The program I am running requires only the directory to be specified in order to save the output files. If I run this with snakemake it is giving me error:
IOError: [Errno 2] No such file or directory: 'BOB/call_images/chrX_194_1967_BOB_78.rpkm.png'
My try:
rule all:
input:
"BOB/call_images/"
rule plot:
input:
hdf="BOB/hdf5/analysis.hdf5",
txt="BOB/calls.txt"
output:
directory("BOB/call_images/")
shell:
"""
python someprogram.py plotcalls --input {input.hdf} --calls {input.txt} --outputdir {output[0]}
"""
Neither this version works:
output:
outdir="BOB/call_images/"
Normally, snakemake will create the output directory for specified output files. The snakemake directory() declaration tells snakemake that the directory is the output, so it leaves it up to the rule to create the output directory.
If you can predict what the output files are called (even if you don't specify them on the command line), you should tell snakemake the output filenames in the output: field. EG:
rule plot:
input:
hdf="BOB/hdf5/analysis.hdf5",
txt="BOB/calls.txt"
output:
"BOB/call_images/chrX_194_1967_BOB_78.rpkm.png"
params:
outdir="BOB/call_images"
shell:
"""
python someprogram.py plotcalls --input {input.hdf} --calls {input.txt} --outputdir {params.outdir}
"""
(The advantage of using the params to define the output dir instead of hard coding it in the shell command is that you can use wildcards there)
If you can't predict the output file name, then you have to manually run mkdir -p {output} as the first step of the shell command.
rule plot:
input:
hdf="BOB/hdf5/analysis.hdf5",
txt="BOB/calls.txt"
output: directory("BOB/call_images")
shell:
"""
mkdir -p {output}
python someprogram.py plotcalls --input {input.hdf} --calls {input.txt} --outputdir {output}
"""
Related
A few days ago I started using Snakemake for the first time. I am having an issue when I am trying to run the megahit rule in my pipeline.
It gives me the following error "Outputs of incorrect type (directories when expecting files or vice versa). Output directories must be flagged with directory(). ......"
So initially it runs and then crashes with the above error. I implemented the solution with the directory() option in my pipeline but I think its not a good practice since, for various reasons, you can loose files without even knowing it.
Is there a way to run the rule without using the directory() ?
I would appreciate any help on the issue!
Thanking you in advance
sra = []
with open("run_ids") as f:
for line in f:
sra.append(line.strip())
rule all:
input:
expand("raw_reads/{sample}/{sample}.fastq", sample=sra),
expand("trimmo/{sample}/{sample}.trimmed.fastq", sample=sra),
expand("megahit/{sample}/final.contigs.fa", sample=sra)
rule download:
output:
"raw_reads/{sample}/{sample}.fastq"
params:
"--split-spot --skip-technical"
log:
"logs/fasterq-dump/{sample}.log"
benchmark:
"benchmarks/fastqdump/{sample}.fasterq-dump.benchmark.txt"
threads: 8
shell:
"""
fasterq-dump {params} --outdir /home/raw_reads/{wildcards.sample} {wildcards.sample} -e {threads}
"""
rule trim:
input:
"raw_reads/{sample}/{sample}.fastq"
output:
"trimmo/{sample}/{sample}.trimmed.fastq"
params:
"HEADCROP:15 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36"
log:
"logs/trimmo/{sample}.log"
benchmark:
"benchmarks/trimmo/{sample}.trimmo.benchmark.txt"
threads: 6
shell:
"""
trimmomatic SE -phred33 -threads {threads} {input} trimmo/{wildcards.sample}/{wildcards.sample}.trimmed.fastq {params}
"""
rule megahit:
input:
"trimmo/{sample}/{sample}.trimmed.fastq"
output:
"megahit/{sample}/final.contigs.fa"
params:
"-m 0.7 -t"
log:
"logs/megahit/{sample}.log"
benchmark:
"benchmarks/megahit/{sample}.megahit.benchmark.txt"
threads: 10
shell:
"""
megahit -r {input} -o {output} -t {threads}
"""
IMHO it is a bad design of the megahit software that it takes a directory as a parameter and outputs into a file in this directory with a hardcoded name. Flagging the filename with directory() doesn't solve the issue, as in this case what you expect to be a file with the .fa extension megahit treats as a directory. The rest of the pipeline is broken in this case.
