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JASPAR (http://jaspar.genereg.net/) is an open-access database containing manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six taxonomic groups. In this 9th release, we expanded the CORE collection with 341 new profiles (148 for plants, 101 for vertebrates, 85 for urochordates, and 7 for insects), which corresponds to a 19% expansion over the previous release. We added 298 new profiles to the Unvalidated collection when no orthogonal evidence was found in the literature. All the profiles were clustered to provide familial binding profiles for each taxonomic group. Moreover, we revised the structural classification of DNA binding domains to consider plant-specific TFs. This release introduces word clouds to represent the scientific knowledge associated with each TF. We updated the genome tracks of TFBSs predicted with JASPAR profiles in eight organisms; the human and mouse TFBS predictions can be visualized as native tracks in the UCSC Genome Browser. Finally, we provide a new tool to perform JASPAR TFBS enrichment analysis in user-provided genomic regions. All the data is accessible through the JASPAR website, its associated RESTful API, the R/Bioconductor data package, and a new Python package, pyJASPAR, that facilitates serverless access to the data.

The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data.We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: (i) full-length RNA-seq for detection of splicing patterns and (ii) high-throughput 5' and 3' tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts. We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings and Saccharomyces cerevisiae cells as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the most commonly used community gene models, TAIR10 and Araport11 for A.thaliana and SacCer3 for S.cerevisiae. In particular, we identify multiple transient transcripts missing from the existing annotations. Our new annotations promise to improve the quality of A.thaliana and S.cerevisiae genome research.Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

Background 5′-end sequencing assays, and Cap Analysis of Gene Expression (CAGE) in particular, have been instrumental in studying transcriptional regulation. 5′-end methods provide genome-wide maps of transcription start sites (TSSs) with base pair resolution. Because active enhancers often feature bidirectional TSSs, such data can also be used to predict enhancer candidates. The current availability of mature and comprehensive computational tools for the analysis of 5′-end data is limited, preventing efficient analysis of new and existing 5′-end data. Results We present CAGEfightR, a framework for analysis of CAGE and other 5′-end data implemented as an R/Bioconductor-package. CAGEfightR can import data from BigWig files and allows for fast and memory efficient prediction and analysis of TSSs and enhancers. Downstream analyses include quantification, normalization, annotation with transcript and gene models, TSS shape statistics, linking TSSs to enhancers via co-expression, identification of enhancer clusters, and genome-browser style visualization. While built to analyze CAGE data, we demonstrate the utility of CAGEfightR in analyzing nascent RNA 5′-data (PRO-Cap). CAGEfightR is implemented using standard Bioconductor classes, making it easy to learn, use and combine with other Bioconductor packages, for example popular differential expression tools such as limma, DESeq2 and edgeR. Conclusions CAGEfightR provides a single, scalable and easy-to-use framework for comprehensive downstream analysis of 5′-end data. CAGEfightR is designed to be interoperable with other Bioconductor packages, thereby unlocking hundreds of mature transcriptomic analysis tools for 5′-end data. CAGEfightR is freely available via Bioconductor: bioconductor.org/packages/CAGEfightR .

Background The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: (i) full-length RNA-seq for detection of splicing patterns and (ii) high-throughput 5′ and 3′ tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts. We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings and Saccharomyces cerevisiae cells as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the most commonly used community gene models, TAIR10 and Araport11 for A.thaliana and SacCer3 for S.cerevisiae. In particular, we identify multiple transient transcripts missing from the existing annotations. Our new annotations promise to improve the quality of A.thaliana and S.cerevisiae genome research. Conclusions Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) for TFs across multiple species in six taxonomic groups. In this 8th release of JASPAR, the CORE collection has been expanded with 245 new PFMs (169 for vertebrates, 42 for plants, 17 for nematodes, 10 for insects, and 7 for fungi), and 156 PFMs were updated (125 for vertebrates, 28 for plants and 3 for insects). These new profiles represent an 18% expansion compared to the previous release. JASPAR 2020 comes with a novel collection of unvalidated TF-binding profiles for which our curators did not find orthogonal supporting evidence in the literature. This collection has a dedicated web form to engage the community in the curation of unvalidated TF-binding profiles. Moreover, we created a Q&A forum to ease the communication between the user community and JASPAR curators. Finally, we updated the genomic tracks, inference tool, and TF-binding profile similarity clusters. All the data is available through the JASPAR website, its associated RESTful API, and through the JASPAR2020 R/Bioconductor package.

