Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Modélisation de l'expression des gènes à partir de données de séquence ADN

International audienceGene expression is tightly controlled to ensure a wide variety of cell types and functions. The development of diseases, particularly cancers, is invariably related to deregulations of these controls. Our objective is to model the link between gene expression and nucleotide composition of different regulatory regions in the genome. We propose to address this problem in a regression framework using a Lasso approach coupled to a regression tree. We use exclusively sequence data and we fit a different model for each cell type. We show that (i) different regulatory regions provide particular and complementary information and that (ii) the only information contained in the nucleotide compositions allows predicting gene expression with an error comparable to that obtained using experimental data. Moreover, the fitted linear model is not as powerful for all genes, but better fit certain groups of genes with particular nucleotides compositions.L'expression des gènes est étroitement contrôlée pour assurer une grande variété de fonctions et de types cellulaires. Le développement des maladies, en particulier les cancers, est invariablement lié à la dérégulation de ces contrôles. Notre objectif est de modéliser le lien entre l'expression des gènes et la composition nucléotidique des différentes régions régulatrices du génome. Nous proposons d'aborder ce problème dans un cadre de régression avec une approche Lasso couplée à un arbre de régression. Nous utilisons exclusivement des données de séquences et nous apprenons un modèle différent pour chaque type cellulaire. Nous montrons (i) que les différentes régions régulatrices apportent des informations diffé-rentes et complémentaires et (ii) que la seule information de leur composition nucléotidique permet de prédire l'expression des gènes avec une erreur comparable à celle obtenue en utilisant des données expérimentales. En outre, le modèle linéaire appris n'est pas aussi performant pour tous les gènes, mais modélise mieux certaines classes de gènes avec des compositions nucléotidiques particulières

Probing instructions for expression regulation in gene nucleotide compositions - Fig 7

<p><b>A: Nucleotide compositions of resident genes distinguish TADs.</b> For each TAD and for each region considered, the percentage of each nucleotide and dinucleotide associated to the embedded genes were compared to that of all other genes using a Kolmogorov-Smirnov test. Red indicates FDR-corrected p-value ≥ 0.05 and yellow FDR-corrected p-value < 0.05. TAD clustering was made using this binary information. Only TADs with at least one p-value < 0.05 are shown (i.e. 87% of the TADs containing at least 10 genes). y-axis from top to bottom: G_INTR, GpC_INTR, CpC_INTR, CpC_3UTR, GpC_3UTR, G_3UTR, GpC_CDS, CpC_CDS, G_CDS, G_DFR, CpC_DFR, GpC_DFR, CpG_INTR, CpG_3UTR, CpG_CDS, CpG_DFR, G_DU, GpC_DD, CpG, DU, CpG_DD, GpC_DU, CpC_DU, CpC_DD, G_DD, GpC_5UTR, CpG_5UTR, G_5UTR, GpC_CORE, CpG_CORE, CpC_CORE, G_CORE, CpC_5UTR, CpT_3UTR, CpT_CDS, CpT_INTR, ApT_INTR, TpA_INTR, A_INTR, ApA_INTR, TpA_3UTR, ApT_3UTR, A_3UTR, ApA_3UTR, ApA_CDS, A_CDS, ApT_CDS, TpA_CDS, A_DD, ApA_DD, ApT_DD, TpA_DD, TpA_DU, ApT_DU, ApA_DU, A_DU, TpA_DFR, ApT_DFR, A_DFR, ApA_DFR, ApA_CORE, A_CORE, ApT_CORE, TpA_CORE, ApA_5UTR, ApT_5UTR, A_5UTR, TpA_5UTR, ApC_DFR, ApC_DD, ApC_DU, TpC_DU, TpC_DFR, ApC_CORE, CpA_DU, CpA_DFR, CpA_CDS, ApC_CDS, ApC_3UTR, TpC_CDS, TpC_CORE, CpT_5UTR, TpC_5UTR, CpT_CORE, TpC_DD, CpA_CORE, ApC_5UTR, CpA_5UTR, ApC_INTR, CpA_DD, CpT_DFR, CpT_DD, CpT_DU, TpC_3UTR, TpC_INTR, CpA_INTR, CpA_3UTR. <b>B: TAD enrichment within groups of genes whose expression is accurately predicted by our model.</b> The enrichment for each TAD (containing more than 10 genes) in each gene group accurately predicted by our model (i.e. groups with mean error < mean errors of the 1st quartile) was evaluated using an hypergeometric test. The fraction of groups with enriched TADs (p-value < 0.05) is represented.</p

Gene classification according to prediction accuracy.

<p>Columns represent the various samples gathered by cancer type. Samples from the same cancer type are ranked by decreasing mean squared prediction error. Lines represent the 3,680 groups of gene obtained with the regression trees (one tree for each of the 241 samples) ranked by decreasing mean squared prediction error. Groups gathering the top 25% well predicted genes (error <∼ 1.77) are indicated in red and light blue.</p

Contribution of additional genomic regions.

<p>Genomic regions were ranked according to their contribution in predicting gene expression. First, all regions were tested separately. Introns yielded the highest Spearman correlation between observed and predicted expressions (in a cross-validation procedure) and was selected as the ‘first’ seed region. Second, each region not already in the model was added separately. 5’UTR in association with introns yielded the best correlation and was therefore selected as the ‘second’ region. Third, the procedure was repeated till all regions were included in the model. The contribution of each region is then visualized starting from the most important (left) to the less important (right). Note that the distance between the second TSS and the first ATG is > 2000 bp for only 189 genes implying that 5’UTR and DD regions overlap. The correlations computed at each steps are indicated in (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005921#pcbi.1005921.s015" target="_blank">S2 Table</a>). ns, non significant.</p