---
title: "The RNA Centric Analysis System Report"
author: "Bora Uyar, Dilmurat Yusuf, Ricardo Wurmus, Altuna Akalin"
date: "`r Sys.Date()`"
output:
  html_document:
    toc: true
    toc_float: true
    number_sections: true
    theme: simplex
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r global_options, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE, eval = FALSE)
```


```{r load_libraries, results='hide'}
library(RCAS)
```


# Introduction

RCAS is an automated system that provides dynamic genome annotations for custom input files that contain transcriptomic regions. Such transcriptomic regions could be, for instance, peak regions detected by CLIP-Seq analysis that detect protein-RNA interactions, RNA modifications (alias the epitranscriptome), CAGE-tag locations, or any other collection of target regions at the level of the transcriptome.

RCAS is designed as a reporting tool for the functional analysis of RNA-binding sites detected by high-throughput experiments. It takes as input a BED format file containing the genomic coordinates of the RNA binding sites and a GTF file that contains the genomic annotation features usually provided by publicly available databases such as Ensembl and UCSC. RCAS performs overlap operations between the genomic coordinates of the RNA binding sites and the genomic annotation features and produces in-depth annotation summaries such as the distribution of binding sites with respect to gene features (exons, introns, 5’/3’ UTR regions, exon-intron boundaries, promoter regions, and whole transcripts). Moreover, by detecting the collection of targeted transcripts, RCAS can carry out functional annotation tables for enriched gene sets (annotated by the Molecular Signatures Database) and GO terms. As one of the most important questions that arise during protein-RNA interaction analysis; RCAS has a module for detecting sequence motifs enriched in the targeted regions of the transcriptome. The final report of RCAS consists of high-quality dynamic figures and tables, which are readily applicable for publications or other academic usage.


#  Data input

RCAS minimally requires as input a BED file and a GTF file.
The BED file should contain coordinates/intervals
of transcriptomic regions
which are located via transcriptomics methods such as Clip-Seq.
The GTF file should provide reference annotation.
The recommended source of GTF files is the 
[ENSEMBLE](http://www.ensembl.org/info/data/ftp/index.html) database. 

For this vignette, in order to demonstrate RCAS functionality,
we use sample BED and GTF data that are built-in the RCAS library,
which can be imported using a common R function: data().
To import custom BED and GTF files,
the user should execute two RCAS functions called importBed() and importGtf().

## Importing sample data
```{r sample_data}
library(RCAS)
data(queryRegions) #sample queryRegions in BED format()
data(gff)          #sample GFF file
```

## Importing custom data
To use importBed() and importGtf(),
the user should provide file paths to the respective
BED file and GTF file.
To reduce memory usage and time consumption,
we advise the user to set `sampleN=10000` to avoid
huge input of intervals.

Before running following the code chunks in your R interpreter,
please remove `#` comment sign.
```{r RCAS_import_data}
#library(RCAS)
#queryRegions <- importBed(filePath = <path to BED file>, sampleN = 10000)
#gff <- importGtf(filePath = <path to GTF file>)
```

# Summarizing Overlaps of Query Regions with Genomic Annotation Features

## Querying the annotation file 
```{r queryGFF}
overlaps <- queryGff(queryRegions = queryRegions, gffData = gff)
#data.table is used to do quick summary operations
overlaps.dt <- data.table(as.data.frame(overlaps)) 
```


### Finding targeted gene types
To find out the distribution of the query regions across gene types:
```{r query_gene_types}
biotype_col <- grep('gene_biotype', colnames(overlaps.dt), value = T)
df <- overlaps.dt[,length(unique(overlappingQuery)), by = biotype_col]
colnames(df) <- c("feature", "count")
df$percent <- round(df$count / length(queryRegions) * 100, 1)
df <- df[order(count, decreasing = TRUE)]
p <- plot_ly(data = df, 
             type = "bar",
             x = df$feature,
             y = df$percent,
             text = paste("count:", df$count), color=df$feature)
layout(p = p, 
       margin = list(l=100, r=100, b=150), 
       xaxis = list(showticklabels = TRUE,  tickangle = 90), 
       yaxis = list(title = paste("percentage of query regions,", 
                                  "n =", length(queryRegions))))

```

## Extending the annotation feature space 

GTF files contain some annotation features (e.g. exons, transcripts) that are 
usually explicitly defined, however, some gene features such as introns,
exon-intron boundaries, promoter regions are only implicitly defined. Such 
implicit features can be extracted from a GTF file using makeTxDb family of 
functions from the GenomicFeatures library.

First we create a GRangesList object, where each list element contains all
the available coordinates of gene features such as transcripts, exons, introns,
5'/3' UTRs, exon-intron boundaries, and promoter regions. 