Long non-coding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes and yet, their functions remain largely unknown. We systematically knockdown 285 lncRNAs expression in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNA exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest to-date lncRNA knockdown dataset with molecular phenotyping (over 1,000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

Cap Analysis of Gene Expression (CAGE) is one of the most popular 5'-end sequencing methods. In a single experiment, CAGE can be used to locate and quantify the expression of both Transcription Start Sites (TSSs) and enhancers. This is workflow is a case study on how to use the CAGEfightR package to orchestrate analysis of CAGE data within the Bioconductor project. This workflow starts from BigWig-files and covers both basic CAGE analyses such as identifying, quantifying and annotating TSSs and enhancers, advanced analysis such as finding interacting TSS-enhancer pairs and enhancer clusters, to differential expression analysis and alternative TSS usage. R-code, discussion and references are intertwined to help provide guidelines for future CAGE studies of the same kind.

Fission yeast, Schizosaccharomyces pombe, is an attractive model organism for transcriptional and chromatin biology research. Such research is contingent on accurate annotation of transcription start sites (TSSs). However, comprehensive genome-wide maps of TSSs and their usage across commonly applied laboratory conditions and treatments for S. pombe are lacking. To this end, we profiled TSS activity genome-wide in S. pombe cultures exposed to heat shock, nitrogen starvation, hydrogen peroxide and two commonly applied media, YES and EMM2, using Cap Analysis of Gene Expression (CAGE). CAGE-based annotation of TSSs is substantially more accurate than existing PomBase annotation; on average, CAGE TSSs fall 50-75 bp downstream of PomBase TSSs and co-localize with nucleosome boundaries. In contrast to higher eukaryotes, S. pombe does not show sharp and dispersed TSS distributions. Our data recapitulate known S. pombe stress expression response patterns and identify stress- and mediaresponsive alternative TSSs. Notably, alteration of growth medium induces changes of similar magnitude as some stressors. We show a link between nucleosome occupancy and genetic variation, and that the proximal promoter region is genetically diverse between S. pombe strains. Our detailed TSS map constitute a central resource for S. pombe gene regulation research.

Upon the first publication of the fifth iteration of the Functional Annotation of Mammalian Genomes collaborative project, FANTOM5, we gathered a series of primary data and database systems into the FANTOM web resource (http://fantom.gsc.riken.jp) to facilitate researchers to explore transcriptional regulation and cellular states. In the course of the collaboration, primary data and analysis results have been expanded, and functionalities of the database systems enhanced. We believe that our data and web systems are invaluable resources, and we think the scientific community will benefit for this recent update to deepen their understanding of mammalian cellular organization. We introduce the contents of FANTOM5 here, report recent updates in the web resource and provide future perspectives. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/)

Summary Alternative splicing is an important mechanism involved in health and disease. Recent work highlights the importance of investigating genome-wide changes in splicing patterns and the subsequent functional consequences. Current computational methods only support such analysis on a gene-by-gene basis. Therefore, we extended IsoformSwitchAnalyzeR R library to enable analysis of genome-wide changes in specific types of alternative splicing and predicted functional consequences of the resulting isoform switches. As a case study, we analyzed RNA-seq data from The Cancer Genome Atlas and found systematic changes in alternative splicing and the consequences of the associated isoform switches. Availability and implementation Windows, Linux and Mac OS: http://bioconductor.org/packages/IsoformSwitchAnalyzeR. Supplementary information Supplementary data are available at Bioinformatics online.

We developed the CAGEfightR R/Biconductor-package for analyzing CAGE data. CAGEfightR allows for fast and memory efficient identification of transcription start sites (TSSs) and predicted enhancers. Downstream analysis, including annotation, quantification, visualization and TSS shape statistics are implemented in easy-to-use functions. The package is freely available at https://bioconductor.org/packages/CAGEfightR

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits., Author summary We discover that genetic variants associated with specific diseases have more in common with each other than we have previously seen. Specifically, variants associated with the same disease tend to be in parts of the genome that are turned on or off in similar complex patterns across many different cell types. We discover that genetic variants associated with specific diseases are found within regulatory elements that share patterns of expression. Specifically, variants associated with the same disease tend to be in parts of the genome that are turned on or off together in similar complex patterns across many different cell types. Knowing this helps us to find new variants associated with some diseases, and to better understand the genetic causes of other diseases. Furthermore, we discover that the genetic causes of inflammatory bowel disease fall into two distinct patterns, indicating that two aetiologically-distinct endotypes of this condition exist. Unlike other methods to learn about disease mechanisms from genetic information, our approach does not require any knowledge or assumptions about the genes themselves–it depends only on the patterns in which parts of the genome are activated in different cell types.

