1 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.

2 Input Settings

3 Annotation Summary for Query Regions

4 motifRG analysis results

5 GO Term Analysis Results

6 Gene Set Enrichment Analysis Results

Table 6 : Significant MSigDB Gene Sets (FDR < 0.1) enriched for genes that overlap query regions

7 Acknowledgements

RCAS is developed in the group of Altuna Akalin (head of the Scientific Bioinformatics Platform) by Bora Uyar (Bioinformatics Scientist), Dilmurat Yusuf (Bioinformatics Scientist) and Ricardo Wurmus (System Administrator) at the Berlin Institute of Medical Systems Biology (BIMSB) at the Max-Delbrueck-Center for Molecular Medicine (MDC) in Berlin.

RCAS is developed as a bioinformatics service as part of the RNA Bioinformatics Center, which is one of the eight centers of the German Network for Bioinformatics Infrastructure (de.NBI).

8 Session Information

## R version 3.3.2 (2016-10-31)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Sierra 10.12.1
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
##  [1] grid      stats4    parallel  methods   stats     graphics  grDevices
##  [8] utils     datasets  base     
## 
## other attached packages:
##  [1] org.Hs.eg.db_3.4.0                RCAS_1.1.1                       
##  [3] motifRG_1.18.0                    BSgenome.Hsapiens.UCSC.hg19_1.4.0
##  [5] BSgenome_1.42.0                   rtracklayer_1.34.1               
##  [7] GenomicRanges_1.26.1              GenomeInfoDb_1.10.1              
##  [9] seqLogo_1.40.0                    Biostrings_2.42.0                
## [11] XVector_0.14.0                    topGO_2.26.0                     
## [13] SparseM_1.74                      GO.db_3.4.0                      
## [15] AnnotationDbi_1.36.0              IRanges_2.8.1                    
## [17] S4Vectors_0.12.0                  Biobase_2.34.0                   
## [19] graph_1.52.0                      BiocGenerics_0.20.0              
## [21] data.table_1.9.8                  DT_0.2                           
## [23] plotly_4.5.6                      ggplot2_2.2.0                    
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.8                lattice_0.20-34           
##  [3] tidyr_0.6.0                Rsamtools_1.26.1          
##  [5] assertthat_0.1             rprojroot_1.1             
##  [7] digest_0.6.10              gridBase_0.4-7            
##  [9] R6_2.2.0                   plyr_1.8.4                
## [11] backports_1.0.4            RSQLite_1.0.0             
## [13] evaluate_0.10              httr_1.2.1                
## [15] zlibbioc_1.20.0            GenomicFeatures_1.26.0    
## [17] lazyeval_0.2.0             Matrix_1.2-7.1            
## [19] rmarkdown_1.2              BiocParallel_1.8.1        
## [21] readr_1.0.0                stringr_1.1.0             
## [23] htmlwidgets_0.8            RCurl_1.95-4.8            
## [25] biomaRt_2.30.0             munsell_0.4.3             
## [27] base64enc_0.1-3            htmltools_0.3.5           
## [29] SummarizedExperiment_1.4.0 tibble_1.2                
## [31] matrixStats_0.51.0         XML_3.98-1.5              
## [33] viridisLite_0.1.3          dplyr_0.5.0               
## [35] GenomicAlignments_1.10.0   bitops_1.0-6              
## [37] jsonlite_1.1               gtable_0.2.0              
## [39] DBI_0.5-1                  magrittr_1.5              
## [41] scales_0.4.1               KernSmooth_2.23-15        
## [43] stringi_1.1.2              impute_1.48.0             
## [45] reshape2_1.4.2             RColorBrewer_1.1-2        
## [47] tools_3.3.2                seqPattern_1.6.0          
## [49] purrr_0.2.2                yaml_2.1.14               
## [51] plotrix_3.6-3              colorspace_1.3-1          
## [53] genomation_1.6.0           knitr_1.15.1