Snakefile

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

import os
import sys

import pandas as pd
import shutil
import glob
from zipfile import ZipFile 

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

# Global config
localrules: finish, rename_star_rpm_for_alfa, prepare_files_for_report, \
    prepare_MultiQC_config

# 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")

rule finish:
    """
        Rule for collecting outputs
    """
    input:
        MultiQC_report = expand(
            os.path.join(
                config['output_dir'],
                "multiqc_summary"),
            output_dir=config["output_dir"])


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:2.7.3a-slim"

    threads: 12

    log:
        stderr = os.path.join(
            config['log_dir'],
            "{organism}_{index_size}_create_index_star.stderr.log"),
        stdout = os.path.join(
            config['log_dir'],
            "{organism}_{index_size}_create_index_star.stdout.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}) \
        1> {log.stdout} 2> {log.stderr}"

rule extract_transcriptome:
    """ Create transcriptome from genome and gene annotations """
    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:
        transcriptome = os.path.join(
                config['output_dir'],
                "transcriptome",
                "{organism}",
                "transcriptome.fa",
            )
    log:
        stderr = os.path.join(
            config['log_dir'],
            "{organism}_extract_transcriptome.log"),
        stdout = os.path.join(
            config['log_dir'],
            "{organism}_extract_transcriptome.log")
    singularity:
        "docker://zavolab/gffread:0.11.7-slim"
    shell:
        "(gffread \
        -w {output.transcriptome} \
        -g {input.genome} {input.gtf}) \
        1> {log.stdout} 2> {log.stderr}"

rule create_index_salmon:
    """
        Create index for Salmon quantification
    """
    input:
        transcriptome = os.path.join(
                config['output_dir'],
                "transcriptome",
                "{organism}",
                "transcriptome.fa",
            )
    output:
        index = directory(
            os.path.join(
                config['salmon_indexes'],
                "{organism}",
                "{kmer}",
                "salmon.idx"))

    params:
        kmerLen = "{kmer}"

    singularity:
        "docker://zavolab/salmon:1.1.0-slim"

    log:
        stderr = os.path.join(
            config['log_dir'],
            "{organism}_{kmer}_create_index_salmon.stderr.log"),
        stdout = os.path.join(
            config['log_dir'],
            "{organism}_{kmer}_create_index_salmon.stdout.log")

    threads: 8

    shell:
        "(salmon index \
        --transcripts {input.transcriptome} \
        --index {output.index} \
        --kmerLen {params.kmerLen} \
        --threads {threads}) \
        1> {log.stdout} 2> {log.stderr}"


rule create_index_kallisto:
    """
        Create index for Kallisto quantification
    """
    input:
        transcriptome = os.path.join(
                config['output_dir'],
                "transcriptome",
                "{organism}",
                "transcriptome.fa",
            )
    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-slim"

    log:
        stderr = os.path.join(
            config['log_dir'],
            "{organism}_create_index_kallisto.stderr.log"),
        stdout = os.path.join(
            config['log_dir'],
            "{organism}_create_index_kallisto.stdout.log")

    shell:
        "(mkdir -p {params.output_dir}; \
        chmod -R 777 {params.output_dir}; \
        kallisto index -i {output.index} {input.transcriptome}) \
        1> {log.stdout}  2> {log.stderr}"


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-slim"

    threads: 1

    log:
        stderr = os.path.join(
            config['log_dir'],
            "extract_transcripts_as_bed12.stderr.log")

    shell:
        "(gtf_transcript_type_to_bed12.pl \
        --anno={input.gtf} \
        --type=protein_coding > {output.bed12}); \
        2> {log.stderr}"


rule index_genomic_alignment_samtools:
    '''
        Index genome bamfile using samtools
    '''
    input:
        bam = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "map_genome",
            "{sample}_Aligned.sortedByCoord.out.bam")

    output:
        bai = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "map_genome",
            "{sample}_Aligned.sortedByCoord.out.bam.bai")

    singularity:
        "docker://zavolab/samtools:1.10-slim"

