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Cathal Seoighe

Researcher at National University of Ireland, Galway

Publications -  114
Citations -  8223

Cathal Seoighe is an academic researcher from National University of Ireland, Galway. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 37, co-authored 108 publications receiving 7381 citations. Previous affiliations of Cathal Seoighe include University College Dublin & South African National Bioinformatics Institute.

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Inference of Candidate Germline Mutator Loci in Humans from Genome-Wide Haplotype Data.

TL;DR: It is demonstrated that an allele that increases the rate of germline mutation can result in a distinctive signature in the genomic region linked to the affected locus, characterized by a number of haplotype with a locally high proportion of derived alleles, against a background of haplotypes carrying a typical proportion ofderived alleles.
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Alterations in hepatic miRNA expression during negative energy balance in postpartum dairy cattle

TL;DR: This study shows that SNEB affects liver miRNA expression and these miRNAs have putative targets in hepatic genes down-regulated under this condition and this study highlights the potential role ofmiRNAs in transcription regulation of hepatic gene expression during SNEB in dairy cattle.
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Promiscuous mRNA splicing under the control of AIRE in medullary thymic epithelial cells.

TL;DR: The results suggest that developing T lymphocytes are exposed to diverse tissue-restricted splice isoforms in the thymus and that AIRE has a direct or indirect role in this process, representing a novel aspect of its role in the maintenance of immune self-tolerance.
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ATR Restrains DNA Synthesis and Mitotic Catastrophe in Response to CDC7 Inhibition.

TL;DR: It is shown that partial CDC7 inhibition induces ATR mainly through ETAA1, and that if ATR is subsequently inhibited, origin firing is unleashed in a CDK- and CDC7-dependent manner.

CellMix: a comprehensive toolbox for gene expression

TL;DR: CellMix is a nR package that incorporates most state-of-the-art deconvolution methods, into an intuitive and extendible framework, providing a single entry point to explore, assess and disentangle gene expression data from heterogeneous samples.