Large-scale clustering of CAGE tag expression data

Background: Recent analyses have suggested that many genes possess multiple transcription start sites (TSSs) that are differentially utilized in different tissues and cell lines. We have identified a huge number of TSSs mapped onto the mouse genome using the cap analysis of gene expression (CAGE) method. The standard hierarchical clustering algorithm, which gives us easily understandable graphical tree images, has difficulties in processing such huge amounts of TSS data and a better method to calculate and display the results is needed. Results: We use a combination of hierarchical and non-hierarchical clustering to cluster expression profiles of TSSs based on a large amount of CAGE data to profit from the best of both methods. We processed the genome-wide expression data, including 159,075 TSSs derived from 127 RNA samples of various organs of mouse, and succeeded in categorizing them into 70-100 clusters. The clusters exhibited intriguing biological features: a cluster supergroup with a ubiquitous expression profile, tissue-specific patterns, a distinct distribution of non-coding RNA and functional TSS groups. Conclusion: Our approach succeeded in greatly reducing the calculation cost, and is an appropriate solution for analyzing large-scale TSS usage data

Non-parametric Bayesian modelling of digital gene expression data

Next-generation sequencing technologies provide a revolutionary tool for generating gene expression data. Starting with a fixed RNA sample, they construct a library of millions of differentially abundant short sequence tags or "reads", which constitute a fundamentally discrete measure of the level of gene expression. A common limitation in experiments using these technologies is the low number or even absence of biological replicates, which complicates the statistical analysis of digital gene expression data. Analysis of this type of data has often been based on modified tests originally devised for analysing microarrays; both these and even de novo methods for the analysis of RNA-seq data are plagued by the common problem of low replication. We propose a novel, non-parametric Bayesian approach for the analysis of digital gene expression data. We begin with a hierarchical model for modelling over-dispersed count data and a blocked Gibbs sampling algorithm for inferring the posterior distribution of model parameters conditional on these counts. The algorithm compensates for the problem of low numbers of biological replicates by clustering together genes with tag counts that are likely sampled from a common distribution and using this augmented sample for estimating the parameters of this distribution. The number of clusters is not decided a priori, but it is inferred along with the remaining model parameters. We demonstrate the ability of this approach to model biological data with high fidelity by applying the algorithm on a public dataset obtained from cancerous and non-cancerous neural tissues

Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data

A set of methods is presented for normalization, quantification of noise and co-expression analysis for gene expression studies using deep sequencing

RECLU:a pipeline to discover reproducible transcriptional start sites and their alternative regulation using capped analysis of gene expression (CAGE)

BACKGROUND: Next generation sequencing based technologies are being extensively used to study transcriptomes. Among these, cap analysis of gene expression (CAGE) is specialized in detecting the most 5’ ends of RNA molecules. After mapping the sequenced reads back to a reference genome CAGE data highlights the transcriptional start sites (TSSs) and their usage at a single nucleotide resolution. RESULTS: We propose a pipeline to group the single nucleotide TSS into larger reproducible peaks and compare their usage across biological states. Importantly, our pipeline discovers broad peaks as well as the fine structure of individual transcriptional start sites embedded within them. We assess the performance of our approach on a large CAGE datasets including 156 primary cell types and two cell lines with biological replicas. We demonstrate that genes have complicated structures of transcription initiation events. In particular, we discover that narrow peaks embedded in broader regions of transcriptional activity can be differentially used even if the larger region is not. CONCLUSIONS: By examining the reproducible fine scaled organization of TSS we can detect many differentially regulated peaks undetected by previous approaches

A code for transcription initiation in mammalian genomes

Genome-wide detection of transcription start sites (TSSs) has revealed that RNA Polymerase II transcription initiates at millions of positions in mammalian genomes. Most core promoters do not have a single TSS, but an array of closely located TSSs with different rates of initiation. As a rule, genes have more than one such core promoter; however, defining the boundaries between core promoters is not trivial. These discoveries prompt a re-evaluation of our models for transcription initiation. We describe a new framework for understanding the organization of transcription initiation. We show that initiation events are clustered on the chromosomes at multiple scales-clusters within clusters-indicating multiple regulatory processes. Within the smallest of such clusters, which can be interpreted as core promoters, the local DNA sequence predicts the relative transcription start usage of each nucleotide with a remarkable 91% accuracy, implying the existence of a DNA code that determines TSS selection. Conversely, the total expression strength of such clusters is only partially determined by the local DNA sequence. Thus, the overall control of transcription can be understood as a combination of large- and small-scale effects; the selection of transcription start sites is largely governed by the local DNA sequence, whereas the transcriptional activity of a locus is regulated at a different level; it is affected by distal features or events such as enhancers and chromatin remodeling