But this issue can be solved in Snakemake like that:
rule megahit:
input:
"trimmo/{sample}/{sample}.trimmed.fastq"
output:
"megahit/{sample}/final.contigs.fa"
# ...
shell:
"""
megahit -r {input} -o megahit/{wildcards.sample} -t {threads}
"""
A better design of the megahit rule would look as follows:
rule megahit:
input:
"trimmo/{sample}/{sample}.trimmed.fastq"
output:
out_dir = directory("megahit/{sample}/"),
fasta = "megahit/{sample}/final.contigs.fa"
log:
"logs/megahit/{sample}.log"
benchmark:
"benchmarks/megahit/{sample}.megahit.benchmark.txt"
threads:
10
shell:
"megahit -r {input} -f -o {output.out_dir} -t {threads}"
This guarantees that the output directory is removed upon failure, while the -f argument to megahit tells it to ignore the fact that the output folder exists (it is created by Snakemake automatically because one of the outputs is a file inside it: final.contigs.fa).
BTW, the -m (--memory) parameter is best implemented as a resource. The only problem though is that snakemake's default resource, mem_mb is in megabytes. One workaround would be as follows:
resources:
mem_mb = mem_mb_limit_for_megahit # could be a fraction of a global constant
params:
mem_bytes = lambda w, resources: round(resources.mem_mb * 1e6)
shell:
"megahit ... -m {params.mem_bytes}"
Is there a way to write a rule so that I don't need to use the wildcards for all inputs/outputs or can I use a "*" to glob rather than using wildcards?? I want to symlink a file that is autocreated in subfolders, to the main directory.
This is the error I get when trying to run the Snakemake:
WildcardError in line 42 of snakemake_guppy_basecall/Snakefile:
Wildcards in input files cannot be determined from output files:
'failpass'
import glob
configfile: "config.yaml"
inputdirectory=config["directory"]
SAMPLES, = glob_wildcards(inputdirectory+"/{sample}.fast5", followlinks=True)
print(SAMPLES)
wildcard_constraints:
sample="\w+\d+_\w+_\w+\d+_.+_\d"
##### target rules #####
rule all:
input:
#expand('basecall/{sample}/sequencing_summary.txt', sample=SAMPLES),
"qc/multiqc.html"
rule make_indvidual_samplefiles:
input:
inputdirectory+"/{sample}.fast5",
output:
"lists/{sample}.txt",
shell:
"basename {input} > {output}"
rule guppy_basecall_persample:
input:
directory=directory(inputdirectory),
samplelist="lists/{sample}.txt",
output:
summary="basecall/{sample}/sequencing_summary.txt",
directory=directory("basecall/{sample}/"),
params:
config["basealgo"]
shell:
"guppy_basecaller -i {input.directory} --input_file_list {input.samplelist} -s {output.directory} -c {params} --compress_fastq -x \"auto\" --gpu_runners_per_device 3 --num_callers 2 --chunks_per_runner 200"
rule guppy_linkfastq:
input:
#glob_wildcards("basecall/{sample}/*/*.fastq.gz"),
"basecall/{sample}/{failpass}/{runid}.fastq.gz",
output:
"basecall/{sample}.fastq.gz",
shell:
"ln -s {input} {output}"
rule fastqc_pretrim:
input:
#"basecall/{sample}/{failpass}/{runid}.fastq.gz",
"basecall/{sample}.fastq.gz"
output:
html="qc/fastqc_pretrim/{sample}.html",
zip="qc/fastqc_pretrim/{sample}_fastqc.zip" # the suffix _fastqc.zip is necessary for multiqc to find the file. If not using multiqc, you are free to choose an arbitrary filename
params: ""
log:
"logs/fastqc_pretrim/{sample}.log"
threads: 1
wrapper:
"v0.75.0/bio/fastqc"
rule multiqc:
input:
#expand("basecall/{sample}.fastq.gz", sample=SAMPLES)
expand("qc/fastqc_pretrim/{sample}_fastqc.zip", sample=SAMPLES)
output:
"qc/multiqc.html"
params:
"" # Optional: extra parameters for multiqc.
