Linking FANTOM5 CAGE peaks to annotations with CAGEscan

An improved method for the highly specific detection of transcription start sites

Precise detection of the transcriptional start site (TSS) is a key for characterizing transcriptional regulation of genes and for annotation of newly sequenced genomes. Here, we describe the development of an improved method, designated 'TSS-seq2.' This method is an iterative improvement of TSS-seq, a previously published enzymatic cap-structure conversion method to detect TSSs in base sequences. By modifying the original procedure, including by introducing split ligation at the key cap-selection step, the yield and the accuracy of the reaction has been substantially improved. For example, TSS-seq2 can be conducted using as little as 5 ng of total RNA with an overall accuracy of 96%; this yield a less-biased and more precise detection of TSS. We then applied TSS-seq2 for TSS analysis of four plant species that had not yet been analyzed by any previous TSS method.

Atlas of primary cell-type-specific sequence models of gene expression and variant effects

Human biology is rooted in highly specialized cell types programmed by a common genome, 98% of which is outside of genes. Genetic variation in the enormous noncoding space is linked to the majority of disease risk. To address the problem of linking these variants to expression changes in primary human cells, we introduce ExPectoSC, an atlas of modular deep-learning-based models for predicting cell-type-specific gene expression directly from sequence. We provide models for 105 primary human cell types covering 7 organ systems, demonstrate their accuracy, and then apply them to prioritize relevant cell types for complex human diseases. The resulting atlas of sequence-based gene expression and variant effects is publicly available in a user-friendly interface and readily extensible to any primary cell types. We demonstrate the accuracy of our approach through systematic evaluations and apply the models to prioritize ClinVar clinical variants of uncertain significance, verifying our top predictions experimentally.

Improving the annotation of the cattle genome by annotating transcription start sites in a diverse set of tissues and populations using Cap Analysis Gene Expression sequencing

Understanding the genomic control of tissue-specific gene expression and regulation can help to inform the application of genomic technologies in farm animal breeding programs. The fine mapping of promoters [transcription start sites (TSS)] and enhancers (divergent amplifying segments of the genome local to TSS) in different populations of cattle across a wide diversity of tissues provides information to locate and understand the genomic drivers of breed- and tissue-specific characteristics. To this aim, we used Cap Analysis Gene Expression (CAGE) sequencing, of 24 different tissues from 3 populations of cattle, to define TSS and their coexpressed short-range enhancers (<1 kb) in the ARS-UCD1.2_Btau5.0.1Y reference genome (1000bulls run9) and analyzed tissue and population specificity of expressed promoters. We identified 51,295 TSS and 2,328 TSS-Enhancer regions shared across the 3 populations (dairy, beef-dairy cross, and Canadian Kinsella composite cattle from 2 individuals, 1 of each sex, per population). Cross-species comparative analysis of CAGE data from 7 other species, including sheep, revealed a set of TSS and TSS-Enhancers that were specific to cattle. The CAGE data set will be combined with other transcriptomic information for the same tissues to create a new high-resolution map of transcript diversity across tissues and populations in cattle for the BovReg project. Here we provide the CAGE data set and annotation tracks for TSS and TSS-Enhancers in the cattle genome. This new annotation information will improve our understanding of the drivers of gene expression and regulation in cattle and help to inform the application of genomic technologies in breeding programs.

A versatile 5′ RACE-Seq methodology for the accurate identification of the 5′ termini of mRNAs

Background

Technological advancements in the era of massive parallel sequencing have enabled the functional dissection of the human transcriptome. However, 5′ ends of mRNAs are significantly underrepresented in these datasets, hindering the efficient analysis of the complex human transcriptome. The implementation of the template-switching mechanism at the reverse transcription stage along with 5′ rapid amplification of cDNA ends (RACE) constitutes the most prominent and efficient strategy to specify the actual 5′ ends of cDNAs. In the current study, we developed a 5′ RACE-seq method by coupling a custom template-switching and 5′ RACE assay with targeted nanopore sequencing, to accurately unveil 5′ termini of mRNA targets.

Results

The optimization of the described 5′ RACE-seq method was accomplished using the human BCL2L12 as control gene. We unveiled that the selection of hybrid DNA/RNA template-switching oligonucleotides as well as the complete separation of the cDNA extension incubation from the template-switching process, significantly increase the overall efficiency of the downstream 5′ RACE. Collectively, our results support the existence of two distinct 5′ termini for BCL2L12, being in complete accordance with the results derived from both direct RNA and PCR-cDNA sequencing approaches from Oxford Nanopore Technologies. As proof of concept, we implemented the described 5′ RACE-seq methodology to investigate the 5′ UTRs of several kallikrein-related peptidases (KLKs) gene family members. Our results confirmed the existence of multiple annotated 5′ UTRs of the human KLK gene family members, but also identified novel, previously uncharacterized ones.

Conclusions

In this work we present an in-house developed 5′ RACE-seq method, based on the template-switching mechanism and targeted nanopore sequencing. This approach enables the broad and in-depth study of 5′ UTRs of any mRNA of interest, by offering a tremendous sequencing depth, while significantly reducing the cost-per reaction compared to commercially available kits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08386-y.

