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Combination of inputs in the snakemake shell script


I have the below bash script that I would like to convert into a snakefile:

mmseqs rbh flye_db megahit_db flye_megahit_rbh --min-seq-id 0.9 mmseq2_tmp --threads 12
mmseqs rbh flye_db metaspades_db flye_metaspades_rbh --min-seq-id 0.9 mmseq2_tmp --threads 12
mmseqs rbh megahit_db metaspades_db megahit_metaspades_rbh --min-seq-id 0.9 mmseq2_tmp --threads 12

I've managed to come up with the following, but was wondering if there's a way to use regex or expand to improve the code further:

rule mmseq2_compare:
    input:
        mm1=expand(os.path.join(RESULTS_DIR, "annotation/mmseq2/{assembler}_db"), assembler="flye"),
        mm2=expand(os.path.join(RESULTS_DIR, "annotation/mmseq2/{assembler}_db"), assembler="megahit"),
        mm3=expand(os.path.join(RESULTS_DIR, "annotation/mmseq2/{assembler}_db"), assembler="metaspades_hybrid")
    output: 
        mo1=os.path.join(RESULTS_DIR, "annotation/mmseq2/flye_megahit_rbh"),
        mo2=os.path.join(RESULTS_DIR, "annotation/mmseq2/flye_metaspades_hybrid_rbh"),
        mo3=os.path.join(RESULTS_DIR, "annotation/mmseq2/megahit_metaspades_hybrid_rbh")
    log: os.path.join(RESULTS_DIR, "annotation/mmseq2/compare.mmseq2.log")
    conda: "cd-hit.yml"
    shell:
        """
        (date &&\
        mmseqs rbh {input.mm1} {input.mm2} {output.mo1} --min-seq-id 0.9 mmseq2_tmp --threads 12 &&\
        mmseqs rbh {input.mm1} {input.mm3} {output.mo2} --min-seq-id 0.9 mmseq2_tmp --threads 12 &&\
        mmseqs rbh {input.mm2} {input.mm3} {output.mo3} --min-seq-id 0.9 mmseq2_tmp --threads 12 &&\
        date) &> >(tee {log})
        """

With the 3 assemblers (flye, megahit and metaspades_hybrid) is there any way to remove the redundancy especially in the 'shell'?

Thank you!

Dry-run output

Building DAG of jobs...
Job counts:
        count   jobs
        1       all
        1       mmseq_compare
        2

[Thu Mar 26 12:25:14 2020]
rule mmseq_compare:
    input: results/annotation/mmseq2/flye_db, results/annotation/mmseq2/megahit_db
    output: results/annotation/mmseq2/flye_megahit_rbh
    jobid: 1
    wildcards: assembler1=flye, assembler2=megahit

mmseqs rbh results/annotation/mmseq2/flye_db results/annotation/mmseq2/megahit_db results/annotation/mmseq2/flye_megahit_rbh --min-seq-id 0.9 mmseq2_tmp --threads 12

[Thu Mar 26 12:25:14 2020]
localrule all:
    input: results/annotation/mmseq2/flye_megahit_rbh, results/annotation/mmseq2/flye_metaspades_hybrid_rbh, results/annotation/mmseq2/megahit_metaspades_hybrid_rbh
    jobid: 0

Job counts:
        count   jobs
        1       all
        1       mmseq_compare
        2
This was a dry-run (flag -n). The order of jobs does not reflect the order of execution.```

Solution

  • Firstly you don't need an expand in your input. This is needed if you wish to create a list of filenames that have the same pattern.

    Next, as long as you already use Unix-type slash in your paths, you may add the RESULTS_DIR into f-strings for readability (but don't forget to double the braces for wildcards).

    Finally, there is no need in having a pipeline of scripts separated with &&: that is what Snakemake is designed for.

    My version of reworked script:

    rule all:
        input:
            expand(f"{RESULTS_DIR}/annotation/mmseq2/{{assembler1}}__{{assembler2}}_rbh", zip,
                   assembler1=["flye", "flye", "megahit"],
                   assembler2=["megahit", "megahit_metaspades_hybrid", "megahit_metaspades_hybrid"])
    
    rule mmseq_compare:
        input:
            f"{RESULTS_DIR}/annotation/mmseq2/{{assembler1}}_db",
            f"{RESULTS_DIR}/annotation/mmseq2/{{assembler2}}_db"
        output: 
            f"{RESULTS_DIR}/annotation/mmseq2/{{assembler1}}__{{assembler2}}_rbh"
        conda:
            "cd-hit.yml"
        shell:
            "mmseqs rbh {input[0]} {input[1]} {output} --min-seq-id 0.9 mmseq2_tmp --threads 12"
    

    I've excluded the date and logging. My solution has a limitaton that the order of execution of the comparisons is undefined: you need to reconsider the logging strategy in this case.