PLIDflow: an open-source workflow for the online analysis of protein-ligand docking using galaxy.

MOTIVATION: Molecular docking is aimed at predicting the conformation of small-molecule (ligands) within an identified binding site (BS) in a target protein (receptor). Protein-ligand docking plays an important role in modern drug discovery and biochemistry for protein engineering. However, efficient docking analysis of proteins requires prior knowledge of the BS, which is not always known. The process which covers BS identification and protein-ligand docking usually requires the combination of different programs, which require several input parameters. This is furtherly aggravated when factoring in computational demands, such as CPU-time. Therefore, these types of simulation experiments can become a complex process for researchers without a background in computer sciences. RESULTS: To overcome these problems, we have designed an automatic computational workflow (WF) to process protein-ligand complexes, which runs from the identification of the possible BSs positions to the prediction of the experimental binding modes and affinities of the ligand. This open-access WF runs under the Galaxy platform that integrates public domain software. The results of the proposed method are in close agreement with state-of-the-art docking software. AVAILABILITY AND IMPLEMENTATION: Software is available at: https://pistacho.ac.uma.es/galaxy-bitlab. CONTACT: euv@uma.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

mORCA: sailing bioinformatics world with mobile devices.

Motivation: Nearly 10 years have passed since the first mobile apps appeared. Given the fact that bioinformatics is a web-based world and that mobile devices are endowed with web-browsers, it seemed natural that bioinformatics would transit from personal computers to mobile devices but nothing could be further from the truth. The transition demands new paradigms, designs and novel implementations. Results: Throughout an in-depth analysis of requirements of existing bioinformatics applications we designed and deployed an easy-to-use web-based lightweight mobile client. Such client is able to browse, select, compose automatically interface parameters, invoke services and monitor the execution of Web Services using the service's metadata stored in catalogs or repositories. Availability and implementation: mORCA is available at http://bitlab-es.com/morca/app as a web-app. It is also available in the App store by Apple and Play Store by Google. The software will be available for at least 2 years. Contact: ortrelles@uma.es. Supplementary information: Source code, final web-app, training material and documentation is available at http://bitlab-es.com/morca.

Computational workflow for the fine-grained analysis of metagenomic samples.

BACKGROUND: The field of metagenomics, defined as the direct genetic analysis of uncultured samples of genomes contained within an environmental sample, is gaining increasing popularity. The aim of studies of metagenomics is to determine the species present in an environmental community and identify changes in the abundance of species under different conditions. Current metagenomic analysis software faces bottlenecks due to the high computational load required to analyze complex samples. RESULTS: A computational open-source workflow has been developed for the detailed analysis of metagenomes. This workflow provides new tools and datafile specifications that facilitate the identification of differences in abundance of reads assigned to taxa (mapping), enables the detection of reads of low-abundance bacteria (producing evidence of their presence), provides new concepts for filtering spurious matches, etc. Innovative visualization ideas for improved display of metagenomic diversity are also proposed to better understand how reads are mapped to taxa. Illustrative examples are provided based on the study of two collections of metagenomes from faecal microbial communities of adult female monozygotic and dizygotic twin pairs concordant for leanness or obesity and their mothers. CONCLUSIONS: The proposed workflow provides an open environment that offers the opportunity to perform the mapping process using different reference databases. Additionally, this workflow shows the specifications of the mapping process and datafile formats to facilitate the development of new plugins for further post-processing. This open and extensible platform has been designed with the aim of enabling in-depth analysis of metagenomic samples and better understanding of the underlying biological processes.

Unraveling Genome Evolution Throughout Visual Analysis: The XCout Portal.

Due to major breakthroughs in sequencing technologies throughout the last decades, the time and cost per sequencing experiment have reduced drastically, overcoming the data generation barrier during the early genomic era. Such a shift has encouraged the scientific community to develop new computational methods that are able to compare large genomic sequences, thus enabling large-scale studies of genome evolution. The field of comparative genomics has proven itself invaluable for studying the evolutionary mechanisms and the forces driving genome evolution. In this line, a full genome comparison study between 2 species requires a quadratic number of comparisons in terms of the number of sequences (around 400 chromosome comparisons in the case of mammalian genomes); however, when studying conserved syntenies or evolutionary rearrangements, many sequence comparisons can be skipped for not all will contain significant signals. Subsequently, the scientific community has developed fast heuristics to perform multiple pairwise comparisons between large sequences to determine whether significant sets of conserved similarities exist. The data generation problem is no longer an issue, yet the limitations have shifted toward the analysis of such massive data. Therefore, we present XCout, a Web-based visual analytics application for multiple genome comparisons designed to improve the analysis of large-scale evolutionary studies using novel techniques in Web visualization. XCout enables to work on hundreds of comparisons at once, thus reducing the time of the analysis by identifying significant signals between chromosomes across multiple species. Among others, XCout introduces several techniques to aid in the analysis of large-scale genome rearrangements, particularly (1) an interactive heatmap interface to display comparisons using automatic color scales based on similarity thresholds to ease detection at first sight, (2) an overlay system to detect individual signal contributions between chromosomes, (3) a tracking tool to trace conserved blocks across different species to perform evolutionary studies, and (4) a search engine to search annotations throughout different species.

Ultra-fast genome comparison for large-scale genomic experiments.