```{r getTxdbFeatures}
txdbFeatures <- getTxdbFeaturesFromGRanges(gff)
```

### Plotting overlap counts between query regions and gene features
To have a global overview of the distribution of query regions across gene
features, we can use the summarizeQueryRegions function.

```{R summarizeQueryRegions}
summary <- summarizeQueryRegions(queryRegions = queryRegions, 
                                 txdbFeatures = txdbFeatures)

df <- data.frame(summary)
df$percent <- round((df$count / length(queryRegions)), 3) * 100
p <- plot_ly( data = df, 
              x = rownames(df), 
              y = df$percent, 
              type = 'bar',
              text = paste("count:", df$count), 
              color = rownames(df)
              )
layout(p = p, 
       xaxis = list(title = 'features'),
       yaxis = list(title = paste("percentage of query regions,", 
                                  "n =", length(queryRegions)
                                  )
                    ), 
       margin = list(b = 150, r = 50)
       )
```

### Obtaining a table of overlap counts between query regions and genes 
To find out which genes overlap with how many queries and categorise overlaps by
gene features; we use getTargetedGenesTable function, which returns a data.frame
object. Then we use datatable function from 'DT' library to print an interactive
table of genes that overlap query Regions. 
```{r getTargetedGenesTable}
dt <- getTargetedGenesTable(queryRegions = queryRegions, 
                           txdbFeatures = txdbFeatures)
dt <- dt[order(transcripts, decreasing = TRUE)]

DT::datatable(dt[1:100], 
          extensions = c('Buttons', 'FixedColumns'), 
          options = list(fixedColumns = TRUE, 
                         scrollX = TRUE,
                         dom = 'Bfrtip',
                         buttons = c('copy', 'print', 'csv','excel', 'pdf')),
          filter = 'bottom'
          )
```

### Profiling the coverage of query regions across gene features 

#### Coverage profile of query regions for 3' UTRs
Coverage profiles can be obtained for a single type of gene feature or a list of
gene features. Here we demonstrate how to get coverage profile of query regions
across 3'UTRs. It might be a good idea to use sampleN parameter to randomly
downsample the target regions to speed up the calculations.

```{r coverageprofile}
cov <- calculateCoverageProfile(queryRegions = queryRegions, 
                               targetRegions = txdbFeatures$threeUTRs, 
                               sampleN = 10000)

plot_ly(data = cov, x = ~bins, y = ~coverage, 
            type = 'scatter', mode = 'lines')

```

#### Coverage profile of query regions for all gene features
Coverage profiles can be obtained for a single type of gene feature or a list of
gene features. Here we demonstrate how to get coverage profile of query regions 
across all available gene features. It might be a good idea to use sampleN
parameter to randomly downsample the target regions to speed up the 
calculations.

```{r coverageprofilelist}
covList <- calculateCoverageProfileList(queryRegions = queryRegions, 
                                       targetRegionsList = txdbFeatures, 
                                       sampleN = 10000)

df <- do.call('cbind', covList)
df <- df[,!grepl(colnames(df), pattern = '*.bins')]
df$bins <- c(1:100)
colnames(df) <- gsub(pattern = ".coverage", replacement = "", x = colnames(df))
mdf <- reshape2::melt(df, id.vars = c('bins'))
colnames(mdf) <- c('bins', 'feature', 'coverage')
p = plot_ly(data = mdf, x = ~bins, y = ~coverage, 
            type = 'scatter', mode = 'lines', color = ~feature)
layout(p)
```


#### Coverage profile of query regions at/around Feature Boundaries

Another way of profiling the signal at different gene features is to look into the depth of
coverage of the query regions at the boundaries such as the transcription start/end sites,  
exon-intron boundaries, and so on. Below, we demonstrate how to get the signal distribution
at the flanking regions of the transcription start/end sites. The signal can be either binned by
a window of certain number of base-pairs (using **getFeatureBoundaryCoverageBin** function) or the signal can be computed for each base-pair around the boundary region including a certain length of flanking bases (using the **getFeatureBoundaryCoverage** function). Plotting the coverage profiles of 
TSS and TES regions side-by-side, we can quickly observe that for this particular dataset, the signal concentrates around the 3' end of the transcript. 