Genomics consortia have produced large datasets profiling the expression of genes, micro-RNAs, enhancers and more across human tissues or cells. There is a need for intuitive tools to select subsets of such data that is the most relevant for specific studies. To this end, we present SlideBase, a web tool which offers a new way of selecting genes, promoters, enhancers and microRNAs that are preferentially expressed/used in a specified set of cells/tissues, based on the use of interactive sliders. With the help of sliders, SlideBase enables users to define custom expression thresholds for individual cell types/tissues, producing sets of genes, enhancers etc. which satisfy these constraints. Changes in slider settings result in simultaneous changes in the selected sets, updated in real time. SlideBase is linked to major databases from genomics consortia, including FANTOM, GTEx, The Human Protein Atlas and BioGPS. Database URL: http://slidebase.binf.ku.dk

JASPAR (http://jaspar.genereg.net) is the leading open-access database of matrix profiles describing the DNA-binding patterns of transcription factors (TFs) and other proteins interacting with DNA in a sequence-specific manner. Its fourth major release is the largest expansion of the core database to date: the database now holds 457 non-redundant, curated profiles. The new entries include the first batch of profiles derived from ChIP-seq and ChIPchip whole-genome binding experiments, and 177 yeast TF binding profiles. The introduction of a yeast division brings the convenience of JASPAR to an active research community. As binding models are refined by newer data, the JASPAR database now uses versioning of matrices: in this release, 12% of the older models were updated to improved versions. Classification of TF families has been improved by adopting a new DNA-binding domain nomenclature. A curated catalog of mammalian TFs is provided, extending the use of the JASPAR profiles to additional TFs belonging to the same structural family. The changes in the database set the system ready for more rapid acquisition of new high-throughput data sources. Additionally, three new special collections provide matrix profile data produced by recent alternative high-throughput approaches. publishedVersion

The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). This resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0560-6) contains supplementary material, which is available to authorized users.

A comprehensive understanding of protein and regulatory networks is strictly dependent on the complete description of the transcriptome of cells. After the determination of the genome sequence of several mammalian species, gene identification is based on in silico predictions followed by evidence of transcription. Conservative estimates suggest that there are about 20 000 protein-encoding genes in the mammalian genome. In the last few years the combination of full-length cDNA cloning, cap-analysis gene expression (CAGE) tag sequencing and tiling arrays experiments have unveiled unexpected additional complexities in the transcriptome. Here we describe the current view of the mammalian transcriptome focusing on transcripts diversity, the growing non-coding RNA world, the organization of transcriptional units in the genome and promoter structures. In-depth analysis of the brain transcriptome has been challenging due to the cellular complexity of this organ. Here we present a computational analysis of CAGE data from different regions of the central nervous system, suggesting distinctive mechanisms of brain-specific transcription.

The analysis of regulatory regions in genome sequences is strongly based on the detection of potential transcription factor binding sites. The preferred models for representation of transcription factor binding specificity have been termed position-specific scoring matrices. JASPAR is an open-access database of annotated, high-quality, matrix-based transcription factor binding site profiles for multicellular eukaryotes. The profiles were derived exclusively from sets of nucleotide sequences experimentally demonstrated to bind transcription factors. The database is complemented by a web interface for browsing, searching and subset selection, an online sequence analysis utility and a suite of programming tools for genome-wide and comparative genomic analysis of regulatory regions. JASPAR is available at http://jaspar. cgb.ki.se.