log:
"logs/multiqc.log"
wrapper:
"0.77.0/bio/multiqc"
I am trying to create a pipeline that does: Get Nanopore f5 sequence files -> run guppy basecaller GPU mode -> use resulting fastq files to run FASTQC -> run multiQC for everything
I get a MissingInputException when I run the following rule:
configfile: "Configs.yaml"
rule download_data_from_ZFIN:
input:
anatomy_item = config["ZFIN_url"]["anatomy_item"],
xpat_stage_anatomy = config["ZFIN_url"]["xpat_stage_anatomy"],
xpat_fish = config["ZFIN_url"]["xpat_fish"],
anatomy_synonyms = config["ZFIN_url"]["anatomy_synonyms"]
output:
anatomy_item = os.path.join(os.getcwd(), config["download_data_from_ZFIN"]["dir"], "anatomy_item.tsv"),
xpat_stage_anatomy = os.path.join(os.getcwd(), config["download_data_from_ZFIN"]["dir"], "xpat_stage_anatomy.tsv"),
xpat_fish = os.path.join(os.getcwd(), config["download_data_from_ZFIN"]["dir"], "xpat_fish.tsv"),
anatomy_synonyms = os.path.join(os.getcwd(), config["download_data_from_ZFIN"]["dir"], "anatomy_synonyms.tsv")
shell:
"wget -O {output.anatomy_item} {input.anatomy_item};" \
"wget -O {output.anatomy_synonyms} {input.anatomy_synonyms};" \
"wget -O {output.xpat_stage_anatomy} {input.xpat_stage_anatomy};" \
"wget -O {output.xpat_fish} {input.xpat_fish};"
And this is the content of my configs.yaml file:
ZFIN_url:
# Zebrafish Anatomy Term
anatomy_item: "https://zfin.org/downloads/file/anatomy_item.txt"
# Zebrafish Gene Expression by Stage and Anatomy Term
xpat_stage_anatomy: "https://zfin.org/downloads/file/xpat_stage_anatomy.txt"
# ZFIN Genes with Expression Assay Records
xpat_fish: "https://zfin.org/downloads/file/xpat_fish.txt"
# Zebrafish Anatomy Term Synonyms
anatomy_synonyms: "https://zfin.org/downloads/file/anatomy_synonyms.txt"
download_data_from_ZFIN:
dir: ZFIN_data
The error message is:
Building DAG of jobs...
MissingInputException in line 10 of /home/zhangdong/works/NGS/coevolution/snakemake/coevolution.rule:
Missing input files for rule download_data_from_ZFIN:
https://zfin.org/downloads/file/anatomy_item.txt
I want to make sure that if this exception is caused by none file input for the input rule?
Note that you can also use remote files as input so you may avoid rule download_data_from_ZFIN altogether. E.g.:
from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
HTTP = HTTPRemoteProvider()
rule all:
input:
'output.txt',
rule one:
input:
# Some file from the web
x= HTTP.remote('https://plasmodb.org/common/downloads/release-49/PbergheiANKA/txt/PlasmoDB-49_PbergheiANKA_CodonUsage.txt', keep_local=True)
output:
'output.txt',
shell:
r"""
# Do something with the remote file
head {input.x} > {output}
"""
The remote file will be downloaded and stored locally under plasmodb.org/common/.../PlasmoDB-49_PbergheiANKA_CodonUsage.txt
Many thanks #dariober, I tried the follwing code and it worked,
import os
from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
configfile: "Configs.yaml"
HTTP = HTTPRemoteProvider()
rule all:
input:
expand(os.path.join(os.getcwd(),config["download_data_from_ZFIN"]["dir"],"{item}.tsv"),
item=list(config["ZFIN_url"].keys()))
rule download_data_from_ZFIN:
input:
lambda wildcards: HTTP.remote(config["ZFIN_url"][wildcards.item], keep_local=True)
output:
os.path.join(os.getcwd(),config["download_data_from_ZFIN"]["dir"],"{item}.tsv")
threads:
1
shell:
"mv {input} > {output}"
Such code is more snakemake-like, but I have two further questions:
Is there a way to specify the output file name for the downloading? Now I use the mv command to achieve that.
Does this remote files function support parallel works? I tried the above code together with --cores 6, but it still download the file one by one.
My problem is related to Running parallel instances of a single job/rule on Snakemake but I believe different.
I cannot create a all: rule for it in advance because the folder of input files will be created by a previous rule and depends on the user initial data
pseudocode
rule1: get a big file (OK)
rule2: split the file in parts in Split folder (OK)
rule3: run a program on each file created in Split
I am now at rule3 with Split containing 70 files like
Split/file_001.fq
Split/file_002.fq
..
