Adam Cribbs on Muck Rack

Adam Cribbs

Oxford
As seen in: Color Research & Application, Nature, Frontiers, PNAS, WFTV-TV (Orlando, FL), WSOC-TV (Charlotte, NC), WPXI-TV (Pittsburgh, PA), LabRoots, The Journal of Biological Chemistry
#MRC CDA fellow| @UniofOxford within dept @ndorms| long-read sequencing| single-cell technology development| multiple myeloma| bone cancer
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Articles

Chromatin-focused genetic and chemical screens identify BRPF1 as a targetable vulnerability in Taxol-resistant triple-negative breast cancer - Experimental & Molecular Medicine

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By Ahmet Cingoz, Ebru Yilmaz, Buse Cevatemre, Hamzah Syed, Adam Cribbs, Ceyda Acilan, Tamer T. Onder
| Nature Verified
Abstract Triple-negative breast cancer (TNBC) is a particularly aggressive and frequently recurring form of breast cancer, where chemotherapy is the primary treatment approach. Unfortunately, the development of resistance to chemotherapy poses a considerable challenge, restricting the already limited therapeutic alternatives for recurrent cases. Here, we generated two Taxol-resistant TNBC cell lines with a dose-escalation method to mimic chemotherapy resistance in vitro.
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mclUMI: Markov clustering of unique molecular identifiers enables dynamic removal of PCR duplicates

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By Jianfeng Sun, Shuang Li, Stefan Canzar, Adam Cribbs
| biorxiv.org
Abstract Molecular quantification in high-throughput sequencing experiments relies on accurate identification and removal of polymerase chain reaction (PCR) duplicates. The use of Unique Molecular Identifiers (UMIs) in sequencing protocols has become a standard approach for distinguishing molecular identities. However, PCR artefacts and sequencing errors in UMIs present a significant challenge for effective UMI collapsing and accurate molecular counting.
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Tresor: An integrated platform for simulating transcriptomic reads with realistic PCR error representation across various RNA sequencing technologies

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By Jianfeng Sun, Adam Cribbs
| biorxiv.org
Abstract The rapid advancement of high-throughput sequencing technologies has spurred the development of numerous computational tools designed to identify gene expression patterns from growing datasets at both bulk and single-cell sequencing levels. The recent advent of longread sequencing technologies has further accelerated the availability and refinement of these tools.
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