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.

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.

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.

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.

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.

mORCA: ubiquitous access to life science web services.

BACKGROUND: Technical advances in mobile devices such as smartphones and tablets have produced an extraordinary increase in their use around the world and have become part of our daily lives. The possibility of carrying these devices in a pocket, particularly mobile phones, has enabled ubiquitous access to Internet resources. Furthermore, in the life sciences world there has been a vast proliferation of data types and services that finish as Web Services. This suggests the need for research into mobile clients to deal with life sciences applications for effective usage and exploitation. RESULTS: Analysing the current features in existing bioinformatics applications managing Web Services, we have devised, implemented, and deployed an easy-to-use web-based lightweight mobile client. This client is able to browse, select, compose parameters, invoke, and monitor the execution of Web Services stored in catalogues or central repositories. The client is also able to deal with huge amounts of data between external storage mounts. In addition, we also present a validation use case, which illustrates the usage of the application while executing, monitoring, and exploring the results of a registered workflow. The software its available in the Apple Store and Android Market and the source code is publicly available in Github. CONCLUSIONS: Mobile devices are becoming increasingly important in the scientific world due to their strong potential impact on scientific applications. Bioinformatics should not fall behind this trend. We present an original software client that deals with the intrinsic limitations of such devices and propose different guidelines to provide location-independent access to computational resources in bioinformatics and biomedicine. Its modular design makes it easily expandable with the inclusion of new repositories, tools, types of visualization, etc.

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.

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.

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.

Partial Activation of SA- and JA-Defensive Pathways in Strawberry upon Colletotrichum acutatum Interaction.

Understanding the nature of pathogen host interaction may help improve strawberry (Fragaria × ananassa) cultivars. Plant resistance to pathogenic agents usually operates through a complex network of defense mechanisms mediated by a diverse array of signaling molecules. In strawberry, resistance to a variety of pathogens has been reported to be mostly polygenic and quantitatively inherited, making it difficult to associate molecular markers with disease resistance genes. Colletotrichum acutatum spp. is a major strawberry pathogen, and completely resistant cultivars have not been reported. Moreover, strawberry defense network components and mechanisms remain largely unknown and poorly understood. Assessment of the strawberry response to C. acutatum included a global transcript analysis, and acidic hormones SA and JA measurements were analyzed after challenge with the pathogen. Induction of transcripts corresponding to the SA and JA signaling pathways and key genes controlling major steps within these defense pathways was detected. Accordingly, SA and JA accumulated in strawberry after infection. Contrastingly, induction of several important SA, JA, and oxidative stress-responsive defense genes, including FaPR1-1, FaLOX2, FaJAR1, FaPDF1, and FaGST1, was not detected, which suggests that specific branches in these defense pathways (those leading to FaPR1-2, FaPR2-1, FaPR2-2, FaAOS, FaPR5, and FaPR10) were activated. Our results reveal that specific aspects in SA and JA dependent signaling pathways are activated in strawberry upon interaction with C. acutatum. Certain described defense-associated transcripts related to these two known signaling pathways do not increase in abundance following infection. This finding suggests new insight into a specific putative molecular strategy for defense against this pathogen.

Transcriptomic Analysis Using Olive Varieties and Breeding Progenies Identifies Candidate Genes Involved in Plant Architecture.

Plant architecture is a critical trait in fruit crops that can significantly influence yield, pruning, planting density and harvesting. Little is known about how plant architecture is genetically determined in olive, were most of the existing varieties are traditional with an architecture poorly suited for modern growing and harvesting systems. In the present study, we have carried out microarray analysis of meristematic tissue to compare expression profiles of olive varieties displaying differences in architecture, as well as seedlings from their cross pooled on the basis of their sharing architecture-related phenotypes. The microarray used, previously developed by our group has already been applied to identify candidates genes involved in regulating juvenile to adult transition in the shoot apex of seedlings. Varieties with distinct architecture phenotypes and individuals from segregating progenies displaying opposite architecture features were used to link phenotype to expression. Here, we identify 2252 differentially expressed genes (DEGs) associated to differences in plant architecture. Microarray results were validated by quantitative RT-PCR carried out on genes with functional annotation likely related to plant architecture. Twelve of these genes were further analyzed in individual seedlings of the corresponding pool. We also examined Arabidopsis mutants in putative orthologs of these targeted candidate genes, finding altered architecture for most of them. This supports a functional conservation between species and potential biological relevance of the candidate genes identified. This study is the first to identify genes associated to plant architecture in olive, and the results obtained could be of great help in future programs aimed at selecting phenotypes adapted to modern cultivation practices in this species.

