Biology subjects

Conesa, A.

Publications and source records attributed to Conesa, A..

29 records · Page 2Linked to original sources

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↗

bioRxiv2020

Transcriptional differences for COVID-19 Disease Map genes between males and females indicate a different basal immunophenotype relevant to the disease

Worldwide COVID-19 epidemiology data indicate clear differences in disease incidence among sex and age groups. Specifically, male patients are at a higher death risk than females. However, whether this difference is the consequence of a pre-existing sex-bias in immune genes or a differential response to the virus has not been studied yet. We created DeCovid, an R shiny app that combines gene expression data of different human tissue from the Genotype-Tissue Expression (GTEx) project and the COVID-19 Disease Map gene collection to explore basal gene expression differences across healthy demographic groups. We used this app to study differential gene expression between men and women for COVID-19 associated genes. We identified that healthy women present higher levels in the expression of interferon genes and the JAK-STAT pathway leading to cell survival.

genomics↗

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

Building gene regulatory networks from single-cell ATAC-seq and RNA-seq using Linked Self-Organizing Maps

Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq and scRNA-seq data that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of single-cells.

genomics↗

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

Vascular brassinosteroid receptors confer drought resistance without penalizing plant growth

Drought represents a major threat to food security. Mechanistic data describing plant responses to drought have been studied extensively and genes conferring drought resistance have been introduced into crop plants. However, plants with enhanced drought resistance usually display lower growth, highlighting the need for strategies to uncouple drought resistance from growth. Here, we show that overexpression of BRL3, a vascular-enriched member of the brassinosteroid receptor family, can confer drought stress tolerance in Arabidopsis. Whereas loss-of-function mutations in the ubiquitously expressed BRI1 receptor leads to drought resistance at the expense of growth, overexpression of BRL3 receptor confers drought tolerance without penalizing overall growth. Systematic analyses reveal that upon drought stress, increased BRL3 triggers the accumulation of osmoprotectant metabolites including proline and sugars. Transcriptomic analysis suggests that this results from differential expression of genes in the vascular tissues. Altogether, this data suggests that manipulating BRL3 expression could be used to engineer drought tolerant crops.

plant biology↗

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

A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies

Over the last few years, RNA-seq has been used to study alterations in alternative splicing related to several diseases. Bioinformatics workflows used to perform these studies can be divided into two groups, those finding changes in the absolute isoform expression and those studying differential splicing. Many computational methods for transcriptomics analysis have been developed, evaluated and compared; however, there are not enough reports of systematic and objective assessment of processing pipelines as a whole. Moreover, comparative studies have been performed considering separately the changes in absolute or relative isoform expression levels. Consequently, no consensus exists about the best practices and appropriate workflows to analyse alternative and differential splicing. To assist the adequate pipeline choice, we present here a benchmarking of nine commonly used workflows to detect differential isoform expression and splicing. We evaluated the workflows performance over three different experimental scenarios where changes in absolute and relative isoform expression occurred simultaneously. In addition, the effect of the number of isoforms per gene, and the magnitude of the expression change over pipeline performances were also evaluated. Our results suggest that workflow performance is influenced by the number of replicates per condition and the conditions heterogeneity. In general, workflows based on DESeq, DEXSeq, Limma and NOISeq performed well over a wide range of transcriptomics experiments. In particular, we suggest the use of workflows based on Limma when high precision is required, and DESeq2 and DEXseq pipelines to prioritize sensitivity. When several replicates per condition are available, NOISeq and Limma pipelines are indicated.

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↗

bioRxiv2017

SQANTI: extensive characterization of long read transcript sequences for quality control in full-length transcriptome identification and quantification

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in very well annotated organisms as mice and humans. Nonetheless, there is a need for studies and tools that characterize these novel isoforms. Here we present SQANTI, an automated pipeline for the classification of long-read transcripts that computes 47 descriptors that can be used to assess the quality of the data and of the preprocessing pipelines. We applied SQANTI to a neuronal mouse transcriptome using PacBio long reads and illustrate how the tool is effective in readily describing the composition of and characterizing the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach, and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, result more frequently in novel ORFs than novel UTRs and are enriched in both general metabolic and neural specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases we find that alternative isoforms are elusive to proteogenomics detection and are variable in protein changes with respect to the principal isoform of their genes. SQANTI allows the user to maximize the analytical outcome of long read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes. SQANTI is available at https://bitbucket.org/ConesaLab/sqanti.

bioinformatics↗