117 lines (105 with data), 4.9 kB

shell.prefix('set -x;')
include: 'common.snakemake'

import os

def get_all_inputs(wildcards):
    available_inputs = dict(
        cutadapt=expand('{output_dir}/cutadapt/{sample_id}.fastq.gz',
            output_dir=output_dir, sample_id=sample_ids),
        clean=expand('{output_dir}/unmapped/{sample_id}/clean.fa.gz',
            output_dir=output_dir, sample_id=sample_ids),
        summarize_cutadapt=expand('{output_dir}/summary/cutadapt.html',
            output_dir=output_dir)
    )
    enabled_inputs = list(available_inputs.keys())
    inputs = []
    for key, l in available_inputs.items():
        if key in enabled_inputs:
            inputs += l
    return inputs

rule all:
    input:
        get_all_inputs


rule cutadapt_se:
    input:
        auto_gzip_input(data_dir + '/fastq/{sample_id}.fastq')
    output:
        trimmed='{output_dir}/cutadapt/{sample_id}.fastq.gz'
    params:
        min_read_length=config['min_read_length'],
        min_base_quality=config['min_base_quality'],
        trim_5p=lambda wildcards: '-u {}'.format(config['trim_5p']) if config['trim_5p'] > 0 else '',
        trim_3p=lambda wildcards: '-u -{}'.format(config['trim_3p']) if config['trim_3p'] > 0 else '',
        adaptor=lambda wildcards: get_cutadapt_adapter_args(wildcards, config['adaptor'], '-a'),
        adaptor_5p=lambda wildcards: get_cutadapt_adapter_args(wildcards, config['adaptor_5p'], '-g'),
        umi_length=config['umi_length']
    log:
        '{output_dir}/log/cutadapt/{sample_id}'
    threads: 2
    run:
        if config['trim_after_adapter']:
            shell('''cutadapt {params.adaptor} {params.adaptor_5p} \
                -m {params.min_read_length} --trim-n -q {params.min_base_quality} {input}  2>&1 \
                | cutadapt {params.trim_5p} {params.trim_3p} \
                -o >(pigz -c -p {threads} > {output.trimmed})  > {log}
                ''')
        else:
            shell('''cutadapt {params.adaptor} {params.adaptor_5p} {params.trim_5p} {params.trim_3p} \
                -m {params.min_read_length} --trim-n -q {params.min_base_quality} \
                -o >(pigz -c -p {threads} > {output.trimmed}) {input} > {log} 2>&1
                ''')


rule summarize_cutadapt_se:
    input:
        lambda wildcards: expand('{output_dir}/log/cutadapt/{sample_id}',
            output_dir=wildcards.output_dir, sample_id=sample_ids)
    output:
        '{output_dir}/summary/cutadapt.txt'
    run:
        import pandas as pd
        
        def parse_number(s):
            return int(''.join(s.split(',')))

        columns = ['sample_id', 'total_reads', 'reads_with_adapters', 'reads_too_short', 'reads_kept',
            'total_bp', 'bp_quality_trimmed', 'bp_kept']
        summary = []
        for filename in input:
            sample_id = os.path.basename(filename)
            record = {'sample_id': sample_id}
            with open(filename, 'r') as fin:
                for line in fin:
                    line = line.strip()
                    if line.startswith('Total reads processed:'):
                        record['total_reads'] = parse_number(line.split()[-1])
                    elif line.startswith('Reads with adapters:'):
                        record['reads_with_adapters'] = parse_number(line.split()[-2])
                    elif line.startswith('Reads that were too short:'):
                        record['reads_too_short'] = parse_number(line.split()[-2])
                    elif line.startswith('Reads written (passing filters):'):
                        record['reads_kept'] = parse_number(line.split()[-2])
                    elif line.startswith('Total basepairs processed:'):
                        record['total_bp'] = parse_number(line.split()[-2])
                    elif line.startswith('Quality-trimmed:'):
                        record['bp_quality_trimmed'] = parse_number(line.split()[-3])
                    elif line.startswith('Total written (filtered):'):
                        record['bp_kept'] = parse_number(line.split()[-3])
            summary.append(record)
        summary = pd.DataFrame.from_records(summary)
        summary = summary.reindex(columns=columns)
        summary.to_csv(output[0], sep='\t', na_rep='NA', index=False, header=True)

rule summarize_cutadapt_jupyter_se:
    input:
        summary='{output_dir}/summary/cutadapt.txt',
        jupyter=package_dir + '/templates/summarize_cutadapt_se.ipynb'
    output:
        jupyter='{output_dir}/summary/cutadapt.ipynb',
        html='{output_dir}/summary/cutadapt.html'
    run:
        shell(nbconvert_command)

rule fastq_to_fasta_se:
    input:
        '{output_dir}/cutadapt/{sample_id}.fastq.gz'
    output:
        '{output_dir}/unmapped/{sample_id}/clean.fa.gz'
    threads:
        config['threads_compress']
    shell:
        '''pigz -d -c -p {threads} {input} | fastq_to_fasta -r | pigz -p {threads} -c > {output}
        '''