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Challenges and Opportunities for Single-cell Computational Proteomics
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DOI：10.1016/j.mcpro.2023.100518.png)
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Abstract
En 中文
Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quan-tifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying as-sumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible op-timizations and solutions that could be used to address the differences in single-cell data.
Keywords:
DATA-INDEPENDENT-ACQUISITION
RELATIVE PROTEIN QUANTIFICATION
MASS-SPECTROMETRY
PEPTIDE IDENTIFICATION
SOFTWARE TOOLS
DATA SETS
TANDEM
STRATEGY
ACCURACY
ABUNDANCES
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