The Landscape of long noncoding RNA classification

•ncRNAs constitute the majority of transcriptional output of the human genome. •Lack of functional knowledge of most of ncRNAs has led to classification with a number of issues. •A new classification is emerging rooted in unbiased whole-genome surveys of RNA. •The new classification should allow for easy data integration across multiple experiments. Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long noncoding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the noncoding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual unambiguous classification framework results in a number of challenges in the annotation and interpretation of noncoding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel the function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then, we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long noncoding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the noncoding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual unambiguous classification framework results in a number of challenges in the annotation and interpretation of noncoding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel the function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then, we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. an altered nucleotide present at 5′ ends of a eukaryotic RNA and vital for its functioning. the Encyclopedia of DNA Elements; a public research consortium launched in September 2003 by the National Human Genome Research Institute. The goal of the project is to identify all functional elements in the human genome sequence. a genomic element that was traced back to a retrovirus integrated into an ancestral genome and since retained. ERV sequences comprise ∼8% of the human genome. a relatively short and typically partial sequence of a longer RNA molecule. an international research consortium established by scientists at RIKEN, Japan in 2000, initially to assign functional annotations to the full-length cDNAs collected during the Mouse Encyclopedia Project. FANTOM has since developed and expanded over time to encompass different fields of transcriptome analysis. an approach designed to detect differentially expressed regions of the genome in the regions where no annotation is available. identical pieces of DNA found at the ends of retroviruses and critical for viral life cycle. LTRs contain elements required for viral gene expression. LTRs of ERVs often retain these elements and thus can initiate or control expression of host transcripts. a subcellular compartment that could be identified in nuclear interchromatin space. a multi-protein complex that reversibly modifies chromatin structure and silences target genes. a microarray design (typically oligonucleotide-based) where probes interrogate an entire genomic region of interest at regular intervals agnostic of genomic annotations. This design differs from other microarrays that target only specific genomic features of interest, like exons of known genes.