ArticleNucleic acids research2010
Building promoter aware transcriptional regulatory networks using siRNA perturbation and deepCAGE.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Single-cell RNA sequencing uncovers the nuclear decoy lincRNA PIRAT as a regulator of systemic monocyte immunity during COVID-19.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Selective Activation of AlternativeGenes · 2018Article
- SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles.Bioinformatics (Oxford, England) · 2018Article
- Mapping Mammalian Cell-type-specific Transcriptional Regulatory Networks Using KD-CAGE and ChIP-seq Data in the TC-YIK Cell Line.Frontiers in genetics · 2015Article
- RNA sequencing: from tag-based profiling to resolving complete transcript structure.Cellular and molecular life sciences : CMLS · 2014Review
- 5' end-centered expression profiling using cap-analysis gene expression and next-generation sequencing.Nature protocols · 2012Article
- Automated workflow for preparation of cDNA for cap analysis of gene expression on a single molecule sequencer.PloS one · 2012Article
- NanoCAGE: a high-resolution technique to discover and interrogate cell transcriptomes.Cold Spring Harbor protocols · 2011Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Perturbation and time-course data sets, in combination with computational approaches, can be used to infer transcriptional regulatory networks which ultimately govern the developmental pathways and responses of cells. Here, we individually knocked down the four transcription factors PU.1, IRF8, MYB and SP1 in the human monocyte leukemia THP-1 cell line and profiled the genome-wide transcriptional response of individual transcription starting sites using deep sequencing based Cap Analysis of Gene Expression. From the proximal promoter regions of the responding transcription starting sites, we derived de novo binding-site motifs, characterized their biological function and constructed a network. We found a previously described composite motif for PU.1 and IRF8 that explains the overlapping set of transcriptional responses upon knockdown of either factor.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.