• Athma Pai

• Julia Salzman, Julia Salzman, Stanford University

Myriad mechanisms diversify the sequence content of RNA transcripts and are of great interest to single cell biology. Currently, these events are detected using tools that first require alignment to a necessarily incomplete reference genome alignment in the first step; this incompleteness is especially prominent in diseases such as cancer. Second, today the next step in analysis requires as a custom choice of bioinformatic procedure to follow it: for example, to detect splicing, RNA editing or V(D)J recombination among others. I will present collaborative work based on a new statistics-first analytic method —SPLASH (Statistically Primary aLignment Agnostic Sequence Homing)— that performs unified, reference-free inference directly on raw sequencing reads without a reference genome or cell metadata. SPLASH is highly efficient and simple to run. As a snapshot of SPLASH, applying to 10,326 primary human single cells in 19 tissues profiled with SmartSeq2, we discover a set of splicing and histone regulators with highly conserved intronic regions that are themselves targets of complex splicing regulation, unreported transcript diversity in the heat shock protein HSP90AA1, and diversification in centromeric RNA expression, V(D)J recombination, RNA editing, and repeat expansions missed by existing methods, as well as unpublished extensions to 10x genomics data.

• Zachary Wakefield, Zachary Wakefield, Boston University
• Steven Mick, Steven Mick, Boston University
• Ana Fiszbein, Ana Fiszbein, Boston University

Alternative splicing (AS) is a highly regulated process occurring in approximately 95% of encoded proteins, however the global implications on the proteome are largely unknown. To explore how AS impacts the proteome on a genome-wide level, we systematically identified every possible isoform switch annotated in the human genome, resulting from alternative first/last exons, alternative splice sites, and retained introns, in a pairwise manner. Additionally, we characterized isoform swaps due to the regulation of a newly identified class of exons known as hybrid exons, which can act as terminal or internal exons. We then performed sequence alignment between each protein pair and classified changes as frame shifts, partial expansions/reductions, and identical proteins. We observed the changing use of hybrid exons between internal and last exons had the most significant impact on protein sequences across all different splicing events. To elucidate the proteomic consequences of AS across phenotypes, we developed SpliceImpactR, a novel open-source R package for protein-domain analysis and isoform-specific domain-derived protein-protein interactions (ISPPI). Using adapted functionality from SpliceImpactR, we quantified the changes in ISPPI and the domain enrichment using the previously identified isoform swaps, revealing varied and unique impacts across each AS type. Applying SpliceImpactR to brain and heart samples from the GTEx database showed over 700 genes with significantly differentially used terminal exons – 33% of the identified swaps classified as strong swaps are caused by changing usage of hybrid exons. Our findings underscore the significance of hybrid exon usage in shaping the proteome diversity expressed in human cells.

• Priyanka Sehgal, Priyanka Sehgal, Division of Cancer Pathobiology
• Ammar Naqvi, Ammar Naqvi, Department of Biomedical and Health Informatics
• Katharina Hayer, Katharina Hayer, Department of Biomedical and Health Informatics
• Makenna Higgins, Makenna Higgins, Division of Cancer Pathobiology
• Julien Jarroux, Julien Jarroux, Center for Neurogenetics
• Taewoo Kim, Taewoo Kim, Center for Neurogenetics
• Pamela Mishra, Pamela Mishra, Division of Cancer Pathobiology
• Jacinta Davis, Jacinta Davis, Division of Cancer Pathobiology
• Charles Dr

Pediatric high-grade gliomas (pHGG) respond poorly to standard therapies, and the development of novel immunotherapeutics (such as chimeric antigen receptor (CAR)-armed T cells) is hindered by the paucity of tumor-specific surface antigens. To overcome this problem, we used various algorithms to compare and contrast splicing patterns in 142 pHGGs vs. adult and fetal brain samples, yielding a list of pHGG-specific splice junctions. After prioritizing events corresponding to extracellular domains, we found that ~40% of them mapped to 3-51 nucleotide-long microexons. One salient example is neural cell adhesion molecule (NRCAM) mRNA, which exhibits skipping of the 18-nt microexon 9 and 30-nt microexon 23 (GTEx nomenclature) in ~70% of pHGG samples. Consequently, the corresponding junctions shows much higher expression levels in pHGGs compared to normal tissues of both neural and non-neural origins. Bulk and single-nuclei (SnISOr) long-read RNA-seq of pHGG organoids using the Oxford Nanopore platform revealed coordinated skipping of both microexons and a uniform expression pattern of the Δex9Δex23 NRCAM isoform across different cell clusters. We validated the surface expression of the corresponding proteoform using live cell biotinylation assay and demonstrated that it increases migration and invasion of KNS42 pHGG cells. We also developed a mouse monoclonal antibody with significantly higher avidity for the Δex9Δex23 vs. the full-length NRCAM isoform. Therefore, the pHGG-specific NRCAM (and possibly other microexon-derived proteoforms) are highly selective and feasible targets for CAR T cell-based immunotherapies.

