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Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_THP1_GSK-LSD1]

ABSTRACT: Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_THP1_GSK-LSD1]

PROVIDER: PRJNA841985 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR19379115.fastq.gz	Fastqsanger.gz
	SRR19379116.fastq.gz	Fastqsanger.gz
	SRR19379117.fastq.gz	Fastqsanger.gz
	SRR19379118.fastq.gz	Fastqsanger.gz
	SRR19379119.fastq.gz	Fastqsanger.gz

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Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework

Project description:Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework

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Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [RNAseq_THP1_GSK-LSD1]

Project description:Genome-wide transcription factor binding profiles provide valuable insights into hematopoietic development and malignancies. Today, advanced computational methods exploit cis-regulatory information to predict complex gene regulatory networks controlled by transcription factors. However, these prediction methods still require large amounts of reference data or experimental expertise. Here, we present a low-requirement network-based computational framework that exploits bidirectional transcription marked regulatory elements to identify important transcription factors driving hematopoietic decision making. Using CAGE-seq we predicted TF binding and confirmed experimentally validated TF important in cell conversion strategies by exploiting bidirectional regions. Next, we applied our framework to predict driving transcription factors in induced normal erythropoiesis and early myelopoiesis, as well as in acute myeloid leukemia associated MLL-AF9-driven immortalisation. Our approach allowed the identification of experimentally validated as well as thus far unexplored transcription factors in these processes.

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Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_THP1_GSK-LSD1]

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Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_MLLAF9-AML]

Project description:Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_MLLAF9-AML]

| PRJNA841986 | ENA

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [RNAseq_THP1_GSK-LSD1]

Project description:Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [RNAseq_THP1_GSK-LSD1]

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Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_MLLAF9-AML 2]

Project description:Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_MLLAF9-AML 2]

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Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework.

Project description:Advanced computational methods exploit gene expression and epigenetic datasets to predict gene regulatory networks controlled by transcription factors (TFs). These methods have identified cell fate determining TFs but require large amounts of reference data and experimental expertise. Here, we present an easy to use network-based computational framework that exploits enhancers defined by bidirectional transcription, using as sole input CAGE sequencing data to correctly predict TFs key to various human cell types. Next, we applied this Analysis Algorithm for Networks Specified by Enhancers based on CAGE (ANANSE-CAGE) to predict TFs driving red and white blood cell development, and THP-1 leukemia cell immortalization. Further, we predicted TFs that are differentially important to either cell line- or primary- associated MLL-AF9-driven gene programs, and in primary MLL-AF9 acute leukemia. Our approach identified experimentally validated as well as thus far unexplored TFs in these processes. ANANSE-CAGE will be useful to identify transcription factors that are key to any cell fate change using only CAGE-seq data as input.

| S-EPMC9636203 | biostudies-literature

Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing

Project description:This SuperSeries is composed of the following subset Series: GSE14694: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (reproducibility) GSE14695: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (standards) Refer to individual Series

2009-06-02 | E-GEOD-14696 | biostudies-arrayexpress

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