Systematic clustering of transcription start site landscapes

Genome-wide, high-throughput methods for transcription start site (TSS) detection have shown that most promoters have an array of neighboring TSSs where some are used more than others, forming a distribution of initiation propensities. TSS distributions (TSSDs) vary widely between promoters and earlier studies have shown that the TSSDs have biological implications in both regulation and function. However, no systematic study has been made to explore how many types of TSSDs and by extension core promoters exist and to understand which biological features distinguish them. In this study, we developed a new non-parametric dissimilarity measure and clustering approach to explore the similarities and stabilities of clusters of TSSDs. Previous studies have used arbitrary thresholds to arrive at two general classes: broad and sharp. We demonstrated that in addition to the previous broad/sharp dichotomy an additional category of promoters exists. Unlike typical TATA-driven sharp TSSDs where the TSS position can vary a few nucleotides, in this category virtually all TSSs originate from the same genomic position. These promoters lack epigenetic signatures of typical mRNA promoters and a substantial subset of them are mapping upstream of ribosomal protein pseudogenes. We present evidence that these are likely mapping errors, which have confounded earlier analyses, due to the high similarity of ribosomal gene promoters in combination with known G addition bias in the CAGE libraries. Thus, previous two-class separations of promoter based on TSS distributions are motivated, but the ultra-sharp TSS distributions will confound downstream analyses if not removed.This work was supported by a grant from the Novo Nordisk Foundation, http://www.novonordiskfonden.dk/. The European Research Council (http:// erc.europa.eu/) has provided financial support to Dr. Sandelin under the EU 7th Framework Programme (FP7/2007-2013)/ERC grant agreement 204135

Systematic clustering of transcription start site landscapes

Genome-wide, high-throughput methods for transcription start site (TSS) detection have shown that most promoters have an array of neighboring TSSs where some are used more than others, forming a distribution of initiation propensities. TSS distributions (TSSDs) vary widely between promoters and earlier studies have shown that the TSSDs have biological implications in both regulation and function. However, no systematic study has been made to explore how many types of TSSDs and by extension core promoters exist and to understand which biological features distinguish them. In this study, we developed a new non-parametric dissimilarity measure and clustering approach to explore the similarities and stabilities of clusters of TSSDs. Previous studies have used arbitrary thresholds to arrive at two general classes: broad and sharp. We demonstrated that in addition to the previous broad/sharp dichotomy an additional category of promoters exists. Unlike typical TATA-driven sharp TSSDs where the TSS position can vary a few nucleotides, in this category virtually all TSSs originate from the same genomic position. These promoters lack epigenetic signatures of typical mRNA promoters and a substantial subset of them are mapping upstream of ribosomal protein pseudogenes. We present evidence that these are likely mapping errors, which have confounded earlier analyses, due to the high similarity of ribosomal gene promoters in combination with known G addition bias in the CAGE libraries. Thus, previous two-class separations of promoter based on TSS distributions are motivated, but the ultra-sharp TSS distributions will confound downstream analyses if not removed

Inference of gene regulatory interactions from deep sequencing data

This thesis describes our work on the inference of gene regulatory interactions from deep sequencing data. In the first part we start with an introduction to the problem of inferring transcription regulatory interactions (Chapter 2). Then we describe detailed methods of promoterome construction in human and mouse genomes from deep CAGE data (Chapter 3), and show how promoters together with gene expression data can be used to infer transcription regulatory interactions (Chapter 4). Finally, we show an application of this methodology to a human macrophage lineage undergoing differentiation accompanied by a detailed experimental validation of the predicted network structure (Chapter 5). Work presented in Chapter 5 comes from the FANTOM4 project. In the second part we focus on the regulatory functions of two small nucleolar RNAs (snoRNAs). In Chapter 8 we describe an atypical function of snoRNAs - regulation of the mRNA alternative splicing process by a particular class of mouse snoRNAs (MBII-52 variants). These are of great interest, as the locus encoding MBII-52 is linked to Prader-Willi Syndrome. Methods include in silico RNA hybridization screens and experimental confirmation of the predictions. In Chapter 9, we report a discovery of the first virus-encoded snoRNA in Epstein-Barr Virus (EBV). Again, we show that the function of this snoRNA (v-snoRNA1) is atypical: it is processed into small, 24 nt long fragments that can function as microRNAs

CAGE-TSSchip: promoter-based expression profiling using the 5'-leading label of capped transcripts

A novel approach that combines CAGE expression analysis with oligonucleotide array technology allows for the accurate and sensitive detection of promoter-based transcriptional activity