Snakefile

"""General purpose RNA-Seq analysis pipeline developed by the Zavolan Lab"""

import os
import sys

import pandas as pd

# Get sample table
samples_table = pd.read_csv(
    config['samples'],
    header=0,
    index_col=0,
    comment='#',
    engine='python',
    sep="\t",
)

# Global config
localrules: finish

# Create log directories
os.makedirs(
    os.path.join(
        os.getcwd(),
        config['log_dir'],
    ),
    exist_ok=True,
)
if cluster_config:
    os.makedirs(
        os.path.join(
            os.getcwd(),
            os.path.dirname(cluster_config['__default__']['out']),
        ),
        exist_ok=True,
    )

# Include subworkflows
include: os.path.join("workflow", "rules", "paired_end.snakefile.smk")
include: os.path.join("workflow", "rules", "single_end.snakefile.smk")

# Final rule
rule finish:
    """Rule for collecting outputs"""
    input:
        outdir1 = expand(
            os.path.join(
                config['output_dir'],
                "{seqmode}",
                "{sample}",
                "mate1_fastqc",
            ),
            zip,
            sample=[i for i in list(samples_table.index.values)],
            seqmode=[
                samples_table.loc[i, 'seqmode']
                for i in list(samples_table.index.values)
            ]
        ),
        salmon_gn_estimates = expand(
            os.path.join(
                config['output_dir'],
                "{seqmode}",
                "{sample}",
                "salmon_quant",
                "quant.genes.sf",
            ),
            zip,
            sample=[i for i in list(samples_table.index.values)],
            seqmode=[
                samples_table.loc[i, 'seqmode']
                for i in list(samples_table.index.values)
            ]
        ),
        pseudoalignment = expand(
            os.path.join(
                config['output_dir'],
                "{seqmode}",
                "{sample}",
                "quant_kallisto",
                "{sample}.kallisto.pseudo.sam",
            ),
            zip,
            sample=[i for i in list(samples_table.index.values)],
            seqmode=[
                samples_table.loc[i, 'seqmode']
                for i in list(samples_table.index.values)
            ]
        ),
        TIN_score = expand(
            os.path.join(
                config['output_dir'],
                "{seqmode}",
                "{sample}",
                "TIN",
                "TIN_score.tsv",
            ),
            zip,
            sample=[i for i in list(samples_table.index.values)],
            seqmode=[
                samples_table.loc[i, 'seqmode']
                for i in list(samples_table.index.values)
            ]
        ),


rule create_index_star:
    """Create index for STAR alignments"""
    input:
        genome = lambda wildcards:
            samples_table['genome'][
                samples_table['organism'] == wildcards.organism
            ][0],
        gtf = lambda wildcards:
            samples_table['gtf'][
                samples_table['organism'] == wildcards.organism
            ][0],
    output:
        chromosome_info = os.path.join(
            config['star_indexes'],
            "{organism}",
            "{index_size}",
            "STAR_index",
            "chrNameLength.txt",
        ),
        chromosomes_names = os.path.join(
            config['star_indexes'],
            "{organism}",
            "{index_size}",
            "STAR_index",
            "chrName.txt",
        ),
    params:
        output_dir = os.path.join(
            config['star_indexes'],
            "{organism}",
            "{index_size}",
            "STAR_index",
        ),
        outFileNamePrefix = os.path.join(
            config['star_indexes'],
            "{organism}",
            "{index_size}",
            "STAR_index/STAR_",
        ),
        sjdbOverhang = "{index_size}",
    singularity:
        "docker://zavolab/star:2.6.0a"
    threads: 12
    log:
        os.path.join(
            config['log_dir'],
            "{organism}_{index_size}_create_index_star.log",
        )
    shell:
        "(mkdir -p {params.output_dir}; \
        chmod -R 777 {params.output_dir}; \
        STAR \
        --runMode genomeGenerate \
        --sjdbOverhang {params.sjdbOverhang} \
        --genomeDir {params.output_dir} \
        --genomeFastaFiles {input.genome} \
        --runThreadN {threads} \
        --outFileNamePrefix {params.outFileNamePrefix} \
        --sjdbGTFfile {input.gtf}) &> {log}"