A-180 Roni Cohen-Fultheim
A-186 Étienne Fafard-Couture
A-163 Andrew Tapia
A-184 Sumit Tarafder
A-157 Ihor Arefiev
A-183 Fozia Masood
A-188 Arsham Mikaeili Namini

• Qian Shi, Qian Shi, Department of Computer Science and Engineering
• Qimin Zhang, Qimin Zhang, Department of Computer Science and Engineering
• Mingfu Shao, Mingfu Shao, Department of Computer Science and Engineering

High-throughput RNA sequencing has become indispensable for decoding gene activities, yet the challenge of reconstructing full-length transcripts persists. Traditional single-sample assemblers frequently produce fragmented transcripts, especially in single-cell RNA-seq data. While algorithms designed for assembling multiple samples exist, they encounter various limitations. We present Aletsch, a new assembler for multiple bulk or single-cell RNA-seq samples. Aletsch incorporates several algorithmic innovations, including a “bridging” system that can effectively integrate multiple samples to restore missed junctions in individual samples, and a new graph-decomposition algorithm that leverages “supporting information across multiple samples to guide the decomposition of complex vertices. A standout feature of Aletsch is its application of a random forest model with 50 well-designed features for scoring transcripts. We demonstrate its robust adaptability across different chromosomes, datasets, and species. Our experiments, conducted on RNA-seq data from several protocols, firmly demonstrate Aletsch’s significant outperformance over existing meta-assemblers. As an example, when measured with the partial area under the precision-recall curve (pAUC) , Aletsch surpasses the leading assemblers TransMeta by 21.2%-57.4% and PsiCLASS by 21.9%-172.5% on human datasets. Aletsch is freely available at https://github.com/Shao-Group/aletsch.

• Beril Erdogdu, Beril Erdogdu, Department of Biomedical Engineering
• Ales Varabyou, Ales Varabyou, Department of Biomedical Engineering
• Stephanie Hicks, Stephanie Hicks, Department of Biomedical Engineering
• Steven Salzberg, Steven Salzberg, Department of Biomedical Engineering
• Mihaela Pertea, Mihaela Pertea, Department of Biomedical Engineering

Differential transcript usage (DTU) plays a crucial role in shaping gene expression diversity across different biological scenarios, influencing cellular functionality and disease development. However, current DTU analysis methods often fail to consider the inherent population heterogeneity seen in complex human traits and diseases. Filling this important gap, our study introduces SPIT, a statistical method specifically designed to identify predominant subgroups and their unique DTU events within populations.

Utilizing an over-smoothed kernel density estimator (KDE), SPIT effectively mitigates technical and biological noise inherent in RNA-Seq data, and detects of multimodality without assumptions about expression pattern distributions. Additionally, SPIT generates an empirical null distribution of isoform abundance variability across datasets, enhancing its accuracy and versatility compared to existing tools.

Applying SPIT to a diverse array of human brain samples, our analysis unveils six significant DTU events associated with Schizophrenia subgroups, underscoring its efficacy in capturing disease heterogeneity. Furthermore, exploration of prenatal and adult brain samples reveals thousands of genes where the dominant isoform undergoes a complete shift between developmental stages and post-birth, providing novel evidence of this phenomenon in human brain development. These findings provide biological significance to specific isoforms previously lacking comprehensive functional understanding, and valuable insights into neurodevelopmental disorders.