```{r transcriptBoundaryCoverage}
cvg <- getFeatureBoundaryCoverage(queryRegions = queryRegions,
                                  featureCoords = txdbFeatures$transcripts,
                                  flankSize = 1000, 
                                  sampleN = 10000)

yLimit <- (as.integer(max(c(cvg$fivePrime, cvg$threePrime))/10)+1)*10

p <- subplot(
  plot_ly(data = cvg, x = ~bases, y = ~fivePrime, type = 'scatter', mode = 'lines'),
  plot_ly(data = cvg, x = ~bases, y = ~threePrime, type = 'scatter', mode = 'lines'),
  margin = 0.05
) %>% layout (xaxis = list(title = 'Distance (bp) to TSS'), 
              xaxis2 = list(title = 'Distance (bp) to TES'), 
              yaxis = list(title = 'coverage', range = c(0, yLimit)),
              yaxis2 = list(title = 'coverage', range = c(0, yLimit)),
                           showlegend = FALSE) 
layout(p) 
```


# Motif Analysis using motifRG

## Calculating enriched motifs
With the RCAS package, a motif analysis is also possible.
RCAS uses motifRG library to find enriched motifs among the query regions.
```{r motif_analysis}
motifResults <- runMotifRG(queryRegions = queryRegions, 
                           genomeVersion = 'hg19', 
                           motifN = 2, nCores = 2)

par(mfrow = c(1,2), mar = c(2,2,2,2))
for (i in 1:length(motifResults$motifs)) {
  motifPattern <- motifResults$motifs[[i]]@pattern
  motifRG::plotMotif(match = motifResults$motifs[[i]]@match$pattern, 
                     main = paste0('Motif-',i,': ',motifPattern),
                     entropy = TRUE)
}
```

## motif analysis: getting motif summary statistics
A summary table from the motif analysis results can be obtained
```{r motif_analysis_table}
summary <- getMotifSummaryTable(motifResults)
DT::datatable(summary, 
          extensions = c('Buttons', 'FixedColumns'), 
          options = list(fixedColumns = TRUE, 
                         scrollX = TRUE,
                         dom = 'Bfrtip',
                         buttons = c('copy', 'print', 'csv','excel', 'pdf')),
          filter = 'bottom'
          )
```


# GO term analysis

## Biological processes enriched among targeted genes
RCAS can perform GO term enrichment analysis to find out
enriched functions in genes that overlap the query regions. Below is demonstrated how to get
biological processes terms ('BP') enriched in the genes that overlap query regions and the top 10 GO terms with most fold change increase relative to the background are provided.   
```{r GO analysis}

#get all genes from the GTF data
backgroundGenes <- unique(gff$gene_id)
#get genes that overlap query regions
targetedGenes <- unique(overlaps$gene_id)

#run TopGO
goBP <- runTopGO(ontology = 'BP', 
                      species = 'human', 
                      backgroundGenes = backgroundGenes, 
                      targetedGenes = targetedGenes)

goBP <- goBP[order(goBP$foldEnrichment, decreasing = TRUE),]
rownames(goBP) <- goBP$GO.ID
goBP <- subset(goBP, select = -c(Annotated,classicFisher, bh, GO.ID))
DT::datatable(goBP[1:10,], 
          extensions = c('Buttons', 'FixedColumns'), 
          options = list(fixedColumns = TRUE, 
                         scrollX = TRUE,
                         dom = 'Bfrtip',
                         buttons = c('copy', 'print', 'csv', 'excel', 'pdf')),
          filter = 'bottom'
          )
```


# Gene Set Enrichment Analysis

## MSIGDB gene sets enriched among targeted genes
RCAS can use gene sets from Molecular Signatures Database
and calculate gene set enrichment analysis (GSEA)
to find out which gene sets are enriched
among the genes targeted by the query regions. 

Below we demonstrate a GSEA case using randomly generated gene sets (in order not to breach MSIGDB licence agreement) that are provided as built-in data in RCAS. The actual MSIGDB gene set annotations must be downloaded by the user from the MSIGDB website. RCAS provides functions to parse the annotations and map them to species other to enable GSEA on other species such as mouse and fly. 

