Dynamic configuration and data security for bioinformatics cloud services with the Laniakea Dashboard.

Technological advances in high-throughput technologies improve our ability to explore the molecular mechanisms of life. Computational infrastructures for scientific applications fulfil a critical role in harnessing this potential. However, there is an ongoing need to improve accessibility and implement robust data security technologies to allow the processing of sensitive data, particularly human genetic data. Scientific clouds have emerged as a promising solution to meet these needs. We present three components of the Laniakea software stack, initially developed to support the provision of private on-demand Galaxy instances. These components can be adopted by providers of scientific cloud services built on the INDIGO PaaS layer. The Dashboard translates configuration template files into user-friendly web interfaces, enabling the easy configuration and launch of on-demand applications. The secret management and the encryption components, integrated within the Dashboard, support the secure handling of passphrases and credentials and the deployment of block-level encrypted storage volumes for managing sensitive data in the cloud environment. By adopting these software components, scientific cloud providers can develop convenient, secure and efficient on-demand services for their users.

Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service.

BACKGROUND: Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of "on-demand" Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server's initial configuration and deployment. RESULTS: "Laniakea@ReCaS", the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform. CONCLUSIONS: During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers' work.

ITSoneWB: profiling global taxonomic diversity of eukaryotic communities on Galaxy.

SUMMARY: ITSoneWB (ITSone WorkBench) is a Galaxy-based bioinformatic environment where comprehensive and high-quality reference data are connected with established pipelines and new tools in an automated and easy-to-use service targeted at global taxonomic analysis of eukaryotic communities based on Internal Transcribed Spacer 1 variants high-throughput sequencing. AVAILABILITY AND IMPLEMENTATION: ITSoneWB has been deployed on the INFN-Bari ReCaS cloud facility and is freely available on the web at http://itsonewb.cloud.ba.infn.it/galaxy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Seismicity of the Gargano promontory (Southern Italy) after 7 years of local seismic network operation: Data release of waveforms from 2013 to 2018.

The University of Bari (Italy), in cooperation with the National Institute of Geophysics and Volcanology (INGV) (Italy), has installed the OTRIONS micro-earthquake network to better understand the active tectonics of the Gargano promontory (Southern Italy). The OTRIONS network operates since 2013 and consists of 12 short period, 3 components, seismic stations located in the Apulian territory (Southern Italy). This data article releases the waveform database collected from 2013 to 2018 and describes the characteristics of the local network in the current configuration. At the end of 2018, we implemented a cloud infrastructure to make more robust the acquisition and storage system of the network through a collaboration with the RECAS-Bari computing centre of the University of Bari (Italy) and of the National Institute of Nuclear Physics (Italy). Thanks to this implementation, waveforms recorded after the beginning of 2019 and the station metadata are accessible through the European Integrated Data Archive (EIDA, https://www.orfeus-eu.org/data/eida/nodes/INGV/).

Laniakea: an open solution to provide Galaxy "on-demand" instances over heterogeneous cloud infrastructures.

BACKGROUND: While the popular workflow manager Galaxy is currently made available through several publicly accessible servers, there are scenarios where users can be better served by full administrative control over a private Galaxy instance, including, but not limited to, concerns about data privacy, customisation needs, prioritisation of particular job types, tools development, and training activities. In such cases, a cloud-based Galaxy virtual instance represents an alternative that equips the user with complete control over the Galaxy instance itself without the burden of the hardware and software infrastructure involved in running and maintaining a Galaxy server. RESULTS: We present Laniakea, a complete software solution to set up a "Galaxy on-demand" platform as a service. Building on the INDIGO-DataCloud software stack, Laniakea can be deployed over common cloud architectures usually supported both by public and private e-infrastructures. The user interacts with a Laniakea-based service through a simple front-end that allows a general setup of a Galaxy instance, and then Laniakea takes care of the automatic deployment of the virtual hardware and the software components. At the end of the process, the user gains access with full administrative privileges to a private, production-grade, fully customisable, Galaxy virtual instance and to the underlying virtual machine (VM). Laniakea features deployment of single-server or cluster-backed Galaxy instances, sharing of reference data across multiple instances, data volume encryption, and support for VM image-based, Docker-based, and Ansible recipe-based Galaxy deployments. A Laniakea-based Galaxy on-demand service, named Laniakea@ReCaS, is currently hosted at the ELIXIR-IT ReCaS cloud facility. CONCLUSIONS: Laniakea offers to scientific e-infrastructures a complete and easy-to-use software solution to provide a Galaxy on-demand service to their users. Laniakea-based cloud services will help in making Galaxy more accessible to a broader user base by removing most of the burdens involved in deploying and running a Galaxy service. In turn, this will facilitate the adoption of Galaxy in scenarios where classic public instances do not represent an optimal solution. Finally, the implementation of Laniakea can be easily adapted and expanded to support different services and platforms beyond Galaxy.

Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019.

Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.

DNA Multiple Sequence Alignment Guided by Protein Domains: The MSA-PAD 2.0 Method.

Multiple sequence alignment (MSA) is a fundamental component in many DNA sequence analyses including metagenomics studies and phylogeny inference. When guided by protein profiles, DNA multiple alignments assume a higher precision and robustness. Here we present details of the use of the upgraded version of MSA-PAD (2.0), which is a DNA multiple sequence alignment framework able to align DNA sequences coding for single/multiple protein domains guided by PFAM or user-defined annotations. MSA-PAD has two alignment strategies, called "Gene" and "Genome," accounting for coding domains order and genomic rearrangements, respectively. Novel options were added to the present version, where the MSA can be guided by protein profiles provided by the user. This allows MSA-PAD 2.0 to run faster and to add custom protein profiles sometimes not present in PFAM database according to the user's interest. MSA-PAD 2.0 is currently freely available as a Web application at https://recasgateway.cloud.ba.infn.it/ .

DTI measurements for Alzheimer's classification.

Diffusion tensor imaging (DTI) is a promising imaging technique that provides insight into white matter microstructure integrity and it has greatly helped identifying white matter regions affected by Alzheimer's disease (AD) in its early stages. DTI can therefore be a valuable source of information when designing machine-learning strategies to discriminate between healthy control (HC) subjects, AD patients and subjects with mild cognitive impairment (MCI). Nonetheless, several studies have reported so far conflicting results, especially because of the adoption of biased feature selection strategies. In this paper we firstly analyzed DTI scans of 150 subjects from the Alzheimer's disease neuroimaging initiative (ADNI) database. We measured a significant effect of the feature selection bias on the classification performance (p-value < 0.01), leading to overoptimistic results (10% up to 30% relative increase in AUC). We observed that this effect is manifest regardless of the choice of diffusion index, specifically fractional anisotropy and mean diffusivity. Secondly, we performed a test on an independent mixed cohort consisting of 119 ADNI scans; thus, we evaluated the informative content provided by DTI measurements for AD classification. Classification performances and biological insight, concerning brain regions related to the disease, provided by cross-validation analysis were both confirmed on the independent test.

BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology.

BACKGROUND: Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. RESULTS: BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on-line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. CONCLUSIONS: Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.

BioMaS: a modular pipeline for Bioinformatic analysis of Metagenomic AmpliconS.

BACKGROUND: Substantial advances in microbiology, molecular evolution and biodiversity have been carried out in recent years thanks to Metagenomics, which allows to unveil the composition and functions of mixed microbial communities in any environmental niche. If the investigation is aimed only at the microbiome taxonomic structure, a target-based metagenomic approach, here also referred as Meta-barcoding, is generally applied. This approach commonly involves the selective amplification of a species-specific genetic marker (DNA meta-barcode) in the whole taxonomic range of interest and the exploration of its taxon-related variants through High-Throughput Sequencing (HTS) technologies. The accessibility to proper computational systems for the large-scale bioinformatic analysis of HTS data represents, currently, one of the major challenges in advanced Meta-barcoding projects. RESULTS: BioMaS (Bioinformatic analysis of Metagenomic AmpliconS) is a new bioinformatic pipeline designed to support biomolecular researchers involved in taxonomic studies of environmental microbial communities by a completely automated workflow, comprehensive of all the fundamental steps, from raw sequence data upload and cleaning to final taxonomic identification, that are absolutely required in an appropriately designed Meta-barcoding HTS-based experiment. In its current version, BioMaS allows the analysis of both bacterial and fungal environments starting directly from the raw sequencing data from either Roche 454 or Illumina HTS platforms, following two alternative paths, respectively. BioMaS is implemented into a public web service available at https://recasgateway.ba.infn.it/ and is also available in Galaxy at http://galaxy.cloud.ba.infn.it:8080 (only for Illumina data). CONCLUSION: BioMaS is a friendly pipeline for Meta-barcoding HTS data analysis specifically designed for users without particular computing skills. A comparative benchmark, carried out by using a simulated dataset suitably designed to broadly represent the currently known bacterial and fungal world, showed that BioMaS outperforms QIIME and MOTHUR in terms of extent and accuracy of deep taxonomic sequence assignments.

