seekr 1.1.1

A library for counting small kmer frequencies in nucleotide sequences.

SEEKR

A library for counting small kmer frequencies in nucleotide sequences.

A webportal is available at seekr.org.

Installation

• To use this library, you have to have Python3.x on your computer. If you don't have it installed, the easiest place to get it is from the Anaconda distribution. Downloading Anaconda will also provide you with most of the dependencies you need to use SEEKR.
• Either download this repository as a .zip file, or use git to clone it.
• Install any missing dependencies by running: pip install -r requirements.txt.

Usage

You can either use SEEKR from the command-line or as a python module. In either case, you will use kmer_counts.py to generate a kmer count matrix of m rows by n columns, where m is the number of transcripts in a fasta file and n is 4^kmer. Then pearson.py can be used to calculate how well correlated all pairwise combinations of sequences are.

Note: Some advanced usages are currently not available from the command-line and require that you import the module.

Here are some quick-start examples if you just want to get going:

Examples

kmer_counts

The default settings produce a binary, normalized numpy file:

$ python kmer_counts.py /path/to/rnas.fa -o /path/to/out.npy

To get a human readable csv file, set the nonbinary flag:

$ python kmer_counts.py /path/to/rnas.fa -o /path/to/out.csv -nb

If you want to add default labels, also set the label flag:

$ python kmer_counts.py /path/to/rnas.fa -o /path/to/out.csv -nb -lb

You can change also change the size of the kmer you're using, and prevent normalization:

$ python kmer_counts.py /path/to/rnas.fa -o /path/to/out.npy -k 4 -nc -ns

pearson

To find Pearson correlations between kmer count profiles, run pearson.py. Running the program and options are similar to kmers_counts.py. Input files for pearson.py will always be the output files from one or more runs of kmer_counts.py. The default setting accept two numpy files and output a third numpy file.

$ python pearson.py /path/to/kc_out.npy /path/to/kc_out.npy -o /path/to/out.npy

The only other options besides the -o flag control binary versus .csv input and output. If you have a non binary input file (i.e. a .csv file) and also want a non binary output file, you can do:

$ python pearson.py /path/to/kc_out.npy /path/to/kc_out.npy -o /path/to/out.npy -nbi -nbo

Advanced usage

A common task is to use the normalization vectors from a large .fa file to analyze specific lncRNAs of interest. Currently, this cannot be done from the command-line, but is still fairly straightforward from within python. This example will serve as a walkthrough on using SEEKR as a module and will take you through creating the normalization vectors (using 4mers) to creating the Pearson's similarity matrix.

import numpy as np
from kmer_counts import BasicCounter
from pearson import pearson

v22_gencode = 'v22_gencode.fa'
gencode_counter = BasicCounter(v22_gencode, k=4)
gencode_counter.get_counts()
np.save('mean.npy', gencode_counter.mean)
np.save('std.npy', gencode_counter.std)

xist = 'xist.fa'
lncRNAs = 'other_lncs.fa'
xist_counter = BasicCounter(xist, '4mers_xist.npy', mean='mean.npy', std='std.npy', k=4)
lncs_counter = BasicCounter(lncRNAs, '4mers_lncs.npy', mean='mean.npy', std='std.npy', k=4)
xist_counter.make_count_file()
lncs_counter.make_count_file()

sim = pearson(xist_counter.counts, lncs_counter.counts, outfile='xist_vs_lncs.npy')

This will write five files to disk:

• mean.npy: Mean vector for gencode human lncRNAs. Once this has been saved, the first portion of the code doesn't need to be run again.
• std.npy: Standard deviation vector for gencode human lncRNAs.
• 4mers_xist.npy: Normalized kmer profile for Xist.
• 5mers_lncs.npy: Normalized kmer profile for other lncRNAs of interest.
• xist_vs_lncs.npy: Pearson's r values for all pairwise comparisons between Xist and the other lncRNAs.

Help

For full documentation of the parameters and flags, you can run kmer_counts.py or pearson.py without any arguments.

$ python kmer_counts.py

Issues

Any suggestions, questions, or problems can be directed to our GitHub Issues page. #TODO update to full link after acceptance.