---
title: "Analysing SNVs with VarCon"
author: "Johannes Ptok"
date: "`r format(Sys.Date(), '%m/%d/%Y')`"
output:
  rmarkdown::html_document:
vignette: >
  %\VignetteIndexEntry{Analysing SNVs with VarCon}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, echo=FALSE, results="hide"}
knitr::opts_chunk$set(tidy = FALSE,
                      cache = FALSE,
                      dev = "png")
```	


# Introduction

Comparing genomic DNA sequences of individuals of the same species
reveals positions where single nucleotide variations (SNVs) occur.
SNVs can influence transcription, RNA processing and translation, 
indicated by lower frequency of variants within sequence elements 
like transcription or translation start sites, splice sites and 
splicing regulatory elements. When localized within the 
coding sequence of a gene, SNVs can, among others, affect which
amino acids are encoded by the altered codon, potentially leading
to disease. Approximately 88% of human SNVs associated with 
disease are, however, not located within the coding sequence of
genes, but within intronic and intergenic sequence segments.
Nevertheless, annotations referring to the coding sequence of a
specific transcript are still widely used, e.g. c.8754+3G>C 
(BRCA2 and Ensembl transcript-ID ENST00000544455), referring to 
the third intronic nucleotide downstream of the splice donor (SD) 
at the position of the 8754th coding nucleotide. Based on its 
position information referring to the coding sequence (c.) or 
alternatively to the genomic (g.) position (e.g. g.1256234A>G),
our tool VarCon retrieves an adjustable SNV sequence neighborhood
from the reference genome. Both intronic and exonic SNVs can lead
to disease by activating cryptic splice sites, generating de 
novo splice sites or altering usage of physiological splices 
sites. However, disruption of splicing can also originate
from SNVs within splice regulatory elements (SREs), potentially
altering their ability to recruit splicing regulatory proteins
(SRPs). The capacity of genomic sequences to recruit 
splicing regulatory proteins to the pre-mRNA transcript can by 
assessed by the HEXplorer score. Highly positive (negative) 
HZEI scores indicate sequence segments, which enhance (repress)
usage of both downstream 5’ splice sites and upstream 3’ splice
sites. .
VarCon therefore calculates the HEXplorer score of the retrieved
nucleotide sequence with and without the variation, to detect 
potential crucial changes in the property of the sequences to 
recruit SRPs.
To visualize possible effects of SNVs on splice sites or splicing
regulatory elements, which play an increasing role in cancer 
diagnostics and therapy, VarCon additionally calculates HBond
scores of SDs and MaxEnt scores[10] of splice acceptor sites
(SA).



# Implementation

VarCon is an R package which can be executed from Windows, Linux 
or Mac OS. It executes a Perl script located in its directory and
therefore relies on prior installation of some version of Perl 
(e.g. Strawberry Perl). Additionally, the human reference genome 
must be downloaded as fasta file (or zipped fasta.gz) with Ensembl 
chromosome names (“1” for chromosome 1) and subsequently uploaded
into the R working environment, using the function 
“prepareReferenceFasta” to generate a large DNAStringset (file
format of the R package Biostrings). In order to translate SNV
positional information, referring to the coding sequence of a 
transcript, two transcript tables are pre-loaded with the VarCon
package. Both contain exon and coding sequence coordinates of every
transcript from Ensembl, and refer either to the genome assembly
GRCh37 or GRCh38. 
Since the transcript table with the GRCh38 genomic coordinates 
(currently from Ensembl version 100) will be updated with further
releases, a new transcript table can be downloaded using the 
Ensembl Biomart interface. Any newly generated transcript table,
however, must contain the same columns and column names as 
described in the documentation of the current transcript tables
for correct integration. Since, for instance, in cancer research
the transcript which is used to refer to genomic positions of 
SNVs is often the same, a gene-to-transcript conversion table
can be used for synonymous usage of certain gene names (or gene
IDs) and transcript IDs (Ensembl ID). VarCon deliberately does
not rely on Biomart queries using the Biomart R package, since 
these might be blocked by firewalls. 
Due to its structure, the VarCon package can accept any genome 
and transcript table combination which is available on Ensembl
and thus additionally permits usage for any other organism 
represented in the Ensembl database[11]. The combination of 
already existing tools like Mutalyzer[12], SeqTailor[13] or
ensembldb[14] can lead to similar results during the variation
conversion and DNA sequence extraction. However, VarCon holds 
additional benefits, namely its straightforward usage even on 
a large-throughput scale, its independence due to the direct 
data entry and its instant graphical representation of splicing
regulatory elements and intrinsic splice site strength.

After upload of the human reference genome, selection of the
appropriate transcript table and a potential gene-to-transcript
conversion table, a transcript ID (or gene name) and an SNV 
(whose positional information either refers to the coding (“c.”)
or genomic (“g.”) sequence) are requested during the execution
of the main function of the package. VarCon then uses the 
information of the transcripts’ exon coordinates to translate
the SNV positional information to a genomic coordinate, if 
needed. Then the genomic sequence around the SNV position is
retrieved from the reference genome in the direction of the
open reading frame and committed to further analysis, both 
with and without the SNV.

For analysis of an SNV impact on splicing regulatory elements,
VarCon calculates the HZEI score profile of reference and 
SNV sequences from the HEXplorer algorithm[7] and visualizes
both in a bar plot. The HEXplorer score assesses splicing
regulatory properties of genomic sequences, their capacity 
to recruit splicing regulatory proteins to the pre-mRNA
transcript. Highly positive (negative) HZEI scores indicate
sequence segments, which enhance (repress) usage of both
downstream 5’ splice sites and upstream 3’ splice sites.

Additionally, intrinsic strengths of SD and SA sites are 
visualized within the HZEI score plot. SD strength is
calculated by the HBond score, based on hydrogen bonds 
formed between a potential SD sequence and all 11 nucleotides 
of the free 5’ end of the U1 snRNA. SA strength is 
calculated by the MaxEnt score, which is essentially based 
on the observed distribution of splice acceptor sequences
within the reference genome, while also taking into account 
dependencies between both non-neighboring and neighboring
nucleotide positions[10]. 

VarCon can either be executed using integrated R package
functions according to the manual on github, or with a GUI
application based on R package shiny. The shiny app (app.R)
can be found within the package directory “/VarCon/shiny/”.
To provide the data needed by VarCon within the shiny app, 
the working directory has to be changed to the app.R source
file location prior to starting the shiny application.


# Applying VarCon to an SNV

The main function of the VarCon package is the `getSeqInfoFromVariation` 
function, which requires the following input parameters: a DNAStringSet 
of the reference genome (e.g. loaded with the integrated `prepareReferenceFasta`
function), the Ensembl transcript ID, the SNV annotation (either refering to 
the coding sequence or genomic sequence), the size of the sequence surrounding 
which should be reported, the transcript table and optionally a gene-to-transcript
conversion table.

First the needed variables are defined, namely the transcript table `transcriptTable`,
which provides the information needed to translate SNV coordinates which refer to
the coding sequence to genomic coordinates. `transcriptID` provides the respective 
transcript, the SNV is refering to. The variable `variation` holds the actual single
nucleotide variation, whose sequence surrounding we will try to retrieve.

