Biology subjects

Tarazona, S.

Publications and source records attributed to Tarazona, S..

12 recordsLinked to original sources

bioRxiv2026

Systematic evaluation of transcript normalization methods and quantification pipelines across long-read sequencing platforms

Long-read RNA sequencing (lrRNA-seq) has transformed transcriptomics by enabling full-length isoform resolution, yet transcript quantification remains confounded by platform-dependent length biases and tool choices. While a quantification normalization step is common in short-read RNA-seq, this has not yet been extended to lrRNA-seq and the field lacks a systematic analysis of normalization strategies applied to long reads data. Here, we systematically benchmarked quantification pipelines and downstream normalization strategies across Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) platforms. Using synthetic spike-in controls (SIRVs and ERCC) alongside matched Illumina short-read benchmarks across mouse tissues and human cell models, we evaluated five normalization methods (CPM, TPM, CQN, EDASeq, and Scaled Median Ratio) across five quantification workflows (Bambu, FLAIR, IsoQuant, Oarfish, and Kallisto). Our framework assessed PacBio Iso-Seq and Kinnex protocols together with ONT cDNA and direct RNA (dRNA) sequencing datasets. We demonstrate that standard Transcripts Per Million (TPM) scaling counteracts short-molecule depletion in PacBio datasets, but severely distorts Nanopore expression estimates. For Nanopore libraries, covariate-aware normalizations (CQN and EDASeq) mitigate length bias without TPM's over-correction penalty. Conversely, relative median ratio normalization (SMR) degrades accuracy and introduces noise in low-variability biological samples. Across computational tools, FLAIR minimized absolute error on short SIRVs, while reference-restricted quantifiers (Oarfish and Kallisto) consistently outperformed transcript reconstruction tools in short-read validation. These findings show that quantification normalization should be applied to lrRNA-seq data and establish platform-specific guidelines for long-read transcriptomics.

bioinformatics↗

bioRxiv2025

Metabolite accumulation mediates the shift between HOC and LOC phases in the Yeast Metabolic Cycle

The Yeast Metabolic Cycle (YMC) is a molecular system that serves as a model to study the internal clock that maintains homeostasis in complex organisms. Traditionally, this ultradian rhythm has been studied in the three phases where mature mRNA transcripts show peak accumulation. However, recent studies have shown that the YMC can be interpreted as a two-phase cycle based on altered redox states, known as the high (HOC) and low oxygen consumption (LOC) phases. The length of the HOC phase is fixed and its frequency is nutrient dependent but the nature of the HOC to LOC transition is poorly defined. Here, we use multivariate statistics to integrate metabolic, chromatin and transcriptional changes across the YMC to study the levels of organization that connect them. Our model reveals that both the HOC-LOC and LOC-HOC phase transitions in the YMC are coordinated by accumulating metabolites, reflecting cellular energetics and redox state. We propose that the cycling behavior of chromatin states, transcription and transcripts is a consequence of accumulating metabolites at phase transitions, which function by modulating protein activity and coordinating biochemical pathways to maintain cellular homeostasis.

cell biology↗

bioRxiv2025

Coxmos: Interpretable survival models for high-dimensional and multi-omic data

BackgroundSurvival analysis in high-dimensional (HD) and multi-block (MB) settings, such as omic and multi-omic studies, poses major methodological challenges due to multicollinearity, low events-per-variable ratios, and limited model interpretability. While Cox Proportional Hazards models remain widely used, their applicability is restricted in HD scenarios. Machine learning approaches can handle such complexity but often lack interpretability. PLS-based survival models offer an attractive compromise, yet existing implementations provide limited support for model optimization, evaluation, and interpretation. ResultsTo overcome these limitations, we introduce Coxmos, a CRAN R package that integrates adapted Cox regression models with HD-adapted variable selection, and optimized PLS-based approaches tailored for HD and MB data. Coxmos also provides validation, comparison, interpretation, and visualization tools. Benchmarking on clinical, single-omic, and multi-omic datasets demonstrated that Coxmos outperformed other state-of-the-art machine learning methods, while enhancing interpretability. With an ovarian cancer case study, we illustrated how Coxmos facilitates model selection, validation, and biological interpretation in a complex multi-omic survival analysis. ConclusionsCoxmos provides a robust, flexible, and interpretable solution for survival analysis in HD and MB data. By combining optimized PLS-based methodologies with rigorous evaluation and rich visualization tools, Coxmos enables reliable survival prediction and facilitates the identification of relevant risk and protective factors in complex biomedical studies. The package fills an important methodological gap and represents a valuable resource for reproducible and interpretable survival modeling in the big data scenario. AvailabilityCoxmos is available at CRAN repository: https://cran.r-project.org/web/packages/Coxmos.

