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PAIRADISE <a name="PAIRADISE"></a>

Paired Replicate Analysis of Allelic Differential Splicing Events

PAIRADISE (PAIred Replicate analysis of Allelic DIfferential Splicing Events) is a method for detecting allele-specific alternative splicing (ASAS) from RNA-seq data. Unlike conventional approaches that detect ASAS events one sample at a time, PAIRADISE aggregates ASAS signals across multiple individuals in a population. By treating the two alleles of an individual as paired, and multiple individuals sharing a heterozygous SNP as replicates, PAIRADISE formulates ASAS detection as a statistical problem for identifying differential alternative splicing from RNA-seq data with paired replicates.

Table of Contents

• Installation
• Usage
• Output
• Shiny App
• Contact

<a name="install"></a>Installation

Dependencies

PAIRADISE requires the following dependencies:

• Python >= (2.7.5)
• R >= (3.2.1)
• STAR >= (2.6.0a) (can be downloaded <a href="https://github.com/alexdobin/STAR/releases/tag/2.6.0a" target="_blank">here</a>)

The folowing Python dependencies are required:

• pysam >= (0.15.2)
• pybedtools >= (0.8.0)
• more-itertools >= (5.0.0)
• numpy >= (1.16.2)

In addition, the following R packages must be installed before installing PAIRADISE:

• nloptr >= (1.2.1)
• doParallel >= (1.0.14)
• foreach >= (1.4.4)
• binom >= (1.1.1)
• ggplot2 >= (3.1.1)

Download and setup

Download PAIRADISE from github:

git clone github.com/Xinglab/PAIRADISE/pairadise

Change your working directory to "pairadise" and run the following commands to configure and install PAIRADISE:

./configure
make
make install

To test if PAIRADISE has been successfully installed, type in the command pairadise_personalize -h. You should see

pairadise_personalize -h
usage: pairadise_personalize [-h] [--gz] [-e E] [--rnaedit] [-v V] [-o O]
                             [-r R]
                             [command [command ...]]

pairadise2

positional arguments:
  command

optional arguments:
  -h, --help  show this help message and exit
  --gz        flag denoting gzipped reads
  -e E        file containing RNA editing positions, downloaded from RADAR
  --rnaedit   flag to check for RNA editing events, must also provide an RNA
              editing file usng -e parameter
  -v V        VCF genotype directory
  -o O        output directory
  -r R        reference fasta file

You may need permission if want to install PAIRADISE to a root PATH. You can bypass this issue by specifying a user R library directory:

R CMD INSTALL -l /user/R/lib src/pairadise_model/

<a name="use"></a>Usage

PAIRADISE requires the following subdirectories and input data to be in the directory where you will be performing your analysis (we'll refer to this as the 'data directory'):

1. genome: Contains the reference genome gtf file, fasta files, and (optionally) RNA editing information. We used the following files:

   1. hg19.fa
   2. Homo_sapiens.GRCh37.75.dna.primary_assembly.fa
   3. Homo_sapiens.Ensembl.GRCh37.75.gtf
   4. Human_AG_all_hg19_v2.txt

2. genotype: Contains the genotype data in the format of vcf files, one vcf file per chromosome. The genotype directory contains one subdirectory for each sample/replicate, each containing that sample's vcf files. The subdirectory names should match the sample names. If data are biological replicates corresponding to one common sample, you only need one subdirectory.

3. input: Contains the fastq files for each sample/replicate. The fastq files should have the .gz extension.

4. scripts: Contains the scripts used to run the PAIRADISE pipeline and statistical model.

The following .txt file should also be in the data directory:

‘sample_name.txt’: Contains the sample ID’s and RNA-seq read lengths. This file contains 3 columns: the first two columns contain the sample names and the last column contains the read length. When the data are biological replicates, column 1 contains the sample IDs of the biological replicates, and column 2 contains the names of the sample.

