ClonalFrameML for analyses of gene families

[juliaped]

Hello,

I have recently started to use ClonalFrameML, as I want to analyse recombination in my data set. Although I understood from the introduction that the program is mainly aimed at genome analyses, I had understood that I could also use it for e.g. gene family analyses. However after reading through a couple of issues I am uncertain of my approach, should ClonalFrameML not be used for single family analyses? Should I instead use ClonalFrame (I wanted to use the ML version for the statistical advantages)?

Also, I read a few issues regarding core genome vs. core gene alignments, and did not fully understand if core gene alignments can be used or not? Are core genome alignments preferred? If core gene alignments can be used, do you have any suggestions on how to convert the concatenated alignment into .xmfa-format (I have not used mauve but mafft for alignment of the individual core gene trees).

Thanks in advance!

Kind regards,
Julia

[xavierdidelot]

Dear Julia,

ClonalFrame and ClonalFrameML have the same requirements in terms of input data, so there is no reason to use ClonalFrame rather than ClonalFrameML.

The best input is a whole-genome alignment as a multi-fasta file. You can also use a gene-by-gene alignment of core genes as a xmfa file, but this is not as good as a whole-genome alignement firstly because it includes less sites and secondly because you lose the information on gene order along the genomes. The xmfa format is not specific to Mauve, and is easy to generate for example if you have alignments of each locus then you can combine them into a xmfa file simply by adding '=' symbols between the loci.

You can also use a xmfa file with any number of loci (eg 7 for MLST) but the less genes are used the more difficult it is to correctly infer the clonal genealogy. If you have only a single loci for example then any recombination event replacing the loci will erase all information about clonal relationships.

Best wishes,
Xavier

[juliaped]

Thanks a lot, I really appreciate it! Now I understand.

Best regards,
Julia