• Alejandro Paniagua, Alejandro Paniagua, Spanish National Research Council
• Jorge Mestre-Tomas, Jorge Mestre-Tomas, Spanish National Research Council
• Liudmyla Kondratova, Liudmyla Kondratova, University of Florida
• Fabian Jetzinger, Fabian Jetzinger, Biobam Bioinformatics
• Stanley Cormack, Stanley Cormack, Imperial College London
• Natalia Vega, Natalia Vega, University of Valencia
• Luis Ferrández-Peral, Luis Ferrández-Peral, Spanish National Research Council
• Carolina Monzó, Carolina Monzó, Spanish National Research Council
• Ana Victoria Conesa Cegarra, Ana Victoria Conesa Cegarra, Spanish National Research Council

Long-read sequencing technologies such as PacBio and Oxford Nanopore are reshaping transcriptomics. The enhanced precision and depth of sequencing from these methods are proving critical for differential isoform expression studies across various conditions. This shift towards long-reads necessitates new best practices for experimental designs, preprocessing, and normalization tailored to these data types.
We set out to provide analysis guidelines for multi-sample long-read transcriptomics experiments. Utilizing a replicated dataset from mouse tissues and three long-read cDNA protocols, including the newest Pacbio Kinnex, we explored biases when constructing count tables.
We evaluated two main approaches: Call&Join (call transcript model for each sample and then combine results) and Join&Call (merge reads from different samples, then call transcripts models and re-quantify), finding that each strategy renders a different transcriptome composition depending on the analysis tool. Sequencing depth and replicate number significantly affect transcript identification, with known transcripts quickly stabilizing and novel ones requiring more depth, and most transcripts detected either by all or just one sample.
We detected variable biases in quantification due to read length and GC content across technologies. For instance, PacBio data showed a parabolic length bias and increased expression levels with higher GC content, although this greatly varied by sample, challenging differential analyses.
Our study highlights that experimental and preprocessing choices profoundly affect the long-read transcriptome count-tables. Length and GC content biases impact quantification, influenced by sample and technology. The results underscore the importance of thoughtful experimental design and preprocessing to ensure accurate transcriptome dataset composition and comparable quantification.

• Yu-Jen Lin, Yu-Jen Lin, University of California
• Amr Alazali, Amr Alazali, University of California
• Zhiqiang Hu, Zhiqiang Hu, University of California
• Steven Brenner, Steven Brenner, University of California

RNA-seq has been widely used to quantify expression and splicing changes in transcriptomes. Although biological consequences arise from changes in both expression and splicing aspects, researchers usually use their impressions to choose only one aspect to analyze, potentially overlooking significant impacts of the other. Even if researchers investigate both, the measurement scales of expression and splicing are different, and thus, their impacts are incomparable. To compare the relative impact of expression and splicing, we have developed RISE.

RISE qualifies the relative impact of expression and splicing changes caused by the treatment. To place the impact of expression and splicing changes on the same scale to compare, we developed the Normalized Variation (NV) measure. NV is defined as the proportion of the between-group variation to the total variation. Finally, we assess whether expression NV (eNV) or splicing NV (sNV) is significantly larger to understand the comparative influence of expression versus splicing alternations in the transcriptome.

To validate our method, we performed RISE analysis on RNA-seq data from knockdown or overexpression experiments of 11 transcription and splicing factors. RISE effectively categorizes transcription and splicing factors by their relative impacts on expression and splicing. As an example application, we applied RISE to 4 studies involving proteins with complex or previously unknown roles in regulating transcriptomes to understand their functions. In summary, RISE enables researchers to systematically compare the relative impact of expression and splicing.

• Eraj Khokhar, Eraj Khokhar, RTI
• Kaitlyn Brokaw, Kaitlyn Brokaw, RTI
• Nida Javeed, Nida Javeed, RTI
• Zachary Kartje, Zachary Kartje, RTI
• Valeria Sanabria, Valeria Sanabria, RTI
• Jonathan Watts, Jonathan Watts, RTI
• Athma Pai, Athma Pai, RTI

Splicing is likely a major contributor to noise in mRNA regulation, with errors in splicing leading to reduced transcriptional efficiency and wasted transcriptional output. Cryptic splicing involves use of low-fidelity or infrequently bound splice sites that often leads to non-productive transcripts, likely targeted for degradation. Substantial evidence suggests that splicing noise is prevalent in homeostatic cell conditions, but the extent to which it occurs is likely underappreciated due to the challenges of identifying cryptic, low-fidelity splice site usage in mature mRNA data. Characterizing splicing noise has become increasingly important since blocking or redirecting the use of noisy splice sites in favor of productive splice sites may provide a novel strategy for up-regulating gene expression in healthy or disease contexts with high levels of splicing noise (e.g., cancer). Here, we tackle these challenges by performing high-throughput sequencing on selectively enriched nuclear nascent RNA, which greatly increases the global detection of cryptic splice sites. We further developed a python package to systematically identify and analyze cryptic, low-fidelity sites in high-throughput sequencing data from nascent, nuclear RNA and RNA from cycloheximide-treated cells. We use these experimental and computational methods to analyze cryptic splicing events in cancer cell lines and identify genomic features, sequence elements, and gene properties associated with the occurrence of cryptic splice sites across and between cell types. Our findings uncover a previously under-appreciated role for stochasticity in regulation of mRNA splicing, identify features predictive of splicing noise, and will aid in developing novel disease therapeutics to inhibit cryptic splicing.