In the last decade, a technological shift in the bioinformatics field has occurred: larger genomes can now be sequenced quickly and cost effectively, resulting in the computational need to efficiently compare large and abundant sequences. Furthermore, detecting conserved similarities across large collections of genomes remains a problem. The size of chromosomes, along with the substantial amount of noise and number of repeats found in DNA sequences (particularly in mammals and plants), leads to a scenario where executing and waiting for complete outputs is both time and resource consuming. Filtering steps, manual examination and annotation, very long execution times and a high demand for computational resources represent a few of the many difficulties faced in large genome comparisons. In this work, we provide a method designed for comparisons of considerable amounts of very long sequences that employs a heuristic algorithm capable of separating noise and repeats from conserved fragments in pairwise genomic comparisons. We provide software implementation that computes in linear time using one core as a minimum and a small, constant memory footprint. The method produces both a previsualization of the comparison and a collection of indices to drastically reduce computational complexity when performing exhaustive comparisons. Last, the method scores the comparison to automate classification of sequences and produces a list of detected synteny blocks to enable new evolutionary studies.

Combining Strengths for Multi-genome Visual Analytics Comparison.

The eclosion of data acquisition technologies has shifted the bottleneck in molecular biology research from data acquisition to data analysis. Such is the case in Comparative Genomics, where sequence analysis has transitioned from genes to genomes of several orders of magnitude larger. This fact has revealed the need to adapt software to work with huge experiments efficiently and to incorporate new data-analysis strategies to manage results from such studies. In previous works, we presented GECKO, a software to compare large sequences; now we address the representation, browsing, data exploration, and post-processing of the massive amount of information derived from such comparisons. GECKO-MGV is a web-based application organized as client-server architecture. It is aimed at visual analysis of the results from both pairwise and multiple sequences comparison studies combining a set of common commands for image exploration with improved state-of-the-art solutions. In addition, GECKO-MGV integrates different visualization analysis tools while exploiting the concept of layers to display multiple genome comparison datasets. Moreover, the software is endowed with capabilities for contacting external-proprietary and third-party services for further data post-processing and also presents a method to display a timeline of large-scale evolutionary events. As proof-of-concept, we present 2 exercises using bacterial and mammalian genomes which depict the capabilities of GECKO-MGV to perform in-depth, customizable analyses on the fly using web technologies. The first exercise is mainly descriptive and is carried out over bacterial genomes, whereas the second one aims to show the ability to deal with large sequence comparisons. In this case, we display results from the comparison of the first Homo sapiens chromosome against the first 5 chromosomes of Mus musculus.

Precise and Parallel Pairwise Metagenomic Comparisons.

The comparison and assessment of similarity across metagenomes are still an open problem. Uncultivated samples suffer from high variability, thus making it difficult for heuristic sequence comparison methods to find precise matches in reference databases. Finer methods are required to provide higher accuracy and certainty, although these come at the expense of larger computation times. Therefore, in this work, we present our software for the highly parallel, fine-grained pairwise alignment of metagenomes. First, an analysis of the computational limitations of performing coarse-grained global alignments in parallel manner is described, and a solution is discussed and employed by our proposal. Second, we show that our development is competitive with state-of-the-art software in terms of speed and consumption of resources, while achieving more accurate results. In addition, the parallel scheme adopted is tested, depicting a performance of up to 98% efficiency while using up to 64 cores. Sequential optimizations are also tested and show a speedup of 9× over our previous proposal.

Training bioinformaticians in High Performance Computing.

In the last decade, bioinformatics has become an indispensable branch of modern science research, experiencing an explosion in financial support, developed applications and data collection. The growth of the datasets that are emerging from research laboratories, industry, the health sector, etc., are increasingly raising the levels of demand in computing power and storage. Processing biological data, in the large scales of these datasets, often requires the use of High Performance Computing (HPC) resources, especially when dealing with certain types of omics data, such as genomic and metagenomic data. Such computational resources not only require substantial investments, but they also involve high maintenance costs. More importantly, in order to keep good returns from the investments, specific training needs to be put in place to ensure that wasting is minimized. Furthermore, given that bioinformatics is a highly interdisciplinary field where several other domains intersect (such as biology, chemistry, physics and computer science), researchers from these areas also require bioinformatics-specific training in HPC, in order to fully take advantage of supercomputing centers. In this document, we describe our experience in training researchers from several different disciplines in HPC, as applied to bioinformatics under the framework of the leading European bioinformatics platform ELIXIR, and analyze both the content and outcomes of the course.

Microbiome overview in swine lungs.

Mycoplasma hyopneumoniae is the etiologic agent of swine enzootic pneumonia. However other mycoplasma species and secondary bacteria are found as inhabitants of the swine respiratory tract, which can be also related to disease. In the present study we have performed a total DNA metagenomic analysis from the lungs of pigs kept in a field condition, with suggestive signals of enzootic pneumonia and without any infection signals to evaluate the bacteria variability of the lungs microbiota. Libraries from metagenomic DNA were prepared and sequenced using total DNA shotgun metagenomic pyrosequencing. The metagenomic distribution showed a great abundance of bacteria. The most common microbial families identified from pneumonic swine's lungs were Mycoplasmataceae, Flavobacteriaceae and Pasteurellaceae, whereas in the carrier swine's lungs the most common families were Mycoplasmataceae, Bradyrhizobiaceae and Flavobacteriaceae. Analysis of community composition in both samples confirmed the high prevalence of M. hyopneumoniae. Moreover, the carrier lungs had more diverse family population, which should be related to the lungs normal flora. In summary, we provide a wide view of the bacterial population from lungs with signals of enzootic pneumonia and lungs without signals of enzootic pneumonia in a field situation. These bacteria patterns provide information that may be important for the establishment of disease control measures and to give insights for further studies.