visual check solely on abnormal results or samples. It should now be possible to evaluate the combination of the NeoSickle^® analytical solution and the NeoScreening^® Laboratory Information Management System in a real-life, prospective study of first-line SCD screening.

DEWE: A novel tool for executing differential expression RNA-Seq workflows in biomedical research

BACKGROUND

Transcriptomics profiling aims to identify and quantify all transcripts present within a cell type or tissue at a particular state, and thus provide information on the genes expressed in specific experimental settings, differentiation or disease conditions. RNA-Seq technology is becoming the standard approach for such studies, but available analysis tools are often hard to install, configure and use by users without advanced bioinformatics skills.

METHODS

Within reason, DEWE aims to make RNA-Seq analysis as easy for non-proficient users as for experienced bioinformaticians. DEWE supports two well-established and widely used differential expression analysis workflows: using Bowtie2 or HISAT2 for sequence alignment; and, both applying StringTie for quantification, and Ballgown and edgeR for differential expression analysis. Also, it enables the tailored execution of individual tools as well as helps with the management and visualisation of differential expression results.

RESULTS

DEWE provides a user-friendly interface designed to reduce the learning curve of less knowledgeable users while enabling analysis customisation and software extension by advanced users. Docker technology helps overcome installation and configuration hurdles. In addition, DEWE produces high quality and publication-ready outputs in the form of tab-delimited files and figures, as well as helps researchers with further analyses, such as pathway enrichment analysis.

CONCLUSIONS

The abilities of DEWE are exemplified here by practical application to a comparative analysis of monocytes and monocyte-derived dendritic cells, a study of clinical relevance. DEWE installers and documentation are freely available at https://www.sing-group.org/dewe.

GC4S: A bioinformatics-oriented Java software library of reusable graphical user interface components

Modern bioinformatics and computational biology are fields of study driven by the availability of effective software required for conducting appropriate research tasks. Apart from providing reliable and fast implementations of different data analysis algorithms, these software applications should also be clear and easy to use through proper user interfaces, providing appropriate data management and visualization capabilities. In this regard, the user experience obtained by interacting with these applications via their Graphical User Interfaces (GUI) is a key factor for their final success and real utility for researchers. Despite the existence of different packages and applications focused on advanced data visualization, there is a lack of specific libraries providing pertinent GUI components able to help scientific bioinformatics software developers. To that end, this paper introduces GC4S, a bioinformatics-oriented collection of high-level, extensible, and reusable Java GUI elements specifically designed to speed up bioinformatics software development. Within GC4S, developers of new applications can focus on the specific GUI requirements of their projects, relying on GC4S for generalities and abstractions. GC4S is free software distributed under the terms of GNU Lesser General Public License and both source code and documentation are publicly available at http://www.sing-group.org/gc4s.