Biology subjects

Aparicio-Puerta, E.

Publications and source records attributed to Aparicio-Puerta, E..

6 recordsLinked to original sources

bioRxiv2026

An assessment of normalization and differential expression methods for miRNA-seq analysis using a realistic benchmark dataset

MicroRNAs are short noncoding RNAs that regulate gene expression and are commonly profiled by small RNA sequencing (miRNA-seq). Despite the widespread use of miRNA-seq, datasets are often analyzed with RNA-seq method such as DESeq2 or edgeR, which do not take into account the specific characteristics of miRNA-seq data. Here, we present a benchmark study of normalization and differential expression approaches using a realistic ground-truth dataset. By mixing mouse RNA of two organs, we generated expression trends while capturing biological and technical variability. Using monotonicity across the dataset and expected fold changes from the mixture design, we assessed normalization and differential expression methods. Normalization benchmarking showed that within-sample scaling, particularly Read Per Million (RPM), best preserved the expected monotonic trends, outperforming cross-sample methods such as TMM, rlog, and VST. These approaches sometimes recovered apparent monotonicity among abundant miRNAs, but inspection of individual profiles suggested likely over-correction. Regarding differential expression, edgeR consistently ranked among the best-performing methods across several metrics, including log2 fold-change estimation, with performance comparable to miRNA-seq-specific tools such as miRglmm and NBSR. DESeq2, edgeR-v4, and limma-based approaches tended to systematically underestimate log2 fold changes. Applying a common RPM-based normalization substantially improved the performance of cross-sample methods, highlighting the strong influence of normalization on differential expression analysis. Overall, our findings support within-sample scaling methods such as RPM for normalization, and edgeR, miRglmm, or NBSR for differential expression. The dataset has been made publicly available, providing a valuable resource for objective method comparison and future miRNA-seq software development.

bioinformatics↗

bioRxiv2026

PARiS: Probabilistic Assignment and Repartitioning of isomiR Sequences: A data-driven method for denoising isomiR read count data

MicroRNAs (miRNAs) are non-coding RNAs, approximately 18-24 nucleotides in length, with important gene regulatory functions. In small RNA sequencing (sRNA-seq), observed isoforms of miRNA, called isomiRs, arise from my biological and technical processes. Alterations in isomiR expression has been linked to a wide variety of human diseases, from cancers to neurological diseases. However, it is difficult to distinguish between technical and biological isomiRs. We present PARiS, an algorithm for the Probabilistic Assignment and Repartitioning of isomiR Sequences, that identifies technical error isomiRs in sRNA-seq data and reassigns them to their most likely biological source. We assess the ability of PARiS to identify and remove error isomiR sequences in a realistic simulation study. Additionally, we compare PARiS to alternative approaches, focusing on downstream miRNA-level differential expression analysis in a variety of settings, including a set of simulated datasets, an experimental benchmark dataset, and three colorectal adenocarcinoma cell lines.

bioinformatics↗

bioRxiv2024

Full-length tRNAs lacking a functional CCA tail are selectively sorted into the lumen of extracellular vesicles

Small extracellular vesicles (sEVs) are heterogenous lipid membrane particles typically less than 200 nm in size and secreted by most cell types either constitutively or upon activation signals. sEVs isolated from biofluids contain RNAs, including small non-coding RNAs (ncRNAs), that can be either encapsulated within the EV lumen or bound to the EV surface. EV-associated microRNAs (miRNAs) are, despite a relatively low abundance, extensively investigated for their selective incorporation and their role in cell-cell communication. In contrast, the sorting of highly-structured ncRNA species is understudied, mainly due to technical limitations of traditional small RNA sequencing protocols. Here, we adapted ALL-tRNAseq to profile the relative abundance of highly structured and potentially methylated small ncRNA species, including transfer RNAs (tRNAs), small nucleolar RNAs (snoRNAs), and Y RNAs in bulk EV preparations. We determined that full-length tRNAs, typically 75 to 90 nucleotides in length, were the dominant small ncRNA species (>60% of all reads in the 18-120 nucleotides size-range) in all cell culture-derived EVs, as well as in human plasma-derived EV samples, vastly outnumbering 21 nucleotides-long miRNAs. Nearly all EV-associated tRNAs were protected from external RNAse treatment, indicating a location within the EV lumen. Strikingly, the vast majority of luminal-sorted, full-length, nucleobase modification-containing EV-tRNA sequences, harbored a dysfunctional 3 CCA tail, 1 to 3 nucleotides truncated, rendering them incompetent for amino acid loading. In contrast, in non-EV associated extracellular particle fractions (NVEPs), tRNAs appeared almost exclusively fragmented or nicked into tRNA-derived small RNAs (tsRNAs) with lengths between 18 to 35 nucleotides. We propose that in mammalian cells, tRNAs that lack a functional 3 CCA tail are selectively sorted into EVs and shuttled out of the producing cell, offering a new perspective into the physiological role of secreted EVs and luminal cargo-selection.

