Pasini's lab CutNtag/CutNrun pipeline

Snakemake-based CutNtag and run pipeline to be run in our PBS-based HPC using singularity containers. The singularity image that is used to run this pipeline is created from this docker container.

Setup

The following files are located inside the folder configuration. In this folder you will find the files with raw data paths (units.tsv), sample metadata (samples.tsv), cluster configuration (cluster.yaml) and pipeline parameters -alignment, peak calling...- (config.yaml).

Raw data

Paths to raw data are located in the file units.tsv. The file has the following structure:

sample	lane	fq1	fq2
name_of_sample	name_of_lane_or_resequencing	path/to/forward.fastq	path/to/reverse.fastq

• The first field correspond to the sample name. This field has to be the same as the sample name that is specified in the samples.tsv file (see below). It is recommended to NOT use underscores in the name of the samples, dashed are prefered. I still don't understand why sometimes I get errors if I use them so before fixing I strongly recommend to use dashes instead.

• The second field corresponds to lane. The idea of this field is to group fastq files corresponding to the same sample (or to samples that have to be merged). For example, if 1 sample arrived in 2 different lanes from a PE experiment, in total there will be 4 fastqs (2 forward and 2 reverse). In this case, one should enter the same sample 2 times, putting in the lane field the corresponding lanes (lane1 and lane2, for example). Actually one can write any word in this field, the idea is to group fastqs from the same sample. All the entries with the same name in the sample field with different lane will be merged in the same fastq. Here an example of how it would be with 1 sample that arrived in 2 lanes:

sample	lane	fq1	fq2
foo	lane1	path/to/forward_lane1.fastq	path/to/reverse_lane1.fastq
foo	lane2	path/to/forward_lane2.fastq	path/to/reverse_lane2.fastq

Here I am using lane1 and lane2 for consistency and making things more clear, but the following would also work:

sample	lane	fq1	fq2
foo	potato	path/to/forward_lane1.fastq	path/to/reverse_lane1.fastq
foo	checazzo	path/to/forward_lane2.fastq	path/to/reverse_lane2.fastq

• Finally the last 2 fields fq1 and fq2 correspond to the paths to the fastq files. fq1 is the FORWARD read and fq2 the REVERSE. The order is very important because they will be sent in that order to the aligner.

Sample metadata

All metadata and information regarding every sample is located in samples.tsv. The file has the following structure:

NAME	INPUT	SPIKE	AB	USER	GENOME	RUN	IS_INPUT
name_of_sample	input_to_use	If the sample contains spikein. true or false	antibody	user	versione of genome (i.e: mm10)	run of the sequencing	if the sample is an input

• For every sample, the NAME field has to contain exactly the same name that was written in the sample column of the units.tsv.

• The INPUT field contains the name of the input corresponding to the given sample. It has to be the name of the input written in the fields sample and NAME from units.tsv and samples.tsv.

• SPIKE: TRUE or FALSE based on if the sample contains spike-in or not.

• AB, USER, RUN: Metadata corresponding to each sample. If there's nothing to fill I usuallt write an X.

• GENOME: Version of the genome used for the alignment. It will be used for peak annotation with ChIPseeker. Right now the accepted values are mm9, mm10, hg19 and hg38.

• IS_INPUT: The options are TRUE or FALSE. If the sample is an input set it to TRUE. Also, in case the sample is an input sequenced just to calculate the ratio sample/spike-in that won't be used to call peaks, set it to TRUE.

Configuration of pipeline parameters

In the root folder of this repository (I say this because there's in another folder a file with the same name) there is the file config.yaml. This files contains the configuration of the software and parameters used in the pipeline. Modify them as you wish. Check always that you are using the correct genome files corresponding to the version that you want to use. Also check the effective genome size that is used by deeptools to calculate the GC bias.

Cluster configuration

cluster.yaml contains the per rule cluster parameters (ncpus, ram, walltime...). It can be modified as desired. In the future I want to remove this file in favour of the new snakemake profiles system (see below), but I still need to understand a little bit better how it works and how to properly do the migration.

Snakemake profiles

In Snakemake 4.1 snakemake profiles were introduced. They are supposed to substitute the classic cluster.json file and make the execution of snakemake more simple. The parameters that will be passed to snakemake (i.e: --cluster, --use-singularity...) now are inside a yaml file (config.yaml) inside the profile folder (in the case of this repository is snakemake_profile). The config.yaml inside snakemake_profile contains the parameters passed to snakemake. So if you were executing snakemake as snakemake --cluster qsub --use-singularity the new config.yaml would be like this:

cluster: qsub
use-singularity: true

Execution of the pipeline

Once you have all the configuration files as desired, it's time to execute the pipeline. For that you have to execute the execute_pipeline.sh script, followed by the name of the rule that you want to execute. If any rule is given it will automatically execute the rule all (which would execute the standard pipeline). Examples:

./execute_pipeline.sh all

is equivalent to

./execute_pipeline.sh

If you want to obtain also broad peaks...

./execute_pipeline.sh all_broad

At the end of the Snakefile you will find all the possible target rules and their corresponding output files.

To Do's

• Migrate 100% to snakemake profiles and stop using the cluster.yaml configuration.