• Teresa Rummel, Teresa Rummel, Faculty for Informatics and Data Science
• Yiliam Cruz Garcia, Yiliam Cruz Garcia, Institute of Biochemistry
• Juliane Müller, Juliane Müller, Institute of Biochemistry
• Christophe Toussaint, Christophe Toussaint, HIRI
• Bhupesh Prusty, Bhupesh Prusty, Institute of Virology
• Antoine-Emmanuel Saliba, Antoine-Emmanuel Saliba, HIRI
• Elmar Wolf, Elmar Wolf, Institute of Biochemistry
• Florian Erhard, Florian Erhard, Faculty for Informatics and Data Science

In single cells, transcription is governed by bursts. The kinetics of transcriptional bursting are defined by the burst frequency, describing how often bursts occur, and the burst size, describing how many mRNA molecules are synthesized during one burst. Knowledge of these parameters of transcriptional bursting is key to gaining a better understanding of gene expression and its regulation.

Transcriptional bursting can be studied in a transcriptome-wide manner using single-cell RNA-seq. However, this approach only works under steady-state conditions and assumes a uniform RNA degradation rate across genes.

To overcome these limitations, we developed a new mathematical model that utilizes temporal data from single-cell metabolic labeling (scSLAM-seq) to quantitatively assess transcriptional bursting. This model enables the estimation of burst frequencies, sizes, and gene-specific degradation rates, applicable also under dynamic, non-steady state conditions such as upon viral infections or cytokine stimulation.

Using scSLAM-seq data we studied the role of MYC in transcriptional regulation in two cell lines with different MYC levels, A375 (high) and MaMel63a (low), after MYC depletion via an auxin inducible degron system. Our findings challenge the view that MYC primarily impacts burst size. Instead, we discovered that changes in burst frequency or a combination of both frequency and size are drivers of transcriptional changes, indicating a more complex role for MYC in gene regulation.

These insights illustrate the benefits of combining advanced sequencing techniques with dynamic modeling to study gene expression, enhancing our understanding of transcriptional mechanisms and providing a framework for analyzing gene responses under various conditions.

• Pavel Sumazin, Pavel Sumazin, Baylor College of Medicine
• Hua-Sheng Chiu, Hua-Sheng Chiu, Baylor College of Medicine
• Sonal Somvanshi, Sonal Somvanshi, Baylor College of Medicine

The characterization of long noncoding RNA (lncRNA) function is a major challenge in RNA biology with applications to basic, translational, and medical research. Our efforts to characterize the regulatory roles of lncRNAs in cancer identified lncRNA species that coordinately regulate both the transcriptional and post-transcriptional processing of their targets. This coordinated regulation results in tight couplings between lncRNAs and their targets and is easier to identify and verify. Our analyses suggested that hundreds of cancer genes are coordinately regulated by lncRNAs in multiple tumor types. As proof of principle, we studied the regulation of DICER1—a cancer gene that regulates microRNA biogenesis—by the lncRNA ZFAS1. ZFAS1 activates DICER1 transcription and blocks its post-transcriptional repression to control the expression of DICER1 and its target microRNAs. Both genes regulate tumor growth and DNA repair. Our analyses suggested that coordinated lncRNA regulation can propagate genomic alterations at lncRNAs to physiologically dysregulate cancer genes.