    threads: 1

    log:
        stderr = os.path.join(
            config["log_dir"],
            "{seqmode}",
            "{sample}",
            "index_genomic_alignment_samtools.stderr.log"),
        stdout = os.path.join(
            config["log_dir"],
            "{seqmode}",
            "{sample}",
            "index_genomic_alignment_samtools.stdout.log")

    shell:
        "(samtools index {input.bam} {output.bai};) \
        1> {log.stdout} 2> {log.stderr}"


rule star_rpm:
    ''' Create stranded bedgraph coverage with STARs RPM normalisation '''
    input: 
        bam = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "map_genome",
            "{sample}_Aligned.sortedByCoord.out.bam"),
        bai = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "map_genome",
            "{sample}_Aligned.sortedByCoord.out.bam.bai")

    output:
        str1 = (os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "STAR_coverage",
            "{sample}_Signal.Unique.str1.out.bg"),
            os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "STAR_coverage",
            "{sample}_Signal.UniqueMultiple.str1.out.bg")),
        str2 = (os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "STAR_coverage",
            "{sample}_Signal.Unique.str2.out.bg"),
            os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "STAR_coverage",
            "{sample}_Signal.UniqueMultiple.str2.out.bg"))

    params:
        out_dir = lambda wildcards, output: os.path.dirname(output.str1[0]),
        prefix = lambda wildcards, output: os.path.join(os.path.dirname(output.str1[0]),
            str(wildcards.sample) + "_"),
        stranded = "Stranded"

    singularity:
        "docker://zavolab/star:2.7.3a-slim"

    log: 
        stderr = os.path.join(
            config["log_dir"],
            "{seqmode}",
            "{sample}",
            "star_rpm_single_end.stderr.log"),
        stdout = os.path.join(
            config["log_dir"],
            "{seqmode}",
            "{sample}",
            "star_rpm_single_end.stdout.log")

    threads: 4

    shell:
        """
        (mkdir -p {params.out_dir}; \
        chmod -R 777 {params.out_dir}; \
        STAR \
        --runMode inputAlignmentsFromBAM \
        --runThreadN {threads} \
        --inputBAMfile {input.bam} \
        --outWigType "bedGraph" \
        --outWigStrand {params.stranded} \
        --outWigNorm "RPM" \
        --outFileNamePrefix {params.prefix}) \
        1> {log.stdout} 2> {log.stderr}
        """


rule rename_star_rpm_for_alfa:
    input:
        str1 = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "STAR_coverage",
            "{sample}_Signal.{unique}.str1.out.bg"),
        str2 = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "STAR_coverage",
            "{sample}_Signal.{unique}.str2.out.bg")
    
    output:
        plus = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "ALFA",
            "{unique}",
            "{sample}_Signal.{unique}.out.plus.bg"),
        minus = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "ALFA",
            "{unique}",
            "{sample}_Signal.{unique}.out.minus.bg")
    
    params:
        orientation = lambda wildcards: samples_table.loc[wildcards.sample, "kallisto_directionality"]
    
    run:
        if params['orientation'] == "--fr":
            shutil.copy2(input['str1'], output['plus'])
            shutil.copy2(input['str2'], output['minus'])
        elif params['orientation'] == "--rf":
            shutil.copy2(input['str1'], output['minus'])
            shutil.copy2(input['str2'], output['plus'])


rule calculate_TIN_scores:
    """
        Caluclate transcript integrity (TIN) score
    """
    input:
        bam = os.path.join(
            config['output_dir'],
            "{seqmode}",
            "{sample}",
            "map_genome",
            "{sample}_Aligned.sortedByCoord.out.bam"),
        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:
        sample = "{sample}"

    log:
        stderr = os.path.join(
            config['log_dir'],
            "{seqmode}",
            "{sample}",
            "calculate_TIN_scores.log")

    threads: 8

    singularity:
        "docker://zavolab/tin_score_calculation:0.2.0-slim"

    shell:
        "(tin_score_calculation.py \
        -i {input.bam} \
        -r {input.transcripts_bed12} \
        -c 0 \
        --names {params.sample} \
        -n 100 > {output.TIN_score};) 2> {log.stderr}"


rule merge_TIN_scores:
    """
        Merge TIN scores tables
    """
    input:
        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)])

    output:
        TIN_scores_merged = os.path.join(
            config['output_dir'],
            "TIN_scores_merged.tsv")

