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RNA modifications, such as N6-methyladenosine (m6A), modulate functions of cellular RNA species. However, quantifying differences in RNA modifications has been challenging. Here we develop a computational method, xPore, to identify differential RNA modifications from nanopore direct RNA sequencing (RNA-seq) data. We evaluate our method on transcriptome-wide m6A profiling data, demonstrating that xPore identifies positions of m6A sites at single-base resolution, estimates the fraction of modified RNA species in the cell and quantifies the differential modification rate across conditions. We apply xPore to direct RNA-seq data from six cell lines and multiple myeloma patient samples without a matched control sample and find that many m6A sites are preserved across cell types, whereas a subset exhibit significant differences in their modification rates. Our results show that RNA modifications can be identified from direct RNA-seq data with high accuracy, enabling analysis of differential modifications and expression from a single high-throughput experiment.

Differences in RNA expression can provide insights into the molecular identity of a cell, pathways involved in human diseases, and variation in RNA levels across patients associated with clinical phenotypes. RNA modifications such as m6A have been found to contribute to molecular functions of RNAs. However, quantification of differences in RNA modifications has been challenging. Here we develop a computational method (xPore) to identify differential RNA modifications from direct RNA sequencing data. We evaluate our method on transcriptome-wide m6A profiling data, demonstrating that xPore identifies positions of m6A sites at single base resolution, estimates the fraction of modified RNAs in the cell, and quantifies the differential modification rate across conditions. We apply the method to direct RNA-Sequencing data from 6 cell lines and find that many m6A sites are preserved, while a subset of m6A sites show significant differences in their modification rates across cell types. Together, we show that RNA modifications can be identified from direct RNA-sequencing with high accuracy, enabling the analysis of differential modifications and expression from a single high throughput experiment.AvailabilityxPore is available as open source software (https://github.com/GoekeLab/xpore)

Transcriptional reactivation of telomerase catalytic subunit (TERT) is a frequent hallmark of cancer, occurring in 90% of human malignancies. However, specific mechanisms driving TERT reactivation remain obscure for many tumor types and in particular gastric cancer (GC), a leading cause of global cancer mortality. Here, through comprehensive genomic and epigenomic analysis of primary GCs and GC cell lines, we identified the transcription factor early B cell factor 1 (EBF1) as a TERT transcriptional repressor and inactivation of EBF1 function as a major cause of TERT upregulation. Abolishment of EBF1 function occurs through 3 distinct (epi)genomic mechanisms. First, EBF1 is epigenetically silenced via DNA methyltransferase, polycomb-repressive complex 2 (PRC2), and histone deacetylase activity in GCs. Second, recurrent, somatic, and heterozygous EBF1 DNA-binding domain mutations result in the production of dominant-negative EBF1 isoforms. Third, more rarely, genomic deletions and rearrangements proximal to the TERT promoter remobilize or abolish EBF1-binding sites, derepressing TERT and leading to high TERT expression. EBF1 is also functionally required for various malignant phenotypes in vitro and in vivo, highlighting its importance for GC development. These results indicate that multimodal genomic and epigenomic alterations underpin TERT reactivation in GC, converging on transcriptional repressors such as EBF1.