bioinformatics↗

bioRxiv2024

MORE interpretable multi-omic regulatory networks to characterize phenotypes

The identification of phenotype-specific regulatory mechanisms is crucial for understanding the molecular basis of diseases and other complex traits. However, the lack of tools capable of constructing multi-omic, condition-specific regulatory networks remains a significant limitation. He re, we introduce MO RE (Multi-Omics Regulation), a novel R package for the inference and comparison of multi-modal regulatory networks publicly available at https://github.com/BiostatOmics/MORE. MORE supports any number and type of omics layers, integrates prior regulatory knowledge, and employs advanced regression-based modelling and variable selection techniques to identify significant regulators of target features. We evaluated MORE on simulated datasets and benchmarked it against state-of-the-art tools. Our tool exhibited superior accuracy in identifying key regulators, model goodness-of-fit, and computational efficiency. Additionally, we applied MORE to an ovarian cancer dataset to uncover tumour subtype-specific regulatory mechanisms associated with distinct survival outcomes. By providing a comprehensive and user-friendly framework for constructing phenotype-specific regulatory networks, MORE addresses a critical gap in the field of multi-omics data integration. Its versatility and effectiveness make it a valuable resource for advancing our understanding of complex molecular interactions and regulatory systems.

bioinformatics↗

bioRxiv2022

MAMBA: a model-driven, constraint-based multiomic integration method

The inclusion of omic data into constraint-based modeling (CBM) has improved metabolic network characterization of biological models. However, the integration of semi-quantitative metabolomic data into CBM remains challenging. Here, we present MAMBA (Metabolic Adjustment via Multiomic Blocks Aggregation), a CBM approach that enables the use of semi-quantitative metabolomic data together with a gene-centric omic data type (e.g. transcriptomics, ChIP-seq or chromatin accessibility data, among others), and the combination of different time points and conditions. MAMBA outperformed other methods in terms of metabolic network characterization and metabolite prediction accuracy. As a case study, we applied MAMBA to a yeast multiomic dataset with time series design where two different yeast strains where exposed to heat stress. MAMBA captured known biology of heat stress in yeast and identified novel affected metabolic pathways. MAMBA was implemented as an integer linear programming (ILP) problem to guarantee efficient computation, and coded for MATLAB (freely available at github.com/ConesaLab/MAMBA).

bioinformatics↗

bioRxiv2021

Acorde: unraveling functionally-interpretable networks of isoform co-usage from single cell data

Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. However, the extent to which alternative isoforms establish co-expression networks that may relevant in cellular function has not been explored yet. Here, we present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships. To achieve this, we developed and validated percentile correlations, a novel approach that overcomes data sparsity and yields accurate co-expression estimates from single-cell data. Next, acorde uses correlations to cluster co-expressed isoforms into a network, unraveling cell type-specific alternative isoform usage patterns. By selecting same-gene isoforms between these clusters, we subsequently detect and characterize genes with co-differential isoform usage (coDIU) across neural cell types. Finally, we predict functional elements from long read-defined isoforms and provide insight into biological processes, motifs and domains potentially controlled by the coordination of post-transcriptional regulation.

bioinformatics↗

bioRxiv2020

STATegra: Multi-omics data integration - A conceptual scheme with a bioinformatics pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.

bioinformatics↗

bioRxiv2019

tappAS: A comprehensive computational framework for the analysis of the functional impact of differential splicing

