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Pseudogenes are defined as regions of the genome that contain defective copies of genes. They exist across almost all forms of life, and in mammalian genomes are annotated in similar numbers to recognized protein-coding genes. Although often presumed to lack function, growing numbers of pseudogenes are being found to play important biological roles. In consideration of their evolutionary origins and inherent limitations in genome annotation practices, we posit that pseudogenes have been classified on a scientifically unsubstantiated basis. We reflect that a broad misunderstanding of pseudogenes, perpetuated in part by the pejorative inference of the ‘pseudogene’ label, has led to their frequent dismissal from functional assessment and exclusion from genomic analyses. With the advent of technologies that simplify the study of pseudogenes, we propose that an objective reassessment of these genomic elements will reveal valuable insights into genome function and evolution.In this Perspective article, Cheetham, Faulkner and Dinger describe our latest understanding of pseudogenes, which are typically defined as defective copies of regular genes. They argue that being open minded about potential functionality, as well as carefully designing functional studies, will lead to a growing appreciation of emerging functional roles of these understudied elements.

The previous decade has seen long non-coding RNAs (lncRNAs) rise from obscurity to being defined as a category of genetic elements, leaving its mark on the field of cancer biology. With the current number of curated lncRNAs increasing by 10,000 in the last five years, the field is moving from annotation of lncRNA expression in various tumours to understanding their importance in the key cancer signalling networks and characteristic behaviours. Here, we summarize the previously identified as well as recently discovered mechanisms of lncRNA function and their roles in the hallmarks of cancer. Furthermore, we identify novel technologies for investigation of lncRNA properties and their function in carcinogenesis, which will be important for their translation to the clinic as novel biomarkers and therapeutic targets.

RNA-sequencing has become the gold standard for whole-transcriptome gene expression quantification. Multiple algorithms have been developed to derive gene counts from sequencing reads. While a number of benchmarking studies have been conducted, the question remains how individual methods perform at accurately quantifying gene expression levels from RNA-sequencing reads. We performed an independent benchmarking study using RNA-sequencing data from the well established MAQCA and MAQCB reference samples. RNA-sequencing reads were processed using five workflows (Tophat-HTSeq, Tophat-Cufflinks, STAR-HTSeq, Kallisto and Salmon) and resulting gene expression measurements were compared to expression data generated by wet-lab validated qPCR assays for all protein coding genes. All methods showed high gene expression correlations with qPCR data. When comparing gene expression fold changes between MAQCA and MAQCB samples, about 85% of the genes showed consistent results between RNA-sequencing and qPCR data. Of note, each method revealed a small but specific gene set with inconsistent expression measurements. A significant proportion of these method-specific inconsistent genes were reproducibly identified in independent datasets. These genes were typically smaller, had fewer exons, and were lower expressed compared to genes with consistent expression measurements. We propose that careful validation is warranted when evaluating RNA-seq based expression profiles for this specific gene set.

Human genome function is underpinned by the primary storage of genetic information in canonical B-form DNA, with a second layer of DNA structure providing regulatory control. I-motif structures are thought to form in cytosine-rich regions of the genome and to have regulatory functions; however, in vivo evidence for the existence of such structures has so far remained elusive. Here we report the generation and characterization of an antibody fragment (iMab) that recognizes i-motif structures with high selectivity and affinity, enabling the detection of i-motifs in the nuclei of human cells. We demonstrate that the in vivo formation of such structures is cell-cycle and pH dependent. Furthermore, we provide evidence that i-motif structures are formed in regulatory regions of the human genome, including promoters and telomeric regions. Our results support the notion that i-motif structures provide key regulatory roles in the genome.

Clinical genomics promises unprecedented precision in understanding the genetic basis of disease. Understanding the impact of variation across the genome is required to realize this potential. Currently, clinical genomics analyses focus on protein-coding genes. However, the noncoding genome is substantially larger than the protein-coding counterpart, and contains structural, regulatory, and transcribed information that needs to be incorporated into genome annotations if the full extent of the opportunity to use genomic information in healthcare is to be realized. This article reviews the challenges and opportunities in unlocking the clinical significance of coding and noncoding genomic information and translating its utility in practice. Medical genetics: The importance of the whole genome Most of the DNA in the genome does not consist of genes that code for proteins, and understanding the function of these less examined parts of our genetic material is essential to fully understand human development and disease. Brian Gloss and Marcel Dinger at the Garvan Institute of Medical Research in Sydney, Australia, review the challenges and opportunities in unraveling the clinical significance of all parts of our DNA. Many regions of DNA that do not encode protein molecules perform crucial functions in regulating the activity and interactions of the protein-coding genes. Variations in these regions may significantly influence the risks and causes of disease. Studying all parts of the genome will be critical for ensuring that the powerful modern techniques of genetic analysis have maximal impact on healthcare.

