Dataset Information

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks.

ABSTRACT: The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce ?-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying "personal" gene regulatory network. We validated ?-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome data and motif discovery to identify master regulators with de novo binding sites that result in the up-regulation of nearby oncogenic drivers. ?-cisTarget is available from http://mucistarget.aertslab.org .

SUBMITTER: Kalender Atak Z

PROVIDER: S-EPMC5575942 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks.

Kalender Atak Zeynep Z Imrichova Hana H Svetlichnyy Dmitry D Hulselmans Gert G Christiaens Valerie V Reumers Joke J Ceulemans Hugo H Aerts Stein S

Genome medicine 20170830 1

The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying "personal" gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome ...[more]

PMID: 28854983

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Project description:BackgroundCryptosporidium parvum is a unicellular eukaryote in the phylum Apicomplexa. It is an obligate intracellular parasite that causes diarrhea and is a significant AIDS-related pathogen. Cryptosporidium parvum is not amenable to long-term laboratory cultivation or classical molecular genetic analysis. The parasite exhibits a complex life cycle, a broad host range, and fundamental mechanisms of gene regulation remain unknown. We have used data from the recently sequenced genome of this organism to uncover clues about gene regulation in C. parvum. We have applied two pattern finding algorithms MEME and AlignACE to identify conserved, over-represented motifs in the 5' upstream regions of genes in C. parvum. To support our findings, we have established comparative real-time -PCR expression profiles for the groups of genes examined computationally.ResultsWe find that groups of genes that share a function or belong to a common pathway share upstream motifs. Different motifs are conserved upstream of different groups of genes. Comparative real-time PCR studies show co-expression of genes within each group (in sub-sets) during the life cycle of the parasite, suggesting co-regulation of these genes may be driven by the use of conserved upstream motifs.ConclusionThis is one of the first attempts to characterize cis-regulatory elements in the absence of any previously characterized elements and with very limited expression data (seven genes only). Using de novo pattern finding algorithms, we have identified specific DNA motifs that are conserved upstream of genes belonging to the same metabolic pathway or gene family. We have demonstrated the co-expression of these genes (often in subsets) using comparative real-time-PCR experiments thus establishing evidence for these conserved motifs as putative cis-regulatory elements. Given the lack of prior information concerning expression patterns and organization of promoters in C. parvum we present one of the first investigations of gene regulation in this important human pathogen.

Dataset Information

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks.

Publications

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks.

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