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--outFileNamePrefix {params.prefix}) \
1> {log.stdout} 2> {log.stderr}"
rule rename_star_rpm_for_alfa:
input:
plus = lambda wildcards:
expand(
os.path.join(
config["output_dir"],
"samples",
"{sample}",
"STAR_coverage",
"{sample}_Signal.{unique}.{plus}.out.bg"),
sample=wildcards.sample,
unique=wildcards.unique,
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plus=get_sample(
'alfa_plus',
search_id='index',
search_value=wildcards.sample)),
minus = lambda wildcards:
expand(
os.path.join(
config["output_dir"],
"samples",
"{sample}",
"STAR_coverage",
"{sample}_Signal.{unique}.{minus}.out.bg"),
sample=wildcards.sample,
unique=wildcards.unique,
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minus=get_sample(
'alfa_minus',
search_id='index',
search_value=wildcards.sample))
output:
plus = os.path.join(
config["output_dir"],
"samples",
"{sample}",
"ALFA",
"{unique}",
"{sample}.{unique}.plus.bg"),
minus = os.path.join(
config["output_dir"],
"samples",
"{sample}",
"ALFA",
"{unique}",
"{sample}.{unique}.minus.bg")
params:
orientation = lambda wildcards:
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get_sample(
'kallisto_directionality',
search_id='index',
search_value=wildcards.sample),
log:
stderr = os.path.join(
config["log_dir"],
"samples",
"{sample}",
"rename_star_rpm_for_alfa__{unique}.stderr.log"),
stdout = os.path.join(
config["log_dir"],
"samples",
"{sample}",
"rename_star_rpm_for_alfa__{unique}.stdout.log")
singularity:
"docker://bash:5.0.16"
shell:
"(cp {input.plus} {output.plus}; \
cp {input.minus} {output.minus};) \
1>{log.stdout} 2>{log.stderr}"
rule generate_alfa_index:
''' Generate ALFA index files from sorted GTF file '''
input:
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get_sample(
'gtf',
search_id='organism',
search_value=wildcards.organism),
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",
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)
log:
os.path.join(
config["log_dir"],
"{organism}_{index_size}_generate_alfa_index.log")
"(alfa -a {input.gtf} \
-g {params.genome_index} \
--chr_len {input.chr_len} \
-p {threads} \
-o {params.out_dir}) &> {log}"
'''
Run ALFA from stranded bedgraph files
'''
input:
plus = os.path.join(
config["output_dir"],
gtf = lambda wildcards:
os.path.join(
config["alfa_indexes"],
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get_sample(
'organism',
search_id='index',
search_value=wildcards.sample),
get_sample(
'index_size',
search_id='index',
search_value=wildcards.sample),
"ALFA",
"sorted_genes.stranded.ALFA_index")
output:
biotypes = os.path.join(
config["output_dir"],
"ALFA_plots.Biotypes.pdf"),
categories = os.path.join(
config["output_dir"],
"ALFA_plots.Categories.pdf"),
table = os.path.join(
config["output_dir"],
out_dir = lambda wildcards, output:
os.path.dirname(output.biotypes),
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get_sample(
'alfa_directionality',
search_id='index',
search_value=wildcards.sample),
genome_index = lambda wildcards, input:
os.path.abspath(
os.path.join(
os.path.dirname(input.gtf),
"sorted_genes")),
"samples",
"{sample}",
"alfa_qc.{unique}.log")
"(cd {params.out_dir}; \
alfa \
-g {params.genome_index} \
--bedgraph {params.plus} {params.minus} {params.name} \
rule prepare_multiqc_config:
'''
Prepare config for the MultiQC
'''
input:
script = os.path.join(
workflow.basedir,
"workflow",
"scripts",
output:
multiqc_config = os.path.join(
config["output_dir"],
"multiqc_config.yaml")
logo_path = config['report_logo'],
multiqc_intro_text = config['report_description'],
