I want to download the fastq files from SRA database using SRR ID using Snakemake. I read a file to get SRR ID using python code.
I want to parse the Variable one by one as input. My code is below.
I want to run command
fastq-dump SRR390728
#SAMPLES = ['SRR390728','SRR400816']
SAMPLES = [line.strip() for line in open("./srrList", 'r')]
rule all:
input:
expand("fastq/{sample}.fastq.log",sample=SAMPLES)
rule download_fastq:
input:
"{sample}"
output:
"fastq/{sample}.fastq.log"
shell:
"fastq-dump {input} > {output}"
Skip input and just call the wildcard in shell command. input needs to be a filepath that needs to already exist or be created as part of the pipeline - neither are true in your case.
rule download_fastq:
output:
"fastq/{sample}.fastq.log"
shell:
"fastq-dump {wildcards.sample} > {output}"
Related
I want to rename and move my fastq.gz files from these:
NAME-BOB_S1_L001_R1_001.fastq.gz
NAME-BOB_S1_L001_R2_001.fastq.gz
NAME-JOHN_S2_L001_R1_001.fastq.gz
NAME-JOHN_S2_L001_R2_001.fastq.gz
to these:
NAME_BOB/reads/NAME_BOB.R1.fastq.gz
NAME_BOB/reads/NAME_BOB.R2.fastq.gz
NAME_JOHN/reads/NAME_JOHN.R1.fastq.gz
NAME_JOHN/reads/NAME_JOHN.R2.fastq.gz
This is my code. The problem I have is the second variable S which I do not know how to specify in the code as I do not need it in my output filename.
workdir: "/path/to/workdir/"
DIR=["BOB","JOHN"]
S=["S1","S2"]
rule all:
input:
expand("NAME_{dir}/reads/NAME_{dir}.R1.fastq.gz", dir=DIR),
expand("NAME_{dir}/reads/NAME_{dir}.R2.fastq.gz", dir=DIR)
rule rename:
input:
fastq1=("fastq/NAME-{dir}_{s}_L001_R1_001.fastq.gz", zip, dir=DIR, s=S),
fastq2=("fastq/NAME-{dir}_{s}_L001_R2_001.fastq.gz", zip, dir=DIR, s=S)
output:
fastq1="NAME_{dir}/reads/NAME_{dir}.R1.fastq.gz",
fastq2="NAME_{dir}/reads/NAME_{dir}.R2.fastq.gz"
shell:
"""
mv {input.fastq1} {output.fastq1}
mv {input.fastq2} {output.fastq2}
"""
There are several problems in your code. First of all, the {dir} in your output and {dir} in your input are two different variables. Actually the {dir} in the output is a wildcard, while the {dir} in the input is a parameter for the expand function (moreover, you even forgot to call this function, and that is the second problem).
The third problem is that the shell section shall contain only a single command. You may try mv {input.fastq1} {output.fastq1}; mv {input.fastq2} {output.fastq2}, but this is not an idiomatic solution. Much better would be to create a rule that produces a single file, letting Snakemake to do the rest of the work.
Finally the S value fully depend on the DIR value, so it becomes a function of {dir}, and that can be solved with a lambda in input:
workdir: "/path/to/workdir/"
DIR=["BOB","JOHN"]
dir2s = {"BOB": "S1", "JOHN": "S2"}
rule all:
input:
expand("NAME_{dir}/reads/NAME_{dir}.{r}.fastq.gz", dir=DIR, r=["R1", "R2"])
rule rename:
input:
lambda wildcards:
"fastq/NAME-{{dir}}_{s}_L001_{{r}}_001.fastq.gz".format(s=dir2s[wildcards.dir])
output:
"NAME_{dir}/reads/NAME_{dir}.{r}.fastq.gz",
shell:
"""
mv {input} {output}
"""
I am trying to process MinION cDNA amplicons using Porechop with Minimap2 and I am getting this error.