Enhancers control the correct temporal and cell-type-specific activation of gene expression in multicellular eukaryotes. Knowing their properties, regulatory activity and targets is crucial to understand the regulation of differentiation and homeostasis. Here we use the FANTOM5 panel of samples, covering the majority of human tissues and cell types, to produce an atlas of active, in vivo-transcribed enhancers. We show that enhancers share properties with CpG-poor messenger RNA promoters but produce bidirectional, exosome-sensitive, relatively short unspliced RNAs, the generation of which is strongly related to enhancer activity. The atlas is used to compare regulatory programs between different cells at unprecedented depth, to identify disease-associated regulatory single nucleotide polymorphisms, and to classify cell-type-specific and ubiquitous enhancers. We further explore the utility of enhancer redundancy, which explains gene expression strength rather than expression patterns. The online FANTOM5 enhancer atlas represents a unique resource for studies on cell-type-specific enhancers and gene regulation Enhancers control the correct temporal and cell-type-specific activation of gene expression in multicellular eukaryotes. Knowing their properties, regulatory activity and targets is crucial to understand the regulation of differentiation and homeostasis. Here we use the FANTOM5 panel of samples, covering the majority of human tissues and cell types, to produce an atlas of active, in vivo-transcribed enhancers. We show that enhancers share properties with CpG-poor messenger RNA promoters but produce bidirectional, exosome-sensitive, relatively short unspliced RNAs, the generation of which is strongly related to enhancer activity. The atlas is used to compare regulatory programs between different cells at unprecedented depth, to identify disease-associated regulatory single nucleotide polymorphisms, and to classify cell-type-specific and ubiquitous enhancers. We further explore the utility of enhancer redundancy, which explains gene expression strength rather than expression patterns. The online FANTOM5 enhancer atlas represents a unique resource for studies on cell-type-specific enhancers and gene regulation.

Genes that are frequently deregulated in cancer are clinically attractive as both potential pan-cancer diagnostic markers and therapeutic targets. Here we compared Cap Analysis of Gene Expression (CAGE) profiles from 225 cancer cell lines and 339 corresponding primary cell samples to identify transcripts that are recurrently deregulated in a broad range of cancer types. CAGE is a 5’ sequence tag technology that globally determines transcription start sites (TSS) in the genome and their expression levels. This allowed us to assess novel aspects of the cancer transcriptome. First, we identified (at promoter resolution) hundreds of protein coding and long non-coding transcripts that are commonly de-regulated in cancer cell lines. Next, we showed that promoters that overlap repetitive elements (especially SINE/Alu and LTR/ERV1 elements) are often upregulated in cancer. In particular, a specific repeat family, REP522 (largely palindromic, unclassified interspersed repeat of ∼1.8Kb in size), was strongly enriched for the most up-regulated promoters. To our knowledge this is the first report implicating REP522 activation in cancer. Then, taking advantage of the fact that CAGE data can be used to estimate the activity of enhancers from balanced bidirectional transcription we identified 90 enhancer RNA producing regions that are recurrently activated in cancer cell lines. With ENCODE ChIA-PET data, we linked 16 of the cancer-activated enhancers to promoters of known cancer related genes. Finally, to confirm that our results are relevant to clinical tumors we performed complementary analysis in RNA-seq data from 4,055 tumors and 563 normal tissues profiled by The Cancer Genome Atlas (TCGA) and we identified a core set of pan-cancer biomarkers (of both coding and non-coding transcripts) that are recurrently perturbed in both the FANTOM5 and TCGA datasets. In summary, our extensive transcriptome analysis identified a comprehensive set of candidate biomarkers with pan-cancer potential, and extended the perspective of enhancers and repetitive elements that are recurrently activated during carcinogenesis. Citation Format: Bogumil Kaczkowski, Yuji Tanaka, Hideya Kawaji, Albin Sandelin, Robin Andersson, Masayoshi Itoh, Timo Lassmann, Yoshihide Hayashizaki, Piero Carninci, Alistair R.R. Forrest, FANTOM5 Consortium. Recurrent transcriptome alterations across multiple cancer types. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2897.

Promoters are crucial for gene regulation. They vary greatly in terms of associated regulatory elements, sequence motifs, the choice of transcription start sites and other features. Several technologies that harness next-generation sequencing have enabled recent advances in identifying promoters and their features, helping researchers who are investigating functional categories of promoters and their modes of regulation. Additional features of promoters that are being characterized include types of histone modifications, nucleosome positioning, RNA polymerase pausing and novel small RNAs. In this Review, we discuss recent findings relating to metazoan promoters and how these findings are leading to a revised picture of what a gene promoter is and how it works.

A central question in cellular biology is how the cell regulates transcription and discerns when and where to initiate it. Locating transcription start sites (TSSs), the signals that specify them, and ultimately elucidating the mechanisms of regulated initiation has therefore been a recurrent theme. In recent years substantial progress has been made towards this goal, spurred by the possibility of applying genome-wide, sequencing-based analysis. We now have a large collection of high-resolution datasets identifying locations of TSSs, protein–DNA interactions, and chromatin features over whole genomes; the field is now faced with the daunting challenge of translating these descriptive maps into quantitative and predictive models describing the underlying biology. We review here the genomic and chromatin features that underlie TSS selection and usage, focusing on the differences between the major classes of core promoters.

