README.md

In yuhuihui2011/vasp_test: Quantification and Visulization of Variations of Splicing in Population

vasp

Quantification and Visulization of Variations of Splicing in Population

Introduction

VaSP is an R package for discovery of genome-wide variable splicing events from short-read RNA-seq data. Based on R package Ballgown, VaSP calculates Single Splicing Strength (3S) score of any intron by the junction count normalized by the gene-level average read coverage (score=junction count/gene-level average read coverage). The 3S scores can be used for further analysis, such as differential splicing analysis between two sample groups and sQTL (splicing Quantitative Trait Locus) identification in a large population. The VaSP package provides a function to find large-effect differential splicing events without the need of genotypic information in an inbred plant population, so called genotype-specific splicing (GSS). Integrated with functions from R package Sushi, VaSP package also provides function to visualization of gene splicing information for publication-quality multi-panel figures.

Installation

Start R and run: ```{r, eval=FALSE} if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("vasp")

## Data input

Users need to follow the manual of R package Ballgown (<https://github.com/alyssafrazee/ballgown>) to creat a ballgown object as an input for the VaSP package. See `?ballgown` for detailed information on creating Ballgown objects. The object can be stored in a `.RDate` file by `save()` .

```{r,eval=FALSE}
library(vasp)
?ballgown
path<-system.file('extdata', package='vasp')
rice.bg<-ballgown(samples = list.dirs(path = path,recursive = F) )

Quick start

Calculate 3S (Single Splicing Strength) scores, find GSS (genotype-specific splicing) events and display the splicing information.

• Calculating 3S scores:

library(vasp)
#> Loading required package: ballgown
#> 
#> Attaching package: 'ballgown'
#> The following object is masked from 'package:base':
#> 
#>     structure
data(rice.bg)
?rice.bg
rice.bg
#> ballgown instance with 33 transcripts and 6 samples
score<-spliceGene(rice.bg, gene="MSTRG.183", junc.type = "score")
tail(round(score,2),2)
#>    Sample_027 Sample_042 Sample_102 Sample_137 Sample_237 Sample_272
#> 58          0       0.02       0.22       0.23       0.23       0.23
#> 59          0       0.00       0.00       0.12       0.12       0.12

• Discovering GSS:

gss <- BMfinder(score, cores = 1) 
#> Warning in BMfinder(score, cores = 1): sample size < 30, the result should be
#> further tested!
#> ---BMfinder: 16 features * 6 samples
#> ---Completed:  a total of 4 bimodal distrubition features found!
gss
#>    Sample_027 Sample_042 Sample_102 Sample_137 Sample_237 Sample_272
#> 55          2          2          1          1          1          1
#> 57          1          1          2          2          2          2
#> 58          1          1          2          2          2          2
#> 59          1          1          1          2          2          2

• Extracing intron information

gss_intron<-structure(rice.bg)$intron
(gss_intron<-gss_intron[gss_intron$id%in%rownames(gss)])
#> GRanges object with 4 ranges and 2 metadata columns:
#>       seqnames          ranges strand |        id transcripts
#>          <Rle>       <IRanges>  <Rle> | <integer> <character>
#>   [1]     Chr1 1179011-1179226      - |        55       15:16
#>   [2]     Chr1 1179011-1179134      - |        57          17
#>   [3]     Chr1 1179011-1179110      - |        58          18
#>   [4]     Chr1 1179011-1179106      - |        59          19
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths
range(gss_intron)
#> GRanges object with 1 range and 0 metadata columns:
#>       seqnames          ranges strand
#>          <Rle>       <IRanges>  <Rle>
#>   [1]     Chr1 1179011-1179226      -
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

• Showing the splicing information

splicePlot(rice.bg,gene='MSTRG.183',samples = sampleNames(rice.bg)[c(1,3,5)],
           start = 1179000, end = 1179300)
#> [1] "yes"

Features

Detailed usage examples are available in the Vignette.

Contributors

• Huihui Yu
• Qian Du
• Chi Zhang

License

The code is freely available under the GPL (>= 2.0) license

yuhuihui2011/vasp_test documentation built on March 5, 2020, 12:50 a.m.