Split/file_069.fq
Could you please help me creating a rule for pigz to run compress the 70 files in parallel to 70 .gz files
I am running with snakemake -j 24 ZipSplit
config["pigt"] gives 4 threads for each compression job and I give 24 threads to snakemake so I expect 6 parallel compressions but my current rule merges the inputs to one archive in a single job instead of parallelizing !?
Should I build the list of input fully in the rule? how?
# parallel job
files, = glob_wildcards("Split/{x}.fq")
rule ZipSplit:
input: expand("Split/{x}.fq", x=files)
threads: config["pigt"]
shell:
"""
pigz -k -p {threads} {input}
"""
I tried to define input directly with
input: glob_wildcards("Split/{x}.fq")
but syntax error occures
# InSilico_PCR Snakefile
import os
import re
from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
HTTP = HTTPRemoteProvider()
# source config variables
configfile: "config.yaml"
# single job
rule GetRawData:
input:
HTTP.remote(os.path.join(config["host"], config["infile"]), keep_local=True, allow_redirects=True)
output:
os.path.join("RawData", config["infile"])
run:
shell("cp {input} {output}")
# single job
rule SplitFastq:
input:
os.path.join("RawData", config["infile"])
params:
lines_per_file = config["lines_per_file"]
output:
pfx = os.path.join("Split", config["infile"] + "_")
shell:
"""
zcat {input} | split --numeric-suffixes --additional-suffix=.fq -a 3 -l {params.lines_per_file} - {output.pfx}
"""
# parallel job
files, = glob_wildcards("Split/{x}.fq")
rule ZipSplit:
input: expand("Split/{x}.fq", x=files)
threads: config["pigt"]
shell:
"""
pigz -k -p {threads} {input}
"""
I think the example below should do it, using checkpoints as suggested by #Maarten-vd-Sande.
However, in your particular case of splitting a big file and compress the output on the fly, you may be better off using the --filter option of split as in
split -a 3 -d -l 4 --filter='gzip -c > $FILE.fastq.gz' bigfile.fastq split/
The snakemake solution, assuming your input file is called bigfile.fastq, split and compress output will be in directory splitting./bigfile/
rule all:
input:
expand("{sample}.split.done", sample= ['bigfile']),
checkpoint splitting:
input:
"{sample}.fastq"
output:
directory("splitting/{sample}")
shell:
r"""
mkdir splitting/{wildcards.sample}
split -a 3 -d --additional-suffix .fastq -l 4 {input} splitting/{wildcards.sample}/
"""
rule compress:
input:
"splitting/{sample}/{i}.fastq",
output:
"splitting/{sample}/{i}.fastq.gz",
shell:
r"""
gzip -c {input} > {output}
"""
def aggregate_input(wildcards):
checkpoint_output = checkpoints.splitting.get(**wildcards).output[0]
return expand("splitting/{sample}/{i}.fastq.gz",
sample=wildcards.sample,
i=glob_wildcards(os.path.join(checkpoint_output, "{i}.fastq")).i)
rule all_done:
input:
aggregate_input
output:
touch("{sample}.split.done")
I'm using snakemake for the first time in order to build a basic pipeline using cutadapt, bwa and GATK (trimming ; mapping ; calling). I would like to run this pipeline on every fastq file contained in a directory, without having to specify their name or whatever in the snakefile or in the config file. I would like to succeed in doing this.
The first two steps (cutadapt and bwa / trimming and mapping) are running fine, but I'm encountering some problems with GATK.
