Custom reference data
The pipeline uses reference data at various stages, such as for alignment, calculating TSS enrichments, and other QC scores. If you're using a common genome assembly, these resources are pre-built and can be easily downloaded using
refgenie pull, as described in the setup instructions. If the resources are not available, you'll have to build them. This document outlines how we created the reference data, so you can recreate it if you need to. The easiest way to do this is use
refgenie build. All you need to do is:
1: Build the fasta asset
You need a FASTA file for your genome. You can insert this file into refgenie like this:
refgenie build -g GENOME -a fasta --files fasta=/path/to/file.fa
2. Build the bowtie2_index
To build a bowtie2_index and have it managed by
refgenie you'll, of course, need bowtie2 already installed. You will also need the requisite FASTA file, which you just added in step 1.
refgenie build -g GENOME -a bowtie2_index
3: Build the ensembl_gtf asset
The ensembl_gtf asset includes several related assets (e.g. pause index gene bodies and TSS's) the pipeline will employ. To build an ensembl_gtf asset, you need an Ensembl GTF file (or equivalent) for your genome. You can have refgenie build and manage this file as follows:
refgenie build -g GENOME -a ensembl-gtf --files ensembl_gtf=/path/to/Homo_sapiens.GRCh38.97.gtf.gz
4: Build the refgene_anno asset
The refgene_anno asset actually includes several related assets that we'll need (e.g. TSS and premature mRNA annotations). To build these, for example for hg38, you will need to download a refGene annotation. Build it for a any genome like so:
refgenie build -g GENOME -a refgene_anno --files refgene=/path/to/refGene.txt.gz
5: Build the feat_annotation asset
feat_annotation asset includes feature annotations used to calculate the FRiF and cFRiF.
Refgenie can automatically build this after you have the above assets installed:
refgenie build -g GENOME -a feat_annotation
That's it! These assets will be automatically detected by PEPPRO if you build them like this with
Create a custom feature annotation file
The pipeline will calculate the fraction (and proportion) of reads in genomic features using the feat_annotation asset, but you can also construct this file yourself.
This annotation file is really just a modified
BED file, with the chromosomal coordinates and type of feature included. For example, the downloadable
hg38_annotations.bed.gz file looks like so:
chr1 28200 30001 Promoter . * chr1 198800 200201 Promoter . * chr1 778000 780001 Promoter . * chr1 817400 817601 Promoter . * chr1 826200 828801 Promoter . * chr1 904200 905201 Promoter . * chr1 923800 924601 Promoter . * chr1 925000 925601 Promoter . * chr1 941800 942201 Promoter . * chr1 958400 961401 Promoter . *
Just like a standard
BED file, the first three fields are:
1. chrom - the name of the chromosome
2. chromStart - the starting position of the feature
3. chromEnd - the ending position of the feature
Column four is the name column, in our case the name of our feature of interest. The fifth column is the score, which would determine how darkly an item would be displayed in a genome browser if you chose to set that or if the information in your file of interest has ascribed a score to the features. The final, sixth, column is the strand column.
After creating your
BED file, you can point the pipeline to it using the
--anno-name option followed with the path to your file. The pipeline will then use that file to determine the fractions of reads that cover those features.