```{r msigdb_analysis}
#geneSets <- parseMsigdb(< path to msigdbFile>)
data(geneSets)
resultsGSEA <- runGSEA(geneSetList = geneSets,
                       backgroundGenes = backgroundGenes, 
                       targetedGenes = targetedGenes)

datatable(resultsGSEA[1:10,],
    extensions = 'FixedColumns',
  options = list(
    dom = 't',
    scrollX = TRUE,
    scrollCollapse = TRUE
  ))

```

RCAS also provides functions to map the MSIGDB annotations from human to fly and mouse. 
``` 
#parse human annotations
refGeneSets <- parseMsigdb(filePath = <path to MSIGDB annotation file>)

#Map the gene sets to other species using orthologous relationships of genes between
#the reference genome (human) and the target genome (e.g. mouse)
orthGeneSets <- createOrthologousGeneSetList(referenceGeneSetList = refGeneSets, 
                                                refGenomeVersion = 'hg19', 
                                                targetGenomeVersion = 'mm9')

#the mapped gene sets can be used for GSEA analysis using the runGSEA command.

```

# Generating a full report 

The users can use the runReport() function to generate full custom reports
including all the analysis modules described above. There are four main parts of the analysis report.

- Annotation summaries via overlap operations 
- GO term analysis 
- MSIGDB analysis 
- Motif analysis

By default, runReport() function aims to run all four modules, while the user
can turn off these individual modules.

Below are example commands to generate reports using these functionalities.

## A test run for human

```
runReport()
```
## A custom run for human
```
runReport( queryFilePath = 'input.BED',
            gffFilePath = 'annotation.gtf',
            msigdbFilePath = 'human_msigdb.gmt')
```
## To turn off certain modules of the report
``` 
runReport( queryFilePath = 'input.BED',
            gffFilePath = 'annotation.gtf',
            msigdbFilePath = 'human_msigdb.gmt',
            motifAnalysis = FALSE,
            goAnalysis = FALSE )
```
## To run the pipeline for species other than human
If the msigdb module is needed, the msigdbFilePath
must be set to the MSIGDB annotations for 'human'.
MSIGDB datasets for other species will be calculated
in the background using the createOrthologousMsigdbDataset
function
```
runReport( queryFilePath = 'input.mm9.BED',
            gffFilePath = 'annotation.mm9.gtf',
            msigdbFilePath = 'human_msigdb.gmt',
            genomeVersion = 'mm9' )
```

## To turn off verbose output and progress bars
```
runReport(quiet = TRUE)
```

# Acknowledgements

RCAS is developed by
[Dr. Altuna Akalin](http://bioinformatics.mdc-berlin.de/team.html#altuna-akalin-phd)
(head of the Scientific Bioinformatics Platform),
[Dr. Bora Uyar](http://bioinformatics.mdc-berlin.de/team.html#bora-uyar-phd)
(Bioinformatics Scientist),
[Dr. Dilmurat Yusuf](http://bioinformatics.mdc-berlin.de/team.html#dilmurat-yusuf-phd)
(Bioinformatics Scientist) and 
[Ricardo Wurmus](http://bioinformatics.mdc-berlin.de/team.html#ricardo-wurmus)
(System Administrator) at the Berlin Institute of Medical Systems Biology
([BIMSB](https://www.mdc-berlin.de/13800178/en/bimsb))
at the Max-Delbrueck-Center for Molecular Medicine
([MDC](https://www.mdc-berlin.de)) in Berlin.

RCAS is developed as a bioinformatics service as part of
the [RNA Bioinformatics Center](http://www.denbi.de/index.php/rbc),
which is one of the eight centers of
the German Network for Bioinformatics Infrastructure
([de.NBI](http://www.denbi.de/)).  