    params:
        TIN_score_merged_paths = ",".join(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)]))

    log:
        stderr = os.path.join(
            config['log_dir'],
            "merge_TIN_scores.stderr.log"),
        stdout = os.path.join(
            config["log_dir"],
            "merge_TIN_scores.stdout.log")

    threads: 1

    singularity:
        "docker://zavolab/tin_score_calculation:0.2.0-slim"

    shell:
        "(tin_score_merge.py \
        --input-files {params.TIN_score_merged_paths} \
        --output-file {output.TIN_scores_merged}) \
        1> {log.stdout} 2> {log.stderr}"


rule plot_TIN_scores:
    """
        Generate TIN scores boxplots
    """
    input:
        TIN_scores_merged = os.path.join(
            config['output_dir'],
            "TIN_scores_merged.tsv"),

    output:
        TIN_boxplot_PNG = os.path.join(
            config['output_dir'],
            "TIN_scores_boxplot.png"),
        TIN_boxplot_PDF = os.path.join(
            config['output_dir'],
            "TIN_scores_boxplot.pdf")

    params:
        TIN_boxplot_prefix = os.path.join(
            config['output_dir'],
            "TIN_scores_boxplot")

    log:
        stderr = os.path.join(
            config['log_dir'],
            "plot_TIN_scores.stderr.log"),
        stdout = os.path.join(
            config["log_dir"],
            "plot_TIN_scores.stdout.log")

    threads: 1

    singularity:
        "docker://zavolab/tin_score_calculation:0.2.0-slim"

    shell:
        "(tin_score_plot.py \
        --input-file {input.TIN_scores_merged} \
        --output-file-prefix {params.TIN_boxplot_prefix}) \
        1> {log.stdout} 2> {log.stderr}"


rule salmon_quantmerge_genes:
    '''
        Merge gene quantifications into a single file
    '''
    input:
        salmon_in = expand(
            os.path.join(
                config["output_dir"],
                "{seqmode}",
                "{sample}",
                "salmon_quant",
                "quant.genes.sf"),
            zip,
            sample=list(samples_table.index.values),
            seqmode=list(samples_table["seqmode"]))

    output:
        salmon_out = os.path.join(
            config["output_dir"],
            "summary_salmon",
            "quantmerge",
            "genes_{salmon_merge_on}.tsv")

    params:
        salmon_dir = expand(
            os.path.join(
                config["output_dir"],
                "{seqmode}",
                "{sample}",
                "salmon_quant"),
            zip,
            sample=list(samples_table.index.values),
            seqmode=list(samples_table["seqmode"])),
        sample_name_list = expand(
            "{sample}",
            sample=list(samples_table.index.values)),
        salmon_merge_on = "{salmon_merge_on}"

    log:
        stderr = os.path.join(
            config["log_dir"],
            "salmon_quantmerge_genes_{salmon_merge_on}.stderr.log"),
        stdout = os.path.join(
            config["log_dir"],
            "salmon_quantmerge_genes_{salmon_merge_on}.stdout.log")

    threads: 1

    singularity:
        "docker://zavolab/salmon:1.1.0-slim"

    shell:
        "(salmon quantmerge \
        --quants {params.salmon_dir} \
        --genes \
        --names {params.sample_name_list} \
        --column {params.salmon_merge_on} \
        --output {output.salmon_out};) \
        1> {log.stdout} 2> {log.stderr}"


rule salmon_quantmerge_transcripts:
    '''
        Merge gene quantifications into a single file
    '''
    input:
        salmon_in = expand(
            os.path.join(
                config["output_dir"],
                "{seqmode}",
                "{sample}",
                "salmon_quant",
                "quant.sf"),
            zip,
            sample=list(samples_table.index.values),
            seqmode=list(samples_table["seqmode"])),

    output:
        salmon_out = os.path.join(
            config["output_dir"],
            "summary_salmon",
            "quantmerge",
            "transcripts_{salmon_merge_on}.tsv")