TSSr: an R package for comprehensive analyses of TSS sequencing data

Transcription initiation is regulated in a highly organized fashion to ensure proper cellular functions. Accurate identification of transcription start sites (TSSs) and quantitative characterization of transcription initiation activities are fundamental steps for studies of regulated transcriptions and core promoter structures. Several high-throughput techniques have been developed to sequence the very 5'end of RNA transcripts (TSS sequencing) on the genome scale. Bioinformatics tools are essential for processing, analysis, and visualization of TSS sequencing data. Here, we present TSSr, an R package that provides rich functions for mapping TSS and characterizations of structures and activities of core promoters based on all types of TSS sequencing data. Specifically, TSSr implements several newly developed algorithms for accurately identifying TSSs from mapped sequencing reads and inference of core promoters, which are a prerequisite for subsequent functional analyses of TSS data. Furthermore, TSSr also enables users to export various types of TSS data that can be visualized by genome browser for inspection of promoter activities in association with other genomic features, and to generate publication-ready TSS graphs. These user-friendly features could greatly facilitate studies of transcription initiation based on TSS sequencing data. The source code and detailed documentations of TSSr can be freely accessed at https://github.com/Linlab-slu/TSSr.

Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer

Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).

Global Analysis of Transcription Start Sites in the New Ovine Reference Genome (Oar rambouillet v1.0)

The overall aim of the Ovine FAANG project is to provide a comprehensive annotation of the new highly contiguous sheep reference genome sequence (Oar rambouillet v1.0). Mapping of transcription start sites (TSS) is a key first step in understanding transcript regulation and diversity. Using 56 tissue samples collected from the reference ewe Benz2616, we have performed a global analysis of TSS and TSS-Enhancer clusters using Cap Analysis Gene Expression (CAGE) sequencing. CAGE measures RNA expression by 5' cap-trapping and has been specifically designed to allow the characterization of TSS within promoters to single-nucleotide resolution. We have adapted an analysis pipeline that uses TagDust2 for clean-up and trimming, Bowtie2 for mapping, CAGEfightR for clustering, and the Integrative Genomics Viewer (IGV) for visualization. Mapping of CAGE tags indicated that the expression levels of CAGE tag clusters varied across tissues. Expression profiles across tissues were validated using corresponding polyA+ mRNA-Seq data from the same samples. After removal of CAGE tags with <10 read counts, 39.3% of TSS overlapped with 5' ends of 31,113 transcripts that had been previously annotated by NCBI (out of a total of 56,308 from the NCBI annotation). For 25,195 of the transcripts, previously annotated by NCBI, no TSS meeting stringent criteria were identified. A further 14.7% of TSS mapped to within 50 bp of annotated promoter regions. Intersecting these predicted TSS regions with annotated promoter regions (±50 bp) revealed 46% of the predicted TSS were "novel" and previously un-annotated. Using whole-genome bisulfite sequencing data from the same tissues, we were able to determine that a proportion of these "novel" TSS were hypo-methylated (32.2%) indicating that they are likely to be reproducible rather than "noise". This global analysis of TSS in sheep will significantly enhance the annotation of gene models in the new ovine reference assembly. Our analyses provide one of the highest resolution annotations of transcript regulation and diversity in a livestock species to date.

TIF-Seq2 disentangles overlapping isoforms in complex human transcriptomes

Eukaryotic transcriptomes are complex, involving thousands of overlapping transcripts. The interleaved nature of the transcriptomes limits our ability to identify regulatory regions, and in some cases can lead to misinterpretation of gene expression. To improve the understanding of the overlapping transcriptomes, we have developed an optimized method, TIF-Seq2, able to sequence simultaneously the 5' and 3' ends of individual RNA molecules at single-nucleotide resolution. We investigated the transcriptome of a well characterized human cell line (K562) and identified thousands of unannotated transcript isoforms. By focusing on transcripts which are challenging to be investigated with RNA-Seq, we accurately defined boundaries of lowly expressed unannotated and read-through transcripts putatively encoding fusion genes. We validated our results by targeted long-read sequencing and standard RNA-Seq for chronic myeloid leukaemia patient samples. Taking the advantage of TIF-Seq2, we explored transcription regulation among overlapping units and investigated their crosstalk. We show that most overlapping upstream transcripts use poly(A) sites within the first 2 kb of the downstream transcription units. Our work shows that, by paring the 5' and 3' end of each RNA, TIF-Seq2 can improve the annotation of complex genomes, facilitate accurate assignment of promoters to genes and easily identify transcriptionally fused genes.

Evidence That STK19 Is Not an NRAS-dependent Melanoma Driver

STK19 was proposed to be a cancer driver, and recent work by Yin et al. (2019) in Cell suggested that the frequently recurring STK19 D89N substitution represents a gain-of-function change, allowing increased phosphorylation of NRAS to enhance melanocyte transformation. Here we show that the STK19 gene has been incorrectly annotated, and that the expressed protein is 110 amino acids shorter than indicated by current databases. The "cancer driving" STK19 D89N substitution is thus outside the coding region. We also fail to detect evidence of the mutation affecting STK19 expression; instead, it is a UV signature mutation, found in the promoter of other genes as well. Furthermore, STK19 is exclusively nuclear and chromatin-associated, while no evidence for it being a kinase was found. The data in this Matters Arising article raise fundamental questions about the recently proposed role for STK19 in melanoma progression via a function as an NRAS kinase, suggested by Yin et al. (2019) in Cell. See also the response by Yin et al. (2020), published in this issue.

Computational Methods for Mapping, Assembly and Quantification for Coding and Non-coding Transcripts

The measurement of gene expression has long provided significant insight into biological functions. The development of high-throughput short-read sequencing technology has revealed transcriptional complexity at an unprecedented scale, and informed almost all areas of biology. However, as researchers have sought to gather more insights from the data, these new technologies have also increased the computational analysis burden. In this review, we describe typical computational pipelines for RNA-Seq analysis and discuss their strengths and weaknesses for the assembly, quantification and analysis of coding and non-coding RNAs. We also discuss the assembly of transposable elements into transcripts, and the difficulty these repetitive elements pose. In summary, RNA-Seq is a powerful technology that is likely to remain a key asset in the biologist's toolkit.