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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

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.