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

Balwierz, Piotr J.; Carninci, Piero; Daub, Carsten O.; Kawai, Jun; Hayashizaki, Yoshihide; Belle, Werner Van; Beisel, Christian; Nimwegen, Erik van

doi:10.1186/gb-2009-10-7-r79

Cited by 133 publications

(139 citation statements)

References 28 publications

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“…PCR amplification and reverse transcription artifacts were found to be nonnegligible in both Balwierz et al (2009) Marioni et al (2008) found that variation across lanes generally follows a Poisson sampling process, they did observe considerably more variation for a nonnegligible number of genes (on the order of 10 2 ). Balanced blocks by multiplexing: To eliminate confounding caused by batch or lane effects, consider the situation in which all samples of RNA are pooled into the same batch and then sequenced in one lane of a flow cell.…”

Section: Balanced Block Designsmentioning

confidence: 99%

Statistical Design and Analysis of RNA Sequencing Data

2010

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Next-generation sequencing technologies are quickly becoming the preferred approach for characterizing and quantifying entire genomes. Even though data produced from these technologies are proving to be the most informative of any thus far, very little attention has been paid to fundamental design aspects of data collection and analysis, namely sampling, randomization, replication, and blocking. We discuss these concepts in an RNA sequencing framework. Using simulations we demonstrate the benefits of collecting replicated RNA sequencing data according to well known statistical designs that partition the sources of biological and technical variation. Examples of these designs and their corresponding models are presented with the goal of testing differential expression.

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Section: Balanced Block Designsmentioning

confidence: 99%

Statistical Design and Analysis of RNA Sequencing Data

2010

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“…The abundance of CAGE tags that map to a putative promoter region provides quantitative measurement of the extent of transcription initiation at that site; this is capable of estimating expression of the associated genes (Balwierz et al 2009;Murata et al 2014). In this way, we sought to identify promoters with differential activity, differentially-expressed genes across the three states surveyed by our CAGE experiment.…”

Section: Differential Activity Of D Pulex Promotersmentioning

confidence: 99%

Promoter Architecture and Sex-Specific Gene Expression in Daphnia pulex

et al. 2016

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Large-scale transcription start site (TSS) profiling produces a high-resolution, quantitative picture of transcription initiation and core promoter locations within a genome. However, application of TSS profiling to date has largely been restricted to a small set of prominent model systems. We sought to characterize the cis-regulatory landscape of the water flea Daphnia pulex, an emerging model arthropod that reproduces both asexually (via parthenogenesis) and sexually (via meiosis). We performed Cap Analysis of Gene Expression (CAGE) with RNA isolated from D. pulex within three developmental states: sexual females, asexual females, and males. Identified TSSs were utilized to generate a "Daphnia Promoter Atlas," i.e., a catalog of active promoters across the surveyed states. Analysis of the distribution of promoters revealed evidence for widespread alternative promoter usage in D. pulex, in addition to a prominent fraction of compactly-arranged promoters in divergent orientations. We carried out de novo motif discovery using CAGEdefined TSSs and identified eight candidate core promoter motifs; this collection includes canonical promoter elements (e.g., TATA and Initiator) in addition to others lacking obvious orthologs. A comparison of promoter activities found evidence for considerable statespecific differential gene expression between states. Our work represents the first global definition of transcription initiation and promoter architecture in crustaceans. The Daphnia Promoter Atlas presented here provides a valuable resource for comparative study of cis-regulatory regions in metazoans, as well as for investigations into the circuitries that underpin meiosis and parthenogenesis.

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“…To account for this, we modeled the stranded signal throughout genes as a mixture of signal originating from promoter regions and background. We modeled the background as a mixture of signal linearly proportional to transcript expression level as measured by stranded RNA-seq of total RNA (Methods) and uniform unstranded signal, which we treated as random noise in a manner similar to Balwierz et al (2009). All CAGE tags that could be explained by our model as RNA background or random noise (18% of mapped tags) were removed from subsequent analysis (Supplemental Methods), resulting in a set of 21 million filtered aligned CAGE tags.…”

Section: Massively Parallel Mapping Of Tsss In the Drosophila Embryo mentioning

confidence: 99%

Genome-wide analysis of promoter architecture in Drosophila melanogaster

Hoskins

Landolin²,

Brown³

et al. 2010

Genome Res.

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Core promoters are critical regions for gene regulation in higher eukaryotes. However, the boundaries of promoter regions, the relative rates of initiation at the transcription start sites (TSSs) distributed within them, and the functional significance of promoter architecture remain poorly understood. We produced a high-resolution map of promoters active in the Drosophila melanogaster embryo by integrating data from three independent and complementary methods: 21 million cap analysis of gene expression (CAGE) tags, 1.2 million RNA ligase mediated rapid amplification of cDNA ends (RLM-RACE) reads, and 50,000 cap-trapped expressed sequence tags (ESTs). We defined 12,454 promoters of 8037 genes. Our analysis indicates that, due to non-promoter-associated RNA background signal, previous studies have likely overestimated the number of promoter-associated CAGE clusters by fivefold. We show that TSS distributions form a complex continuum of shapes, and that promoters active in the embryo and adult have highly similar shapes in 95% of cases. This suggests that these distributions are generally determined by static elements such as local DNA sequence and are not modulated by dynamic signals such as histone modifications. Transcription factor binding motifs are differentially enriched as a function of promoter shape, and peaked promoter shape is correlated with both temporal and spatial regulation of gene expression. Our results contribute to the emerging view that core promoters are functionally diverse and control patterning of gene expression in Drosophila and mammals.

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

Cited by 133 publications

References 28 publications

Statistical Design and Analysis of RNA Sequencing Data

Statistical Design and Analysis of RNA Sequencing Data

Promoter Architecture and Sex-Specific Gene Expression in Daphnia pulex

Genome-wide analysis of promoter architecture in Drosophila melanogaster

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