Long noncoding RNAs (lncRNAs) comprise a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins. Recent genome-wide studies in humans and the mouse have annotated lncRNAs expressed in cell lines and adult tissues, but a systematic analysis of lncRNAs expressed during vertebrate embryogenesis has been elusive. To identify lncRNAs with potential functions in vertebrate embryogenesis, we performed a time-series of RNA-seq experiments at eight stages during early zebrafish development. We reconstructed 56,535 high-confidence transcripts in 28,912 loci, recovering the vast majority of expressed RefSeq transcripts while identifying thousands of novel isoforms and expressed loci. We defined a stringent set of 1133 noncoding multi-exonic transcripts expressed during embryogenesis. These include long intergenic ncRNAs (lincRNAs), intronic overlapping lncRNAs, exonic antisense overlapping lncRNAs, and precursors for small RNAs (sRNAs). Zebrafish lncRNAs share many of the characteristics of their mammalian counterparts: relatively short length, low exon number, low expression, and conservation levels comparable to that of introns. Subsets of lncRNAs carry chromatin signatures characteristic of genes with developmental functions. The temporal expression profile of lncRNAs revealed two novel properties: lncRNAs are expressed in narrower time windows than are protein-coding genes and are specifically enriched in early-stage embryos. In addition, several lncRNAs show tissue-specific expression and distinct subcellular localization patterns. Integrative computational analyses associated individual lncRNAs with specific pathways and functions, ranging from cell cycle regulation to morphogenesis. Our study provides the first systematic identification of lncRNAs in a vertebrate embryo and forms the foundation for future genetic, genomic, and evolutionary studies., National Human Genome Research Institute (U.S.) (Grant 1RO1HG005111-01)

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.

Background A central question in molecular biology is how transcriptional regulatory elements (TREs) act in combination. Recent high-throughput data provide us with the location of multiple regulatory regions for multiple regulators, and thus with the possibility of analyzing the multivariate distribution of the occurrences of these TREs along the genome. Results We present a model of TRE occurrences known as the Hawkes process. We illustrate the use of this model by analyzing two different publically available data sets. We are able to model, in detail, how the occurrence of one TRE is affected by the occurrences of others, and we can test a range of natural hypotheses about the dependencies among the TRE occurrences. In contrast to earlier efforts, pre-processing steps such as clustering or binning are not needed, and we thus retain information about the dependencies among the TREs that is otherwise lost. For each of the two data sets we provide two results: first, a qualitative description of the dependencies among the occurrences of the TREs, and second, quantitative results on the favored or avoided distances between the different TREs. Conclusions The Hawkes process is a novel way of modeling the joint occurrences of multiple TREs along the genome that is capable of providing new insights into dependencies among elements involved in transcriptional regulation. The method is available as an R package from http://www.math.ku.dk/~richard/ppstat/.

Chromosomal translocations of transcription factors generating fusion proteins with aberrant transcriptional activity are common in acute leukemia. In acute promyelocytic leukemia (APL), the promyelocytic leukemia-retinoic-acid receptor alpha (PML-RARA) fusion protein, which emerges as a consequence of the t(15;17) translocation, acts as a transcriptional repressor that blocks neutrophil differentiation at the promyelocyte (PM) stage. In this study, we used publicly available microarray data sets and identified signatures of genes dysregulated in APL by comparison of gene expression profiles of APL cells and normal PMs representing the same stage of differentiation. We next subjected our identified APL signatures of dysregulated genes to a series of computational analyses leading to (i) the finding that APL cells show stem cell properties with respect to gene expression and transcriptional regulation, and (ii) the identification of candidate drugs and drug targets for therapeutic interventions. Significantly, our study provides a conceptual framework that can be applied to any subtype of AML and cancer in general to uncover novel information from published microarray data sets at low cost. In a broader perspective, our study provides strong evidence that genomic strategies might be used in a clinical setting to prospectively identify candidate drugs that subsequently are validated in vitro to define the most effective drug combination for individual cancer patients on a rational basis.