    params:
        salmon_dir = expand(
            os.path.join(
                config["output_dir"],
                "{seqmode}",
                "{sample}",
                "salmon_quant"),
            zip,
            sample=list(samples_table.index.values),
            seqmode=list(samples_table["seqmode"])),
        sample_name_list = expand(
            "{sample}",
            sample=list(samples_table.index.values)),
        salmon_merge_on = "{salmon_merge_on}"

    log:
        stderr = os.path.join(
            config["log_dir"],
            "salmon_quantmerge_transcripts_{salmon_merge_on}.stderr.log"),
        stdout = os.path.join(
            config["log_dir"],
            "salmon_quantmerge_transcripts_{salmon_merge_on}.stdout.log")

    threads: 1

    singularity:
        "docker://zavolab/salmon:1.1.0-slim"

    shell:
        "(salmon quantmerge \
        --quants {params.salmon_dir} \
        --names {params.sample_name_list} \
        --column {params.salmon_merge_on} \
        --output {output.salmon_out}) \
        1> {log.stdout} 2> {log.stderr}"


#################################################################################
### ALFA: Annotation Landscape For Aligned reads
#################################################################################

directionality = {"--fr": "fr-firststrand", "--rf": "fr-secondstrand"}


rule generate_alfa_index:
    ''' Generate ALFA index files from sorted GTF file '''
    input:
        gtf = lambda wildcards: samples_table["gtf"][samples_table["organism"]==wildcards.organism][0],
        chr_len = os.path.join(
            config["star_indexes"],
            "{organism}",
            "{index_size}",
            "STAR_index",
            "chrNameLength.txt"),

    output:
        index_stranded = os.path.join(config["alfa_indexes"], 
            "{organism}", 
            "{index_size}", 
            "ALFA", 
            "sorted_genes.stranded.ALFA_index"),
        index_unstranded = os.path.join(config["alfa_indexes"], 
            "{organism}", 
            "{index_size}", 
            "ALFA", 
            "sorted_genes.unstranded.ALFA_index")

    params:
        genome_index = "sorted_genes",
        out_dir = lambda wildcards, output: os.path.dirname(output.index_stranded)

    threads: 4

    singularity: 
        "docker://zavolab/alfa:1.1.1-slim"

    log: 
        os.path.join(config["log_dir"], "{organism}_{index_size}_generate_alfa_index.log")

    shell:
        """
        alfa -a {input.gtf} \
            -g {params.genome_index} \
            --chr_len {input.chr_len} \
            -p {threads} \
            -o {params.out_dir} &> {log}
        """


rule alfa_qc:
    ''' Run ALFA from stranded bedgraph files '''
    input:
        plus = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "ALFA",
            "{unique}",
            "{sample}_Signal.{unique}.out.plus.bg"),
        minus = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "ALFA",
            "{unique}",
            "{sample}_Signal.{unique}.out.minus.bg"),
        gtf = lambda wildcards: os.path.join(config["alfa_indexes"], 
            samples_table.loc[wildcards.sample, "organism"], 
            str(samples_table.loc[wildcards.sample, "index_size"]), 
            "ALFA", 
            "sorted_genes.stranded.ALFA_index")

    output:
        biotypes = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "ALFA",
            "{unique}",
            "ALFA_plots.Biotypes.pdf"),
        categories = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "ALFA",
            "{unique}",
            "ALFA_plots.Categories.pdf"),
        table = os.path.join(
            config["output_dir"],
            "{seqmode}",
            "{sample}",
            "ALFA",
            "{unique}",
            "{sample}.ALFA_feature_counts.tsv")

    params:
        out_dir = lambda wildcards, output: os.path.dirname(output.biotypes),
        alfa_orientation = lambda wildcards: directionality[samples_table.loc[wildcards.sample, "kallisto_directionality"]],
        in_file_plus = lambda wildcards, input: os.path.basename(input.plus),
        in_file_minus = lambda wildcards, input: os.path.basename(input.minus),
        genome_index = lambda wildcards, input: os.path.abspath(os.path.join(os.path.dirname(input.gtf), "sorted_genes")),
        name = "{sample}"

    singularity:
        "docker://zavolab/alfa:1.1.1-slim"

    log: 
        os.path.abspath(os.path.join(
            config["log_dir"], 
            "{seqmode}", 
            "{sample}", 
            "alfa_qc.{unique}.log"))