rule create_index_salmon:
    """Create index for Salmon quantification"""
    input:
        transcriptome = lambda wildcards:
            samples_table['tr_fasta_filtered'][
                samples_table['organism'] == wildcards.organism
            ][0]
    output:
        index = directory(
            os.path.join(
                config['salmon_indexes'],
                "{organism}",
                "{kmer}",
                "salmon.idx",
            )
        ),
    params:
        kmerLen = "{kmer}",
    singularity:
        "docker://zavolab/salmon:0.11.0"
    log:
        os.path.join(
            config['log_dir'],
            "{organism}_{kmer}_create_index_salmon.log"
        )
    threads: 8
    shell:
        "(salmon index \
        --transcripts {input.transcriptome} \
        --index {output.index} \
        --kmerLen {params.kmerLen} \
        --threads {threads}) &> {log}"


rule create_index_kallisto:
    """Create index for Kallisto quantification"""
    input:
        transcriptome = lambda wildcards:
            samples_table['tr_fasta_filtered'][
                samples_table['organism'] == wildcards.organism
            ][0],
    output:
        index = os.path.join(
            config['kallisto_indexes'],
            "{organism}",
            "kallisto.idx",
        ),
    params:
        output_dir = os.path.join(
            config['kallisto_indexes'],
            "{organism}",
        ),
    singularity:
        "docker://zavolab/kallisto:0.46.1"
    log:
        os.path.join(
            config['log_dir'],
            "{organism}_create_index_kallisto.log"
        )
    shell:
        "(mkdir -p {params.output_dir}; \
        chmod -R 777 {params.output_dir}; \
        kallisto index -i {output.index} {input.transcriptome}) &> {log}"


rule extract_transcripts_as_bed12:
    """Convert transcripts to BED12 format"""
    input:
        gtf = lambda wildcards:
            samples_table['gtf'][0],
    output:
        bed12 = os.path.join(
            config['output_dir'],
            "full_transcripts_protein_coding.bed",
        ),
    singularity:
        "docker://zavolab/gtf_transcript_type_to_bed12:0.1.0"
    threads: 1
    log:
        os.path.join(
            config['log_dir'],
            "extract_transcripts_as_bed12.log",
        )
    shell:
        "gtf_transcript_type_to_bed12.pl \
        --anno={input.gtf} \
        --type=protein_coding \
        1> {output.bed12} \
        2> {log}"


rule calculate_TIN_scores:
    """Caluclate transcript integrity (TIN) score"""
    input:
        bai = os.path.join(
            config['output_dir'],
            "{seqmode}",
            "{sample}",
            "map_genome",
            "{sample}_Aligned.sortedByCoord.out.bam.bai"
        ),
        transcripts_bed12 = os.path.join(
            config['output_dir'],
            "full_transcripts_protein_coding.bed"
        ),
    output:
        TIN_score = os.path.join(
            config['output_dir'],
            "{seqmode}",
            "{sample}",
            "TIN",
            "TIN_score.tsv",
        ),
    params:
        bam = os.path.join(
            config['output_dir'],
            "{seqmode}",
            "{sample}",
            "map_genome",
            "{sample}_Aligned.sortedByCoord.out.bam"
        ),
        sample = "{sample}",
    log:
        os.path.join(
            config['log_dir'],
            "{seqmode}",
            "{sample}",
            "calculate_TIN_scores.log",
        )
    threads: 8
    singularity:
        "docker://zavolab/tin_score_calculation:0.1.0"
    shell:
        "tin_score_calculation.py \
        -i {params.bam} \
        -r {input.transcripts_bed12} \
        -c 0 \
        --names {params.sample} \
        -n 100 \
        1> {output.TIN_score} \
        2> {log}"