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Exploring the landscape of transcriptome 3’ end diversity (TREND) by applying TRENDseq

ABSTRACT: Purpose: To identify key drivers of transcriptome 3’end diversity (TREND) in neuroblastoma in a genome wide scale. To develop and apply TRENDseq – a tailored method for genome-wide interrogation of the TREND landscape in highly multiplexed libraries. Methods: RNA from BE(2)-C cells transfected with control or specific siRNA’s against indicated TREND regulator were processed according to TRENDseq protocol and sequenced using Illumina HiSeq 2500 or NextSeq 500. Raw data was aligned to human genome hg38 and processed using TRENDseq analysis pipeline. BE(2)-C differentiation samples were processed using the 3’READS protocol (Hoque M et al. 2014, PMID 24590784) Conclusions: We analyzed the diversity of the transcriptome 3’end in response to depletion of 174 potential TREND regulators by RNAi. TRENDseq is capable to deconvolute the dynamics of TREND from highly multiplexed libraries. The screening revealed regulators of various levels of gene expression control (e.g. transcription, splicing, mRNA turnover, etc.) to play an important role in the diversification of the transcriptome 3’ end, affecting over 3600 genes altogether. Data types: 1 – 198 siRNA depletion experiments including 174 knockdowns of putative TREND regulators and 24 mock control samples. 2 – Biological replicates of top TREND regulators depletion and TRENDseq with and without PCF11 co-depletion 3 – Additional independent replicates of PCF11 knockdown 4 – BE(2)-C 3’READS upon differentiation

ORGANISM(S): Homo sapiens

PROVIDER: GSE95057 | GEO | 2018/12/14

REPOSITORIES: GEO

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