def gq_seq_utils_exploratory_analysis_rnaseq_denovo_filtered(self):
"""
Exploratory analysis using the gqSeqUtils R package using a subset of filtered transcripts
"""
# Run exploratory analysis on filtered components
# Extract filtered components from counts file
jobs=[]
exploratory_output_dir = os.path.join("filtered_assembly","exploratory")
counts_file = os.path.join("filtered_assembly", "isoforms.counts.matrix")
trinotate_annotation_report_filtered = os.path.join("trinotate", "trinotate_annotation_report.tsv" + ".isoforms_filtered.tsv")
trinotate_annotation_report_filtered_header="trinotate/trinotate_annotation_report.tsv.isoforms_filtered_header.tsv"
lengths_file=os.path.join("differential_expression", "isoforms.lengths.tsv")
lengths_filtered_file = os.path.join("filtered_assembly", "isoforms.lengths.tsv")
jobs.append(concat_jobs([
Job(command="mkdir -p " + exploratory_output_dir),
Job([trinotate_annotation_report_filtered],
[trinotate_annotation_report_filtered_header],
command="sed '1s/^/ \\n/' " + trinotate_annotation_report_filtered + " > " + trinotate_annotation_report_filtered_header),
tools.py_parseMergeCsv([ trinotate_annotation_report_filtered_header, os.path.join("differential_expression", "isoforms.counts.matrix") ],
"\\\\t",
counts_file,
"\'\'",
left_join=True,
exclude="\'\'"
),
tools.py_parseMergeCsv([ trinotate_annotation_report_filtered_header, lengths_file ],
"\\\\t",
lengths_filtered_file,
"\'\' transcript_id",
left_join=True,
exclude="\' \'"
)
], name="filter_annotated_components_exploratory"
)
)
# gqSeqUtils function call
jobs.append(concat_jobs([
Job(command="mkdir -p " + exploratory_output_dir),
gq_seq_utils.exploratory_analysis_rnaseq_denovo(
counts_file,
lengths_filtered_file,
exploratory_output_dir
)
], name="gq_seq_utils_exploratory_analysis_rnaseq_denovo"))
# Render Rmarkdown Report
jobs.append(
rmarkdown.render(
job_input = os.path.join(exploratory_output_dir, "index.tsv"),
job_name = "gq_seq_utils_exploratory_analysis_rnaseq_denovo_filtered_report",
input_rmarkdown_file = os.path.join(self.report_template_dir, "RnaSeqDeNovoAssembly.gq_seq_utils_exploratory_analysis_rnaseq_filtered.Rmd") ,
render_output_dir = 'report',
module_section = 'report',
prerun_r = 'report_dir="report/filtered_assembly"; exploratory_dir="' + exploratory_output_dir + '";'
)
)
return jobs
def differential_expression(self):
"""
Performs differential gene expression analysis using [DESEQ](http://bioconductor.org/packages/release/bioc/html/DESeq.html) and [EDGER](http://www.bioconductor.org/packages/release/bioc/html/edgeR.html).
Merge the results of the analysis in a single csv file. Also, performs Gene Ontology analysis for RNA-Seq denovo Assembly using the Bioconductor's R package [goseq](http://www.bioconductor.org/packages/release/bioc/html/goseq.html).
Generates GO annotations for differential genes and isoforms expression analysis, based on associated GOTERMS generated by trinotate.
"""
output_directory = "differential_expression"
jobs = []
trinotate_annotation_report = os.path.join("trinotate", "trinotate_annotation_report.tsv")
report_dir= 'report'
input_rmarkdown_file=os.path.join(self.report_template_dir, "RnaSeqDeNovoAssembly.differential_expression_goseq.Rmd")
# Run DGE and merge dge results with annotations
for item in "genes","isoforms":
jobs.append(concat_jobs( self.differential_expression_and_goseq_rsem(output_directory, item, trinotate_annotation_report)
, name= "differential_expression_" + item)
)
# DGE Report
# Render Rmarkdown Report
output_files = []
for job_item in jobs:
output_files.extend([output_file for output_file in job_item.output_files if output_file not in output_files])
jobs.append(
rmarkdown.render(
job_input = output_files,
job_name = "differential_expression_goseq_rnaseq_denovo_report",
input_rmarkdown_file = input_rmarkdown_file,
render_output_dir = 'report',
module_section = 'report',
prerun_r = 'design_file="' + os.path.relpath(self.args.design.name, self.output_dir) +
'"; report_dir="' + report_dir + '"; source_dir="' + output_directory + '"; ' + 'top_n_results=10; contrasts=c("' + '","'.join(contrast.name for contrast in self.contrasts) + '");'
)
)
return jobs
def gq_seq_utils_exploratory_analysis_rnaseq_denovo(self):
"""
Exploratory analysis using the gqSeqUtils R package.
