title: RDDpred: a condition-specific RNA-editing prediction model from RNA-seq data.
abstract: BACKGROUND
  RNA-editing is an important post-transcriptional RNA sequence modification performed by two catalytic enzymes, "ADAR"(A-to-I) and "APOBEC"(C-to-U). By utilizing high-throughput sequencing technologies, the biological function of RNA-editing has been actively investigated. Currently, RNA-editing is considered to be a key regulator that controls various cellular functions, such as protein activity, alternative splicing pattern of mRNA, and substitution of miRNA targeting site. DARNED, a public RDD database, reported that there are more than 300-thousands RNA-editing sites detected in human genome(hg19). Moreover, multiple studies suggested that RNA-editing events occur in highly specific conditions. According to DARNED, 97.62 % of registered editing sites were detected in a single tissue or in a specific condition, which also supports that the RNA-editing events occur condition-specifically. Since RNA-seq can capture the whole landscape of transcriptome, RNA-seq is widely used for RDD prediction. However, significant amounts of false positives or artefacts can be generated when detecting RNA-editing from RNA-seq. Since it is difficult to perform experimental validation at the whole-transcriptome scale, there should be a powerful computational tool to distinguish true RNA-editing events from artefacts.
  
  
  RESULTS
  We developed RDDpred, a Random Forest RDD classifier. RDDpred reports potentially true RNA-editing events from RNA-seq data. RDDpred was tested with two publicly available RNA-editing datasets and successfully reproduced RDDs reported in the two studies (90 %, 95 %) while rejecting false-discoveries (NPV: 75 %, 84 %).
  
  
  CONCLUSIONS
  RDDpred automatically compiles condition-specific training examples without experimental validations and then construct a RDD classifier. As far as we know, RDDpred is the very first machine-learning based automated pipeline for RDD prediction. We believe that RDDpred will be very useful and can contribute significantly to the study of condition-specific RNA-editing. RDDpred is available at http://biohealth.snu.ac.kr/software/RDDpred .
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. mdy89@snu.ac.kr.
authors.forename: Min-su
authors.initials: MS
authors.lastname: Kim
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. Jotunnheim@snu.ac.kr.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Hur
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. sunkim.bioinfo@snu.ac.kr.|Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea. sunkim.bioinfo@snu.ac.kr.|Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea. sunkim.bioinfo@snu.ac.kr.
authors.forename: Sun
authors.initials: S
authors.lastname: Kim
mesh_terms: D030541:Databases, Genetic; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D020407:Internet; D020641:Polymorphism, Single Nucleotide; D012313:RNA / Q000737:chemistry / Q000378:metabolism*; D017393:RNA Editing; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA; D014584:User-Computer Interface
chemical_list: D012313:RNA
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