Scope

Next-generation sequencing and ever-expanding "omics" technologies have resulted in the generation of vast quantities of data on plant genomes, proteomes, transcriptomes, and more. Plant Bioinformatics applies computational tools to draw meaning from these huge datasets.

As the bioinformatics field grows rapidly Plant Bioinformatics will be at the forefront of publishing research across the breadth of the discipline, from opinions and hypothesis through to original research, new software designs, and new methods that will advance the field with a specific focus for plant scientists. The discipline spans levels of investigation from single genes to complex systems.

Reports may be as brief and concise as necessary to accurately describe new analytical tools and approaches.

Plant Bioinformatics particularly encourages the submission of papers that discuss new or improved algorithms, software, and online resources. Articles of interest include but are not limited to:

● Computational analysis of large multidimensional numeric datasets, including gene-expression; gene and protein interaction data; metabolite and small molecule profiling. 

●      Prediction of the function of novel domains, motifs, genes, and proteins using omics data.

●      Computational analysis of nucleotide or amino acid sequences and structures from genomic data.

●      Computational analysis and phylogenetic approaches to biological questions.

●      Computational analysis of the evolution of all life forms.

●      Mathematical or statistical modeling of all of the above.

●      Three-dimensional genome topology construction and epigenetic regulation modeling.

●      Algorithms that provide meaningful insights into biology from the analysis of plant genomic data.

Please consider the requirements for experimental studies as listed below:

Quantitative analysis needs to be performed on a minimum number of 3 biological replicates in order to enable an assessment of significance and ensure depth of the analysis. This includes quantitative omics studies as well as phenotypic measurements, quantitative assays, and qPCR expression analysis. Studies that do not comply with these replication requirements will not be considered for review.

Studies falling in the categories below will also not be considered for review, unless they are extended to provide meaningful insights into gene/protein function and/or the biology of the subject described.:

●    Comparative transcriptomic analyses that only reports a collection of differentially expressed genes, some validated by qPCR under different conditions or treatments;

●      Descriptive studies that merely define gene families using basic phylogenetics and assign cursory functional attributions (e.g. expression profiles, hormone or metabolites levels, promoter analysis, informatic parameters).

Requirements for multiple independent alleles should also be considered:

Studies using transgenic or mutant lines (plants and algae) should be based on data from multiple independent alleles (at least 2) displaying a common and stable phenotype. Examples include T-DNA, transposon, RNAi, CRISPR/Cas9, chemically induced, overexpressors, reporter fusions (GUS, FPs, LUC) etc. Qualitative data can be presented from a single allele but should be indicative of observations from multiple alleles which should be explicitly stated in the text. Quantitative data should be derived from multiple alleles (at least 2) and should be displayed separately for each allele (with at least 3 independent replications for each allele). Studies reporting single alleles may be considered acceptable when:

i) Complementation via transformation is used for confirmation.

ii) The allele has been previously characterized and published and is representative of multiple independent lines.

iii) Systems where genetic transformation is difficult or not yet possible, alternative evidence should be presented supporting the reported allele.

Submission

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