Review: High-performance computing to detect epistasis in genome scale data sets.

It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.

Breaking the computational barriers of pairwise genome comparison.

BACKGROUND: Conventional pairwise sequence comparison software algorithms are being used to process much larger datasets than they were originally designed for. This can result in processing bottlenecks that limit software capabilities or prevent full use of the available hardware resources. Overcoming the barriers that limit the efficient computational analysis of large biological sequence datasets by retrofitting existing algorithms or by creating new applications represents a major challenge for the bioinformatics community. RESULTS: We have developed C libraries for pairwise sequence comparison within diverse architectures, ranging from commodity systems to high performance and cloud computing environments. Exhaustive tests were performed using different datasets of closely- and distantly-related sequences that span from small viral genomes to large mammalian chromosomes. The tests demonstrated that our solution is capable of generating high quality results with a linear-time response and controlled memory consumption, being comparable or faster than the current state-of-the-art methods. CONCLUSIONS: We have addressed the problem of pairwise and all-versus-all comparison of large sequences in general, greatly increasing the limits on input data size. The approach described here is based on a modular out-of-core strategy that uses secondary storage to avoid reaching memory limits during the identification of High-scoring Segment Pairs (HSPs) between the sequences under comparison. Software engineering concepts were applied to avoid intermediate result re-calculation, to minimise the performance impact of input/output (I/O) operations and to modularise the process, thus enhancing application flexibility and extendibility. Our computationally-efficient approach allows tasks such as the massive comparison of complete genomes, evolutionary event detection, the identification of conserved synteny blocks and inter-genome distance calculations to be performed more effectively.

Computational Synteny Block: A Framework to Identify Evolutionary Events.

MOTIVATION: The identification and accurate description of large genomic rearrangements is crucial for the study of Evolutionary Events among species and implicitly defining breakpoints. Although there is a number of software tools available to perform this task, they usually either a) require a collection of pre-computed non-conflicting High-scoring Segment Pairs (HSPs) and gene annotations; or b) involve working at protein level (what excludes non-coding regions) ; or c) need many parameters to adjust the software behaviour and performance; or d) imply working with duplications, repeats and tandem repeats, which complicates the identification of rearrangements task. Although there are many programs specialized in the detection of these repetitions, they are not designed for the identification of main genomic rearrangements.

Early and delayed long-term transcriptional changes and short-term transient responses during cold acclimation in olive leaves.

Low temperature severely affects plant growth and development. To overcome this constraint, several plant species from regions having a cool season have evolved an adaptive response, called cold acclimation. We have studied this response in olive tree (Olea europaea L.) cv. Picual. Biochemical stress markers and cold-stress symptoms were detected after the first 24 h as sagging leaves. After 5 days, the plants were found to have completely recovered. Control and cold-stressed plants were sequenced by Illumina HiSeq 1000 paired-end technique. We also assembled a new olive transcriptome comprising 157,799 unigenes and found 6,309 unigenes differentially expressed in response to cold. Three types of response that led to cold acclimation were found: short-term transient response, early long-term response, and late long-term response. These subsets of unigenes were related to different biological processes. Early responses involved many cold-stress-responsive genes coding for, among many other things, C-repeat binding factor transcription factors, fatty acid desaturases, wax synthesis, and oligosaccharide metabolism. After long-term exposure to cold, a large proportion of gene down-regulation was found, including photosynthesis and plant growth genes. Up-regulated genes after long-term cold exposure were related to organelle fusion, nucleus organization, and DNA integration, including retrotransposons.

Sma3s: a three-step modular annotator for large sequence datasets.

Automatic sequence annotation is an essential component of modern 'omics' studies, which aim to extract information from large collections of sequence data. Most existing tools use sequence homology to establish evolutionary relationships and assign putative functions to sequences. However, it can be difficult to define a similarity threshold that achieves sufficient coverage without sacrificing annotation quality. Defining the correct configuration is critical and can be challenging for non-specialist users. Thus, the development of robust automatic annotation techniques that generate high-quality annotations without needing expert knowledge would be very valuable for the research community. We present Sma3s, a tool for automatically annotating very large collections of biological sequences from any kind of gene library or genome. Sma3s is composed of three modules that progressively annotate query sequences using either: (i) very similar homologues, (ii) orthologous sequences or (iii) terms enriched in groups of homologous sequences. We trained the system using several random sets of known sequences, demonstrating average sensitivity and specificity values of ~85%. In conclusion, Sma3s is a versatile tool for high-throughput annotation of a wide variety of sequence datasets that outperforms the accuracy of other well-established annotation algorithms, and it can enrich existing database annotations and uncover previously hidden features. Importantly, Sma3s has already been used in the functional annotation of two published transcriptomes.

