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Delprato A.,French National Center for Scientific Research | Aransay A.M.,CIC bioGUNE and CIBERehd | Kollmus H.,Helmholtz Center for Infection Research | Schughart K.,Helmholtz Center for Infection Research | And 4 more authors.
Mammalian Genome | Year: 2013

The second scientific meeting of the European systems genetics network for the study of complex genetic human disease using genetic reference populations (SYSGENET) took place at the Center for Cooperative Research in Biosciences in Bilbao, Spain, December 10-12, 2012. SYSGENET is funded by the European Cooperation in the Field of Scientific and Technological Research (COST) and represents a network of scientists in Europe that use mouse genetic reference populations (GRPs) to identify complex genetic factors influencing disease phenotypes (Schughart, Mamm Genome 21:331-336, 2010). About 50 researchers working in the field of systems genetics attended the meeting, which consisted of 27 oral presentations, a poster session, and a management committee meeting. Participants exchanged results, set up future collaborations, and shared phenotyping and data analysis methodologies. This meeting was particularly instrumental for conveying the current status of the US, Israeli, and Australian Collaborative Cross (CC) mouse GRP. The CC is an open source project initiated nearly a decade ago by members of the Complex Trait Consortium to aid the mapping of multigenetic traits (Threadgill, Mamm Genome 13:175-178, 2002). In addition, representatives of the International Mouse Phenotyping Consortium were invited to exchange ongoing activities between the knockout and complex genetics communities and to discuss and explore potential fields for future interactions. © 2013 Springer Science+Business Media New York. Source

Mosen-Ansorena D.,CIC bioGUNE and CIBERehd | Telleria N.,Clinical Analyses Service at the San Carlos Clinical Hospital | Veganzones S.,Dominion | la Orden V.D.,Clinical Analyses Service at the San Carlos Clinical Hospital | And 2 more authors.
BMC Genomics | Year: 2014

Background: Deviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles.Results: We introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation.Conclusions: seqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow. © 2014 Mosen-Ansorena et al.; licensee BioMed Central Ltd. Source

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