ORCID Profile
0000-0002-9314-5705
Current Organisation
Children's Medical Research Institute
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Publisher: Oxford University Press (OUP)
Date: 23-07-2018
DOI: 10.1093/BIOINFORMATICS/BTY660
Abstract: Large-scale peptide mass spectrometry (MS)/MS reference libraries are essential for the comprehensive analysis of data-independent acquisition (DIA) MS datasets, providing a comprehensive set of spectra for identification and quantification of proteins. We have developed a novel web-based R-package (iSwathX) for combining reference libraries that is compatible with different DIA analysis software. This open-source web GUI automates the process of normalization and combination of spectral libraries and provides a user-friendly method for performing library format conversions, analysis and visualizations, with no need for programing familiarity. iSwathX is freely accessible at biolinfo.shinyapps.io/iSwathX with the R-package and Shiny source code available from GitHub (noor/iSwathX). Supplementary data are available at Bioinformatics online.
Publisher: Oxford University Press (OUP)
Date: 11-02-2020
DOI: 10.1093/BIB/BBZ163
Abstract: Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.
Publisher: Wiley
Date: 03-2021
DOI: 10.1002/CPZ1.69
Abstract: Data‐independent‐acquisition mass spectrometry (DIA‐MS) is a state‐of‐the‐art proteomic technique for high‐throughput identification and quantification of peptides and proteins. Interpretation of DIA‐MS data relies on the use of a spectral library, which is optimally created from data acquired from the same s les in data‐dependent acquisition (DDA) mode. As DIA‐MS quantification relies on the spectral libraries, having a high‐quality, non‐redundant, and comprehensive spectral library is essential. This article describes the major steps for creating a high‐quality spectral library using a combination of multiple complementary search engines. We discuss appropriate strategies to control the false discovery rate for the final spectral library as a result of merging multiple searches. © 2021 The Authors Current Protocols © 2021 Wiley Periodicals LLC. This article was corrected on 25 July 2022. See the end of the full text for details. Basic Protocol 1 : Searching DDA‐MS files with multiple search engines Basic Protocol 2 : Merging results from multiple search engines Basic Protocol 3 : Creating spectral libraries from merged results Alternate Protocol : Using CLI for automating tasks Support Protocol : Creating concatenated FASTA files
Publisher: American Chemical Society (ACS)
Date: 22-03-2021
DOI: 10.1021/ACS.JPROTEOME.0C00898
Abstract: Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma s les, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).
Publisher: Wiley
Date: 06-2020
DOI: 10.1002/CPBI.101
Publisher: Public Library of Science (PLoS)
Date: 02-10-2015
Publisher: Elsevier
Date: 2019
Publisher: Royal Society of Chemistry (RSC)
Date: 2017
DOI: 10.1039/C7RA00762K
Abstract: R-Spondins regulate the WNT/β-catenin signaling pathway by interacting with leucine rich-repeat containing G-protein coupled receptors (LGR4–6).
Publisher: Elsevier BV
Date: 2022
DOI: 10.1016/J.JPROT.2021.104384
Abstract: The collection of blood plasma is minimally invasive, and the fluid is a rich source of proteins for biomarker studies in both humans and animals. Plasma protein analysis by mass spectrometry (MS) can be challenging, though modern data acquisition strategies, such as sequential window acquisition of all theoretical fragment ion spectra (SWATH), enable reproducible quantitation of hundreds of proteins in non-depleted plasma from humans and laboratory model animals. Although there is strong potential to enhance veterinary and translational research, SWATH-based plasma proteomics in non-laboratory animals is virtually non-existent. One limitation to date is the lack of comprehensively annotated genomes to aid protein identification. The current study established plasma peptide spectral repositories for sheep and cattle that enabled quantification of over 200 proteins in non-depleted plasma using SWATH approach. Moreover, bioinformatics pipeline was developed to leverage inter-species homologies to enhance the depth of baseline libraries and plasma protein quantification in bovids. Finally, the practical utility of using bovid libraries for SWATH data extraction in taxonomically related non-domestic ungulate species (giraffe) has been demonstrated. SIGNIFICANCE: Ability to quickly generate comprehensive spectral libraries is limiting the applicability of data-independent acquisition, such as SWATH, to study proteomes of non-laboratory animals. We describe an approach to obtain relatively shallow foundational plasma repositories from domestic ruminants and employ homology searches to increase the depth of data, which we subsequently extend to unsequenced ungulates using SWATH method. When applied to cross-species proteomics, the number of proteins quantified by our approach far exceeds what is traditionally used in plasma protein tests.
Publisher: Elsevier BV
Date: 10-2019
DOI: 10.1016/J.THERIOGENOLOGY.2019.05.036
Abstract: Reproduction efficiency of male animals is one of the key factors influencing the sustainability of livestock. Mass spectrometry (MS) based proteomics has become an important tool for studying seminal plasma proteomes. In this review, we summarize bioinformatics analysis strategies for current proteomics approaches, for identifying novel biomarkers of reproductive robustness.
Publisher: Elsevier
Date: 2019
Publisher: Cold Spring Harbor Laboratory
Date: 03-03-2023
DOI: 10.1101/2023.03.03.530910
Abstract: Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign s le runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.
Publisher: Public Library of Science (PLoS)
Date: 12-11-2015
Publisher: MDPI AG
Date: 20-12-2018
DOI: 10.3390/IJMS20010016
Abstract: Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra—many of which likely harbour PTMs—through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant s les such as plasma and other body fluids, and well-established tools and options for data interpretation.
Location: Pakistan
No related grants have been discovered for Zainab Noor.