A showcase of functions using Biopython, FastqGeneralIterator, pandas, and matplotlib for teaching Big Data parsing for NGS

cd DataViz_For_Fastq

gunzip Life_Expectancy_US/US_A_USALEEP.csv.gz

Alternatively, in a multi-user environment, load Python module:

module load Python/3.9.1

Exact path to bin/python may differ according to Python version.

virtualenv -p /apps/x86_64/python/3.9.1/bin/python ./

bin/pip install biopython

bin/pip install matplotlib

bin/pip install pandas

bin/pip install mpld3

bin/pip install plotly

bin/python BioPython_LengthViolinFastq_Reads.py Viral_MiSeq/SC2_X.R1.fastq Viral_MiSeq/SC2_X.R2.fastq Viral_iSeq/SC2_Y.R1.fastq Viral_iSeq/SC2_Y.R2.fastq --outputType P

BioPython_PHREDViolinFastq_Reads.py Viral_MiSeq/SC2_X.R1.fastq Viral_MiSeq/SC2_X.R2.fastq --outputType P --titleString "Raw Reads PHRED Scores" --showQ30 Y

Note: For faster runtime, --showQ30 N

bin/python Pandas_ParseForViolin_CSV.py US_A_USALEEP.csv --outputType P --stateCodes 6 56 --titleString "U.S., California, and Wyoming Life Expectancy,"

bin/python Plotly_ParseForViolin_to_HTML.py Life_Expectancy_US/US_A_USALEEP.csv --stateCodes 15 6 30 --titleString "U.S., Hawaii, California, and Montana"

Note: For Plotly_ParseForViolin_to_HTML.py, output path should be set to a folder that enables point-and-click opening of the output HTML.