First, I have to generate g.vcf files from bam files. I'm doing this using these rules:
configfile: "config.yaml"
import os
import glob
rule all:
input:
"merge_calling.g.vcf"
rule cutadapt:
input:
read="data/Raw_reads/{sample}_R1_{run}.fastq.gz",
read2="data/Raw_reads/{sample}_R2_{run}.fastq.gz"
output:
R1=temp("trimmed_reads/{sample}_R1_{run}.fastq.gz"),
R2=temp("trimmed_reads/{sample}_R2_{run}.fastq.gz")
threads:
10
shell:
"cutadapt -q {config[Cutadapt][Quality_value]} -m {config[Cutadapt][min_length]} -a {config[Cutadapt][forward_adapter]} -A {config[Cutadapt][reverse_adapter]} -o {output.R1} -p '{output.R2}' {input.read} {input.read2}"
rule bwa_map:
input:
genome="data/genome.fasta",
read=expand("trimmed_reads/{{sample}}_{pair}_{{run}}.fastq.gz", pair=["R1", "R2"])
output:
temp("mapped_bam/{sample}_{run}.bam")
threads:
10
params:
rg="#RG\\tID:{sample}\\tPL:ILLUMINA\\tSM:{sample}"
shell:
"bwa mem -t 2 -R '{params.rg}' {input.genome} {input.read} | samtools view -Sb - > {output}"
rule picard_sort:
input:
"mapped_bam/{sample}.bam"
output:
"sorted_reads/{sample}.bam"
shell:
"java -Xmx4g -jar /home/alexandre/picard-tools/picard.jar SortSam I={input} O={output} SO=coordinate VALIDATION_STRINGENCY=SILENT"
rule picard_rmdup:
input:
bam="sorted_reads/{sample}.bam"
output:
"rmduped_reads/{sample}.bam",
"picard_stats/{sample}.bam"
params:
reads="rmduped_reads/{sample}.bam",
stats="picard_stats/{sample}.bam",
shell:
"java -jar -Xmx2g /home/alexandre/picard-tools/picard.jar MarkDuplicates "
"I={input.bam} "
"O='{params.reads}' "
"VALIDATION_STRINGENCY=SILENT "
"MAX_FILE_HANDLES_FOR_READ_ENDS_MAP=1000 "
"REMOVE_DUPLICATES=TRUE "
"M='{params.stats}'"
rule samtools_index:
input:
"rmduped_reads/{sample}.bam"
output:
"rmduped_reads/{sample}.bam.bai"
shell:
"samtools index {input}"
rule GATK_raw_calling:
input:
bam="rmduped_reads/{sample}.bam",
bai="rmduped_reads/{sample}.bam.bai",
genome="data/genome.fasta"
output:
"Raw_calling/{sample}.g.vcf",
shell:
"java -Xmx4g -jar /home/alexandre/GenomeAnalysisTK-3.7/GenomeAnalysisTK.jar -ploidy 2 --emitRefConfidence GVCF -T HaplotypeCaller -R {input.genome} -I {input.bam} --genotyping_mode DISCOVERY -o {output}"
These rules work fine. For example, if I have the files :
Cla001d_S281_L001_R1_001.fastq.gz
Cla001d_S281_L001_R2_001.fastq.gz
I can create one bam file (Cla001d_S281_L001_001.bam) and from that bam file create a GVCF file (Cla001d_S281_L001_001.g.vcf). I have a lot of sample like this one, and I need to create one GVCF file for each, and then merge these GVCF files into one file. The problem is that I'm unable to give the list of the file to merge to the following rule:
rule GATK_merge:
input:
???
output:
"merge_calling.g.vcf"
shell:
"java -Xmx4g -jar /home/alexandre/GenomeAnalysisTK-3.7/GenomeAnalysisTK.jar "
"-T CombineGVCFs "
"-R data/genome.fasta "
"--variant {input} "
"-o {output}"
I tried several things in order to do that, but cannot succeed. The problem is the link between the two rules (GATK_raw_calling and GATK_merge that is supposed to merge the output of GATK_raw_calling). I can't output one single file if I'm specifying the output of GATK_raw_calling as the input of the following rule (Wildcards in input files cannot be determined from output files), and I'm unable to make a link between the two rules if I'm not specifying these files as an input...
Is there a way to succeed in doing that? The difficulty is that I'm not defining a list of names or whatever, I think.
Thanks you in advance for your help.
You can try to generate a list of sample IDs using glob_wildcards on the initial fastq.gz files:
sample_ids, run_ids = glob_wildcards("data/Raw_reads/{sample}_R1_{run}.fastq.gz")
Then, you can use this to expand the input of GATK_merge:
rule GATK_merge:
input:
expand("Raw_calling/{sample}_{run}.g.vcf",
sample=sample_ids, run=run_ids)
If the same run ID always come with the same sample ID, you will need to zip instead of expanding, in order to avoid non-existing combinations:
rule GATK_merge:
input:
["Raw_calling/{sample}_{run}.g.vcf".format(
sample=sample_id,
run=run_id) for sample_id, run_id in zip(sample_ids, run_ids)]
You can achieve this by using a python function as an input for your rule, as described in the snakemake documentation here.
Could look like this for example:
# Define input files
def gatk_inputs(wildcards):
files = expand("Raw_calling/{sample}.g.vcf", sample=<samples list>)
return files
# Rule
rule gatk:
input: gatk_inputs
output: <output file name>
run: ...
Hope this helps.