1) Prerequisite: Python >= 2.5 and R.
2) Type `make` in the KmerGenie directory
* To enable larger values of k, e.g. 200, type `make k=200`.
reads_file is either a FASTA, FASTQ, FASTA.gz, FASTQ.gz file or a list of filenames, with one file name per line.
type ./kmergenie to see extra options
Input reads should be exactly those the de novo assembler will use to create contigs, i.e. the list of all single and paired-end reads.
The order does not matter, KmerGenie treats the reads as an unordered set of k-mers. Orientation of the reads also does not matter.
With Velvet, if you have mate-pairs, Velvet uses them to create contigs, so do include them in KmerGenie.
Otherwise, if the mate-pairs are used only for scaffolding (i.e. asm_flag=2 in SOAPdenovo), do not include them.
By default, KmerGenie will perform another pass to estimate k more precisely. To skip it, specify "--one-pass".
To run multiple instances of KmerGenie on the same folder, specify the -o and -t parameters (with output prefixes, and less threads per instance).
file containing the predicted number of genomic kmers for each k value
files containing approximate histograms for each k value
plots of the histograms and the fits
last line is the best k value