Gregory W. Schwartz
This program will take a fasta file and find the diversity and rarefaction curve. The sequences must be aligned.
Depends on "fasta": https://github.com/GregorySchwartz/fasta.git
Please cite this paper if you are using this program
Thanks to stack installation is a lot easier:
stack install diversityThe binary should now be in your path (local bin).
Let's say we have a bunch of made up sequences,
>1
AAT
>2
AAT
>3
A-G
>4
--G
>5
TCG
>6
TTG
>7
TAG
>8
GGGthat we want to know the diversity of at each position. We can put those
sequences in a file input.fasta and check the diversity at each position:
diversity -i input.fasta -o output.csv -w 1 -r 1We can see in output.csv that each position has a diversity associated with
it. This diversity is calculated using order 1 in this case with a window length
of 1, meaning that at each position we are looking at a single character, in
this case a nucleotide. Gaps are ignored at each position.
Now, what if we wanted to find the diversity using a sliding window of two
nucleotides? That is, position 1 would analyze AA AA AG N/A TC TT TA GG, where
gaps are artificial and so we skip over them and we don't start on a gap. We can
achieve this by setting the window length to 2:
diversity -i input.fasta -o output.csv -w 2 -r 1The output is similar, but now there are only positions 1 and 2 as we cannot
start at 3 if there is no fragment of length 2 or more. If we wanted to look at
the entire sequence, we could forego the -w flag and just use a to treat the
entire sequence as a single fragment. This analysis would be useful for just
finding the diversity of a collection of sequences ignoring position.
We can also find the diversity of any kind of entity: say we had input.txt
containing:
cat
cat
cat
dog
dog
cat
parrotWe can find the diversity of species in this file using:
diversity -i input.txt -o output.csv -L -aWe are saying that we just have a list of entities separated by line (-L) and
we want to look at the entire entity, not positional (-a).
To look at the rarefaction curves and see if we have sampled enough species, we can use
diversity -i input.txt -o output.csv -c rarefaction_output.csv -L -ato get the rarefaction curve at each position (in this case just the entire
entity). We can use -f to speed up this calculation, but at the cost of
accuracy. In addition, -f cannot work on large data sets (they will show up
as NaN). In this case, you must use the slower, but more accurate default
method. The output for the curve is similar to the diversity output, with
label, window, position, and weight columns. However, there are three additional
columns: subsample, expected_richness, and mad. The rarefaction curve tells us
if we have sufficiently sampled enough if the curve plateaus. To check if the
curve levels off, we can plot the subsample column as the x axis and the
expected_richness column as the y axis. The mad column is only for empirical
richness, described below. The other file generated with -O contains a
percent_above column, which tells us the percent of subsamples that are above
95% of the height of the curve. This can give us a quick glance at the curve to
know if the curve "plateaus" (unless there are NaNs, which result in a nonsense
percentage). You must be careful when looking at this percentage, however, as it
might be high due to a very low number of sequences, say 2 or 3, as it's a
percent of those low values so it might be artificially high in those cases, but
you can tell by the weight. There is also an additional sampling column, which
says how many more samples are needed to get the proportion g of the estimated
richness in the assemblage. That estimated number is included under the S_est
column, where the observed richness is in the richness column. If the additional
sampling has a value of NaN, the number of entities in two samples is probably 0
so the value cannot be calculated.
For very large samples, the theoretical calculation of rarefaction may take approximately an eternity to execute with a high probability of memory issues (however there have been performance improvements so use this paragraph only if needed). To overcome this issue, we look to empirical rarefaction. We can literally take random subsamples from the population in -R runs, resulting in a median of the values as well as the median absolute deviation for variation in the runs. You must use this method (in individual based rarefaction only, triggered automatically when the number of random runs, -R, is specified) or else you will run into performance issues with large data sets.
For an overview of diversity
For a positional and sliding window use case that uses this program
Germline Amino Acid Diversity in B Cell Receptors is a Good Predictor of Somatic Selection Pressures
Usage: diversity [-l|--input-label LABEL] [-r|--input-order [1]|INT]
[-w|--input-window [1]|INT] [-i|--input-fasta FILE]
[-S|--input-sample-field INT] [-C|--input-count-field INT]
[-I|--input-subsampling INT INT (INT)] [-g|--input-g Double]
[-f|--fast-bin] [-R|--input-runs INT] [-G|--keep-gaps]
[-n|--remove-N] [-a|--whole-sequence] [-L|--list] [-s|--sample]
[-d|--rarefaction-df] [-t|--std] [-O|--output-rarefaction FILE]
[-c|--output-rarefaction-curve FILE] [-o|--output FILE]
Quantify the diversity of a population
Available options:
-h,--help Show this help text
-l,--input-label LABEL The label for this particular dataset (to
differentiate the file in batch analyses)
-r,--input-order [1]|INT The order of true diversity
-w,--input-window [1]|INT
The length of the sliding window for generating
fragments
-i,--input-fasta FILE The fasta file containing the germlines and clones
-S,--input-sample-field INT
The index for the sample ID in the header separated
by '|' (1 indexed)
-C,--input-count-field INT
The index for the number of this type in the header
separated by '|' (1 indexed). Used if there are
multiple copies of one entry, so a '4' in the header
would indicate that this entity occurred 4 times.
Defaults to 0, meaning that this field is ignored and
count each sequence as occurring just once
-I,--input-subsampling INT INT (INT)
The start point, interval, and optional endpoint of
subsamples in the rarefaction curve. For instance, '1
1 4' would be 1, 2, 3, 4 '2 6 14' would be 2, 8, 14,
... Note: input is a string so use quotations around
the entry and it always includes the number of
subsamples overall in the result. Excluding the
endpoint results in the number of samples the
endpoint, so '1 1' would be 1, 2, 3, ..., N
-g,--input-g Double Used for calculating the number of individuals (or
samples) needed before the proportion g of the total
number of estimated species is reached. Sobs / Sest <
g < 1
-f,--fast-bin Whether to use a much faster, but approximated,
binomial coefficient for the rarefaction analysis.
This method results in NaNs for larger numbers, so in
that case you you should use the slower, more
accurate default method
-R,--input-runs INT The number of runs for empirical resampling
rarefaction. This method does not compute the
theoretical, it reports the actual median and median
absolute deviation (MAD) values of this many runs. If
this value is not 0, empirical rarefaction is
automatically enabled (individual based only, not for
sample based)
-G,--keep-gaps Do not remove '.' and '-' characters from the
analysis. This flag will thus treat these characters
as additional entities rather than be ignored as
artificial biological gaps in a sequence
-n,--remove-N Remove 'N' and 'n' characters
-a,--whole-sequence Ignore window length and only analyze the entire
sequence for diversity and rarefaction curves.
-L,--list Analyze a diversity of species in a list separated by
lines instead of a fasta file
-s,--sample Whether to use sample based rarefaction (requires
sample ID field from input-sample-field)
-d,--rarefaction-df Whether to output the rarefaction curve as a data
frame
-t,--std Whether to output to stdout or to a file if no file
is supplied
-O,--output-rarefaction FILE
The csv file containing the rarefaction values (the
percent of the rarefaction curve that is above 95% of
the height of the rarefaction curve). Expects a
string, so you need a string even with std
-c,--output-rarefaction-curve FILE
The csv file containing the rarefaction curve.
Expects a a string, so you need a string even with
std
-o,--output FILE The csv file containing the diversities at each
position. expects a string, so you need a string even
with std