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The first version of the CHAIMELEON repository deployed includes a set of resources and services that fulfil the most important use cases identified. A cloud-based architecture has been defined, implemented and deployed on UPV’s cloud premises. The architecture components are described using an infrastructure-as-code approach so it can be deployed in any other cloud offering supported. The CHAIMELEON repository is defined, taking into account privacy and security by design on the access to the data stored. This is implemented around three main concepts: the user role, which defines the actions and permissions of the different actors interacting with the repository; the dataset, which is a citable set of medical images and associated clinical data; and the processing applications, which are applications that run in the platform to access and process the data from the datasets. The CHAIMELEON repository has undergone a Data Protection Impact Assessment (DPIA) risk analysis which led to several refinements and improvements. With this scenario in mind, the initial version of the CHAIMELEON repository provides the management of the life-cycle of the datasets, the access to the data and its processing using efficient and user-friendly applications from a catalogue. This functionality is demonstrated in two videos showing the interactions to register in the platform and access the data (https://www.youtube.com/watch?v=cIyiAPl9ezw&t=398s) and several operations concerning the management of the life cycle (https://www.youtube.com/watch?v=oH4glJNxPDc&t=2s).

In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models., This work was supported by the project DEEP-Hybrid-DataCloud ‘‘Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud’’ that has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant 777435.

Background The specific characteristics of copy number variations (CNVs) require specific methods of detection and characterization. We developed the Easy One-Step Amplification and Labeling procedure for CNV detection (EOSAL-CNV), a new method based on proportional amplification and labeling of amplicons in 1 PCR. Methods We used tailed primers for specific amplification and a pair of labeling probes (only 1 labeled) for amplification and labeling of all amplicons in just 1 reaction. Products were loaded directly onto a capillary DNA sequencer for fragment sizing and quantification. Data obtained could be analyzed by Microsoft Excel spreadsheet or EOSAL-CNV analysis software. We developed the protocol using the LDLR (low density lipoprotein receptor) gene including 23 samples with 8 different CNVs. After optimizing the protocol, it was used for genes in the following multiplexes: BRCA1 (BRCA1 DNA repair associated), BRCA2 (BRCA2 DNA repair associated), CHEK2 (checkpoint kinase 2), MLH1 (mutL homolog 1) plus MSH6 (mutS homolog 6), MSH2 (mutS homolog 2) plus EPCAM (epithelial cell adhesion molecule) and chromosome 17 (especially the TP53 [tumor protein 53] gene). We compared our procedure with multiplex ligation-dependent probe amplification (MLPA). Results The simple procedure for CNV detection required 150 min, with 240 samples, EOSAL-CNV excluded the presence of CNVs in all controls, and in all cases, results were identical using MLPA and EOSAL-CNV. Analysis of the 17p region in tumor samples showed 100% similarity between fluorescent in situ hybridization and EOSAL-CNV. Conclusions EOSAL-CNV allowed reliable, fast, easy detection and characterization of CNVs. It provides an alternative to targeted analysis methods such as MLPA.

Usually, the information known a priori about a newly sequenced organism is limited. Even resequencing the same organism can generate unpredictable output. We introduce MuffinInfo, a FastQ/Fasta/SAM information extractor implemented in HTML5 capable of offering insights into next-generation sequencing (NGS) data. Our new tool can run on any software or hardware environment, in command line or graphically, and in browser or standalone. It presents information such as average length, base distribution, quality scores distribution, k-mer histogram, and homopolymers analysis. MuffinInfo improves upon the existing extractors by adding the ability to save and then reload the results obtained after a run as a navigable file (also supporting saving pictures of the charts), by supporting custom statistics implemented by the user, and by offering user-adjustable parameters involved in the processing, all in one software. At the moment, the extractor works with all base space technologies such as Illumina, Roche, Ion Torrent, Pacific Biosciences, and Oxford Nanopore. Owing to HTML5, our software demonstrates the readiness of web technologies for mild intensive tasks encountered in bioinformatics.

Analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial amount of resources. In EUBra-BIGSEA (Europe–Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications) we address such problems in a comprehensive and integrated way. EUBra-BIGSEA provides a platform for building up data analytic workflows on top of elastic cloud services without requiring skills related to either programming or cloud services. The approach combines cloud orchestration, Quality of Service and automatic parallelisation on a platform that includes a toolbox for implementing privacy guarantees and data quality enhancement as well as advanced services for sentiment analysis, traffic jam estimation and trip recommendation based on estimated crowdedness., The work shown in this article has been funded jointly by European Commission under the Cooperation Programme, Horizon2020 grant agreement No 690116 (EUBra-BIGSEA) and the Min-istériode Ciência,Tecnologiae Inovação(MCTI) from Brazil

The sequencing market has increased steadily over the last few years, with different approaches to read DNA information prone to different types of errors. Multiple studies demonstrated the impact of sequencing errors on different applications of next-generation sequencing (NGS), making error correction a fundamental initial step. Different methods in the literature use different approaches and fit different types of problems. We analyzed 50 methods divided into five main approaches (k-spectrum, suffix arrays, multiple-sequence alignment, read clustering, and probabilistic models). They are not published as a part of a suite (stand-alone), and target raw, unprocessed data without an existing reference genome (de novo). These correctors handle one or more sequencing technologies using the same or different approaches. They face general challenges (sometimes with specific traits for specific technologies) such as repetitive regions, uncalled bases, and ploidy. Even assessing their performance is a challenge in itself because of the approach taken by various authors, the unknown factor (de novo), and the behavior of the third-party tools employed in the benchmarks. This study aims to help the researcher in the field to advance the field of error correction, the educator to have a brief but comprehensive companion, and the bioinformatician to choose the right tool for the right job. WIREs Comput Mol Sci 2016, 6:111–146. doi: 10.1002/wcms.1239 For further resources related to this article, please visit the WIREs website.

Error correction is typically the first step of de Novo genome assembly from NGS data. This step has an important impact on the quality and speed of the assembly process. However, the majority of available stand-alone error correction solutions can only detect and correct mismatches. Therefore, these solutions only support correcting reads generated by Illumina sequencers. Several solutions support insertions and deletions (indels) and are capable of working with multiple technologies. However, these solutions are limited by correction performance and resource consumption. In this paper, we introduce MuffinEc, an indel-aware multi-technology correction method for NGS data. This method uses a greedy approach to create groups of reads and subsequently corrects them using their consensus. MuffinEc surpasses existing solutions by offering better correction ratios for multiple technologies. This method also exploits parallel processing via OpenMP and uses less computational resources than similar programs, thereby being capable of handling large datasets. MuffinEc is open source and freely available at http://muffinec.sourceforge.net., Funding:. This work was supported by Generalitat Valenciana with a Santiago Grisolia Scholarship, reference GRISOLIA/2013/013.