Biology subjects

Vaquero-Garcia, J.

Publications and source records attributed to Vaquero-Garcia, J..

4 recordsLinked to original sources

bioRxiv2021

RNA splicing analysis using heterogeneous and large RNA-seq1datasets

The ubiquity of RNA-seq has led to many methods that use RNA-seq data to analyze variations in RNA splicing. However, available methods are not well suited for handling heterogeneous and large datasets. Such datasets scale to thousands of samples across dozens of experimental conditions, exhibit increased variability compared to biological replicates, and involve thousands of unannotated splice variants resulting in increased transcriptome complexity. We describe here a suite of algorithms and tools implemented in the MAJIQ v2 package to address challenges in detection, quantification, and visualization of splicing variations from such datasets. Using both large scale synthetic data and GTEx v8 as benchmark datasets, we demonstrate that the approaches in MAJIQ v2 outperform existing methods. We then apply MAJIQ v2 package to analyze differential splicing across 2,335 samples from 13 brain subregions, demonstrating its ability to offer new insights into brain subregion-specific splicing regulation.

bioinformatics↗

bioRxiv2019

Mapping RNA splicing variations in clinically-accessible and non-accessible tissues to facilitate Mendelian disease diagnosis using RNA-seq

PurposeRNA-seq is a promising approach to improve diagnoses by detecting pathogenic aberrations in RNA splicing that are missed by DNA sequencing. RNA-seq is typically performed on clinically-accessible tissues (CATs) from blood and skin. RNA tissue-specificity makes it difficult to identify aberrations in relevant but non-accessible tissues (non-CATs). We determined how RNA-seq from CATs represent splicing in and across genes and non-CATs.\n\nMethodsWe quantified RNA splicing in 801 RNA-seq samples from 56 different adult and fetal tissues from GTEx and ArrayExpress. We identified genes and splicing events in each non-CAT and determined when RNA-seq in each CAT would inadequately represent them. We developed an online resource, MAJIQ-CAT, for exploring our analysis for specific genes and tissues.\n\nResultsIn non-CATs, 39.7% of genes have splicing that is inadequately represented by at least one CAT. 6.2% of genes have splicing inadequately represented by all CATs. A majority (52.8%) of inadequately represented genes are lowly expressed in CATs (TPM < 1), but 6.2% are inadequately represented despite being well expressed (TPM > 10).\n\nConclusionMany splicing events in non-CATs are inadequately evaluated using RNA-seq from CATs. MAJIQ-CAT allows users to explore which accessible tissues, if any, best represent splicing in genes and tissues of interest.

genomics↗

bioRxiv2018

LeafCutter vs. MAJIQ and comparing software in the fast-moving field of genomics

In a recent publication, Li et al. introduced LeafCutter, a new method for detecting and quantifying differential splicing of RNA from RNASeq data. In this work, Li et al. first compared LeafCutter to existing methods, then used it for a study of splicing variations and sQTL analysis from a large set of GTEx samples. While the study was elaborate and comprehensive, we want to highlight several issues with the comparative analysis performed by Li et al. We argue these issues created an inaccurate and misleading representation of other tools, namely MAJIQ and rMATS. More broadly, we believe the points we raise regarding the comparative analysis by Li et al. are representative of general issues we all, as authors, editors, and reviewers, are faced with and must address in the current times of fast paced genomics and computational research.

bioinformatics↗

bioRxiv2017

Outlier detection for improved differential splicing quantification from RNA-Seq experiments withreplicates

A key component in many RNA-Seq based studies is the production of multiple replicates for varying experimental conditions. Such replicates allow to capture underlying biological variability and control for experimental ones. However, during data production researchers often lack clear definitions to what constitutes a \"bad\" replicate which should be discarded and if data from failed replicates is published downstream analysis by groups using this data can be hampered. Here we develop a probability model to weigh a given RNA-Seq experiment as a representative of an experimental condition when performing alternative splicing analysis. Using both synthetic and real life data we demonstrate that this model detects outlier samples which are consistently and significantly different compared to samples from the same condition. Using both synthetic and real life data we perform extensive evaluation of the algorithm in different scenarios involving perturbed samples, mislabeled samples, no-signal groups, and different levels of coverage, and show it compares favorably with current state of the art tools.\n\nAvailabilityProgram and code will be available at majiq.biociphers.org

bioinformatics↗