Gianluigi Zanetti

The paper introduces a microservices-based framework for scalable data analysis, validated in metabolomics, enabling dynamic scaling and integration of software tools.

PhenoMeNal provides a cloud-based platform for metabolomics data analysis, integrating tools and workflows to enable reproducible and scalable research.

This paper introduces openEHR archetypes for integrating genomic data into electronic health records and compares them to HL7 FHIR standard.

The paper proposes using Kafka streams to improve genomic data processing pipelines by enabling scalable, fault-tolerant, and composable workflows.

wft4galaxy provides automated testing for Galaxy workflows to enable continuous integration and defect detection.

This paper demonstrates a scalable sequence alignment pipeline for Illumina data using Apache YARN, achieving linear scalability and fault tolerance.