MAPI: a software framework for distributed biomedical applications.

BACKGROUND: The amount of web-based resources (databases, tools etc.) in biomedicine has increased, but the integrated usage of those resources is complex due to differences in access protocols and data formats. However, distributed data processing is becoming inevitable in several domains, in particular in biomedicine, where researchers face rapidly increasing data sizes. This big data is difficult to process locally because of the large processing, memory and storage capacity required. RESULTS: This manuscript describes a framework, called MAPI, which provides a uniform representation of resources available over the Internet, in particular for Web Services. The framework enhances their interoperability and collaborative use by enabling a uniform and remote access. The framework functionality is organized in modules that can be combined and configured in different ways to fulfil concrete development requirements. CONCLUSIONS: The framework has been tested in the biomedical application domain where it has been a base for developing several clients that are able to integrate different web resources. The MAPI binaries and documentation are freely available at http://www.bitlab-es.com/mapi under the Creative Commons Attribution-No Derivative Works 2.5 Spain License. The MAPI source code is available by request (GPL v3 license).

De novo assembly and functional annotation of the olive (Olea europaea) transcriptome.

Olive breeding programmes are focused on selecting for traits as short juvenile period, plant architecture suited for mechanical harvest, or oil characteristics, including fatty acid composition, phenolic, and volatile compounds to suit new markets. Understanding the molecular basis of these characteristics and improving the efficiency of such breeding programmes require the development of genomic information and tools. However, despite its economic relevance, genomic information on olive or closely related species is still scarce. We have applied Sanger and 454 pyrosequencing technologies to generate close to 2 million reads from 12 cDNA libraries obtained from the Picual, Arbequina, and Lechin de Sevilla cultivars and seedlings from a segregating progeny of a Picual × Arbequina cross. The libraries include fruit mesocarp and seeds at three relevant developmental stages, young stems and leaves, active juvenile and adult buds as well as dormant buds, and juvenile and adult roots. The reads were assembled by library or tissue and then assembled together into 81 020 unigenes with an average size of 496 bases. Here, we report their assembly and their functional annotation.

Genetic and genome-wide transcriptomic analyses identify co-regulation of oxidative response and hormone transcript abundance with vitamin C content in tomato fruit.

BACKGROUND: L-ascorbic acid (AsA; vitamin C) is essential for all living plants where it functions as the main hydrosoluble antioxidant. It has diverse roles in the regulation of plant cell growth and expansion, photosynthesis, and hormone-regulated processes. AsA is also an essential component of the human diet, being tomato fruit one of the main sources of this vitamin. To identify genes responsible for AsA content in tomato fruit, transcriptomic studies followed by clustering analysis were applied to two groups of fruits with contrasting AsA content. These fruits were identified after AsA profiling of an F8 Recombinant Inbred Line (RIL) population generated from a cross between the domesticated species Solanum lycopersicum and the wild relative Solanum pimpinellifollium. RESULTS: We found large variability in AsA content within the RIL population with individual RILs with up to 4-fold difference in AsA content. Transcriptomic analysis identified genes whose expression correlated either positively (PVC genes) or negatively (NVC genes) with the AsA content of the fruits. Cluster analysis using SOTA allowed the identification of subsets of co-regulated genes mainly involved in hormones signaling, such as ethylene, ABA, gibberellin and auxin, rather than any of the known AsA biosynthetic genes. Data mining of the corresponding PVC and NVC orthologs in Arabidopis databases identified flagellin and other ROS-producing processes as cues resulting in differential regulation of a high percentage of the genes from both groups of co-regulated genes; more specifically, 26.6% of the orthologous PVC genes, and 15.5% of the orthologous NVC genes were induced and repressed, respectively, under flagellin22 treatment in Arabidopsis thaliana. CONCLUSION: Results here reported indicate that the content of AsA in red tomato fruit from our selected RILs are not correlated with the expression of genes involved in its biosynthesis. On the contrary, the data presented here supports that AsA content in tomato fruit co-regulates with genes involved in hormone signaling and they are dependent on the oxidative status of the fruit.