"""
jobs = []
# gqSeqUtils function call
jobs.append(concat_jobs([
Job(command="mkdir -p exploratory"),
gq_seq_utils.exploratory_analysis_rnaseq_denovo(
os.path.join("differential_expression", "genes.counts.matrix"),
os.path.join("differential_expression", "genes.lengths.tsv"),
"exploratory"
)
], name="gq_seq_utils_exploratory_analysis_rnaseq_denovo"))
# Render Rmarkdown Report
jobs.append(
rmarkdown.render(
job_input = os.path.join("exploratory", "index.tsv"),
job_name = "gq_seq_utils_exploratory_analysis_rnaseq_denovo_report",
input_rmarkdown_file = os.path.join(self.report_template_dir, "RnaSeqDeNovoAssembly.gq_seq_utils_exploratory_analysis_rnaseq.Rmd") ,
render_output_dir = 'report',
module_section = 'report', # TODO: this or exploratory?
prerun_r = 'report_dir="report";' # TODO: really necessary or should be hard-coded in exploratory.Rmd?
)
)
return jobs
def gq_seq_utils_exploratory_analysis_rnaseq_light(self):
"""
Exploratory analysis using the gqSeqUtils R package adapted for RnaSeqLight
"""
jobs = []
abundance_file=os.path.join(self.output_dir,"kallisto/All_readsets", "all_readsets.abundance_genes.csv")
# gqSeqUtils function call
jobs.append(concat_jobs([
Job(command="mkdir -p exploratory"),
gq_seq_utils.exploratory_analysis_rnaseq_light(
abundance_file,
config.param('gq_seq_utils_exploratory_analysis_rnaseq_light', 'genes', type='filepath'),
"exploratory"
)
], name="gq_seq_utils_exploratory_analysis_rnaseq_light", samples=self.samples))
jobs.append(
rmarkdown.render(
job_input = os.path.join("exploratory", "index.tsv"),
job_name = "report.gq_seq_utils_exploratory_analysis_rnaseq",
input_rmarkdown_file = os.path.join(self.report_template_dir, "RnaSeqLight.gq_seq_utils_exploratory_analysis_rnaseq_light.Rmd"),
samples = self.samples,
render_output_dir = 'report',
module_section = 'report',
prerun_r = 'report_dir="report";'
)
)
return jobs
def gq_seq_utils_exploratory_analysis_rnaseq(self):
"""
Exploratory analysis using the gqSeqUtils R package.
"""
jobs = []
# gqSeqUtils function call
sample_fpkm_readcounts = [[
sample.name,
os.path.join("cufflinks", sample.name, "isoforms.fpkm_tracking"),
os.path.join("raw_counts", sample.name + ".readcounts.csv")
] for sample in self.samples]
jobs.append(concat_jobs([
Job(command="mkdir -p exploratory"),
gq_seq_utils.exploratory_analysis_rnaseq(
os.path.join("DGE", "rawCountMatrix.csv"),
"cuffnorm",
config.param('gq_seq_utils_exploratory_analysis_rnaseq', 'genes', type='filepath'),
"exploratory"
)
], name="gq_seq_utils_exploratory_analysis_rnaseq"))
# Render Rmarkdown Report
jobs.append(
rmarkdown.render(
job_input = os.path.join("exploratory", "index.tsv"),
job_name = "gq_seq_utils_exploratory_analysis_rnaseq_report",
input_rmarkdown_file = os.path.join(self.report_template_dir, "RnaSeq.gq_seq_utils_exploratory_analysis_rnaseq.Rmd") ,
render_output_dir = 'report',
module_section = 'report', # TODO: this or exploratory?
prerun_r = 'report_dir="report";' # TODO: really necessary or should be hard-coded in exploratory.Rmd?