A major goal in post-genome biology is the complete mapping of the gene regulatory networks for every organism. Identification of regulatory elements is a prerequisite for realizing this ambitious goal. A common problem is finding regulatory patterns in promoters of a group of co-expressed genes, but contemporary methods are challenged by the size and diversity of regulatory regions in higher metazoans. Two key issues are the small amount of information contained in a pattern compared to the large promoter regions and the repetitive characteristics of genomic DNA, which both lead to “pattern drowning”. We present a new computational method for identifying transcription factor binding sites in promoters using a discriminatory approach with a large negative set encompassing a significant sample of the promoters from the relevant genome. The sequences are described by a probabilistic model and the most discriminatory motifs are identified by maximizing the probability of the sets given the motif model and prior probabilities of motif occurrences in both sets. Due to the large number of promoters in the negative set, an enhanced suffix array is used to improve speed and performance. Using our method, we demonstrate higher accuracy than the best of contemporary methods, high robustness when extending the length of the input sequences and a strong correlation between our objective function and the correct solution. Using a large background set of real promoters instead of a simplified model leads to higher discriminatory power and markedly reduces the need for repeat masking; a common pre-processing step for other pattern finders., Author Summary In the years following the sequencing of the human genome focus have shifted towards trying to understand how this blueprint results in the diversity of cells that we observe. Part of the answer lies in the regulation of transcription and how the proteins responsible for this recognize where they should attach to the DNA. This is a well studied problem, but most methods developed for this have a hard time dealing with the heterogeneity of the mammalian genomes. Here we present a method that greatly improves the efficiency of this search by contrasting the DNA with a large number of background DNA sequences. This enables us to handle repetitive segments of the genome that may be functional, but are usually considered intractable by most methods.

Finding the regulatory mechanisms responsible for gene expression remains one of the most important challenges for biomedical research. A major focus in cellular biology is to find functional transcription factor binding sites (TFBS) responsible for the regulation of a downstream gene. As wet-lab methods are time consuming and expensive, it is not realistic to identify TFBS for all uncharacterized genes in the genome by purely experimental means. Computational methods aimed at predicting potential regulatory regions can increase the efficiency of wet-lab experiments significantly. Here, methods for building quantitative models describing the binding preferences of transcription factors based on literature-derived data are presented, as well as a general protocol for scanning promoters using cross-species comparison as a filter (phylogenetic footprinting).

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view about chromatin structure has emerged, including its interrelationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded novel mechanistic and evolutionary insights about the functional landscape of the human genome. Together, these studies are defining a path forward to pursue a more-comprehensive characterisation of human genome function.

JASPAR is the most complete open-access collection of transcription factor binding site (TFBS) matrices. In this new release, JASPAR grows into a meta-database of collections of TFBS models derived by diverse approaches. We present JASPAR CORE--an expanded version of the original, non-redundant collection of annotated, high-quality matrix-based transcription factor binding profiles, JASPAR FAM--a collection of familial TFBS models and JASPAR phyloFACTS--a set of matrices computationally derived from statistically overrepresented, evolutionarily conserved regulatory region motifs from mammalian genomes. JASPAR phyloFACTS serves as a non-redundant extension to JASPAR CORE, enhancing the overall breadth of JASPAR for promoter sequence analysis. The new release of JASPAR is available at http://jaspar.genereg.net.

ConSite is a user-friendly, web-based tool for finding cis-regulatory elements in genomic sequences. Predictions are based on the integration of binding site prediction generated with high-quality transcription factor models and cross-species comparison filtering (phylogenetic footprinting). By incorporating evolutionary constraints, selectivity is increased by an order of magnitude as compared to single-sequence analysis. ConSite offers several unique features, including an interactive expert system for retrieving orthologous regulatory sequences. Programming modules and biological databases that form the foundation of the ConSite service are freely available to the research community. ConSite is available at http:/www.phylofoot.org/consite.

The compilation of multiple metazoan genome sequences and the deluge of large-scale expression data have combined to motivate the maturation of bioinformatics methods for the analysis of sequences that regulate gene transcription. Historically, these bioinformatics methods have been plagued by poor predictive specificity, but new bioinformatics algorithms that accelerate the identification of regulatory regions are drawing disgruntled users back to their keyboards. However, these new approaches and software are not without problems. Here, we introduce the purpose and mechanisms of the leading algorithms, with a particular emphasis on metazoan sequence analysis. We identify key issues that users should take into consideration in interpreting the results and provide an online training example to help researchers who wish to test online tools before taking an independent foray into the bioinformatics of transcription regulation.

