Frequently Asked Questions¶

What is this curvulin in the Complexity panel?¶

It is a local genome variability profile, which we call complexity profile. The higher the values, the more often changes occurs in this region of genome. By change we mean: gene insertions/deletions/translocations. Large-scale changes (e.g., large inversions) have little effect on complexity value; large scale means larger than the window parameter. Complexity profiles calculated with window values 20, 50, 100 are available at gcb.rcpcm.org, to obtain complexity profiles with other windows sizes you need to use stand-alone version.

For two reasons. Firstly, only fixed variants are observed, so we measure not variability per se, but combination of genome variability and fitness cost of the genome changes. Secondly, not only the quantity, but also the pattern of the changes contributes to the complexity value. The more overlapping changes occur, the greater their contribution.

Circles correspond to the genes, or more precisely, to the orthogroups, inferred from the set of genomes. Lines (graph edges) connects genes, which are adjacent in at least one of the genomes (the larger the number of genomes these genes are located next to each other, the thicker the line).

By majority rule. We consider how many times the name of a gene occurs in the names of genes within the same orthogroup, and we choose the most common one.

Not always. GCB by default skips paralogues genes, but they can still be analysed by switching Draw paralogues toggle in the left slide bar.

from genome annotations (i.e. Genebank or RefSeq database) by searching for particular gene.
from DOOR database of operons [Mao, Xizeng, et al., Nucleic acids research, 2014] (currently available here ).
from papers, in which genome coordinates are specified.

Export graph to JSON (left panel->File->Graph->Download JSON), and then import it in a Cytoscape (File->Import->Network from File), or other graph editing software.

You should use standalone application in this case. Command-line tools are available here, local webserver is available here.

Most likely yes, via Windows Subsystem for Linux, but we only tested GCB on Linux (Ubuntu, CentOS).

We recomend using phylogenetically narrow groups of genomes as input to the GCB analysis (species, and even better - clades of the phylogenetic tree consisting of several tens of genomes). In this case local variability of genomes is not confused by large-scale changes in the genome (e.g., inversions). If you have a genome of interest, it’s a good idea to take in addition to it 50-100 closest genomes.

In case of bacterial (not viral) genomes, it is recommended to run orthology inference on computational cluster. However, you may try it, and if orthogroups inference will be completed, then it is OK to continue the analysis. Local server application can be run on a standart PC.

It depends on computational resources you have. If you have a computational cluster, then hundreds of genomes will be OK in most cases. If you have problems running analysis for your project feel free to contact us, we will try to help you (e.g., run orthology inference on our side).