)
)
report_file = os.path.join("report", "RnaSeq.cuffnorm.md")
jobs.append(
Job(
[os.path.join("cufflinks", "AllSamples","merged.gtf")],
[report_file],
command="""\
mkdir -p report && \\
zip -r report/cuffAnalysis.zip cufflinks/ cuffdiff/ cuffnorm/ && \\
cp \\
{report_template_dir}/{basename_report_file} \\
{report_file}""".format(
report_template_dir=self.report_template_dir,
basename_report_file=os.path.basename(report_file),
report_file=report_file
),
report_files=[report_file],
name="cuffnorm_report")
)
return jobs
def kallisto_count_matrix(self):
jobs=[]
kallisto_directory="kallisto"
all_readset_directory="All_readsets"
output_dir=os.path.join(self.output_dir,kallisto_directory, all_readset_directory)
#per trancripts
input_abundance_files_transcripts = [os.path.join(self.output_dir,kallisto_directory, readset.sample.name, "abundance_transcripts.tsv") for readset in self.readsets]
job_name_transcripts="kallisto_count_matrix.transcripts"
data_type_transcripts="transcripts"
job=tools.r_create_kallisto_count_matrix(input_abundance_files_transcripts, output_dir, data_type_transcripts, job_name_transcripts)
job.samples = self.samples
jobs.append(job)
#per genes
input_abundance_files_genes = [os.path.join(self.output_dir,kallisto_directory, readset.sample.name, "abundance_genes.tsv") for readset in self.readsets]
job_name_genes="kallisto_count_matrix.genes"
data_type_genes="genes"
job=tools.r_create_kallisto_count_matrix(input_abundance_files_genes, output_dir, data_type_genes, job_name_genes)
job.samples = [readset.sample]
jobs.append(job)
#copy tx2genes file
jobs.append(
Job(
[os.path.join(self.output_dir, "kallisto", "All_readsets","all_readsets.abundance_genes.csv"), os.path.join(self.output_dir, "kallisto", "All_readsets","all_readsets.abundance_transcripts.csv")],
[],
command="""\
cp \\
{tx2genes_file} \\
{report_dir}""".format(
tx2genes_file=config.param('kallisto', 'transcript2genes', type="filepath"),
report_dir="report"
),
name="report.copy_tx2genes_file",
samples=self.samples
)
)
# Create kallisto report
jobs.append(
rmarkdown.render(
job_input = [os.path.join(self.output_dir, "kallisto", "All_readsets","all_readsets.abundance_genes.csv"), os.path.join(self.output_dir, "kallisto", "All_readsets","all_readsets.abundance_transcripts.csv")],
job_name = "report.kallisto_count_matrix",
input_rmarkdown_file = os.path.join(self.report_template_dir, "RnaSeqLight.kallisto.Rmd"),
samples = self.samples,
render_output_dir = 'report',
module_section = 'report',
prerun_r = 'report_dir="report";'
)
)
return jobs
def trinotate(self):
"""
Perform transcriptome functional annotation and analysis using [Trinotate](http://trinotate.sourceforge.net/).
All functional annotation data is integrated into a SQLite database and a whole annotation report is created.
"""
jobs = []
swissprot_db = os.path.basename(config.param("blastx_trinity_uniprot", "swissprot_db", type='prefixpath'))
transdecoder_pep = os.path.join("trinotate", "transdecoder", "Trinity.fasta.transdecoder.pep")
job = trinotate.trinotate(
swissprot_db = swissprot_db ,
trinity_fasta = os.path.join("trinity_out_dir", "Trinity.fasta"),
swissprot_blastx = os.path.join("blast", "blastx_Trinity_" + swissprot_db + ".tsv"),
transdecoder_pep = transdecoder_pep,
transdecoder_pfam = os.path.join("trinotate", "transdecoder", "Trinity.fasta.transdecoder.pfam"),
swissprot_blastp = os.path.join("trinotate", "blastp", "blastp_" + os.path.basename(transdecoder_pep) + "_" + swissprot_db + ".tsv"),
rnammer = os.path.join("trinotate", "rnammer", "Trinity.fasta.rnammer.gff"),
signalp = os.path.join("trinotate", "signalp", "signalp.gff"),
tmhmm = os.path.join("trinotate", "tmhmm", "tmhmm.out"),
trinotate_sqlite = os.path.join("trinotate", "Trinotate.sqlite"),
trinotate_report = os.path.join("trinotate", "trinotate_annotation_report.tsv")
)
job.samples = self.samples
jobs.append(job)
# Render Rmarkdown Report
jobs.append(
rmarkdown.render(
job_input = os.path.join("trinotate", "trinotate_annotation_report.tsv"),
job_name = "trinotate_report",
input_rmarkdown_file = os.path.join(self.report_template_dir, "RnaSeqDeNovoAssembly.trinotate.Rmd") ,
render_output_dir = 'report',
module_section = 'report',
prerun_r = 'report_dir="report"; source_dir="trinotate";'
)
)
return jobs
def trinotate(self):
"""
Perform transcriptome functional annotation and analysis using [Trinotate](http://trinotate.sourceforge.net/).
