Locus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics

Lu, Wenyuan; Liu, Xiaohui; Liu, Shanshan; Cao, Weiqian; Zhang, Yang; Yang, Pengyuan

doi:10.1038/srep43959

Cited by 16 publications

(19 citation statements)

References 37 publications

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“…These features predict the relative hydrophobicity of peptide sequences and thus RTs for LC used with MS [24][25][26][27][28][29][30]. The predicted RTs can be improved by implementing machine learning algorithms that incorporate confident, observed peptides as training data [14,18,[31][32][33][34]. Predicted peptide RTs are mostly used for scheduling targeted MS/MS analyses where acquisition time is limited, e.g., multiple reaction monitoring [21].…”

Section: Introductionmentioning

confidence: 99%

DART-ID increases single-cell proteome coverage

Chen¹,

Franks

Slavov³

2018

Preprint

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Section: Introductionmentioning

confidence: 99%

DART-ID increases single-cell proteome coverage

Chen¹,

Franks

Slavov³

2018

Preprint

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“…Peptide retention time (RT) in a liquid chromatography tandem mass spectrometry (LC-MS/MS) experiment, i.e., the time points when peptides elute from the LC column as recorded by the instrument, is an intrinsic feature of a peptide. Recent studies have demonstrated the potential of predicting peptide RT on the basis of peptide sequence [26][27][28][29][30][31] . If the RT can be predicted with high accuracy for all peptides, the predicted RT of the peptide in each PSM can be compared with the observed RT associated with the spectrum to determine the quality of the PSMs.…”

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confidence: 99%

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

et al. 2020

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Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.

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“…LsRP (Locus‐specific Retention Predictor) (http://elude.sbc.su.se/) allows for precise prediction of peptide retention time (RT), which is based on amino acid locus information and Support Vector Regression (SVR) algorithms . Indeed, LsRP provides a prediction correlation coefficient of 0.95∼0.99 and outperforms other methods and has tracked up to 30% extra peptides in an extraction RT window of two minutes, thus allowing further opportunities for precision proteomics.…”

Section: Data Preprocessing and Analysismentioning

confidence: 99%

Updates on resources, software tools, and databases for plant proteomics in 2016–2017

Misra¹

2018

Electrophoresis

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Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.

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Locus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics

Cited by 16 publications

References 37 publications

DART-ID increases single-cell proteome coverage

DART-ID increases single-cell proteome coverage

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Updates on resources, software tools, and databases for plant proteomics in 2016–2017

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