```{r, eval=TRUE}
library(VarCon)
## Defining exemplary input data
transcriptTable <- transCoord
transcriptID <- "pseudo_ENST00000650636"
variation <- "c.412T>G/p.(T89M)"
```

With the variables set, the function `getSeqInfoFromVariation` can now be
used to retrieve information about the SNV, like position and surrounding
reference sequence. As an input the function requires a DNAStringSet of 
the reference genome, a fitting transcript table referring to the same 
reference genome assembly, the actual single nucleotide variation of 
interest refering either to the genomic (g.) or coding (c.) position, 
the respective transcript ID, and the size of the sequence window around
the SNV.

```{r, eval=TRUE}
library(VarCon)
results <- getSeqInfoFromVariation(referenceDnaStringSet, transcriptID,
variation, ntWindow=20, transcriptTable)
results
```

The resulting list `results` holds 8 named elements, like the transcript ID, 
the variation, the genomic coordinate and surrounding sequences with and
without the SNV. The elements `$ref_nuc` and `$alt_nuc` state the nucleotide 
at the SNV position on the strand the respective transcript is encoded.

In case the user would like to enter gene names instread of transcript IDs,
during repeated entries of the same transcript ID for every gene, a 
gene2transcript conversion table can be provided the the `getSeqInfoFromVariation`
function. Here we first define the gene name, which we want to use instead
of the previously entered pseudo-transcript ID. Now we define the gene2transcript
table by generating a data frame with the gene name, the gene ID and the transcript
ID.

```{r, eval=TRUE}
## Define gene 2 transcript table
geneName <- "Example_gene"
gene2transcript <- data.frame(gene_name = "Example_gene",
gene_ID = "pseudo_ENSG00000147099", transcriptID = "pseudo_ENST00000650636")
```

Only changing the entered transcript name to a gene name and defining a 
gene2transcript conversion table, enables to use the `getSeqInfoFromVariation`
with gene names, in case the same transcript ID is used as a reference for 
a specific gene.

```{r, eval=TRUE}
## Use function with gene name
results <- getSeqInfoFromVariation(referenceDnaStringSet, geneName,
variation, ntWindow=20, transcriptTable, gene2transcript=gene2transcript)
results
```

The resulting list holds the same information as in the example above.

The `results` object can now be visualized using the function
`generateHEXplorerPlot` which will generate a HEXplorer plot 
stating the HEXplorer profile of the nucleotide surrounding 
and the strength of surrouning splice sites.


VarCon can alternatively be used as an shiny user interface using the `startVarConApp()`.


# Session info

```{r sessionInfo}
sessionInfo()
```


