lacklab/MuSTARRd

(Analysis of Saturation) Mu(tagenesis) STARR(seq) d(ata)

Overview

Topics: bioinformatics sequencing starrseq ngs snakemake

Latest release: None, Last update: 2023-02-15

Linting: linting: failed, Formatting:formatting: passed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/lacklab/MuSTARRd . --tag None

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

In order to configure your analysis, make changes to config.yaml.

  1. SAMPLES: A tab-separated file with the following example should be provided to specify the samples:
sample condition type
ETOH.R1 EtOH RNA
ETOH.R2 EtOH RNA
ETOH.R3 EtOH RNA
DHT.R1 DHT RNA
DHT.R2 DHT RNA
DHT.R3 DHT RNA
INPUT DNA

sample: Sample name

condition: Treatment condition (blank if none; blank for DNA)

type: Sample type (RNA or DNA)

  1. UNITS: A tab-separated file with the following example should be provided to specify the units (replicates or lanes):
sample unit fq1 fq2
ETOH.R1 1 reads/EM_LNCaP_R1_EtOH_L1_1.fq.gz reads/EM_LNCaP_R1_EtOH_L1_2.fq.gz
ETOH.R2 1 reads/EM_LNCaP_R2_EtOH_L1_1.fq.gz reads/EM_LNCaP_R2_EtOH_L1_2.fq.gz
ETOH.R3 1 reads/EM_LNCaP_R3_EtOH_L1_1.fq.gz reads/EM_LNCaP_R3_EtOH_L1_2.fq.gz
ETOH.R3 2 reads/EM_LNCaP_R3_EtOH_L2_1.fq.gz reads/EM_LNCaP_R3_EtOH_L2_2.fq.gz
DHT.R1 1 reads/EM_LNCaP_R1_DHT_L1_1.fq.gz reads/EM_LNCaP_R1_DHT_L1_2.fq.gz
DHT.R2 1 reads/EM_LNCaP_R2_DHT_L1_1.fq.gz reads/EM_LNCaP_R2_DHT_L1_2.fq.gz
DHT.R3 1 reads/EM_LNCaP_R3_DHT_L1_1.fq.gz reads/EM_LNCaP_R3_DHT_L1_2.fq.gz
INPUT 1 reads/NL18_L2_1.fq.gz reads/NL18_L2_2.fq.gz

sample: Sample name (same as in samples.tsv)

unit: Unit no

fq1: Path to the 1st FASTQ file

fq2: Path to the 2nd FASTQ file

  1. PRIMERS: A tab-separated file with the following specificiations (and example row) should be provided to specify the regions of analysis:
Region name Forward primer Reverse primer Chromosome Start End
overlapped_read_114 AGCGCGGCTTAGTGA TACCAGGAGACTATTTCCAACA chr8 6456903 6457213

This file shouldn't have a header. The primers should be 5' to 3' and the positions should be BED-like (0-based).

  1. REF

    1. FA: Path to the FASTA file of the reference genome
    2. BWA_IDX: Path to the BWA index files (prefix)
    3. FIXED: Path to the FASTA file matching the wild type plasmids (or the same as FA)

  2. SEQ

    1. UMI1_LEN: Length of the 5' UMI
    2. UMI2_LEN: Length of the 3' UMI
    3. EXPECTED_TLEN: Template (insert) length of the plasmids (excluding UMIs but including primers)

Linting and formatting

Linting results

Lints for rule merge_DNA_reads (line 1, /tmp/tmpew7ma_mb/workflow/rules/association.smk):
    * Do not access input and output files individually by index in shell commands:
      When individual access to input or output files is needed (i.e., just
      writing '{input}' is impossible), use names ('{input.somename}') instead
      of index based access.
      Also see:
      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#rules
    * No log directive defined:
      Without a log directive, all output will be printed to the terminal. In
      distributed environments, this means that errors are harder to discover.
      In local environments, output of concurrent jobs will be mixed and become
      unreadable.
      Also see:
      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files

Lints for rule map_DNA_reads (line 32, /tmp/tmpew7ma_mb/workflow/rules/association.smk):
    * Do not access input and output files individually by index in shell commands:
      When individual access to input or output files is needed (i.e., just
      writing '{input}' is impossible), use names ('{input.somename}') instead
      of index based access.

... (truncated)

Formatting results

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