Scope

The discipline of Computational Genomics sits at the interface between genomics, the quantitative sciences (such as mathematics, statistics, computer science) and engineering, and spans levels of investigation from single genes to systems. This section aims to rapidly publish new methods, research findings, opinions, and hypothesis articles on all aspects of the discipline.

As computational genomics bridges data interpretation, technology and software development, the section welcomes Original Research and Methods articles describing new analytical approaches and tools - which may be as brief as required for accurate presentation. Additionally, reviews of new approaches can be submitted as Technology Reports or Review articles.

The Computational Genomics Section particularly encourages the submission of papers that discuss new or improved algorithms, software and online resources dealing with all of the below (including some form of validation is highly encouraged):

·       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 and structures from genomic data;

·       Computational analysis and phylogenetic approaches to biological questions;

·       Computational analysis of evolution of all life forms;

·       Mathematical or statistical modeling of all of the above;

·       Algorithms which provide meaningful insights into biology from the analysis of genomic data

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. Studies relating to the prediction of clinical outcome require some validation of findings:

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

·       Re-analysis of existing genomic, transcriptomic data which attempts to identify a candidate set of diagnostic or prognostic markers for disease.  

·       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).

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