timflutre / quantgen Goto Github PK

See this thread on GATK forum.

Currently, it is "4c"by default.

In "utils.cpp", the function elapsedTime() doesn't return the number of days if the program ran for more than one day, and instead the number of hours starts again from 0.

Would it be posible to change the stopifnot condition l271-273 in utilis_fatsqc.R so that the script can also deal with fastqc outputs whith user-provided adapter sequences?
Thanks,

N.

In "get_summary_stats.cpp", no permutation P-value should be equal to 0. That's why Brian Browning explains in his paper describing PRESTO (BMC Bioinformatics 2008): "If k out of P permutations have a maximum test statistic greater than the maximum test statistic for the original data, the multiple-testing adjusted P-value for the experiment is (k+1)/(P+1)".

verbosity level for demultiplex.py = verbosity level of test_demultiplex.py -1
verbosity argument could be an indicator variable (0 or 1)

Samtools v >= 1.3

Picard which requires R

check gbs.py and demultiplex.py

gbs.py doesn't work with Python 2.6
-> Python v >= 2.7

Then add it in the "info" page of the game and in the code.

In "get_summary_stats.cpp", use the Fisher transformation of the Spearman rank correlation coefficient (see here). Compare to the approximation leading to a Student distribution, with the Fisher transformation I can get a Z score, as used by Fu et al.

If there is one (pair of) fastq file(s) per lane, then one simply indicates the lane ID, say "4", in the "lane" column of the sample file.

If there is more than one (pair of) fastq file(s) per lane, say 3, e.g. if a double barcoding was used, then one can concatenate the lane ID "4", with other numbers, say "41", "42" and "43", in the "lane" column of the sample file.

This could be mentioned in the help message.

e.g. via capture

In "get_summary_stats.cpp", allow to skip parameter "--links" usually used to precise which SNPs are in cis of which genes. To do so efficiently, compute summary stats for each SNP by reading the genotype file line by line. This will therefore allow to easily compute summary stats for all SNPs, whether they are in cis or trans of any gene.

in step 4