Biology subjects

Novoa, E. M.

Publications and source records attributed to Novoa, E. M..

27 records · Page 2Linked to original sources

bioRxiv2021

ModPhred: an integrative toolkit for the analysis and storage of nanopore sequencing DNA and RNA modification data

MotivationDNA and RNA modifications can now be identified using Nanopore sequencing. However, we currently lack a flexible software to efficiently encode, store, analyze and visualize DNA and RNA modification data. ResultsHere we present ModPhred, a versatile toolkit that facilitates DNA and RNA modification analysis from nanopore sequencing reads in a user-friendly manner. ModPhred integrates probabilistic DNA and RNA modification information within the FASTQ and BAM file formats, can be used to encode multiple types of modifications simultaneously, and its output can be easily coupled to genomic track viewers, facilitating the visualization and analysis of DNA and RNA modification information in individual reads in a simple and computationally efficient manner. Availability and ImplementationModPhred is available at https://github.com/novoalab/modPhred, is implemented in Python3, and is released under an MIT license. Supplementary DataSupplementary Data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Subcellular relocalization and nuclear redistribution of the RNA methyltransferases TRMT1 and TRMT1L upon neuronal activation

RNA modifications are dynamic chemical entities that regulate RNA fate, and an avenue for environmental response in neuronal function. However, which RNA modifications may be playing a role in neuronal plasticity and environmental responses is largely unknown. Here we characterize the biochemical function and cellular dynamics of two human RNA methyltransferases previously associated with neurological dysfunction, TRMT1 and its homolog, TRMT1-like (TRMT1L). Using a combination of next-generation sequencing, LC-MS/MS, patient-derived cell lines and knockout mouse models, we confirm the previously reported dimethylguanosine (m 2,2 G) activity of TRMT1 in tRNAs, as well as reveal that TRMT1L, whose activity was unknown, is responsible for methylating a subset of cytosolic tRNA Ala (AGC) isoacceptors at position 26. Using a cellular in vitro model that mimics neuronal activation and long term potentiation, we find that both TRMT1 and TRMT1L change their subcellular localization upon neuronal activation. Specifically, we observe a major subcellular relocalization from mitochondria and other cytoplasmic domains (TRMT1) and nucleoli (TRMT1L) to different small punctate compartments in the nucleus, which are as yet uncharacterized. This phenomenon does not occur upon heat shock, suggesting that the relocalization of TRMT1 and TRMT1L is not a general reaction to stress, but rather a specific response to neuronal activation. Our results suggest that subcellular relocalization of RNA modification enzymes play a role in neuronal plasticity and transmission of information, presumably by addressing new targets.

molecular biology↗

bioRxiv2020

Decoding ribosomal RNA modification dynamics at single molecule resolution

A broad diversity of modifications decorate RNA molecules. Originally conceived as static components, evidence is accumulating that some RNA modifications may be dynamic, contributing to cellular responses to external signals and environmental circumstances. A major difficulty in studying these modifications, however, is the need of tailored protocols to map each modification type individually. Here, we present a new approach that uses direct RNA nanopore sequencing to identify and quantify RNA modifications present in native RNA molecules. First, we show that each RNA modification type results in a distinct and characteristic base-calling error signature, which we validate using a battery of genetic strains lacking either pseudouridine (Y) or 2-O-methylation (Nm) modifications. We then demonstrate the value of these signatures for de novo prediction of Y modifications transcriptome-wide, confirming known Y-modified sites as well as uncovering novel Y sites in mRNAs, ncRNAs and rRNAs, including a previously unreported Pus4-dependent Y modification in yeast mitochondrial rRNA, which we validate using orthogonal methods. To explore the dynamics of pseudouridylation across environmental stresses, we treat the cells with oxidative, cold and heat stresses, finding that yeast ribosomal rRNA modifications do not change upon environmental exposures, contrary to the general belief. By contrast, our method reveals many novel heat-sensitive Y-modified sites in snRNAs, snoRNAs and mRNAs, in addition to recovering previously reported sites. Finally, we develop a novel software, nanoRMS, which we show can estimate per-site modification stoichiometries from individual RNA molecules by identifying the reads with altered current intensity and trace profiles, and quantify the RNA modification stoichiometry changes between two conditions. Our work demonstrates that Y RNA modifications can be predicted de novo and in a quantitative manner using native RNA nanopore sequencing.

molecular biology↗

bioRxiv2019

Barcoding and demultiplexing Oxford Nanopore native RNA sequencing reads with deep residual learning

Nanopore sequencing has enabled sequencing of native RNA molecules without conversion to cDNA, thus opening the gates to a new era for the unbiased study of RNA biology. However, a formal barcoding protocol for direct sequencing of native RNA molecules is currently lacking, limiting the efficient processing of multiple samples in the same flowcell. A major limitation for the development of barcoding protocols for direct RNA sequencing is the error rate introduced during the base-calling process, especially towards the 5 and 3 ends of reads, which complicates sequence-based barcode demultiplexing. Here, we propose a novel strategy to barcode and demultiplex direct RNA sequencing nanopore data, which does not rely on base-calling or additional library preparation steps. Specifically, custom DNA oligonucleotides are ligated to RNA transcripts during library preparation. Then, raw current signal corresponding to the DNA barcode is extracted and transformed into an array of pixels, which is used to determine the underlying barcode using a deep convolutional neural network classifier. Our method, DeePlexiCon, implements a 20-layer residual neural network model that can demultiplex 93% of the reads with 95.1% specificity, or 60% of reads with 99.9% specificity. The availability of an efficient and simple barcoding strategy for native RNA sequencing will enhance the use of direct RNA sequencing by making it more cost-effective to the entire community. Moreover, it will facilitate the applicability of direct RNA sequencing to samples where the RNA amounts are limited, such as patient-derived samples.