MissingInputException in line 16 of /home/sean/Desktop/reo/antisera project/20200813/MinIONAmplicon.smk:
Missing input files for rule minimap2:
8413_19_strict/BC01.fastq.g
I understand what the error telling me, I just understand why its being its not trying to make the rule before it. Porechop is being used to check for all the possible barcodes and will output more than one fastq file if it finds more than barcode in the directory. However since I know what barcode I am looking for I made a barcodes section in the config.yaml file so I can map them together.
I think the error is happening because my target output for Porechop doesn't match the input for minimap2 but I do not know how to correct this problem as there can be multiple outputs from porechop.
I thought I was building a path for the input file for the minimap2 rule and when snakemake discovered that the porechop output was not there it would make it, but that is not what is happening.
Here is my pipeline so far,
configfile: "config.yaml"
rule all:
input:
expand("{sample}.bam", sample = config["samples"])
rule porechop_strict:
input:
lambda wildcards: config["samples"][wildcards.sample]
output:
directory("{sample}_strict/")
shell:
"porechop -i {input} -b {output} --barcode_threshold 85 --threads 8 --require_two_barcodes"
rule minimap2:
input:
lambda wildcards: "{sample}_strict/" + config["barcodes"][wildcards.sample]
output:
"{sample}.bam"
shell:
"minimap2 -ax map-ont -t8 ../concensus.fasta {input} | samtools sort -o {output}"
and the yaml file
samples: {
'8413_19': relabeled_reads/8413_19.raw.fastq.gz,
'8417_19': relabeled_reads/8417_19.raw.fastq.gz,
'8445_19': relabeled_reads/8445_19.raw.fastq.gz,
'8466_19_104': relabeled_reads/8466_19_104.raw.fastq.gz,
'8466_19_105': relabeled_reads/8466_19_105.raw.fastq.gz,
'8467_20': relabeled_reads/8467_20.raw.fastq.gz,
}
barcodes: {
'8413_19': BC01.fastq.gz,
'8417_19': BC02.fastq.gz,
'8445_19': BC03.fastq.gz,
'8466_19_104': BC04.fastq.gz,
'8466_19_105': BC05.fastq.gz,
'8467_20': BC06.fastq.gz,
}
First of all, you can always debug the problems like that specifying the flag --printshellcmds. That would print all shell commands that Snakemake runs under the hood; you may try to run them manually and locate the problem.
As for why your rule doesn't produce any output, my guess is that samtools requires explicit filenames or - to use stdin:
Samtools is designed to work on a stream. It regards an input file '-'
as the standard input (stdin) and an output file '-' as the standard
output (stdout). Several commands can thus be combined with Unix
pipes. Samtools always output warning and error messages to the
standard error output (stderr).
So try that:
shell:
"minimap2 -ax map-ont -t8 ../concensus.fasta {input} | samtools sort -o {output} -"
So I am not 100% sure why this way works, I imagine it has to do with the way snakemake looks at the targets however here is the solution I found for it.
rule minimap2:
input:
"{sample}_strict"
params:
suffix=lambda wildcards: config["barcodes"][wildcards.sample]
output:
"{sample}.bam"
shell:
"minimap2 -ax map-ont -t8 ../consensus.fasta\
{input}/{params.suffix} | samtools sort -o {output}"
by using the params feature in snakemake I was able to match up the correct barcode to the sample name. I am not sure why I could just do that as the input itself, but when I returned the input to the match the output of the previous rule it works.
I have some ONT sequencing runs that have been basecalled on the MINIT. As such, when I demultiplex with guppy_barcoder, I get a directory of fastq files for each barcode. I want to use snakemake as a workflow manager to take these fastq files through our analyses, but this involves swapping the {barcode} for {sample} at some point.