Traditionally, the functional analysis of gene expression data has used pathway and network enrichment algorithms. These methods are usually gene rather than transcript centric and hence fall short to unravel functional roles associated to posttranscriptional regulatory mechanisms such as Alternative Splicing (AS) and Alternative PolyAdenylation (APA), jointly referred here as Alternative Transcript Processing (AltTP). Moreover, short-read RNA-seq has serious limitations to resolve full-length transcripts, further complicating the study of isoform expression. Recent advances in long-read sequencing open exciting opportunities for studying isoform biology and function. However, there are no established bioinformatics methods for the functional analysis of isoform-resolved transcriptomics data to fully leverage these technological advances. Here we present a novel framework for Functional Iso-Transcriptomics analysis (FIT). This framework uses a rich isoform-level annotation database of functional domains, motifs and sites -both coding and non-coding- and introduces novel analysis methods to interrogate different aspects of the functional relevance of isoform complexity. The Functional Diversity Analysis (FDA) evaluates the variability at the inclusion/exclusion of functional domains across annotated transcripts of the same gene. Parameters can be set to evaluate if AltTP partially or fully disrupts functional elements. FDA is a measure of the potential of a multiple isoform transcriptome to have a functional impact. By combining these functional labels with expression data, the Differential Analysis Module evaluates the relative contribution of transcriptional (i.e. gene level) and post-transcriptional (i.e. transcript/protein levels) regulation on the biology of the system. Measures of isoform relevance such as Minor Isoform Filtering, Isoform Switching Events and Total Isoform Usage Change contribute to restricting analysis to biologically meaningful changes. Finally, novel methods for Differential Feature Inclusion, Co-Feature Inclusion, and the combination of UTR-lengthening with Alternative Polyadenylation analyses carefully dissects the contextual regulation of functional elements resulting from differential isoforms usage. These methods are implemented in the software tappAS, a user-friendly Java application that brings FIT to the hands of non-expert bioinformaticians supporting several model and non-model species. tappAS complements statistical analyses with powerful browsing tools and highly informative gene/transcript/CDS graphs.\n\nWe applied tappAS to the analysis of two mouse Neural Precursor Cells (NPCs) and Oligodendrocyte Precursor Cells (OPCs) whose transcriptome was defined by PacBio and quantified by Illumina. Using FDA we confirmed the high potential of AltTP regulation in our system, in which 90% of multi-isoform genes presented variation in functional features at the transcript or protein level. The Differential Analysis module revealed a high interplay between transcriptional and AltTP regulation in neural development, mainly controlled by differential expression, but where AltTP acts the main driver of important neural development biological mechanisms such as vesicle trafficking, signal transduction and RNA processing. The DFI analysis revealed that, globally, AltTP increased the availability of functional features in differentiated neural cells. DFI also showed that AltTP is a mechanism for altering gene function by changing cellular localization and binding properties of proteins, via the differential inclusion of NLS, transmembrane domains or DNA binding motifs, for example. Some of these findings were experimentally validated by others and us.\n\nIn summary, we propose a novel framework for the functional analysis of transcriptomes at isoform resolution. We anticipate the tappAS tool will be an important resource for the adoption of the Functional Iso-Transcriptomics analysis by functional genomics community.

bioinformatics↗

bioRxiv2019

STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

bioinformatics↗

bioRxiv2018

MOSim: Multi-Omics Simulation in R

As multi-omics sequencing technologies advance, the need for simulation tools capable of generating realistic and diverse (bulk and single-cell) multi-omics datasets for method testing and benchmarking becomes increasingly important. We present MOSim, an R package that simulates both bulk (via mosim function) and single-cell (via sc_mosim function) multi-omics data. The mosim function generates bulk transcriptomics data (RNA-seq) and additional regulatory omics layers (ATAC-seq, miRNA-seq, ChIP-seq, Methyl-seq and Transcription Factors), while sc_mosim simulates single-cell transcriptomics data (scRNA-seq) with scATAC-seq and Transcription Factors as regulatory layers. The tool supports various experimental designs, including simulation of gene co-expression patterns, biological replicates, and differential expression between conditions. MOSim enables users to generate quantification matrices for each simulated omics data type, capturing the heterogeneity and complexity of bulk and single-cell multi-omics datasets. Furthermore, MOSim provides differentially abundant features within each omics layer and elucidates the active regulatory relationships between regulatory omics and gene expression data at both bulk and single-cell levels. By leveraging MOSim, researchers will be able to generate realistic and customizable bulk and single-cell multi-omics datasets to benchmark and validate analytical methods specifically designed for the integrative analysis of diverse regulatory omics data. Key PointsO_LIMOSim is capable of generating synthetic datasets for a broad spectrum of omics types, supporting bulk RNA-seq, ChIP-seq, ATAC-seq, miRNA-seq, Methyl-seq, and transcription factor data, as well as single-cell omics, including scRNA-seq, scATAC-seq, and transcription factors. C_LIO_LIMOSim enables the robust simulation of complex, many-to-many regulatory relationships across molecular layers, faithfully capturing intricate regulatory patterns. C_LIO_LIOffering extensive options for customization, MOSims flexible experimental design and parameterization empowers users to simulate count matrices and multilayer regulatory networks, tailoring simulations to diverse experimental scenarios and omics types. C_LI

bioinformatics↗

bioRxiv2018

PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data

The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental to understand interconnections across molecular layers and to fully leverage the biology discovery power offered by the multi-omics approach.\n\nWe present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information.\n\nUnlike other visualization tools, PaintOmics 3 covers a complete pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, etc. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at http://bioinfo.cipf.es/paintomics/.

bioinformatics↗

bioRxiv2017

Identification and visualization of differential isoform expression in RNA-seq time series

As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. The package is freely available under the LGPL license from the Bioconductor web site (http://bioconductor.org).

bioinformatics↗