MSA-PAD: DNA multiple sequence alignment framework based on PFAM accessed domain information.

Here we present the MSA-PAD application, a DNA multiple sequence alignment framework that uses PFAM protein domain information to align DNA sequences encoding either single or multiple protein domains. MSA-PAD has two alignment options: gene and genome mode.

Draft Genome Sequence of the Xylella fastidiosa CoDiRO Strain.

We determined the draft genome sequence of the Xylella fastidiosa CoDiRO strain, which has been isolated from olive plants in southern Italy (Apulia). It is associated with olive quick decline syndrome (OQDS) and characterized by extensive scorching and desiccation of leaves and twigs.

Deep sequencing of the small RNAs derived from two symptomatic variants of a chloroplastic viroid: implications for their genesis and for pathogenesis.

Northern-blot hybridization and low-scale sequencing have revealed that plants infected by viroids, non-protein-coding RNA replicons, accumulate 21-24 nt viroid-derived small RNAs (vd-sRNAs) similar to the small interfering RNAs, the hallmarks of RNA silencing. These results strongly support that viroids are elicitors and targets of the RNA silencing machinery of their hosts. Low-scale sequencing, however, retrieves partial datasets and may lead to biased interpretations. To overcome this restraint we have examined by deep sequencing (Solexa-Illumina) and computational approaches the vd-sRNAs accumulating in GF-305 peach seedlings infected by two molecular variants of Peach latent mosaic viroid (PLMVd) inciting peach calico (albinism) and peach mosaic. Our results show in both samples multiple PLMVd-sRNAs, with prevalent 21-nt (+) and (-) RNAs presenting a biased distribution of their 5' nucleotide, and adopting a hotspot profile along the genomic (+) and (-) RNAs. Dicer-like 4 and 2 (DCL4 and DCL2, respectively), which act hierarchically in antiviral defense, likely also mediate the genesis of the 21- and 22-nt PLMVd-sRNAs. More specifically, because PLMVd replicates in plastids wherein RNA silencing has not been reported, DCL4 and DCL2 should dice the PLMVd genomic RNAs during their cytoplasmic movement or the PLMVd-dsRNAs generated by a cytoplasmic RNA-dependent RNA polymerase (RDR), like RDR6, acting in concert with DCL4 processing. Furthermore, given that vd-sRNAs derived from the 12-14-nt insertion containing the pathogenicity determinant of peach calico are underrepresented, it is unlikely that symptoms may result from the accidental targeting of host mRNAs by vd-sRNAs from this determinant guiding the RNA silencing machinery.

The GENIUS Grid Portal and robot certificates: a new tool for e-Science.

BACKGROUND: Grid technology is the computing model which allows users to share a wide pletora of distributed computational resources regardless of their geographical location. Up to now, the high security policy requested in order to access distributed computing resources has been a rather big limiting factor when trying to broaden the usage of Grids into a wide community of users. Grid security is indeed based on the Public Key Infrastructure (PKI) of X.509 certificates and the procedure to get and manage those certificates is unfortunately not straightforward. A first step to make Grids more appealing for new users has recently been achieved with the adoption of robot certificates. METHODS: Robot certificates have recently been introduced to perform automated tasks on Grids on behalf of users. They are extremely useful for instance to automate grid service monitoring, data processing production, distributed data collection systems. Basically these certificates can be used to identify a person responsible for an unattended service or process acting as client and/or server. Robot certificates can be installed on a smart card and used behind a portal by everyone interested in running the related applications in a Grid environment using a user-friendly graphic interface. In this work, the GENIUS Grid Portal, powered by EnginFrame, has been extended in order to support the new authentication based on the adoption of these robot certificates. RESULTS: The work carried out and reported in this manuscript is particularly relevant for all users who are not familiar with personal digital certificates and the technical aspects of the Grid Security Infrastructure (GSI). The valuable benefits introduced by robot certificates in e-Science can so be extended to users belonging to several scientific domains, providing an asset in raising Grid awareness to a wide number of potential users. CONCLUSION: The adoption of Grid portals extended with robot certificates, can really contribute to creating transparent access to computational resources of Grid Infrastructures, enhancing the spread of this new paradigm in researchers' working life to address new global scientific challenges. The evaluated solution can of course be extended to other portals, applications and scientific communities.

