Transcriptional activation of the embryonic genome (EGA) is a major developmental landmark enabling the embryo to become independent from maternal control. The magnitude and control of transcriptional reprogramming during this event across mammals remains poorly understood. Here, we developed Smart-seq+50 for high sensitivity, full-length transcript coverage and simultaneous capture of 50 transcript information from single cells and single embryos. Using Smart-seq+50, we profiled 34 developmental stages in 5 mammalian species and provide an extensive characterization of the transcriptional repertoire of early development before, during, and after EGA. We demonstrate widespread transposable element (TE)-driven transcription across species, including, remarkably, of DNA transposons. We identify 19,657 TE-driven genic transcripts, suggesting extensive TE co-option in early development over evolutionary timescales. TEs display similar expression dynamics across species and species-specific patterns, suggesting shared and divergent regulation. Our work provides a powerful resource for understanding transcriptional regulation of mammalian development.

The field of epitranscriptomics is undergoing a technology-driven revolution. During past decades, RNA modifications like N6-methyladenosine (m6A), pseudouridine (ψ), and 5-methylcytosine (m5C) became acknowledged for playing critical roles in cellular processes. Direct RNA sequencing by Oxford Nanopore Technologies (ONT) enabled the detection of modifications in native RNA, by detecting noncanonical RNA nucleosides properties in raw data. Consequently, the field's cutting edge has a heavy component in computer science, opening new avenues of cooperation across the community, as exchanging data is as impactful as exchanging samples. Therefore, we seize the occasion to bring scientists together within the RNA Modification and Processing (RMaP) challenge to advance solutions for RNA modification detection and discuss ideas, problems and approaches. We show several computational methods to detect the most researched mRNA modifications (m6A, ψ, and m5C). Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. The RMaP challenge marks a substantial step towards improving algorithms' comparability, reliability, and consistency in RNA modification prediction. It points out the deficits in this young field that need to be addressed in further challenges.

The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N6-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N6-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.

MOTIVATION:Nanopore sequencing by Oxford Nanopore Technologies (ONT) enables direct analysis of DNA and RNA by capturing raw electrical signals. Different nanopore chemistries have varied k-mer lengths, current levels, and standard deviations, which are stored in "k-mer models." In cases where official models are lacking or unsuitable for specific sequencing conditions, tailored k-mer models are crucial to ensure precise signal-to-sequence alignment, analysis and interpretation. The process of transforming raw signal data into nucleotide sequences, known as basecalling, is a fundamental step in nanopore sequencing. RESULTS:In this study, we leverage the move table produced by ONT's basecalling software to create a lightweight de novo k-mer model for RNA004 chemistry. We demonstrate the validity of our custom k-mer model by using it to guide signal-to-sequence alignment analysis, achieving high alignment rates (97.48%) compared to larger default models. Additionally, our 5-mer model exhibits similar performance as the default 9-mer models another analysis, such as detection of m6A RNA modifications. We provide our method, termed Poregen, as a generalizable approach for creation of custom, de novo k-mer models for nanopore signal data analysis. AVAILABILITY AND IMPLEMENTATION:Poregen is an open source package under an MIT license: https://github.com/hiruna72/poregen.

Single cell and spatial transcriptomics have dramatically changed how we can profile RNA from heterogenous biological samples. Combining single cell and spatial profiling with long read RNA-Seq promises to enable the discovery and quantification of individual RNA isoforms at the single-cell level. However, highly multiplexed data such as from a single cell experiment only generates a limited number of reads for each cell, constituting a major challenge for transcript discovery and quantification with existing approaches that usually have limited power for samples with low sequencing depth. Here we present Bambu-Clump, a computational method that performs transcript discovery and quantification from single cell and spatial long read RNA-Seq data using information from both each cell and the cell cluster. Using this approach, Bambu-Clump provides the most accurate transcript discovery compared to other existing methods, and improves transcript quantification compared to methods that rely on estimates derived from single cells. We apply Bambu-Clump to identify fusion transcripts in single-cells, compare 5' and 3' selection protocols, and identify novel isoform cell-type markers in spatial mouse brain data. Together, Bambu-Clump provides an easy-to-use, efficient, and accurate method for analysing individual isoform expression for single cells and cell clusters across multiple datasets and replicates from long read RNA-Seq. ### Competing Interest Statement Jonathan Göke received travel and accommodation expenses to speak at the Oxford Nanopore Community Meeting 2018. All other authors declare no competing interest.

Although KDM5C is one of the most frequently mutated genes in X-linked intellectual disability 1 , the exact mechanisms that lead to cognitive impairment remain unknown. Here we use human patient-derived induced pluripotent stem cells and Kdm5c knockout mice to conduct cellular, transcriptomic, chromatin and behavioural studies. KDM5C is identified as a safeguard to ensure that neurodevelopment occurs at an appropriate timescale, the disruption of which leads to intellectual disability. Specifically, there is a developmental window during which KDM5C directly controls WNT output to regulate the timely transition of primary to intermediate progenitor cells and consequently neurogenesis. Treatment with WNT signalling modulators at specific times reveal that only a transient alteration of the canonical WNT signalling pathway is sufficient to rescue the transcriptomic and chromatin landscapes in patient-derived cells and to induce these changes in wild-type cells. Notably, WNT inhibition during this developmental period also rescues behavioural changes of Kdm5c knockout mice. Conversely, a single injection of WNT3A into the brains of wild-type embryonic mice cause anxiety and memory alterations. Our work identifies KDM5C as a crucial sentinel for neurodevelopment and sheds new light on KDM5C mutation-associated intellectual disability. The results also increase our general understanding of memory and anxiety formation, with the identification of WNT functioning in a transient nature to affect long-lasting cognitive function.