Background: Evolutionarily conserved sequences within or adjoining orthologous genes often serve as critical cis-regulatory regions. Recent studies have identified long, non-coding genomic regions that are perfectly conserved between human and mouse, termed ultra-conserved regions (UCRs). Here, we focus on UCRs that cluster around genes involved in early vertebrate development; genes conserved over 450 million years of vertebrate evolution. Results Based on a high resolution detection procedure, our UCR set enables novel insights into vertebrate genome organization and regulation of developmentally important genes. We find that the genomic positions of deeply conserved UCRs are strongly associated with the locations of genes encoding key regulators of development, with particularly strong positional correlation to transcription factor-encoding genes. Of particular importance is the observation that most UCRs are clustered into arrays that span hundreds of kilobases around their presumptive target genes. Such a hallmark signature is present around several uncharacterized human genes predicted to encode developmentally important DNA-binding proteins. Conclusion The genomic organization of UCRs, combined with previous findings, suggests that UCRs act as essential long-range modulators of gene expression. The exceptional sequence conservation and clustered structure suggests that UCR-mediated molecular events involve greater complexity than traditional DNA binding by transcription factors. The high-resolution UCR collection presented here provides a wealth of target sequences for future experimental studies to determine the nature of the biochemical mechanisms involved in the preservation of arrays of nearly identical non-coding sequences over the course of vertebrate evolution.

Diverse computational and experimental efforts are required to elucidate the control circuitry regulating the transcription of human genes. The fusion of gene-specific promoter analyses with large microarray studies and bioinformatics advances has produced optimism that significant progress can be made in unravelling this complex network. Within bioinformatics, past emphasis for improved pattern discovery has been placed upon "phylogenetic footprinting", the identification of sequences conserved over moderate periods of evolution (e.g. human and mouse comparisons). We introduce a new direction in bioinformatics based on the constraints imposed by the structures of DNA-binding proteins. For most structurally related families of transcription factors, there are clear similarities in the sequences of the sites to which they bind. On the basis of this observation, we construct familial binding profiles for well-characterized transcription factor families. The profiles are shown to classify correctly the structural class of mediating transcription factors for novel motifs in 88% of cases. By incorporating the familial profiles into pattern discovery procedures, we demonstrate that functional binding sites can be found in genomic sequences of dramatically greater length than is possible otherwise. Thus, incorporating familial models can overcome the signal-to-noise challenge that has hindered the transition from microarray data to regulatory control sequences for human genes. Biochemically motivated constraints upon sequence diversity of binding sites will complement the genetically motivated constraints imposed in "phylogenetic footprinting" algorithms.

Dramatic progress in deciphering the regulatory controls in Saccharomyces cerevisiae has been enabled by the fusion of high-throughput genomics technologies with advanced sequence analysis algorithms. Sets of genes likely to function together and with similar expression profiles have been identified in diverse studies. By fusing an advanced pattern recognition algorithm for identification of transcription factor binding sites with a new method for the quantitative comparison of binding properties of transcription factors, we provide an integrated means to move from expression data to biological insights. The Yeast Regulatory Sequence Analysis system, YRSA, combines standard functions with a novel pattern characterization procedure in an intuitive interface designed for use by a broad range of scientists. The features of the system include automated retrieval of user-defined promoter sequences, binding site discovery by pattern recognition, graphical displays of the observed pattern and positions of similar sequences in the specified genes, and comparison of the new pattern against a collection of binding patterns for characterized transcription factors. The comprehensive YRSA system was used to study the regulatory mechanisms of yeast regulons. Analysis of the regulatory controls of a battery of genes induced by DNA damaging agents supports a putative mediating role for the cell-cycle checkpoint regulatory element MCB. YRSA is available at http://yrsa.cgb.ki.se. [YRSA: ancient Scandinavian name meaning old she-bear (Latin Ursus arctos = brown bear/grizzly).]

We present a data filtration and processing pipeline for analysis of nanoCAGE-seq, a variant of the CAGE method allowing for very small amounts of input material, and thus expanding the number of tissue- and cell types available for proteome profiling. For this inherently noisy method, rigorous filter methods, including tag clustering, cluster width and profile filtering, and variance filtering rescue bona fide promoters, allowing for detection of promoter usage, inter-sample promoter switching and detection of new putative promoters. We present result from nanoCAGE from two different studies.