All functional annotation data is integrated into a SQLite database and a whole annotation report is created.
"""
jobs = []
swissprot_db = os.path.basename(config.param("blastx_trinity_uniprot", "swissprot_db", type='prefixpath'))
transdecoder_pep = os.path.join("trinotate", "transdecoder", "Trinity.fasta.transdecoder.pep")
jobs.append( trinotate.trinotate(
swissprot_db = swissprot_db ,
trinity_fasta = os.path.join("trinity_out_dir", "Trinity.fasta"),
swissprot_blastx = os.path.join("blast", "blastx_Trinity_" + swissprot_db + ".tsv"),
transdecoder_pep = transdecoder_pep,
transdecoder_pfam = os.path.join("trinotate", "transdecoder", "Trinity.fasta.transdecoder.pfam"),
swissprot_blastp = os.path.join("trinotate", "blastp", "blastp_" + os.path.basename(transdecoder_pep) + "_" + swissprot_db + ".tsv"),
rnammer = os.path.join("trinotate", "rnammer", "Trinity.fasta.rnammer.gff"),
signalp = os.path.join("trinotate", "signalp", "signalp.gff"),
tmhmm = os.path.join("trinotate", "tmhmm", "tmhmm.out"),
trinotate_sqlite = os.path.join("trinotate", "Trinotate.sqlite"),
trinotate_report = os.path.join("trinotate", "trinotate_annotation_report.tsv")
)
)
# Render Rmarkdown Report
jobs.append(
rmarkdown.render(
job_input = os.path.join("trinotate", "trinotate_annotation_report.tsv"),
job_name = "trinotate_report",
input_rmarkdown_file = os.path.join(self.report_template_dir, "RnaSeqDeNovoAssembly.trinotate.Rmd") ,
render_output_dir = 'report',
module_section = 'report',
prerun_r = 'report_dir="report"; source_dir="trinotate";'
)
)
return jobs
def verify_bam_id(self):
"""
verifyBamID is a software that verifies whether the reads in particular file match previously known
genotypes for an individual (or group of individuals), and checks whether the reads are contaminated
as a mixture of two samples. verifyBamID can detect sample contamination and swaps when external
genotypes are available. When external genotypes are not available, verifyBamID still robustly
detects sample swaps.
"""
# Known variants file
population_AF = config.param('verify_bam_id',
'population_AF',
required=False)
known_variants_annotated = config.param('verify_bam_id',
'verifyBamID_variants_file',
required=False)
verify_bam_id_directory = "verify_bam_id"
variants_directory = "variants"
jobs = []
verify_bam_results = []
jobs.append(
Job([known_variants_annotated],
[variants_directory, verify_bam_id_directory],
command="mkdir -p " + variants_directory + " " +
verify_bam_id_directory,
name="verify_bam_id_create_directories"))
for sample in self.samples:
alignment_directory = os.path.join("alignment", sample.name)
candidate_input_files = [[
os.path.join(alignment_directory,
sample.name + ".sorted.dup.recal.bam")
]]
candidate_input_files.append([
os.path.join(alignment_directory,
sample.name + ".sorted.dedup.bam")
])
candidate_input_files.append([
os.path.join(alignment_directory,
sample.name + ".sorted.mdup.bam")
]) # this one is for RnaSeq pipeline
[input_bam] = self.select_input_files(candidate_input_files)
output_prefix = os.path.join(verify_bam_id_directory, sample.name)
coverage_bed = bvatools.resolve_readset_coverage_bed(
sample.readsets[0])
# Run verifyBamID
job = verify_bam_id.verify(input_bam, known_variants_annotated,
output_prefix)
job.name = "verify_bam_id_" + sample.name
job.samples = [sample]
jobs.append(job)
verify_bam_results.extend([output_prefix + ".selfSM"])
# Coverage bed is null if whole genome experiment
target_bed = coverage_bed if coverage_bed else ""
# Render Rmarkdown Report
jobs.append(
rmarkdown.render(
job_input=verify_bam_results,
job_name="verify_bam_id_report",
input_rmarkdown_file=os.path.join(
self.report_template_dir, "Illumina.verify_bam_id.Rmd"),
render_output_dir='report',
module_section='report',
