Covest estimates the coverage and genome size, just from k-mer abundance histogram computed from DNA sequences reads.
Project description
Tool that estimates coverage (and genome size) of dna sequence from reads.
Requirements
python 3.4+
Installation
We suggest to install CovEst in python3 virtual environment.
pip install covest
For development:
pip install -e . from the project directory
Usage
type covest --help for the usage.
Basic Usage:
covest histogram -m model -k K -r read_length
You can specify the read file using -s reads.fa parameter for more precise genome size computation.
default K is 21
default read length is 100
currently, the supported models are:
basic: for simple genomes without repeats
repeat: for genomes with repetitive sequences
Input Histogram Specification:
The input histogram can be generated from the read data using jellyfish.
jellyfish count -m K -C reads.fa -o table.jf
jellyfish histo table.jf -o reads.hist
The format of the histogram is just list of lines. Each lines contains an index and value separated by space.
Output Specification:
CovEst outputs it’s results in simple subset of YAML format for best human readability and possibility of machine processing.
The output are lines containing key: value. The most important keys are coverage and genome_size (or genome_size_reads if read file was specified).
Other included tools
geset.py tool for estimation genome size from reads and known coverage
prepare_experient.py tool for experiment pipeline setup
experiment_table.py tool which collects data from experiment and create a nice table (html, tex, and csv formats are supported)
Read simulator:
generate_sequence.py random sequence generator
read_simulator.py tool for generating random reads form the sequence
Copyright and citation
CovEst is licenced under GNU GPLv3 license.
- CovEst is research software, so you should cite us when you use it in scientific publications!
Hozza, M., Vinař, T., & Brejová, B. (2015, September). How Big is that Genome? Estimating Genome Size and Coverage from k-mer Abundance Spectra. In String Processing and Information Retrieval (pp. 199-209). Springer International Publishing.
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