preqclr

preqclr is a software tool that reports on quality for long read sequencing data without the use of a reference genome. With the emergence of new long read sequencing technology such as Pacbio Single Molecule, Real-Time (SMRT) Sequencing technology and Oxford Nanopore Technologies (ONT), there is a need for a method that assesses sequencing quality prior to analyses. This tool enables users to visualize metrics of quality.

There are two components to preqclr:

1. calculate
2. report

preqclr generates a PDF report with the following plots:

• Estimated genome size
• Read length distribution
• Estimated coverage distribution
• Per read GC content distribution
• Estimated coverage vs read length
• Total number of bases as a function of minimum read length
• NG(X)

Dependencies

To create the files needed:

• minimap2 (for overlap detection)
• miniasm (optional for NGX plots)

For the calculation step:

• C++ compiler with C++11 support

For the report generation step:

• Python2.7.11
• matplotlib
• BioPython
• setuptools (to download report script dependencies)

Install

To install from source:

git clone --recursive https://github.com/simpsonlab/preqclr.git
cd preqc-lr
make

# download report script dependencies
# create virtual environment
virtualenv preqclr-venv
source preqclr-venv/bin/activate
python setup.py install

Quick usage

# STEP 0: overlap detection and contig lengths
    minimap2 -x ava-ont reads.fq reads.fq > overlaps.paf
    miniasm -f reads.fq overlaps.paf > layout.gfa

# STEP 1: calculate data for plots
    ./preqclr -r reads.fq \
              --paf overlaps.paf \
              --gfa layout.gfa \
              -n ecoli_sample.pacbio \
              --verbose

# STEP 2: create a PDF report
python preqclr-report.py -i ecoli_sample.pacbio.preqclr --verbose 

Example report

Tips

• When using minimaps, we recommend using the settings optimized for PacBio reads (-x ava-pb) and ONT reads (-x ava-ont).

Learn

• Documentation here
• Quickstart tutorial here

Acknowledgements

preqclr uses overlap and assembly information from minimap2 and miniasm, respectively. To parse the output PAF file, and efficiently read fasta files we used kseq and PAF parser in miniasm. I would like to thank Heng Li for developing these tools.