• Stefan Prodic, Stefan Prodic, Australian National University
• Alice Cleynen, Alice Cleynen, Université de Montpellier
• Akanksha Srivastava, Akanksha Srivastava, Australian National University
• Shafi Mahmud, Shafi Mahmud, Australian National University
• Madhu Kanchi, Madhu Kanchi, Australian National University
• Agin Ravindran, Agin Ravindran, Australian National University
• Nikolay Shirokikh, Nikolay Shirokikh, Australian National University
• Eduardo Eyras, Eduardo Eyras, Australian National University

The epitranscriptome contains over 170 chemical modifications that play a pivotal role in regulating RNA properties and function across various RNA classes. High-throughput methods for RNA modification detection remain limited and current approaches are hindered by extensive protocols that lack isoform-level resolution and restrict studies to a single modification per experiment, limiting comprehensive exploration of the dynamic and diverse epitranscriptome. Here we describe SWARM, a robust approach for the detection of m6A, m5C, pseudouridine, and ac4C from the same sample in individual RNA isoforms. SWARM exploits nanopore direct RNA sequencing signals that capture continuous native individual RNA molecules. SWARM attains unmatched accuracy in single-molecule modification detection for multiple RNA modifications through innovative neural network and training strategy applied to a broad array of diverse nanopore signals. We apply SWARM to numerous independent datasets and highlight replicable and accurate detection of modified sites in the transcriptome (messenger RNA and long non-coding RNA) and their modification rates, showing extensive agreement with experimentally validated sites. Our analysis shows that SWARM delivers confident detection of multiple RNA modifications from a single sample and provides a robust framework for comparing RNA modification landscapes between samples. We also provide an efficient workflow that opens a wealth of possibilities towards uncovering diverse RNA modification landscapes in countless contexts. SWARM enables a significant leap towards deciphering the dynamics and functional relevance of the epitranscriptome.

• Fatima Mostefai, Fatima Mostefai, Université de Montréal; Montreal Heart Institute
• Jean-Christophe Grenier, Jean-Christophe Grenier, Montreal Heart Institute
• Raphaël Poujol, Raphaël Poujol, Montreal Heart Institute
• Julie Hussin, Julie Hussin, Université de Montréal; Montreal Heart Institute

SARS-CoV-2, an RNA virus, has evolved into multiple variants by accumulating mutations during transmission (inter-host) and infection (intra-host). De novo mutations arise in viral genomes during infection, and analyzing these mutations in sequencing data may predict emerging variants. Intra-host single nucleotide variants (iSNVs) can be identified by analyzing RNA sequencing (RNA-seq) reads from infections. However, sequencing artifacts introduced during the RNA-seq process can result in erroneous iSNVs. We aim to identify true intra-host mutations from viral RNA-seq data and propose metrics to refine RNA-seq analysis.

We developed a two-step workflow to isolate de novo iSNVs, focusing on the SARS-CoV-2 RNA-seq dataset. Initially, we processed a dataset of RNA-seq libraries, ensuring high-quality library preparation through whole-genome quality control. We then used these libraries for iSNV calling, using metrics such as Alternative Allele Frequency (AAF) and Strand Bias Likelihood (S) metrics to distinguish iSNVs from sequencing artifacts. We also used dimensionality reduction representations, such as PHATE and t-SNE, to visualize and analyze library structures complemented with an explainability metric.

We applied our workflow to a comprehensive SARS-CoV-2 RNA-seq dataset, distinguishing between de novo and consensus iSNVs, which is crucial for understanding viral intra-host evolution. We identified batch effects from sequencing centers and refined the AAF and S metrics for artifact resolution. Analyzing libraries from 2020 to 2023, we observed low intra-host diversity per infection, significant diversity in the spike gene, and strong purifying selection. This workflow enhances the precision and depth of RNA-seq and viral genomic analyses, advancing studies in RNA viruses.

• Ashley Laughney

Predicting protein function from sequence, also known as genotype-to-phenotype mapping, remains a central challenge in biology. This is because most proteins are highly pleiotropic; meaning they can perform more than one function and participate in a wide range of biological processes. As such, perturbations to a single gene often affects multiple, independent cellular responses. Integrating innovative systems and synthetic biology approaches with a hypothesis-driven framework, I will describe tools my lab has developed to map genome-encoded components to complex cellular and in vivo functions at scale. We focus on cancer metastasis as our model of a multicellular, evolutionary process and develop approaches that ask how activation of the very same protein or signaling pathway can lead to diverse functional outputs through (i) the evolution of distinct modular domains, (ii) intra-cellular genetic interactions (epistasis) and (iii) inter-cellular signaling networks (multicellular programs). We apply these emerging techniques to understand how highly pleiotropic proteins - such as an immune-related protein called Stimulator of Interferon Genes (STING) - switches from a tumor-suppressor to pro-tumoral function during the evolution of cancer metastasis.