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RNA-seq Quantification of Alternative Polyadenylation (QAPA)

Analysis of alternative polyadenylation (APA) from RNA-seq data (human and mouse). QAPA consists of two main components:

  1. Extraction and annotation of 3′ UTR sequences from gene models
  2. Calculation of relative usage of alternative 3′ UTR isoforms based on transcript-level abundance.

Note that QAPA itself does not perform transcript quantification. It relies on other tools such as Sailfish and Salmon.


Installation

QAPA consists of both Python (3.5+) and R scripts. A conda virtual environment can be created using the provided environment.yml file.

  1. Clone the repo:

     git clone https://github.com/morrislab/qapa.git
     cd qapa
    
  2. (Optional) Install mamba for faster Conda management

     conda install -c conda-forge mamba
    
  3. Create the environment

    mamba env create -f environment.yml
    conda activate qapa
    

Usage

QAPA has three sub-commands:

  1. build: Generate a 3′ UTR library from annotations
  2. fasta: Extract sequences for indexing by transcript quantification tools
  3. quant: Calculate relative usage of alternative 3′ UTR isoforms

1) Build 3′ UTRs from annotation (build)

Download pre-compiled libraries

Pre-compiled libraries for human and mouse are available for download below. Otherwise, continue reading to build from scratch.

Updated in v1.3.0, the following libraries are pre-compiled with Polyasite V2:

(Old) Versions v1.2.3 and earlier:

Prepare annotation files

To run build, gene and poly(A) annotation sources need to be prepared:

A. Gene annotation

  1. Ensembl gene metadata table from Biomart.

    Human and mouse tables are provided in the examples folder. To obtain a fresh copy, download a table of Ensembl Genes from Biomart with the following attributes:

    1. Ensembl Gene ID
    2. Ensembl Transcript ID
    3. Gene Type
    4. Transcript Type
    5. Gene Name

    Alternatively, download via MySQL (see here for more details):

     mysql --user=anonymous --host=martdb.ensembl.org --port=5316 -A ensembl_mart_89 \
         -e "select stable_id_1023 as 'Gene stable ID', stable_id_1066 as 'Transcript stable ID', \
         biotype_1020 as 'Gene type', biotype_1064 as 'Transcript type', \
         display_label_1074 as 'Gene name' from mmusculus_gene_ensembl__transcript__main" \
         > ensembl_identifiers.txt
    

    To change the species, replace the table name (e.g. for human, use hsapiens_gene_ensembl__transcript__main).

  2. GENCODE gene prediction annotation table

    Download from UCSC Table Browser or alternatively via MySQL (see here for more details). For example, to download mm10 gene predictions:

     mysql --user=genome --host=genome-mysql.cse.ucsc.edu -A \
         -e "select * from wgEncodeGencodeBasicVM9" mm10 > gencode.basic.txt
    

    Alternatively, if you are starting from a GTF/GFF file, you can convert it to genePred format using the UCSC tool gtfToGenePred:

     gtfToGenePred -genePredExt custom_genes.gtf custom_genes.genePred
    

    Note that it is important to include the -genePredExt option!

B. Poly(A) site annotation (optional)

This step can be skipped, otherwise continue reading. Two options are available:

Option 1: standard approach using PolyASite and GENCODE poly(A) track (as described in the paper)

  1. PolyASite database

    Download BED files (human or mouse) from http://polyasite.unibas.ch/.

  2. GENCODE poly(A) sites track

    Download from UCSC Table Browser or alternatively via MySQL. For example, to download mm10 annotations:

     mysql --user=genome --host=genome-mysql.cse.ucsc.edu -A \
         -e "select chrom, txStart, txEnd, name2, score, strand \
         from wgEncodeGencodePolyaVM9 where name2 = 'polyA_site'" -N mm10 \
         > gencode.polyA_sites.bed
    

Option 2: use custom BED track of poly(A) sites

A custom BED file of poly(A) sites can be used to annotate 3′ UTRs. Each entry must contain the start (0-based) and end coordinate of a poly(A) site.

