ORCID Profile
0000-0003-4647-436X
Current Organisation
Children's Medical Research Institute
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Publisher: Springer Science and Business Media LLC
Date: 13-03-2014
Publisher: Springer Science and Business Media LLC
Date: 30-07-2020
DOI: 10.1038/S41467-020-17641-3
Abstract: Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight s les containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.
Publisher: Cold Spring Harbor Laboratory
Date: 03-2022
DOI: 10.1101/2022.02.26.482008
Abstract: The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downs ling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at cellmodelpassports.sanger.ac.uk , is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.
Publisher: Unpublished
Date: 2022
Publisher: Cold Spring Harbor Laboratory
Date: 28-09-2023
Publisher: Elsevier BV
Date: 08-2022
Publisher: Springer Science and Business Media LLC
Date: 29-08-2016
Publisher: CSIRO Publishing
Date: 2017
DOI: 10.1071/CH17270
Abstract: The reaction of (–)-β-pinene with KCN under a mild bridged Ritter reaction gave (1S,5R,6S)-2,2,6-trimethyl-3-aza-bicyclo[3.3.1]non-3-en-6-yl acetate that subsequently reacted to provide an unexpected (1S,4R,5R,6S)-4-cyano-2,2,6-trimethyl-3-azabicyclo[3.3.1]nonane-6-yl acetate. The structure of the compound was determined by high-resolution mass spectrometry, and IR and NMR spectroscopy and confirmed by single crystal X-ray crystallography. The compound crystallises in the monoclinic P21 space group, with unit cell parameters a 8.6120 (17), b 7.4570 (15), c 11.189 (2) Å, and β 110.16 (3)°.
Publisher: Elsevier BV
Date: 2017
No related grants have been discovered for Steven Williams.