Long non-coding RNAs (lncRNAs) represent a major fraction of the transcriptome in multicellular organisms. Although a handful of well-studied lncRNAs are broadly recognized as biologically meaningful, the fraction of such transcripts out of the entire collection of lncRNAs remains a subject of vigorous debate. Here we review the evidence for and against biological functionalities of lncRNAs and attempt to arrive at potential modes of lncRNA functionality that would reconcile the contradictory conclusions. Finally, we discuss different strategies of phenotypic analyses that could be used to investigate such modes of lncRNA functionality.

Long non-coding (lnc) RNAs represent a fascinating class of transcripts that remains highly controversial mainly due to ambiguity surrounding overall biological relevance of these RNAs. Multitude of reverse genetics studies showing functionality of lncRNAs are unfortunately based on assays that are either plagued by non-specific effects and/or cannot unambiguously assign observed phenotypes to the transcript per se. Here, we show application of the novel CRISPR/Cas13 RNA knockdown system that has superior specificity compared to other transcript-targeting knockdown methods like RNAi. We applied this method to a novel widespread subclass of nuclear lncRNAs — very long intergenic non-coding (vlinc) RNAs — in a high-throughput phenotypic assay based on survival challenge in response to anticancer drug treatments. We used multiple layers of controls including mismatch control for each targeting gRNA to ensure uncovering true phenotype-transcript relationships. We found evidence supporting importance for cellular survival for up to 60% of the tested protein-coding mRNAs and, importantly, 64% of vlincRNAs. Overall, this study demonstrates utility of CRISPR/Cas13 as a highly sensitive and specific tool for reverse genetics study of both protein-coding genes and lncRNAs. Furthermore, importantly, this approach provides evidence supporting biological significance of the latter transcripts in anticancer drug response.

Single-strand breaks (SSBs) represent the major form of DNA damage, yet techniques to map these lesions genome-wide with nucleotide-level precision are limited. Here, we present a method, termed SSiNGLe, and demonstrate its utility to explore the distribution and dynamic changes in genome-wide SSBs in response to different biological and environmental stimuli. We validate SSiNGLe using two very distinct sequencing techniques and apply it to derive global profiles of SSBs in different biological states. Strikingly, we show that patterns of SSBs in the genome are non-random, specific to different biological states, enriched in regulatory elements, exons, introns, specific types of repeats and exhibit differential preference for the template strand between exons and introns. Furthermore, we show that breaks likely contribute to naturally occurring sequence variants. Finally, we demonstrate strong links between SSB patterns and age. Overall, SSiNGLe provides access to unexplored realms of cellular biology, not obtainable with current approaches.