Single Cell Analysis with Snakemake Pipelines: From Data Download to Processing

public public 1yr ago 0 bookmarks
View Workflow
Report
sc_snakemake
sc_snakemake
View Workflow

Snakemake Pipelines for Single Cell. This pipeline is based on snakemake . It is probably worth reading through the introductory material before using this code.

To get started, you must have the following on your path

  • Salmon (ensure that the index specified in your config is the same as the salmon in your path)

  • Samtools

  • Python (snakemake is only supported by Python 3)

  • baton

Make sure the python requirements are fulfilled using the following: 

pip install -r requirements.txt

How to Use

Generally, full use of the pipeline involves downloading the data from iRODS first before processing the data.

These two steps are not done withing the same snakemake workflow, as the iRODS download is limited to 16 simultaneous processes, and in snakemake you cannot specify a job limit per step within the same workflow.

Both steps are directed by a shared config.json file. An example file is included in the repository. Please copy and create your own file per new processing task instead of using the provided example file, as this will make updating this repository easier.

There are two ways of running the pipeline - either using the bash scripts, which call snakemake using the required cluster syntax, or by using the individual .snake files as specified in the original repository .

Running the pipeline

The pipeline bash script can be used as follows (assuming your config is in ~/workflows/config.json): 

pipeline.sh -c ~/workflows/config.json

The config file

The config file should specify, as a minimum:

  • The directory where the cram / fastq files are for analysis

  • The lustre folder for temporary files

  • The salmon index directory (created with the same version as the salmon in your path)

  • A file mapping transcript to gene ID's

  • The study ID

  • The query of what runs / lanes to fetch from the study

  • The file pattern (e.g. {run}_{lane}#{tag_index} )

The query parameter specifies the iRODS query. The syntax is in JSON.

Code Snippets

 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
    shell:
        "multiqc {salmon_results} -o {out_folder}".format(
            salmon_results=os.path.join(RESULTS_FOLDER, "quant"),
            out_folder = os.path.join(RESULTS_FOLDER, "qc", "multiqc_salmon"))

rule read_salmon_qcs:
    # Combine the salmon QC output into a single table
    input:
        expand(os.path.join(RESULTS_FOLDER, "quant", "{sample}", 
               "quant.genes.sf"), sample=final_samples)
    output:
        os.path.join(RESULTS_FOLDER, "qc", "salmon_qc.csv"),
    log:
        os.path.join(LOG_FOLDER, "parsing", "salmon_qc.log")
    run:
        qcs = read_qcs(pattern=os.path.join(RESULTS_FOLDER, "quant", "*"))
        qcs.to_csv(output[0])

rule read_reads:
    # Combine the read count into a single table
    input:
        expand(os.path.join(RESULTS_FOLDER, "quant", "{sample}", 
               "quant.genes.sf"), sample=final_samples)
    output:
        os.path.join(RESULTS_FOLDER, "results", "counts.csv")
    log:
        os.path.join(LOG_FOLDER, "parsing", "reads.log")
    run:
        results = read_quants(os.path.join(RESULTS_FOLDER, "quant", "*"), 
                              col="NumReads")
        results.to_csv(output[0])

rule read_tpm:
    # Combine the computed TPM into a single table
    input:
        expand(os.path.join(RESULTS_FOLDER, "quant", "{sample}", 
               "quant.genes.sf"), sample=final_samples)
    output:
        os.path.join(RESULTS_FOLDER, "results", "tpm.csv"),
    log:
        os.path.join(LOG_FOLDER, "parsing", "tpm.log")
    run:
        results = read_quants(pattern=os.path.join(RESULTS_FOLDER, "quant", "*"))
        results.to_csv(output[0])