MOTIVATION:The process of analyzing high throughput sequencing data often requires the identification and extraction of specific target sequences. This could include tasks, such as identifying cellular barcodes and UMIs in single-cell data, and specific genetic variants for genotyping. However, existing tools, which perform these functions are often task-specific, such as only demultiplexing barcodes for a dedicated type of experiment, or are not tolerant to noise in the sequencing data. RESULTS:To overcome these limitations, we developed Flexiplex, a versatile and fast sequence searching and demultiplexing tool for omics data, which is based on the Levenshtein distance and thus allows imperfect matches. We demonstrate Flexiplex's application on three use cases, identifying cell-line-specific sequences in Illumina short-read single-cell data, and discovering and demultiplexing cellular barcodes from noisy long-read single-cell RNA-seq data. We show that Flexiplex achieves an excellent balance of accuracy and computational efficiency compared to leading task-specific tools. AVAILABILITY AND IMPLEMENTATION:Flexiplex is available at https://davidsongroup.github.io/flexiplex/.

Nanopore sequencing by Oxford Nanopore Technologies (ONT) enables direct analysis of DNA and RNA by capturing raw electrical signals. Different nanopore chemistries have varied k-mer lengths, current levels, and standard deviations, which are stored in k-mer models. Particularly in cases where official models are lacking or unsuitable for specific sequencing conditions, tailored k-mer models are crucial to ensure precise signal-to-sequence alignment and interpretation. The process of transforming raw signals into nucleotide sequences, known as basecalling, is a fundamental step in nanopore sequencing. In this study, we leverage the basecaller’s move table to create a lightweight denovo k-mer model for RNA004 chemistry. We showcase the effectiveness of our custom k-mer model through high alignment rates (97.48%) compared to larger default models. Additionally, our 5-mer model exhibits similar performance as the default 9-mer models in m6A methylation detection. ### Competing Interest Statement I.W.D. manages a fee-for-service sequencing facility at the Garvan Institute of Medical Research and is a customer of Oxford Nanopore Technologies but has no further financial relationship. H.G. and I.W.D. have previously received travel and accommodation expenses from Oxford Nanopore Technologies. J.G. received reimbursement for travel and accommodation from Oxford Nanopore Technologies to present at the Nanopore Community Meeting in San Francisco in 2018. The authors declare no other competing financial or non-financial interests.

Exhaustive list of sense-gene promoters considered in our analysis.

Supplementary Data - Main File

Most approaches to transcript quantification rely on fixed reference annotations; however, the transcriptome is dynamic and depending on the context, such static annotations contain inactive isoforms for some genes, whereas they are incomplete for others. Here we present Bambu, a method that performs machine-learning-based transcript discovery to enable quantification specific to the context of interest using long-read RNA-sequencing. To identify novel transcripts, Bambu estimates the novel discovery rate, which replaces arbitrary per-sample thresholds with a single, interpretable, precision-calibrated parameter. Bambu retains the full-length and unique read counts, enabling accurate quantification in presence of inactive isoforms. Compared to existing methods for transcript discovery, Bambu achieves greater precision without sacrificing sensitivity. We show that context-aware annotations improve quantification for both novel and known transcripts. We apply Bambu to quantify isoforms from repetitive HERVH-LTR7 retrotransposons in human embryonic stem cells, demonstrating the ability for context-specific transcript expression analysis. Leveraging long-read RNA-seq data and machine learning, Bambu facilitates accurate transcript discovery and quantification.

ST1: Primer list; ST2: The most differentially expressed 401 transcripts (top 1%) in MADM tumors compared with normal brains; ST3: Upregulated 544 genes specifically in MADM tumors not in GL261; ST4: The mutational and expression status of 162 epigenome-associated genes in LGGs and GBMs; ST5: Significantly upregulated and downregulated genes by Ezh2 depletion; ST6: Representative genes with H3K4me3 alone, H3K27me3 alone or bivalent modifications in Ezh2 wild-type P8 precancerous cells and Ezh2 wild-type MADM tumor cells (top 50 genes with highest relative value); ST7: Gene ontology of upregulated genes by Ezh2 depletion.

This file explains the additional details of Materials and Methods

List of ncNATs expressed in TCGA tumors, for which their associated sense genes have two active alternative promoters

SF1: Loss of p53 and NF1 expression in MADM GFP-positive precancerous cells and tumor cells; SF2: Clustering analysis of gene expression status in human gliomas; SF3: Expression levels of epigenetic genes in MADM tumors, human IDH-wild-type DAGs-MF and DAGs-nonMF; SF4: Histone modifications and expression status in Ezh2 wild-type MADM tumors; SF5: Copy number analysis of genes located on chromosomes 7 and 10 in the TM31 cell line; SF6: Relationship between Ezh2 expression and RAS signaling pathway; SF7: Inhibition of EZH2/PRC2 as a specific treatment strategy for IDH-wild-type DAGs-nonMF.

This file is the legends of Supplementary FigureS1-S7

List of 353 unique ncNATs expressed in tumors of one or multiple tumor types from TCGA.

List of ncNATs expressed in TCGA and GTEx non-tumor samples, for which their associated sense genes have two active alternative promoters

List of 521 unique ncNATs expressed in non-tumor samples of one or multiple cancer/tissue types from TCGA and GTEx.