Commands

Once the data files have been prepared, we can then use build to create the 3' UTR library. See qapa build -h for usage details. The following describes several example use cases:

  1. To extract 3′ UTRs from annotation, run:

    qapa build --db ensembl_identifiers.txt -g gencode.polyA_sites.bed -p clusters.mm10.bed gencode.basic.txt > output_utrs.bed
    
  2. If using a custom BED file, replace the -g and -p options with -o:

    qapa build --db ensembl_identifiers.txt -o custom_sites.bed gencode.basic.txt > output_utrs.bed
    
  3. If using a custom genePred file converted from GTF, supply the file as in 1. (e.g. the first positional argument):

    qapa build --db ensembl_identifiers.txt -o custom_sites.bed custom_genes.genePred > output_utrs.bed
    
  4. If bypassing the poly(A) annotation step, include the -N option:

    qapa build -N --db ensembl_identifiers.txt gencode.basic.txt > output.utrs.bed
    

Results will be saved in the file output_utrs.bed (default is STDOUT). It is important that the sequence IDs are not modified as it will be parsed by quant below.

Additional notes:

  • 3' UTRs that contain introns will be skipped.
  • Chromosome names that contain underscores are currently not supported and will be skipped.

2) Extract 3′ UTR sequences (fasta)

To extract sequences from the BED file prepared by build, a reference genome in FASTA format is required. e.g. http://hgdownload.soe.ucsc.edu/downloads.html.

Then, run the command:

qapa fasta -f genome.fa output_utrs.bed output_sequences.fa

Essentially fasta is a wrapper that calls bedtools getfasta. Note that genome.fa must be uncompressed. Sequences will be saved in output_sequences.fa.

3) Quantify 3′ UTR isoform usage (quant)

Expression quantification of 3′ UTR isoforms must be carried out first using the FASTA file prepared by fasta as the index. For example, to index the sequences using Salmon:

salmon index -t output_sequences.fa -i utr_library

Following expression quantification, QAPA expects the results to be located inside its own sub-directory. For example, typical Sailfish/Salmon results may appear with the following directory structure:

project/
  |-- sample1/quant.sf
  |-- sample2/quant.sf
  |-- (etc.)

The quant sub-command calls two R scripts: create_merged_table.R and compute_pau.R. The first script combines the quantifications from each sample into a single table. The second script computes the relative proportion of each isoform in a gene, measured as Poly(A) Usage (PAU) (compute_pau.R).

qapa quant --db ensembl_identifiers.mm10.txt project/sample*/quant.sf > pau_results.txt

Results will be saved in the file pau_results.txt (default is STDOUT).

For advanced usage, the R scripts can be run on its own. Run create_merged_table.R -h and compute_pau.R -h for usage details.

The final output format is as follows:

Column Description
APA_ID unique identifier consisting of in the format <Ensembl Gene ID>_<number>_<(P|D|S)>, where P = proximal, D = distal, and S = single
Transcript one or more Ensembl Transcript IDs
Gene Ensembl Gene ID
Gene_Name gene symbol
Chr chromosome
LastExon.Start start coordinate of last exon
LastExon.End end coordinate of last exon
Strand + or -
UTR3.Start start coordinate of 3′ UTR
UTR3.End end coordinate of 3′ UTR
Length length of the 3′ UTR
Num_Events number of PAS per gene
sample1.PAU PAU estimate for sample1
sample2.PAU PAU estimate for sample2
sample1.TPM TPM estimate for sample1
sample2.TPM TPM estimate for sample2

Citation

Ha, K.C.H., Blencowe, B.J., Morris, Q. (2018). QAPA: a new method for the systematic analysis of alternative polyadenylation from RNA-seq data. Genome Biol. 19, 45. [link]

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