BARCODE=['barcode01', 'barcode02', 'barcode03', 'barcode04']
SAMPLE=['sample01', 'sample02', 'sample03', 'sample04']
rule all:
input:
directory(expand("Sequencing_reads/demultiplexed/{barcode}", barcode=BARCODE)), #guppy_barcoder
expand("Sequencing_reads/gathered/{sample}_ONT.fastq", sample=SAMPLE), #getting all of the fastq files with the same barcode assigned to the correct sample
rule demultiplex:
input:
glob.glob("Sequencing_reads/fastq_pass/*fastq")
output:
directory(expand("Sequencing_reads/demultiplexed/{barcode}", barcode=BARCODE))
shell:
"guppy_barcoder --input_path Sequencing_reads/fastq_pass --save_path Sequencing_reads/demultiplexed -r "
rule gather:
input:
rules.demultiplex.output
output:
"Sequencing_reads/gathered/{sample}_ONT.fastq"
shell:
"cat Sequencing_reads/demultiplexed/{wildcards.barcode}/*fastq > {output.fastq} "
This does give me an error:
RuleException in line 32 of /home/eriny/sandbox/ONT_unicycler_pipeline/ONT_pipeline.smk:
'Wildcards' object has no attribute 'barcode'
But I actually think I'm missing something conceptually. I would like rule gather to be something like:
cat Sequencing_reads/demultiplexed/barcode01/*fastq > Sequencing_reads/gathered/sample01_ONT.fastq
I have tried setting up some dictionaries so that sample and barcode are given the same key, but my syntax must be broken.
I'm hoping to find a 1:1 way to map one variable name onto another.
I'm hoping to find a 1:1 way to map one variable name onto another.
I think the sample to dictionary is a possibility combined with a lambda as input function to get the barcode assign to a sample. For example:
BARCODE=['barcode01', 'barcode02', 'barcode03', 'barcode04']
SAMPLE=['sample01', 'sample02', 'sample03', 'sample04']
sam2bar= dict(zip(SAMPLE, BARCODE))
rule all:
input:
expand("Sequencing_reads/gathered/{sample}_ONT.fastq", sample=SAMPLE), #getting all of the fastq files with the same barcode assigned to the correct sample
rule demultiplex:
input:
glob.glob("Sequencing_reads/fastq_pass/*fastq"),
output:
done= touch('demux.done'), # This signals that guppy has completed
shell:
"guppy_barcoder --input_path Sequencing_reads/fastq_pass --save_path Sequencing_reads/demultiplexed -r "
rule gather:
input:
done= 'demux.done',
fastq= lambda wc: glob.glob("Sequencing_reads/demultiplexed/%s/*fastq" % sam2bar[wc.sample])
output:
fastq= "Sequencing_reads/gathered/{sample}_ONT.fastq"
shell:
"cat {input.fastq} > {output.fastq} "
How can I make sure in rule all that the output folder was well created?
Should I add each expected result file?
somehow relates to snakemake define folder as output but in my case the specified 'output' is a combination of a path to a dir and a prefix for all results files (they wil be multiple)
the following command creates a folder path Analysis/MosDepth and adds to that path the files:
gt0.mosdepth.global.dist.txt
gt0.mosdepth.region.dist.txt
gt0.per-base.bed.gz
gt0.per-base.bed.gz.csi
gt0.regions.bed.gz
gt0.regions.bed.gz.csi
rule MosDepth:
input:
bam = "Analysis/Minimap2/"+UnpackedRawFastq+".bam",
bed = "ReferenceData/"+UnpackedGenomeGFF+"_exons.bed"
output:
pfx = "Analysis/MosDepth/gt0"
threads: config["threads"]
shell:
"mosdepth -t {threads} -b {input.bed} {output.pfx} {input.bam}"
I currently have only one of the files in rule all:, is this enough or is there a better way to ensure that the mosdepth has run well and not redo it in a later re-run?
rule all:
input:
"Analysis/MosDepth/gt0.regions.bed.gz"
I would recommend sth like this:
mos_out = ['gt0.mosdepth.global.dist.txt', 'gt0.mosdepth.region.dist.txt', 'gt0.per-base.bed.gz', 'gt0.per-base.bed.gz.csi', 'gt0.regions.bed.gz', 'gt0.regions.bed.gz.csi']
rule MosDepth:
input:
bam = "Analysis/Minimap2/"+UnpackedRawFastq+".bam",
bed = "ReferenceData/"+UnpackedGenomeGFF+"_exons.bed"
output:
expand("Analysis/MosDepth/{mos_out}", mos_out=mos_out)
params:
pfx = "Analysis/MosDepth/gt0"
threads: config["threads"]
shell:
"mosdepth -t {threads} -b {input.bed} {params.pfx} {input.bam}"
If one of the output files is not created by the rule, snakemake will remove all the output files for you, and throw an error.
