We welcome manuscripts providing accessible and reusable large-scale data, computational biology or software resources of broad interest targeting one or more of the subject areas of Physiologia Plantarum.
Data resources can be at any level from the genome through to phenotype but must add new and novel value. Resource articles should highlight the value of the resource to the broad community and should contain a succinct summary of the biological aims and findings obtained from the resource.
However, resource articles can be descriptive and exploratory in nature. Raw and processed data or source code must be available.
To ease writing and reading, those articles follow a specific format. Please refer to “Resource article“.
Nathaniel Street
Umeå Plant Science Centre
Department of Plant Physiology
Umeå University, Sweden
nathaniel.street@umu.se
Area of expertise: Genomics; natural variation; metatranscriptomics; genome assembly; co-expression networks
Handling Editors
Alisdair Fernie
Max-Planck-Institute of Molecular Plant Physiology
Potsdam, Germany
fernie@mpimp-golm.mpg.de
Area of expertise: primary metabolism, source sink interactions, metabolomics, TCA cycle, metabolic regulation, QTL anaylsis, GWAS
Jian-Feng Mao
Umeå Plant Science Center
Sweden
jianfeng.mao@umu.se
Area of expertise: Genome assembly, Transposable Elements, Ecological Genomics, Genome Evolution
Marek Albert Mutwil
Nanyang Technological University
Singapore
mutwil@gmail.com
Area of expertise: gene function prediction, specialized metabolism, stress resilience, databases, algorithms
Pirita Paajanen
John Innes Centre
UK
pirita.paajanen@jic.ac.uk
Area of experise: RNASeq, Genomics, bioinformatics
Jarkko Salojärvi
School of Biological Sciences
Nanyang Technological University
Singapore
jarkko@ntu.edu.sg
Area of expertise: Population genomics, computational modelling, evolution, molecular ecology
Aalt Dirk Jan van Dijk
Bioinformatics Group
Wageningen University
The Netherlands
aaltjan.vandijk@wur.nl
Area of expertise: machine learning, algorithms, gene function prediction, protein structure