Running the PAIRADISE pipeline

Preparation:

Run the following command to create multiple .qsub files, each of which performs different stages of the PAIRADISE analysis:

python scripts/run_preprocess.py sample_name.txt population_name path/to/data/directory/

For example, if we are analyzing the Geuvadis CEU population and we are in the data directory, the above command becomes:

python scripts/run_preprocess.py CEU.txt CEU ./

We will continue using CEU as our running example.

Step 1: Personalization, mapping, and assignment

Submit each of the qsub files corresponding to Step 1 of the PAIRADISE pipeline (personalization, mapping, and assignment):

qsub -cwd -l h_vmem=90G qsub_files/pairadise_step1_NA12843.qsub

The above command performs Step 1 for one sample: NA12843. You should run an analogous qsub command for each sample.

Note: Step 1 can take a long time to run. For the CEU dataset, the typical run-time for one sample ranged from 2-5 hours.

Step 2: Joint annotation

Once Step 1 has been completed for every sample, submit the qsub file corresponding to Step 2 (joint annotation):

qsub -cwd -l h_vmem=10G qsub_files/pairadise_step2_annotation.qsub

Step 3: Counting

Once Step 2 is finished, submit each of the qsub files corresponding to Step 3 of the PAIRADISE pipeline (counting):

qsub -cwd -l h_vmem=15G qsub_files/pairadise_step3_NA12843.qsub

The above command performs Step 3 for one sample: NA12843. You should run an analogous qsub command for each sample.

Step 4: Merging the counts

Once Step 3 has been completed for every sample, submit the qsub file corresponding to Step 4 (merging the counts):

qsub -cwd -l h_vmem=10G qsub_files/pairadise_step4_merge.qsub

Step 5: Statistical modeling and visualization

Finally, we run the PAIRADISE statistical model and create plots of the significant events.

qsub -cwd -l h_vmem=10G qsub_files/pairadise_step5_stat.qsub

<a name="output"></a>Output

PAIRADISE outputs a table of significant ASAS events (default significance is set to FDR <= 10%). Each row of the output table corresponds to a significant ASAS event and contains the following columns:

1. ExonID: Gene name, chromosome number and strand, SNP name, genomic location, reference and alternative alleles.
2. IJC_REF: Exon inclusion counts for the reference allele.
3. SJC_REF: Exon skipping counts for the reference allele.
4. IJC_ALT: Exon inclusion counts for the alternative allele.
5. SJC_ALT: Exon skipping counts for the alternative allele.
6. incLen: Effective length of the exon inclusion isoform.
7. skpLen: Effective length of the exon skipping isoform.
8. pval: Raw (unadjusted) PAIRADISE p-value.
9. qval: PAIRADISE p-value FDR adjusted using the Benjamini-Hochberg method.
10. IncLevel1: Naive psi values for reference allele samples.
11. IncLevel2: Naive psi values for alternative allele samples.
12. AvgIncLevel1: Average psi value for reference allele samples.
13. AvgIncLevel2: Average psi value for alternative allele samples.
14. IncLevelDifference: Difference in average psi values between reference and alternative allele samples.
15. AvgTotalCount1: Average total number of read counts for reference allele samples.
16. AvgTotalCount2: Average total number of read counts for alternative allele samples.
17. SampleName: Sample names.
18. RefAltAllele: Reference allele and alternative allele (in the format Ref|Alt).
19. AF: Allele frequencies.

For visualizing the PAIRADISE results, we recommend uploading the PAIRADISE output table directly into the accompanying Shiny app. Here's an example of a significant ASAS event visualized using our Shiny app:

<a name="shiny"></a>Shiny App

We have developed a data visualization tool in R Shiny for visualizing the results of PAIRADISE. The app can be accessed <a href="https://xingshiny.research.chop.edu/PAIRADISE/" target="_blank">here</a>. Have fun!

<a name="contact"></a>Contact

Levon Demirdjian levondem@gmail.com

Yi Xing xingyi@email.chop.edu

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