    shell:
        """ 
        cd {params.out_dir}; \
        (alfa -g {params.genome_index} \
            --bedgraph {params.in_file_plus} {params.in_file_minus} {params.name} \
            -s {params.alfa_orientation}) &> {log}
        """


rule alfa_qc_all_samples:
    ''' Run ALFA from stranded bedgraph files on all samples '''
    input:
        tables = [os.path.join(
            config["output_dir"],
            samples_table.loc[sample1, "seqmode"],
            str(sample1),
            "ALFA",
            "{unique}",
            sample1 + ".ALFA_feature_counts.tsv")
            for sample1 in list(samples_table.index.values)]

    output:
        biotypes = os.path.join(
            config["output_dir"],
            "ALFA",
            "{unique}",
            "ALFA_plots.Biotypes.pdf"),
        categories = os.path.join(
            config["output_dir"],
            "ALFA",
            "{unique}",
            "ALFA_plots.Categories.pdf")

    params:
        out_dir = lambda wildcards, output: os.path.dirname(output.biotypes)

    log: 
        os.path.abspath(
            os.path.join(config["log_dir"], 
            "alfa_qc_all_samples.{unique}.log"))

    singularity:
        "docker://zavolab/alfa:1.1.1-slim"

    shell:
        """
        (alfa -c {input.tables} -o {params.out_dir}) &> {log}
        """


rule alfa_concat_results:
    input:
        expand(os.path.join(
            config["output_dir"],
            "ALFA",
            "{unique_type}",
            "ALFA_plots.{annotation}.pdf"),
            unique_type = ["Unique", "UniqueMultiple"],
            annotation = ["Categories", "Biotypes"])

    output:
        expand(os.path.join(
            config["output_dir"],
            "ALFA",
            "ALFA_plots.concat.png"))

    params:
        density = 300

    log: 
        os.path.abspath(
            os.path.join(config["log_dir"], 
            "alfa_qc_all_samples.concat.log"))

    singularity:
        "docker://zavolab/imagemagick:6.9.10-slim"

    shell:
        """
        convert -append -density {params.density} \
            {input} {output} &> {log}
        """


rule prepare_files_for_report:
    '''
        Re-structure the results and add comments for MultiQC parsing
    '''
    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)]),
        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_boxplot_PNG = os.path.join(
            config['output_dir'],
            "TIN_scores_boxplot.png"),
        TIN_boxplot_PDF = os.path.join(
            config['output_dir'],
            "TIN_scores_boxplot.pdf"),
        salmon_merge_genes = expand(
            os.path.join(
                config["output_dir"],
                "summary_salmon",
                "quantmerge",
                "genes_{salmon_merge_on}.tsv"),
            salmon_merge_on=["tpm", "numreads"]),
        salmon_merge_transcripts = expand(
            os.path.join(
                config["output_dir"],
                "summary_salmon",
                "quantmerge",
                "transcripts_{salmon_merge_on}.tsv"),
            salmon_merge_on=["tpm", "numreads"]),
        star_rpm = expand(
            os.path.join(
                config["output_dir"],
                "{seqmode}",
                "{sample}",
                "STAR_coverage",
                "{sample}_Signal.UniqueMultiple.str1.out.bg"),
                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)]),
        alfa_concat_out = os.path.join(
            config["output_dir"],
            "ALFA",
            "ALFA_plots.concat.png")

    output:
        samples_dir = directory(os.path.join(
            "{output_dir}",
            "samples"))
    params:
        results_dir = config["output_dir"],
        log_dir = config["log_dir"],
        log_samples_dir = os.path.join(
            config["log_dir"],
            "samples")
    log:
        LOG_local_log = \
            os.path.join("{output_dir}", "local_log", \
                "prepare_files_for_report.log")
    run:

        # remove "single/paired end" from the results directories
        os.mkdir(output.samples_dir)
        # move paired end results
        paired_end_dir = glob.glob(
            os.path.join(
                params.results_dir,
                "paired_end",
                "*"))
        for s in paired_end_dir:
            sample_name = s.split("/")[-1]
            shutil.copytree(
                s, \
                os.path.join(
                    params.results_dir,