molecular biology↗

bioRxiv2021

Unbiased and UMI-informed sequencing of cell-free miRNAs at single-nucleotide resolution

Terminal nucleotidyl transferases are enzymes that add non-templated nucleotides to RNA molecules. In the case of microRNAs, this process was shown to be functionally relevant for their maturation process and generation of isomiRs with non-canonical mRNA targets. Deconvolution of these posttranscriptional modifications is challenging in particular for extracellular miRNAs that are considered as a target for minimally-invasive diagnostics. Massively parallel RNA sequencing is the only method that can truthfully reveal isomiR diversity in biological samples and determine relative quantities. Improvements aside, current small RNA sequencing strategies remain imprecise. We developed IsoSeek that diverges from these methods by making use of randomized 5- and 3-adapters combined with a 10N unique molecular identifier (UMI). Using synthetic miRNA and isomiR spike-in sets and testing depletion and RNA competition strategies in 7 sequencing rounds of >100 samples, we rigorously optimized and validated the technical accuracy of the IsoSeek method. In genetically-altered HEK293, we characterized the terminal uridylase (TUT4/TUT7) dependent miRNA uridylome and discovered extensive uridylation of disease-associated miRNAs. Notably, 3-uridylated isomiR profiles of plasma extracellular vesicles (EVs) rely on UMI-correction. Thus, IsoSeek advances our knowledge of cell-free miRNAs and supports development into non-invasive biomarkers.

genomics↗

bioRxiv2021

GeneCodis 4: Expanding the modular enrichment analysis to regulatory elements

GeneCodis is a web-based tool for functional enrichment analysis that allows researchers to integrate different sources of annotations. It extracts sets of significant concurrent annotations and assigns a statistical score to evaluate those that are significantly enriched in the input list. Since its first release in 2007, it has been widely employed to analyze lists of genes in order to interpret the underlying biological mechanisms. Here we present GeneCodis 4, a new release that expands the functional analysis provided by the application, to accept regulatory elements, including lists of, transcription factors, CpG sites and miRNAs. It also incorporates new annotation databases and improved interactive visualizations functionalities to explore results. GeneCodis 4 is freely available at https://genecodis.genyo.es.

bioinformatics↗

bioRxiv2018

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API

BackgroundMicroRNAs (miRNAs) are small RNA molecules ([~]22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, a lack of consensus on miRNA/isomiR analyses exists, and the resulting diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods.\n\nFindingsTo overcome this situation, we present here a community-based project, miRTOP (miRNA Transcriptomic Open Project) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream analysis tools that are compatible with any existing detection and quantification tool. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, miRge2.0, isomiR-SEA, sRNAbench, and Prost!, as well as BAM files. Its open architecture enables any tool or pipeline to output results in mirGFF3.\n\nConclusionsCollectively a comprehensive isomiR categorization system, along with the accompanying mirGFF3 and mirtop API provide a complete solution for the standardization of miRNA and isomiR analysis, enabling data sharing, reporting, comparative analyses, and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps to miRNA detection, annotation, and quantification.

bioinformatics↗