The bologna annotation resource: a non hierarchical method for the functional and structural annotation of protein sequences relying on a comparative large-scale genome analysis.

Protein sequence annotation is a major challenge in the postgenomic era. Thanks to the availability of complete genomes and proteomes, protein annotation has recently taken invaluable advantage from cross-genome comparisons. In this work, we describe a new non hierarchical clustering procedure characterized by a stringent metric which ensures a reliable transfer of function between related proteins even in the case of multidomain and distantly related proteins. The method takes advantage of the comparative analysis of 599 completely sequenced genomes, both from prokaryotes and eukaryotes, and of a GO and PDB/SCOP mapping over the clusters. A statistical validation of our method demonstrates that our clustering technique captures the essential information shared between homologous and distantly related protein sequences. By this, uncharacterized proteins can be safely annotated by inheriting the annotation of the cluster. We validate our method by blindly annotating other 201 genomes and finally we develop BAR (the Bologna Annotation Resource), a prediction server for protein functional annotation based on a total of 800 genomes (publicly available at http://microserf.biocomp.unibo.it/bar/).

Genome-wide identification of coding and non-coding conserved sequence tags in human and mouse genomes.

BACKGROUND: The accurate detection of genes and the identification of functional regions is still an open issue in the annotation of genomic sequences. This problem affects new genomes but also those of very well studied organisms such as human and mouse where, despite the great efforts, the inventory of genes and regulatory regions is far from complete. Comparative genomics is an effective approach to address this problem. Unfortunately it is limited by the computational requirements needed to perform genome-wide comparisons and by the problem of discriminating between conserved coding and non-coding sequences. This discrimination is often based (thus dependent) on the availability of annotated proteins. RESULTS: In this paper we present the results of a comprehensive comparison of human and mouse genomes performed with a new high throughput grid-based system which allows the rapid detection of conserved sequences and accurate assessment of their coding potential. By detecting clusters of coding conserved sequences the system is also suitable to accurately identify potential gene loci. Following this analysis we created a collection of human-mouse conserved sequence tags and carefully compared our results to reliable annotations in order to benchmark the reliability of our classifications. Strikingly we were able to detect several potential gene loci supported by EST sequences but not corresponding to as yet annotated genes. CONCLUSION: Here we present a new system which allows comprehensive comparison of genomes to detect conserved coding and non-coding sequences and the identification of potential gene loci. Our system does not require the availability of any annotated sequence thus is suitable for the analysis of new or poorly annotated genomes.

Gene analogue finder: a GRID solution for finding functionally analogous gene products.

BACKGROUND: To date more than 2,1 million gene products from more than 100000 different species have been described specifying their function, the processes they are involved in and their cellular localization using a very well defined and structured vocabulary, the gene ontology (GO). Such vast, well defined knowledge opens the possibility of compare gene products at the level of functionality, finding gene products which have a similar function or are involved in similar biological processes without relying on the conventional sequence similarity approach. Comparisons within such a large space of knowledge are highly data and computing intensive. For this reason this project was based upon the use of the computational GRID, a technology offering large computing and storage resources. RESULTS: We have developed a tool, GENe AnaloGue FINdEr (ENGINE) that parallelizes the search process and distributes the calculation and data over the computational GRID, splitting the process into many sub-processes and joining the calculation and the data on the same machine and therefore completing the whole search in about 3 days instead of occupying one single machine for more than 5 CPU years. The results of the functional comparison contain potential functional analogues for more than 79000 gene products from the most important species. 46% of the analyzed gene products are well enough described for such an analysis to individuate functional analogues, such as well-known members of the same gene family, or gene products with similar functions which would never have been associated by standard methods. CONCLUSION: ENGINE has produced a list of potential functionally analogous relations between gene products within and between species using, in place of the sequence, the gene description of the GO, thus demonstrating the potential of the GO. However, the current limiting factor is the quality of the associations of many gene products from non-model organisms that often have electronic associations, since experimental information is missing. With future improvements of the GO, this limit will be reduced. ENGINE will manifest its power when it is applied to the whole GODB of more than 2,1 million gene products from more than 100000 organisms. The data produced by this search is planed to be available as a supplement to the GO database as soon as we are able to provide regular updates.