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fixed summary file when only fastas
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@erinyoung
erinyoung committed Jun 20, 2023
1 parent c6ce94b commit d9efe47
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Showing 2 changed files with 31 additions and 24 deletions.
53 changes: 30 additions & 23 deletions bin/summary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -302,32 +302,39 @@
summary_df.drop("size_accession", axis=1, inplace=True)
summary_df['warnings'] = summary_df['warnings'] + summary_df['size_warning']

if "fastqc_total sequences" in summary_df:
if "fastqc_total sequences" and 'fastqc_avg_length' in summary_df:
summary_df['total_bases'] = summary_df['fastqc_total sequences'].astype('Int32') * summary_df['fastqc_avg_length'].astype(float)
else:
elif 'quast_Total length' in summary_df:
summary_df['total_bases'] = summary_df['quast_Total length'].astype(float)

if 'total_bases' in summary_df:
summary_df['coverage'] = summary_df['total_bases'].astype(float) / summary_df['size_size'].astype(float)
summary_df['coverage_for_1.5M_genome'] = summary_df['total_bases'].astype(float) / 1500000
summary_df['coverage_for_2M_genome'] = summary_df['total_bases'].astype(float) / 2000000
summary_df['coverage_for_2.5M_genome'] = summary_df['total_bases'].astype(float) / 2500000
summary_df['coverage_for_3M_genome'] = summary_df['total_bases'].astype(float) / 3000000
summary_df['coverage_for_3.5M_genome'] = summary_df['total_bases'].astype(float) / 3500000
summary_df['coverage_for_4M_genome'] = summary_df['total_bases'].astype(float) / 4000000
summary_df['coverage_for_4.5M_genome'] = summary_df['total_bases'].astype(float) / 4500000
summary_df['coverage_for_5M_genome'] = summary_df['total_bases'].astype(float) / 5000000
summary_df['coverage_for_5.5M_genome'] = summary_df['total_bases'].astype(float) / 5500000
summary_df['coverage_for_6M_genome'] = summary_df['total_bases'].astype(float) / 6000000
summary_df['coverage_for_6.5M_genome'] = summary_df['total_bases'].astype(float) / 6500000
summary_df['coverage_for_7M_genome'] = summary_df['total_bases'].astype(float) / 7000000
summary_df['coverage_for_7.5M_genome'] = summary_df['total_bases'].astype(float) / 7500000
summary_df['coverage_for_8M_genome'] = summary_df['total_bases'].astype(float) / 8000000
summary_df['coverage_for_8.5M_genome'] = summary_df['total_bases'].astype(float) / 8500000
summary_df['coverage_for_9M_genome'] = summary_df['total_bases'].astype(float) / 9000000
summary_df['coverage_for_9.5M_genome'] = summary_df['total_bases'].astype(float) / 9500000
summary_df['coverage_for_10M_genome'] = summary_df['total_bases'].astype(float) / 10000000
summary_df['coverage_warning'] = summary_df['coverage'].apply(lambda x: "Low coverage," if x <= 20 else "")
summary_df['warnings'] = summary_df['warnings'] + summary_df['coverage_warning']

summary_df['coverage'] = summary_df['total_bases'].astype(float) / summary_df['size_size'].astype(float)
summary_df['coverage_for_1.5M_genome'] = summary_df['total_bases'].astype(float) / 1500000
summary_df['coverage_for_2M_genome'] = summary_df['total_bases'].astype(float) / 2000000
summary_df['coverage_for_2.5M_genome'] = summary_df['total_bases'].astype(float) / 2500000
summary_df['coverage_for_3M_genome'] = summary_df['total_bases'].astype(float) / 3000000
summary_df['coverage_for_3.5M_genome'] = summary_df['total_bases'].astype(float) / 3500000
summary_df['coverage_for_4M_genome'] = summary_df['total_bases'].astype(float) / 4000000
summary_df['coverage_for_4.5M_genome'] = summary_df['total_bases'].astype(float) / 4500000
summary_df['coverage_for_5M_genome'] = summary_df['total_bases'].astype(float) / 5000000
summary_df['coverage_for_5.5M_genome'] = summary_df['total_bases'].astype(float) / 5500000
summary_df['coverage_for_6M_genome'] = summary_df['total_bases'].astype(float) / 6000000
summary_df['coverage_for_6.5M_genome'] = summary_df['total_bases'].astype(float) / 6500000
summary_df['coverage_for_7M_genome'] = summary_df['total_bases'].astype(float) / 7000000
summary_df['coverage_for_7.5M_genome'] = summary_df['total_bases'].astype(float) / 7500000
summary_df['coverage_for_8M_genome'] = summary_df['total_bases'].astype(float) / 8000000
summary_df['coverage_for_8.5M_genome'] = summary_df['total_bases'].astype(float) / 8500000
summary_df['coverage_for_9M_genome'] = summary_df['total_bases'].astype(float) / 9000000
summary_df['coverage_for_9.5M_genome'] = summary_df['total_bases'].astype(float) / 9500000
summary_df['coverage_for_10M_genome'] = summary_df['total_bases'].astype(float) / 10000000
summary_df['coverage_warning'] = summary_df['coverage'].apply(lambda x: "Low coverage," if x <= 20 else "")
summary_df['warnings'] = summary_df['warnings'] + summary_df['coverage_warning']
else:
summary_df['total_bases'] = "Undetermined"
summary_df['coverage'] = "Undetermined"
summary_df['coverage_warning'] = "Coverage is undetermined"
summary_df['warnings'] = summary_df['warnings'] + summary_df['coverage_warning']

##########################################
# creating files #
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2 changes: 1 addition & 1 deletion nextflow.config
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ manifest {
author = 'Erin Young'
homePage = 'https://github.com/UPHL-BioNGS/Grandeur'
mainScript = 'grandeur.nf'
version = '3.2.20230613'
version = '3.2.20230620'
defaultBranch = 'main'
description = 'Grandeur is short-read de novo assembly pipeline with serotyping.'
}
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