Skip to main content

Discrete mixture model based ploidy inference tool

Project description



PloidPy is a program written in Python designed to infer ploidy from next-generation reads aligned to a haploid reference genome. The program makes use of a stastical model representing the distribution of specific nucleotide counts and selects the most probable ploidy on the basis of a minimum AIC.


Installation of PloidPy is relatively simple and can be done easily using pip. The most recent version can be installed using the following command:

pip install git+git://


In order to run PloidPy, the following dependencies are required:

  • Python 3.6+
  • NumPy
  • SciPy
  • Statsmodels
  • matplotlib
  • seaborn
  • pysam

Getting Started

Once installed, PloidPy can be run on an indexed BAM file. For this tutorial we will download some BAM files created using simulated data from the ploidyNGS repository.


After downloading these files, we have to index all of these files using samtools.

samtools index Ploidy2.bowtie2.sorted.bam
samtools index Ploidy3.bowtie2.sorted.bam
samtools index Ploidy4.bowtie2.sorted.bam

From here we can extract the minor allele count (MAC) and total read coverage from each of the BAM files using PloidPy process_bam. In the following example the Phred-like mapping threshold is set to 15

PloidPy process_bam --bam Ploidy2.bowtie2.sorted.bam --out diploid.count --quality 15
PloidPy process_bam --bam Ploidy3.bowtie2.sorted.bam --out triploid.count --quality 15
PloidPy process_bam --bam Ploidy4.bowtie2.sorted.bam --out tetraploid.count --quality 15

In addition to providing raw data to be used, the output from this also calculates the mean base call error probability (p_err) from each file. This will be important for both filtering the data and ploidy evaluation of unfiltered data. In the examples above the diploid, triploid, and tetraploid individuals have p_err values of 0.0012170473202784014, 0.001184570850292672, and 0.0011672555287687418 respectively.

After this has been done, one of two steps can be taken. Either inferring ploidy using the unfiltered data with an error component incorporated into the model or filtering the data and running the model with no error component. One can filter out the data from each individual using the following commands:

PloidPy filter --count_file diploid.count --error_prob 0.0012170473202784014 --out diploid.count.filtered
PloidPy filter  --count_file triploid.count --error_prob 0.001184570850292672 --out triploid.count.filtered
PloidPy filter  --count_file tetraploid.count --error_prob 0.0011672555287687418 --out tetraploid.count.filtered

With this we can evaluate the ploidy of each individual, using the PloidPy assess subcommand. When using filtered data, one should exclude the "--error_prob" argument which tells the program not to incorporate the error model into the evaluation. In this example, ploidies of 2n to 8n will be evaluated, although this can be done with any set of ploidies greater than or equal to 2n. This can be done with the following commands:

PloidPy assess --count_file diploid.count.filtered --out diploid.filtered.tsv --ploidies 2 3 4 5 6 7 8
PloidPy assess --count_file triploid.count.filtered --out triploid.filtered.tsv --ploidies 2 3 4 5 6 7 8
PloidPy assess --count_file tetraploid.count.filtered --out tetraploid.filtered.tsv --ploidies 2 3 4 5 6 7 8

The evaluation on unfiltered can similarly be done with the following commands:

PloidPy assess --count_file diploid.count --out diploid.tsv --ploidies 2 3 4 5 6 7 8 --error_prob 0.0012170473202784014
PloidPy assess --count_file triploid.count --out triploid.tsv --ploidies 2 3 4 5 6 7 8 --error_prob 0.001184570850292672
PloidPy assess --count_file tetraploid.count --out tetraploid.tsv --ploidies 2 3 4 5 6 7 8 --error_prob 0.0011672555287687418

As you can see, in both cases all of the predictions were correct! Additional information can be found in the *tsv files. Each column represents the following:

Column Meaning
Ploidy Ploidy model
Log_Likelihood Log likelihood of ploidy model
AIC AIC value of ploidy model
Het_Weights Weight parameter of each heterozygous state component
Uniform Weight Weight parameter of the uniform component of the model

Additionally, the filtered data can be used to produce helpful figures to visualize the joint distribution of TRC and MAC values. This can be done with the following commands:

PloidPy histo --count_file diploid.count.filtered --out diploid
PloidPy histo --count_file triploid.count.filtered --out triploid
PloidPy histo --count_file tetraploid.count.filtered --out tetraploid

And here are the images!




Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for PloidPy-oaolayin, version 0.1.0
Filename, size File type Python version Upload date Hashes
Filename, size PloidPy_oaolayin-0.1.0-py3-none-any.whl (10.3 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size PloidPy-oaolayin-0.1.0.tar.gz (7.7 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page