Biology subjects

Julien Gagneur

Publications and source records attributed to Julien Gagneur.

5 recordsLinked to original sources

bioRxiv2016

Genetic diagnosis of Mendelian disorders via RNA sequencing

Across a large variety of Mendelian disorders, 50 to 75% of undiagnosed patients do not receive a genetic diagnosis by whole exome sequencing indicative of underlying regulatory variants. In contrast, whole genome sequencing allows the discovery of all genetic variants, but their significant number, coupled with a poor understanding of the non-coding genome, makes their prioritization challenging. Here, we demonstrate the power of directly sequencing transcriptomes by providing a genetic diagnosis for 11% (5 of 47) of unsolved mitochondrial disease patients and strong candidates for other patients. We found a median of 1 aberrantly expressed gene, 5 aberrant splicing events, and 6 mono-allelically expressed rare variants in patient-derived fibroblasts and established disease-causing roles for each kind. Private exons often arose from sites that are weakly spliced in other individuals, providing an important clue for future variant prioritization. One such intronic exon-creating variant was found in three unrelated families in the complex I assembly factor TIMMDC1, which we consequently established as a novel disease-associated gene. In conclusion, our study expands the diagnostic tools for detecting non-exonic variants of Mendelian disorders and provides examples of intronic loss-of-function variants with pathological relevance.

Genetics↗

bioRxiv2016

Genome-wide generalized additive models

MotivationChromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used approach to study protein-DNA interactions. Often, the quantities of interest are the differential occupancies relative to controls, between genetic backgrounds, treatments, or combinations thereof. Current methods for differential occupancy of ChIP-seq data rely however on binning or sliding window techniques, for which the choice of the window and bin sizes are subjective.\n\nResultsHere, we present GenoGAM (Genome-wide Generalized Additive Model), which brings the well-established and flexible generalized additive models framework to genomic applications using a data parallelism strategy. We model ChIP-Seq read count frequencies as products of smooth functions along chromosomes. Smoothing parameters are objectively estimated from the data by cross-validation, eliminating ad-hoc binning and windowing needed by current approaches. GenoGAM provides base-level and region-level significance testing for full factorial designs. Application to a ChIP-Seq dataset in yeast showed increased sensitivity over existing differential occupancy methods while controlling for type I error rate. By analyzing a set of DNA methylation data and illustrating an extension to a peak caller, we further demonstrate the potential of GenoGAM as a generic statistical modeling tool for genome-wide assays.\n\nAvailabilitySoftware is available from Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/GenoGAM.html\n\nContactgagneur@in.tum.de\n\nSupplementary informationSupplementary information is available at Bioinformatics online.

Bioinformatics↗

bioRxiv2016

Accurate promoter and enhancer identification in 127 ENCODE and Roadmap Epigenomics cell types and tissues by GenoSTAN

Accurate maps of promoters and enhancers are required for understanding transcriptional regulation. Promoters and enhancers are usually mapped by integration of chromatin assays charting histone modifications, DNA accessibility, and transcription factor binding. However, current algorithms are limited by unrealistic data distribution assumptions. Here we propose GenoSTAN (Genomic STate ANnotation), a hidden Markov model overcoming these limitations. We map promoters and enhancers for 127 cell types and tissues from the ENCODE and Roadmap Epigenomics projects, todays largest compendium of chromatin assays. Extensive benchmarks demonstrate that GenoSTAN consistently identifies promoters and enhancers with significantly higher accuracy than previous methods. Moreover, GenoSTAN-derived promoters and enhancers showed significantly higher enrichment of complex trait-associated genetic variants than current annotations. Altogether, GenoSTAN provides an easy-to-use tool to define promoters and enhancers in any system, and our annotation of human transcriptional cis-regulatory elements constitutes a rich resource for future research in biology and medicine.

Genomics↗

bioRxiv2015

Meiotic interactors of a mitotic gene TAO3 revealed by functional analysis of its rare variant

Studying the molecular consequences of rare genetic variants has the potential of identifying novel and hereto uncharacterized pathways causally contributing to phenotypic variation. Here we characterize the functional consequences of a rare coding variant of TAO3, previously reported to significantly contribute to sporulation efficiency variation in Saccharomyces cerevisiae. During mitosis TAO3 interacts with CBK1, a conserved NDR kinase and a component of RAM network. The RAM network genes are involved in regulation cell separation and polarization. We demonstrate that the role of the rare allele TAO3(4477C) in meiosis is distinct from its role in mitosis by being independent of ACE2, which is a RAM network target gene. By quantitatively measuring cell morphological dynamics and conditionally expressing TAO3(4477C) allele during sporulation, we show that TAO3 has an early role in meiosis. This early role of TAO3 coincides with entry of cells into meiotic division. Time-resolved transcriptome analyses during early sporulation phase identified regulators of carbon and lipid metabolic pathways as candidate mediators. We experimentally show that during sporulation the TAO3 allele genetically interacts with ERT1 and PIP2, the regulators of tricarboxylic acid cycle and gluconeogenic enzymes, respectively. We thus uncover meiotic functions of TAO3, a mitotic gene and propose ERT1 and PIP2 as novel regulators of sporulation efficiency. Our results demonstrate that study of causal effects of genetic variation on the underlying molecular network has the potential to provide more extensive comprehension of the pathways driving a complex trait. This can help identify prospective personalized targets for intervention in complex diseases.

Genetics↗

bioRxiv2015

Determinants of RNA metabolism in the Schizosaccharomyces pombe genome

To decrypt the regulatory code of the genome, sequence elements must be defined that determine the kinetics of RNA metabolism and thus gene expression. Here we attempt such decryption in an eukaryotic model organism, the fission yeast S. pombe. We first derive an improved genome annotation that redefines borders of 36% of expressed mRNAs and adds 487 non-coding RNAs (ncRNAs). We then combine RNA labeling in vivo with mathematical modeling to obtain rates of RNA synthesis and degradation for 5,484 expressed RNAs and splicing rates for 4,958 introns. We identify functional sequence elements in DNA and RNA that control RNA metabolic rates, and quantify the contributions of individual nucleotides to RNA synthesis, splicing, and degradation. Our approach reveals distinct kinetics of mRNA and ncRNA metabolism, separates antisense regulation by transcription interference from RNA interference, and provides a general tool for studying the regulatory code of genomes.

Genomics↗