genomics↗

bioRxiv2019

Parallel and scalable workflow for the analysis of Oxford Nanopore direct RNA sequencing datasets

The direct RNA sequencing platform offered by Oxford Nanopore Technologies allows for direct measurement of RNA molecules without the need of conversion to complementary DNA, fragmentation or amplification. As such, it is virtually capable of detecting any given RNA modification present in the molecule that is being sequenced, as well as provide polyA tail length estimations at the level of individual RNA molecules. Although this technology has been publicly available since 2017, the complexity of the raw Nanopore data, together with the lack of systematic and reproducible pipelines, have greatly hindered the access of this technology to the general user. Here we address this problem by providing a fully benchmarked workflow for the analysis of direct RNA sequencing reads, termed MasterOfPores. The pipeline converts raw current intensities into multiple types of processed data, providing metrics of the quality of the run, quality-filtering, base-calling and mapping. The output of the pipeline can in turn be used to compute per-gene counts, RNA modifications, and prediction of polyA tail length and RNA isoforms. The software is written using the NextFlow framework for parallelization and portability, and relies on Linux containers such as Docker and Singularity for achieving better reproducibility. The MasterOfPores workflow can be executed on any Unix-compatible OS on a computer, cluster or cloud without the need of installing any additional software or dependencies, and is freely available in Github (https://github.com/biocorecrg/master_of_pores). This workflow will significantly simplify the analysis of nanopore direct RNA sequencing data by non-bioinformatics experts, thus boosting the understanding of the (epi)transcriptome with single molecule resolution.

bioinformatics↗

bioRxiv2019

Accurate detection of m6A RNA modifications in native RNA sequences

The field of epitranscriptomics has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here we show that using Oxford Nanopore Technologies, N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Our results open new avenues to investigate the universe of RNA modifications with single nucleotide resolution, in individual RNA molecules.

molecular biology↗

bioRxiv2018

Elucidation of Codon Usage Signatures across the Domains of Life

Due to the degeneracy of the genetic code, multiple codons are translated into the same amino acid. Despite being synonymous, these codons are not equally used. Selective pressures are thought to drive the choice among synonymous codons within a genome, while GC content, which is generally attributed to mutational drift, is the major determinant of interspecies codon usage bias. Here we find that in addition to the bias caused by GC content, inter-species codon usage signatures can also be detected. More specifically, we show that a single amino acid, arginine, is the major contributor to codon usage bias differences across domains of life. We then exploit this finding, and show that the identified domain-specific codon bias signatures can be used to classify a given sequence into its corresponding domain with high accuracy. Considering that species belonging to the same domain share similar tRNA decoding strategies, we then wondered whether the inclusion of codon autocorrelation patterns might improve the classification performance of our algorithm. However, we find that autocorrelation patterns are not domain-specific, and surprisingly, are unrelated to tRNA reusage, in contrast to the common belief. Instead, our results reveal that codon autocorrelation patterns are a consequence of codon optimality throughout a sequence, where highly expressed genes display autocorrelated optimal codons, whereas lowly expressed genes display autocorrelated non-optimal codons.

evolutionary biology↗

bioRxiv2018

mRNA structure dynamics identifies the embryonic RNA regulome

RNA folding plays a crucial role in RNA function. However, our knowledge of the global structure of the transcriptome is limited to steady-state conditions, hindering our understanding of how RNA structure dynamics influences gene function. Here, we have characterized mRNA structure dynamics during zebrafish development. We observe that on a global level, translation guides structure rather than structure guiding translation. We detect a decrease in structure in translated regions, and we identify the ribosome as a major remodeler of RNA structure in vivo. In contrast, we find that 3-UTRs form highly folded structures in vivo, which can affect gene expression by modulating miRNA activity. Furthermore, we find that dynamic 3-UTR structures encode RNA decay elements, including regulatory elements in nanog and cyclin A1, key maternal factors orchestrating the maternal-to-zygotic transition. These results reveal a central role of RNA structure dynamics in gene regulatory programs.

molecular biology↗

bioRxiv2017

Best practices for genome-wide RNA structure analysis: combination of mutational profiles and drop-off information

Genome-wide RNA structure maps have recently become available through the coupling of in vivo chemical probing reagents with next-generation sequencing. Initial analyses relied on the identification of truncated reverse transcription reads to identify the chemically modified nucleotides, but recent studies have shown that mutational signatures can also be used. While these two methods have been employed interchangeably, here we show that they actually provide complementary information. Consequently, analyses using exclusively one of the two methodologies may disregard a significant portion of the structural information. We also show that the identity and sequence environment of the modified nucleotide greatly affect the odds of introducing a mismatch or causing reverse transcriptase drop-off. Finally, we identify specific mismatch signatures generated by dimethyl sulfate probing that can be exploited to remove false positives typically produced in RNA structurome analyses, and how these signatures vary depending on the reverse transcription enzyme used.

bioinformatics↗