Background: The TATA box, one of the most well studied core promoter elements, is associated with induced, context-specific expression. The lack of precise transcription start site (TSS) locations linked with expression information has impeded genome-wide characterization of the interaction between TATA and the pre-initiation complex. Results: Using a comprehensive set of 5.66 × 106 sequenced 5' cDNA ends from diverse tissues mapped to the mouse genome, we found that the TATA-TSS distance is correlated with the tissue specificity of the downstream transcript. To achieve tissue-specific regulation, the TATA box position relative to the TSS is constrained to a narrow window (-32 to -29), where positions -31 and -30 are the optimal positions for achieving high tissue specificity. Slightly larger spacings can be accommodated only when there is no optimally spaced initiation signal; in contrast, the TATA box like motifs found downstream of position -28 are generally nonfunctional. The strength of the TATA binding protein-DNA interaction plays a subordinate role to spacing in terms of tissue specificity. Furthermore, promoters with different TATA-TSS spacings have distinct features in terms of consensus sequence around the initiation site and distribution of alternative TSSs. Unexpectedly, promoters that have two dominant, consecutive TSSs are TATA depleted and have a novel GGG initiation site consensus. Conclusion: In this report we present the most comprehensive characterization of TATA-TSS spacing and functionality to date. The coupling of spacing to tissue specificity at the transcriptome level provides important clues as to the function of core promoters and the choice of TSS by the pre-initiation complex.

Background: Mammalian promoters do not initiate transcription at single, well defined base pairs, but rather at multiple, alternative start sites spread across a region. We previously characterized the static structures of transcription start site usage within promoters at the base pair level, based on large-scale sequencing of transcript 5' ends. Results: In the present study we begin to explore the internal dynamics of mammalian promoters, and demonstrate that start site selection within many mouse core promoters varies among tissues. We also show that this dynamic usage of start sites is associated with CpG islands, broad and multimodal promoter structures, and imprinting. Conclusion: Our results reveal a new level of biologic complexity within promoters - fine-scale regulation of transcription starting events at the base pair level. These events are likely to be related to epigenetic transcriptional regulation.

SUMMARY Transcription factors have recently been shown to colocalize in hotspot regions of the genome, which are further clustered into super-enhancers. However, the detailed molecular organization of transcription factors at hotspot regions is poorly defined. Here, we have used digital genomic footprinting to precisely define factor localization at a genome-wide level during the early phase of 3T3-L1 adipocyte differentiation, which allows us to obtain detailed molecular insight into how transcription factors target hotspots. We demonstrate the formation of ATF-C/EBP heterodimers at a composite motif on chromatin, and we suggest that this may be a general mechanism for integrating external signals on chromatin. Furthermore, we find evidence of extensive recruitment of transcription factors to hotspots through alternative mechanisms not involving their known motifs and demonstrate that these alternative binding events are functionally important for hotspot formation and activity. Taken together, these findings provide a framework for understanding transcription factor cooperativity in hotspots.

The proper activities of enhancers and gene promoters are essential for coordinated transcription within a cell. Although diverse methodologies have been developed to identify enhancers and promoters, most have tacitly assumed that these elements are distinct. However, studies have unexpectedly shown that regulatory elements may have both enhancer and promoter functions. Here we review these results, focusing on the factors that determine the promoter and/or enhancer activity of regulatory elements. We discuss emerging models that define regulatory elements by accessible DNA and their non-mutually-exclusive abilities to drive transcription initiation (promoter activity) and/or to enhance transcription at other such regions (enhancer activity).

Background: With the increasing depth and decreasing costs of RNA-sequencing researchers are now able to profile the transcriptome with unprecedented detail. These advances not only allow for precise approximation of gene expression levels, but also for the characterization of alternative transcript usage/switching between conditions. Recent software improvements in full-length transcript deconvolution prompted us to develop spliceR, an R package for classification of alternative splicing and prediction of coding potential. Results: spliceR uses the full-length transcripts output from RNA-seq assemblers, to detect single- and multiple exon skipping, alternative donor and acceptor sites, intron retention, alternative first or last exon usage, and mutually exclusive exon events. For each of these events spliceR also annotates the genomic coordinates of the differentially spliced elements facilitating downstream sequence analysis. Furthermore, isoform fraction values are calculated for effective post-filtering, i.e. identification of transcript switching between conditions. Lastly spliceR predicts the coding potential, as well as the potential nonsense mediated decay (NMD) sensitivity of each transcript. Conclusions: spliceR is a easy-to-use tool that allows detection of alternative splicing, transcript switching and NMD sensitivity from RNA-seq data, extending the usability of RNA-seq and assembly technologies. spliceR is implemented as an R package and is freely available from the Bioconductor repository (http://www.bioconductor.org/packages/2.13/bioc/html/spliceR.html).