The recent discovery that most of the eukaryotic genome is transcribed has focused interest on the importance of non-coding transcripts. Long non-coding RNAs are emerging as a class with wide-ranging functions in gene regulation. In mammals and other eukaryotes most of the genome is transcribed in a developmentally regulated manner to produce large numbers of long non-coding RNAs (ncRNAs). Here we review the rapidly advancing field of long ncRNAs, describing their conservation, their organization in the genome and their roles in gene regulation. We also consider the medical implications, and the emerging recognition that any transcript, regardless of coding potential, can have an intrinsic function as an RNA.

Genes specifying long non-coding RNAs (lncRNAs) occupy a large fraction of the genomes of complex organisms. The term ‘lncRNAs’ encompasses RNA polymerase I (Pol I), Pol II and Pol III transcribed RNAs, and RNAs from processed introns. The various functions of lncRNAs and their many isoforms and interleaved relationships with other genes make lncRNA classification and annotation difficult. Most lncRNAs evolve more rapidly than protein-coding sequences, are cell type specific and regulate many aspects of cell differentiation and development and other physiological processes. Many lncRNAs associate with chromatin-modifying complexes, are transcribed from enhancers and nucleate phase separation of nuclear condensates and domains, indicating an intimate link between lncRNA expression and the spatial control of gene expression during development. lncRNAs also have important roles in the cytoplasm and beyond, including in the regulation of translation, metabolism and signalling. lncRNAs often have a modular structure and are rich in repeats, which are increasingly being shown to be relevant to their function. In this Consensus Statement, we address the definition and nomenclature of lncRNAs and their conservation, expression, phenotypic visibility, structure and functions. We also discuss research challenges and provide recommendations to advance the understanding of the roles of lncRNAs in development, cell biology and disease.This Consensus Statement addresses the definition, nomenclature and classification of long non-coding RNAs, and provides a shared viewpoint on their features and functions. The authors also discuss research challenges and provide recommendations to advance our understanding of long non-coding RNAs.

The assumption that RNA can be readily classified into either protein-coding or non-protein-coding categories has pervaded biology for close to 50 years. Until recently, discrimination between these two categories was relatively straightforward: most transcripts were clearly identifiable as protein-coding messenger RNAs (mRNAs), and readily distinguished from the small number of well-characterized non-protein-coding RNAs (ncRNAs), such as transfer, ribosomal, and spliceosomal RNAs. Recent genome-wide studies have revealed the existence of thousands of noncoding transcripts, whose function and significance are unclear. The discovery of this hidden transcriptome and the implicit challenge it presents to our understanding of the expression and regulation of genetic information has made the need to distinguish between mRNAs and ncRNAs both more pressing and more complicated. In this Review, we consider the diverse strategies employed to discriminate between protein-coding and noncoding transcripts and the fundamental difficulties that are inherent in what may superficially appear to be a simple problem. Misannotations can also run in both directions: some ncRNAs may actually encode peptides, and some of those currently thought to do so may not. Moreover, recent studies have shown that some RNAs can function both as mRNAs and intrinsically as functional ncRNAs, which may be a relatively widespread phenomenon. We conclude that it is difficult to annotate an RNA unequivocally as protein-coding or noncoding, with overlapping protein-coding and noncoding transcripts further confounding this distinction. In addition, the finding that some transcripts can function both intrinsically at the RNA level and to encode proteins suggests a false dichotomy between mRNAs and ncRNAs. Therefore, the functionality of any transcript at the RNA level should not be discounted.

In parallel, whole chromosome tiling array interrogation of the RNA content of a variety of human tissues and cell lines revealed that, collectively, at least 93% of genomic bases are transcribed in one cell type or another [1],[10]-[13]. Since it is well established that highly expressed mRNAs dominate the non-ribosomal portion of the polyA+ transcriptome [7],[8],[10],[14]-[19], normalization approaches were used to reduce the quantity of highly expressed transcripts in these cDNA analyses [7],[8], and are implicit in tiling array approaches. [...]any estimate of the pervasiveness of transcription requires inclusion of all data sources, and less than exhaustive analyses can only provide lower bounds for transcriptional complexity.

We develop GeneRAIN, a suite of Transformer-based models that learn gene expression relationships from 410 K human bulk RNA-seq samples. Featuring a novel Binning-By-Gene normalization technique, our models capture diverse biological information beyond expression. We introduce GeneRAIN-vec, a multifaceted vectorized gene representation that outperforms those from existing models. We demonstrate knowledge transfer from protein-coding genes to Make 62.5 million biological attribute predictions for 13,030 long noncoding RNAs. This work advances Transformer and self-supervised deep learning applications to expression data, enhancing biological exploration.