EMBnet.journalPub Date : 2019-02-05DOI: 10.14806/EJ.24.0.917
Yen Hoang, J. Pfeil, Maja Zagorščak, Axel Thieffry, Eftim Zdravevski, Živa Ramšak, Petre Lameski, Sabrina K. Schulze, Eleni D Papakonstantinou, L. Papageorgiou, Tarry Singh, Ariel Duarte-López, M. Pérez-Casany
{"title":"Report on the “Advanced Big Data Training School for Life Sciences”, Barcelona 3th-7th September 2018","authors":"Yen Hoang, J. Pfeil, Maja Zagorščak, Axel Thieffry, Eftim Zdravevski, Živa Ramšak, Petre Lameski, Sabrina K. Schulze, Eleni D Papakonstantinou, L. Papageorgiou, Tarry Singh, Ariel Duarte-López, M. Pérez-Casany","doi":"10.14806/EJ.24.0.917","DOIUrl":"https://doi.org/10.14806/EJ.24.0.917","url":null,"abstract":"The “Advanced Big Data Training School for Life Sciences” took place during September 3-7, 2018, organized by the Data Management Group (DAMA-UPC) at the Technical University of Catalonia (UPC) in Barcelona, Spain. It is the follow-up training school of the first “Big Data Training School for Life Sciences”, held in Uppsala, Sweden, in September 2017, which was defined and structured at the “Think Tank Hackathon”, held in Ljubljana, Slovenia, in February 2018. The aim of this training school was to get participants acquainted with emerging Big Data processing techniques in the field of Computational Biology and Bioinformatics.This article explains in detail the development of the training school, the covered contents and the interaction of the participants within and out of the training event by the student, organizer and lecturer perspective.","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86904117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A genomic data mining pipeline for 15 species of the genus <i>Olea</i>.","authors":"Constantinos Salis, Eleni Papakonstantinou, Katerina Pierouli, Athanasios Mitsis, Lia Basdeki, Vasileios Megalooikonomou, Dimitrios Vlachakis, Marianna Hagidimitriou","doi":"10.14806/ej.24.0.922","DOIUrl":"https://doi.org/10.14806/ej.24.0.922","url":null,"abstract":"<p><p>In the big data era, conventional bioinformatics seems to fail in managing the full extent of the available genomic information. The current study is focused on olive tree species and the collection and analysis of genetic and genomic data, which are fragmented in various depositories. Extra virgin olive oil is classified as a medical food, due to nutraceutical benefits and its protective properties against cancer, cardiovascular diseases, age-related diseases, neurodegenerative disorders, and many other diseases. Extensive studies have reported the benefits of olive oil on human health. However, available data at the nucleotide sequence level are highly unstructured. Towards this aim, we describe an <i>in-silico</i> approach that combines methods from data mining and machine learning pipelines to ontology classification and semantic annotation. Fusing and analysing all available olive tree data is a step of uttermost importance in classifying and characterising the various cultivars, towards a comprehensive approach under the context of food safety and public health.</p>","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"24 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6583798/pdf/nihms-1031557.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37071598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"NOTCH3 and CADASIL syndrome: a genetic and structural overview.","authors":"Eleni Papakonstantinou, Flora Bacopoulou, Dimitrios Brouzas, Vasileios Megalooikonomou, Domenica D'Elia, Erik Bongcam-Rudloff, Dimitrios Vlachakis","doi":"10.14806/ej.24.0.921","DOIUrl":"10.14806/ej.24.0.921","url":null,"abstract":"<p><p>CADASIL syndrome is a rare disease that belongs to a group of disorders called leukodystrophies. It is well established that NOTCH3 gene on chromosome 19 is primarily responsible for the development of the CADASIL syndrome. Herein, an attempt is made to shed light on the actual molecular mechanism underlying CADASIL syndrome, through insights extracted from comprehensive evolutionary studies and in silico modelling on Notch 3 protein. In particular, we suggest the use of optical coherence tomography angiography for the detection of early signs of small vessel diseases, which are the major precursors to a repertoire of neurodegenerative conditions, including CADASIL.</p>","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"24 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6583802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37085692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
EMBnet.journalPub Date : 2018-04-18DOI: 10.14806/EJ.24.0.912
Sabrina K. Schulze, Živa Ramšak, Yen Hoang, Eftim Zdravevski, J. Pfeil, Ariel Duarte-López, Uwe Baier, Maja Zagorščak
{"title":"Proceedings of the “Think Tank Hackathon’’, Big Data Training School for Life Sciences Follow-up, Ljubljana 6th – 7th February 2018","authors":"Sabrina K. Schulze, Živa Ramšak, Yen Hoang, Eftim Zdravevski, J. Pfeil, Ariel Duarte-López, Uwe Baier, Maja Zagorščak","doi":"10.14806/EJ.24.0.912","DOIUrl":"https://doi.org/10.14806/EJ.24.0.912","url":null,"abstract":"On 6 th and 7 th February 2018, a Think Tank took place in Ljubljana, Slovenia. It was a follow-up of the “Big Data Training School for Life Sciences” held in Uppsala, Sweden, in September 2017. The focus was on identifying topics of interest and optimising the programme for a forthcoming “Advanced” Big Data Training School for Life Science, that we hope is again supported by the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15110). The Think Tank aimed to go into details of several topics that were - to a degree - covered by the former training school. Likewise, discussions embraced the recent experience of the attendees in light of the new knowledge obtained by the first edition of the training school and how it comes from the perspective of their current and upcoming work. The 2018 training school should strive for and further facilitate optimised applications of Big Data technologies in life sciences. The attendees of this hackathon entirely organised this workshop.","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"55 1","pages":"912"},"PeriodicalIF":0.0,"publicationDate":"2018-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90778230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
EMBnet.journalPub Date : 2018-04-18DOI: 10.14806/EJ.24.0.910
L. Papageorgiou, Picasi Eleni, S. Raftopoulou, Meropi Mantaiou, V. Megalooikonomou, D. Vlachakis
{"title":"Genomic big data hitting the storage bottleneck","authors":"L. Papageorgiou, Picasi Eleni, S. Raftopoulou, Meropi Mantaiou, V. Megalooikonomou, D. Vlachakis","doi":"10.14806/EJ.24.0.910","DOIUrl":"https://doi.org/10.14806/EJ.24.0.910","url":null,"abstract":"During the last decades, there is a vast data explosion in bioinformatics. Big data centres are trying to face this data crisis, reaching high storage capacity levels. Although several scientific giants examine how to handle the enormous pile of information in their cupboards, the problem remains unsolved. On a daily basis, there is a massive quantity of permanent loss of extensive information due to infrastructure and storage space problems. The motivation for sequencing has fallen behind. Sometimes, the time that is spent to solve storage space problems is longer than the one dedicated to collect and analyse data. To bring sequencing to the foreground, scientists have to slide over such obstacles and find alternative ways to approach the issue of data volume. Scientific community experiences the data crisis era, where, out of the box solutions may ease the typical research workflow, until technological development meets the needs of Bioinformatics.","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75811460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}