prerun_r='source_dir="' + verify_bam_id_directory +
'"; report_dir="report" ; params=list(verifyBamID_variants_file="'
+ known_variants_annotated + '", dbnsfp_af_field="' +
population_AF + '", coverage_bed="' + target_bed + '");'))
return jobs
def differential_expression_filtered(self):
"""
Differential Expression and GOSEQ analysis based on filtered transcripts and genes
"""
output_directory = os.path.join("filtered_assembly","differential_expression")
jobs = []
trinotate_annotation_report = os.path.join("trinotate", "trinotate_annotation_report.tsv")
report_dir= os.path.join("report","filtered_assembly")
input_rmarkdown_file=os.path.join(self.report_template_dir, "RnaSeqDeNovoAssembly.differential_expression_goseq_filtered.Rmd")
# Filter input files
trinotate_annotation_report_filtered = trinotate_annotation_report + ".isoforms_filtered.tsv"
trinotate_annotation_report_filtered_header={}
trinotate_annotation_report_filtered_header["isoforms"] = trinotate_annotation_report + ".isoforms_filtered_header.tsv"
trinotate_annotation_report_filtered_header["genes"]= trinotate_annotation_report + ".genes_filtered_header.tsv"
counts_ids = { 'genes':"Genes", 'isoforms':"Isoforms" }
trinotate_filters = None if not config.param('filter_annotated_components', 'filters_trinotate', required=False) else config.param('filter_annotated_components', 'filters_trinotate', required=False).split("\n")
source_directory = "differential_expression"
# Create the files containing filtered isoforms and genes with headers
jobs.append(concat_jobs([
Job(command="mkdir -p " + output_directory ),
Job([trinotate_annotation_report_filtered],
[trinotate_annotation_report_filtered_header["genes"]],
command="cat " + trinotate_annotation_report_filtered + " | awk 'BEGIN{OFS=\"_\";FS=\"_\"}{print $1,$2}' | uniq | sed '1s/^/ \\n/' " + " > " + trinotate_annotation_report_filtered_header["genes"]
),
Job([trinotate_annotation_report_filtered],
[trinotate_annotation_report_filtered_header["isoforms"]],
command="sed '1s/^/ \\n/' " + trinotate_annotation_report_filtered + " > " + trinotate_annotation_report_filtered_header["isoforms"])
],name="differential_expression_filtered_get_trinotate")
)
# Run DGE and merge dge results with annotations
for item in "genes","isoforms":
matrix = os.path.join(output_directory, item + ".counts.matrix.symbol")
job=tools.py_parseMergeCsv([ trinotate_annotation_report_filtered_header[item], os.path.join(source_directory, item + ".counts.matrix.symbol") ],
"\\\\t",
matrix,
"\'\' " + counts_ids[item],
left_join=True,
exclude="\' \'")
jobs.append(concat_jobs([
job,
Job([os.path.join(source_directory, item +".lengths.tsv.noheader.tsv")],
[os.path.join(output_directory, item +".lengths.tsv.noheader.tsv")],
command="cp " + os.path.join(source_directory, item +".lengths.tsv.noheader.tsv") + " " + os.path.join(output_directory, item +".lengths.tsv.noheader.tsv")),
concat_jobs(self.differential_expression_and_goseq_rsem(output_directory, item, trinotate_annotation_report), name="differential_expression_filtered_" + item)
], name="differential_expression_filtered_" + item)
)
# Dependencies for report
output_files = []
for job_item in jobs:
output_files.extend([output_file for output_file in job_item.output_files if output_file not in output_files])
# DGE Report
# Render Rmarkdown Report
jobs.append(
rmarkdown.render(
job_input = output_files,
job_name = "differential_expression_goseq_rnaseq_denovo_filtered_report",
input_rmarkdown_file = input_rmarkdown_file ,
render_output_dir = 'report',
module_section = 'report',
prerun_r = 'report_dir="' + report_dir + '"; source_dir="' + output_directory + '"; ' + 'top_n_results=10; contrasts=c("' + '","'.join(contrast.name for contrast in self.contrasts) + '");'
)
)
return jobs
