Spliceogen

Spliceogen is an integrative, scalable tool for the discovery of splice-altering variants. Variants are assessed for their potential to create or disrupt any of the cis motifs which guide splice site definition: donors, acceptors, branchpoints, enhancers and silencers. Spliceogen integrates predictions from MaxEntScan¹, GeneSplicer², ESRseq³ and Branchpointer⁴. Spliceogen accepts standard VCF/BED inputs and handles both SNVs and indels.

Author and maintainer: Steve Monger - [email protected]

Getting Started

Instructions for installation and obtaining the required genome annotation files.

Installation:

Navigate to your desired installation directory and clone this repository:

git clone https://github.com/VCCRI/Spliceogen.git Spliceogen

Required annotation files:

-Any whole genome FASTA (.fa)

-Any GTF genome annotation (.gtf)

Downloading required files:

Browse and download FASTA/GTF versions from Gencode

Alternatively, some recent (as of 2019) hg38 releases can be retrieved using:

> wget ftp://ftp.ensembl.org/pub/release-95/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.alt.fa.gz
> gunzip Homo_sapiens.GRCh38.dna.alt.fa.gz
> wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.basic.annotation.gtf.gz
> gunzip gencode.v29.basic.annotation.gtf.gz

Spliceogen Dependencies:

-Bedtools (tested on v2.26.0)

-Java

Branchpointer dependencies:

To include (optional) Branchpointer predictions, users require:

-R (tested on v3.4.3)

-Branchpointer

-a BSgenome

The current Bioconductor release of Branchpointer supports SNV predictions. To install it from an R prompt:

> source("https://bioconductor.org/biocLite.R")
> biocLite("branchpointer")

The development version of Branchpointer also supports indels. To install this version instead:

> library(devtools)
> install_github("betsig/branchpointer_dev")

From an R prompt, install the hg38 BSgenomes package using the below command. For hg19, edit the 3rd line to "hg19".

> if (!requireNamespace("BiocManager", quietly = TRUE))
>     install.packages("BiocManager")
> BiocManager::install("BSgenome.Hsapiens.UCSC.hg38", version = "3.8")

Docker:

Alternatively, a docker image is provided with all dependencies installed. With docker installed, the basic command is:

> docker run -it mictro/spliceogen:latest /bin/bash

Or to run it with access to a local directory (containing your VCF/BED/GTF/FASTA files), use the command below. Replace $(pwd) with the path of your directory. The name of the destination directory (/my_dir) can be changed to anything.

> docker run -it -v $(pwd):/my_dir mictro/spliceogen:latest /bin/bash

Then move to the Spliceogen directory:

> cd app/Spliceogen

Running Spliceogen

Basic Usage:

> cd path/to/Spliceogen
> ./RUN.sh -inputVCF path/to/singleOrMultipleFiles.vcf -fasta path/to/hgXX.fa -gtf path/to/annotation.gtf

Small VCF, BED, GTF and FASTA files are provided to demonstrate input and output formats. Run this small example using the following command:

> ./RUN.sh -inputVCF toy/toy.vcf -gtf toy/toy.gtf -fasta toy/toy.fa

BED input:

For BED inputs, replace the -inputVCF flag with -inputBED.

Including Branchpointer:

To include Branchpointer predictions, include the branchpointer flag and specify the genome build:

*basic usage command* -branchpointer hgXX

Or for branchpointer_dev which handles both SNPs and indels, use the flag -branchpointerIndels hgXX

Output

Files

All scores and predictions can be found in the Spliceogen/output directory in a tab delimited format suitable for ANNOVAR annotation. Multiple output files are provided for each input VCF/BED. This includes one master file containing all scores for all variants, as well as several additional files containing only variants identified as most likely to be disruptive, ranked in descending order. The specific files generated are as follows:

1. "$file"_out.txt:

   Contains all scores generated for every variant, sorted in ascending chromosomal/start position order.

2. "$file"_withinSS.txt:

   Contains all variants that overlap annotated splice sites. The overlapping splice sites are denoted by their exonID and "_donor" or "_acceptor". Variants are sorted by donor/acceptor score decrease, such that the variants most likely to disrupt existing donor/acceptor splice sites appear at the top of this file.

3. "$file"_donorCreating.txt

   Contains variants outside of existing splice sites that are predicted to create donor motifs. Variants are ranked by P value, based on a logistic regression model trained on the MaxEntScan and GeneSplicer scores of a set of known donor creating variants derived from Shiraishi et al., 2018⁵.

4. "$file"_acceptorCreating.txt

   Same as above, but for acceptor creating variants.

5. "$file"_bpOutput.txt

   Contains Branchpointer prediction scores, including whether the variant is predicted to create or remove a branchpoint, based on the recommended Branchpointer thresholds.

Column labels:

The following abbreviations are used in the output headers:

mes = MaxEntScan

gs = GeneSplicer

don = Donor

acc = Acceptor

ref = Reference allele

alt = Alternative allele

ESS = Silencer (ESRseq)

ESE = Enhancer (ESRseq)

withinSS = within splice site

donCreateP = donor creation logistic regression P value

accCreateP = acceptor creation logistic regression P value

So for example, the column "gsDonRef" contains GeneSplicer scores representing donor motif strength for the reference sequence, whereas "mesDonAlt" consists of MaxEntScan scores representing acceptor motif strength for the alternative sequence.

Database

Genome-wide SNV databases are available for download. These cover all possible SNVs at every genomic position within all protein-coding multi-exon transcripts (based on Gencode v29). Both hg19 and hg38 are available.

An extensive version of the database containing MaxEntScan, GeneSplicer and ESRseq prediction scores for every possible SNV is available.

Alternatively, for users only interested in identifying variants which disrupt or create donor/acceptor motifs (and not silencer/enhancer predictions), a leaner version of the database is provided. This version includes donor/acceptor predictions for all SNVs within splice sites, as well as all SNVs outside of splice sites that are likely to create donor/acceptor motifs. The database size is vastly reduced by excluding the large majority of SNVs which do not overlap splice sites and are not predicted to create a donor/acceptor motif.

Scalability

Spliceogen is highly scalable, so we recommend that running the tool will be preferable to downloading the database for most users. Advantages of running the tool include allowing predictions for indels and branchpoints, as well as the flexibility of user selection and customisation of GTF annotations.

Predictions are generated at a rate of 2.3 million variants/compute hour, with peak memory usage less than 500Mb. Benchmarking was performed using a single compute node with 1 CPU allocated, without Branchpointer predictions.

References:

1. Yeo, G., Burge, C., "Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals", J Comput Biol. 2004; 11(2-3):377-94

2. Pertea, M., Lin, X., Salzberg, S., "GeneSplicer: a new computational method for splice site prediction", Nucleic Acids Res. 2001; 29(5):1185-90

3. Shendong, K., et al., "Quantitative evaluation of all hexamers as exonic splicing elements", Genome Res. 2011; 21(8):1360-1374

4. Signal, B., et al., "Machine learning annotation of human branchpoints", Bioinformatics. 2018; 34(6):920-927

5. Shiraishi, Y., et al., "A comprehensive characterization of cis-acting splicing-associated variants in human cancer", Genome Res. 2018; 28(8):1111-1125