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Ian Sudbery

Researcher at University of Sheffield

Publications -  34
Citations -  4103

Ian Sudbery is an academic researcher from University of Sheffield. The author has contributed to research in topics: Gene & Cell cycle. The author has an hindex of 17, co-authored 31 publications receiving 3091 citations. Previous affiliations of Ian Sudbery include Harvard University & Wellcome Trust Sanger Institute.

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Sequencing depth and coverage: key considerations in genomic analyses

TL;DR: The issue of sequencing depth in the design of next-generation sequencing experiments is discussed and current guidelines and precedents on the issue of coverage are reviewed for four major study designs, including de novo genome sequencing, genome resequencing, transcriptome sequencing and genomic location analyses.
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UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy

TL;DR: It is shown that errors in the UMI sequence are common and network-based methods to account for these errors when identifying PCR duplicates are introduced, demonstrating the value of properly accounting for errors in UMIs.
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KDM2B links the Polycomb Repressive Complex 1 (PRC1) to recognition of CpG islands

TL;DR: It is discovered that KDM2B (FBXL10) specifically recognizes non-methylated DNA in CGIs and recruits the polycomb repressive complex 1 (PRC1), which contributes to histone H2A lysine 119 ubiquitylation (H2AK119ub1) and gene repression.
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Apoptosis induced by environmental stresses and amphotericin B in Candida albicans.

TL;DR: A demonstration of apoptosis in a medically important fungal pathogen Candida albicans was associated with an accumulation of cells in the G2/M phase of the cell cycle, and under some apoptosis-inducing conditions, significant proportions of yeast cells switched to hyphal growth before dying.
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Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

TL;DR: Alevin’s approach to UMI deduplication considers transcript-level constraints on the molecules from which UMIs may have arisen and accounts for both gene-unique reads and reads that multimap between genes, which addresses the inherent bias in existing tools which discard gene-ambiguous reads.