rule quantify:
    input:
        forward=os.path.join(LUSTRE, "{sample}_" + FORWARD + ".fastq"),
        reverse=os.path.join(LUSTRE, "{sample}_" + REVERSE + ".fastq")
    output:
        os.path.join(RESULTS_FOLDER, "quant", "{sample}", "quant.genes.sf")
    log:
        lambda wildcards: os.path.join(
            LOG_FOLDER, "salmon", "{sample}.log".format(sample=wildcards.sample))
    params:
        out_folder=lambda wildcards: os.path.join(RESULTS_FOLDER, "quant",
                                                  wildcards.sample),
        index=config['index'],
        gene_map=config['gene_map']
    shell:
        "if [ $(wc -c < '{input.forward}') -le 1200 ]; then "
        "touch {params.out_folder}/quant.genes.sf; "
        "else "
        "salmon quant -i {params.index} "
        "-g {params.gene_map} "
        "-l IU "
        "-1 {input.forward} -2 {input.reverse} "
        "-o {params.out_folder}; "
        "fi; "
SnakeMake MultiQC Quant Salmon From line 51 of master/Snakefile 
65
66
67
run:
    qcs = read_qcs(pattern=os.path.join(RESULTS_FOLDER, "quant", "*"))
    qcs.to_csv(output[0])
SnakeMake Quant From line 65 of master/Snakefile 
78
79
80
81
run:
    results = read_quants(os.path.join(RESULTS_FOLDER, "quant", "*"), 
                          col="NumReads")
    results.to_csv(output[0])
SnakeMake Quant From line 78 of master/Snakefile 
92
93
94
run:
    results = read_quants(pattern=os.path.join(RESULTS_FOLDER, "quant", "*"))
    results.to_csv(output[0])
SnakeMake Quant From line 92 of master/Snakefile 
130
131
shell:
    "cp {input} {output}"
SnakeMake From line 130 of master/Snakefile 
142
143
shell:
    "cp {input} {output}"
SnakeMake From line 142 of master/Snakefile 
162
163
shell:
    "cat {input} > {output}"
SnakeMake From line 162 of master/Snakefile 
182
183
shell:
    "cat {input} > {output}"
SnakeMake From line 182 of master/Snakefile 
200
201
shell:
    "multiqc {params.fastqc_folder} -o {params.multiqc_folder}"
SnakeMake MultiQC From line 200 of master/Snakefile 
219
220
shell:
    "fastqc -o {params.out_dir} {input.forward} {input.reverse}"
SnakeMake FastQC From line 219 of master/Snakefile 
235
236
237
shell:
    "samtools sort -m 10G -n -T %s {input} | "
    "samtools fastq -F 0xB00 -1 {output.forward} -2 {output.reverse} -"
SnakeMake SAMtools From line 235 of master/Snakefile
ShowHide 8 more snippets with no or duplicated tags.

Login to post a comment if you would like to share your experience with this workflow.

Do you know this workflow well? If so, you can request seller status , and start supporting this workflow.

Free

Created: 1yr ago
Updated: 1yr ago
Maitainers: public
URL: https://github.com/Teichlab/sc_snakemake
Name: sc_snakemake
Version: 1
Badge:
workflow icon

Insert copied code into your website to add a link to this workflow.

Downloaded: 0
Copyright: Public Domain
License: None
  • Future updates

Related Workflows

psychip_snakemake
Show Details View Workflow
ENCODE pipeline for histone marks developed for the psychENCODE project
public

public

psychip pipeline is an improved version of the ENCODE pipeline for histone marks developed for the psychENCODE project. The o...
raw sequence reads Alignment Sequence alignment report macs2 ucsc-bedclip bedGraphToBigWig BEDTools BWA Picard SAMtools Snakemake
Free
74
ncov_2
Show Details View Workflow
Near-real time tracking of SARS-CoV-2 in Connecticut
public

public

Repository containing scripts to perform near-real time tracking of SARS-CoV-2 in Connecticut using genomic data. This pipeli...
JSON nextclade Augur Biopython FOCUS Pandas Snakemake bs4 epiweeks geopy matplotlib numpy pycountry pycountry-convert uszipcode
Free
28
cellranger-snakemake-gke
Show Details View Workflow
snakemake workflow to run cellranger on a given bucket using gke.
public

public

A Snakemake workflow for running cellranger on a given bucket using Google Kubernetes Engine. The usage of this workflow ...
macs2 ucsc-bedclip bedGraphToBigWig BEDTools BWA Picard SAMtools Snakemake
Free
74
atlas
Show Details View Workflow
ATLAS - Three commands to start analyzing your metagenome data
public

public

Metagenome-atlas is a easy-to-use metagenomic pipeline based on snakemake. It handles all steps from QC, Assembly, Binning, t...
raw sequence reads Genome assembly Annotation track checkm2 gunc prodigal snakemake-wrapper-utils MEGAHIT Atlas BBMap Biopython BioRuby Bwa-mem2 cd-hit CheckM DAS Diamond eggNOG-mapper v2 MetaBAT 2 Minimap2 MMseqs MultiQC Pandas Picard pyfastx SAMtools SemiBin Snakemake SPAdes SqueezeMeta TADpole VAMB CONCOCT ete3 gtdbtk h5py networkx numpy plotly psutil utils metagenomics
Free
175
rna-seq-star-deseq2
Show Details View Workflow
RNA-seq workflow using STAR and DESeq2
public

public

This workflow performs a differential gene expression analysis with STAR and Deseq2. The usage of this workflow is described ...
raw sequence reads Gene expression profiling ID list snakemake-wrapper-utils biomaRt PCAtools BiocParallel BWA Cutadapt DESeq2 GFFutils MultiQC Pandas SAMtools Snakemake STAR cli stringr tidyverse RNA-Seq
Free
41
dna-seq-gatk-variant-calling
Show Details View Workflow
This Snakemake pipeline implements the GATK best-practices workflow
public

public

This Snakemake pipeline implements the GATK best-practices workflow for calling small germline variants. The usage of thi...
VCF raw sequence reads Variant calling genetic variants gatk rust-bio-tools snakemake-wrapper-utils tabix BCFtools BWA FastQC MultiQC Pandas Picard SAMtools Snakemake Trimmomatic Variant Effect Predictor (VEP) common matplotlib numpy seaborn DNA
Free
57