I have multiple studies and I must make two files (a .notsad and .txt file) for each of the n number of studies. After these are created, I must run a command which runs per chromosome and uses the same two input files (.notsad, .txt) for each chromosome within a given study. So:
mycommand.py study1.notsad study1_filter.txt chr1.bad.gz --out chr1_filter.bad.gz
mycommand.py study1.notsad study1_filter.txt chr2.bad.gz --out chr2_filter.bad.gz
...
mycommand.py study2.notsad study2_filter.txt chr1.bad.gz --out chr1_filter.bad.gz
...
However Im having trouble getting this to run. Im getting an error:
WildcardError in line 33 of /scripts/Snakefile:
Wildcards in input files cannot be determined from output files:
'ds_lower'
My rules so far:
import os
import glob
ROOT = "/rootdir/"
ORIGINAL_DATA_FOLDER="original/"
PROCESS_DATA_FOLDER="process/"
ORIGINAL_DATA_SOURCE=ROOT+ORIGINAL_DATA_FOLDER
PROCESS_DATA_SOURCE=ROOT+PROCESS_DATA_FOLDER
DATASETS = [name for name in os.listdir(ORIGINAL_DATA_SOURCE) if os.path.isdir(os.path.join(ORIGINAL_DATA_SOURCE, name))]
LOWERCASE_DATASETS = [dataset.lower() for dataset in DATASETS]
CHROMOSOME = list(range(1,23))
rule all:
input:
expand(PROCESS_DATA_SOURCE+"{ds}/chr{chr}_filtered.gen.gz", ds=DATASETS, chr=CHROMOSOME)
rule run_command:
input:
ORIGINAL_DATA_SOURCE+"{ds}/chr{chr}.bad.gz", # Matches 22 chroms
PROCESS_DATA_SOURCE+"{ds}/{ds_lower}_filter.txt", # But this should be common to all chr runs for this study.
PROCESS_DATA_SOURCE+"{ds}/{ds_lower}.notsad" # This one as well.
output:
PROCESS_DATA_SOURCE+"{ds}/chr{chr}_filtered.gen.gz"
shell:
# Run command that uses each of the previous files and runs per chromosome
"mycommand.py {input.2} {input.1} {input.0} --out {output}"
rule write_txt_file:
input:
ORIGINAL_DATA_SOURCE+"{ds}/{ds_lower}_info.txt"
output:
PROCESS_DATA_SOURCE+"{ds}/{ds_lower}_filter.txt"
shell:
"touch {output}"
rule write_notsad_file:
input:
ORIGINAL_DATA_SOURCE+"{ds}/_{ds_lower}.sad"
output:
PROCESS_DATA_SOURCE+"{ds}/{ds_lower}.notsad"
shell:
"touch {output}"
UPDATE
Changing inputs for rule run_command to lambda functions did work.
rule run_command:
input:
ORIGINAL_DATA_SOURCE+"{ds}/chr{chr}.gen.gz",
lambda wildcards: PROCESS_DATA_SOURCE + f"{wildcards.ds}/{wildcards.ds.lower()}_filter.txt",
lambda wildcards: PROCESS_DATA_SOURCE + f"{wildcards.ds}/{wildcards.ds.lower()}.sample"
output:
PROCESS_DATA_SOURCE+"{ds}/chr{chr}_filtered.gen.gz"
run:
# Run command that uses each of the previous files and runs per chromosome
"mycommand.py {input.2} {input.1} {input.0} --out {output}"
All the wildcards used in input need to be present in output. In rule run_command, wildcard {ds_lower} is present only in input but not in output.