url = config['report_url']
log:
stderr = os.path.join(
config["log_dir"],
"prepare_multiqc_config.stderr.log"),
stdout = os.path.join(
config["log_dir"],
"prepare_multiqc_config.stdout.log")
shell:
--intro-text '{params.multiqc_intro_text}' \
--custom-logo {params.logo_path} \
--url '{params.url}') \
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sample=pd.unique(samples_table.index.values),
mate="fq1"),
fastqc_pe = expand(
os.path.join(
config['output_dir'],
"samples",
"{sample}",
"fastqc",
"{mate}"),
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sample=[i for i in pd.unique(
samples_table[samples_table['seqmode'] == 'pe'].index.values)],
mate="fq2"),
pseudoalignment = expand(
os.path.join(
config['output_dir'],
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sample=[i for i in pd.unique(samples_table.index.values)],
seqmode=[get_sample('seqmode', search_id='index', search_value=i)
for i in pd.unique(samples_table.index.values)]),
TIN_score = expand(
os.path.join(
config['output_dir'],
"samples",
"{sample}",
"TIN",
"TIN_score.tsv"),
sample=pd.unique(samples_table.index.values)),
tables = lambda wildcards:
expand(
os.path.join(
config["output_dir"],
"samples",
"{sample}",
"ALFA",
"{unique}",
"{sample}.ALFA_feature_counts.tsv"),
sample=pd.unique(samples_table.index.values),
unique=["Unique", "UniqueMultiple"]),
zpca_salmon = expand(os.path.join(
config["output_dir"],
"zpca",
"pca_salmon_{molecule}"),
molecule=["genes", "transcripts"]),
zpca_kallisto = expand(os.path.join(
config["output_dir"],
"zpca",
"pca_kallisto_{molecule}"),
molecule=["genes", "transcripts"]
),
config["output_dir"],
"multiqc_config.yaml")
output:
multiqc_report = directory(
os.path.join(
config["output_dir"],
"multiqc_summary"))
results_dir = os.path.join(
config["output_dir"]),
log_dir = config["log_dir"]
stderr = os.path.join(
config["log_dir"],
stdout = os.path.join(
config["log_dir"],
"docker://zavolab/multiqc-plugins:1.0.0"
shell:
"(multiqc \
--outdir {output.multiqc_report} \
--config {input.multiqc_config} \
{params.results_dir} \
{params.log_dir};) \
1> {log.stdout} 2> {log.stderr}"
sort bedGraphs in order to work with bedGraphtobigWig
config["output_dir"],
"samples",
"{sample}",
"ALFA",
"{unique}",
sorted_bg = os.path.join(
config["output_dir"],
"samples",
"{sample}",
"bigWig",
"{unique}",
"{sample}_{unique}_{strand}.sorted.bg")
singularity:
"docker://cjh4zavolab/bedtools:2.27"
stderr = os.path.join(
config["log_dir"],
"sort_bg_{unique}_{strand}.stderr.log")
"(sortBed \
-i {input.bg} \
> {output.sorted_bg};) 2> {log.stderr}"
bedGraphtobigWig, for viewing in genome browsers
sorted_bg = os.path.join(
config["output_dir"],
"samples",
"{sample}",
"bigWig",
"{unique}",
"{sample}_{unique}_{strand}.sorted.bg"),
chr_sizes = lambda wildcards:
os.path.join(
config['star_indexes'],
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get_sample(
'organism',
search_id='index',
search_value=wildcards.sample),
get_sample(
'index_size',
search_id='index',
search_value=wildcards.sample),
"STAR_index",
"chrNameLength.txt")
bigWig = os.path.join(
config["output_dir"],
"samples",
"{sample}",
"bigWig",
"{unique}",
"{sample}_{unique}_{strand}.bw")
singularity:
"docker://zavolab/bedgraphtobigwig:4-slim"
stderr = os.path.join(
config["log_dir"],
"samples",
"{sample}",
"bigwig_{unique}_{strand}.stderr.log"),
stdout = os.path.join(
config["log_dir"],
"samples",
"{sample}",
"bigwig_{unique}_{strand}.stdout.log")
"(bedGraphToBigWig \
{input.sorted_bg} \
{input.chr_sizes} \
{output.bigWig};) \
1> {log.stdout} 2> {log.stderr}"