Assembling the Community-Scale Discoverable Human Proteome

Wang, Mingxun; Jian, Wang; Carver, Jeremy; Pullman, Benjamin; Won, Seong; Bandeira, Nuno

doi:10.1016/j.cels.2018.08.004

Cited by 144 publications

(163 citation statements)

References 52 publications

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“…Public datasets stored in PRIDE, together with existing ones in other open proteomics repositories (e.g. the CPTAC portal 25 or MassIVE 26 ) present an opportunity to be systematically reanalysed and integrated, in order to confirm the original results, potentially obtain new insights and be able to answer biologically relevant questions orthogonal to those posed in the original studies. Such integrative meta-analyses have already been successfully employed in different omics data types, especially in genomics and transcriptomics 27 .…”

Section: Introductionmentioning

confidence: 99%

An integrated landscape of protein expression in human cancer

Jarnuczak

Najgebauer

Barzine

et al. 2019

Preprint

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Using public proteomics datasets, mostly available through the PRIDE database, we assembled a proteomics resource for 191 cancer cell lines and 246 clinical tumour samples, across 13 cancer lineages. We found that baseline protein abundance in cell lines was generally representative of tumours. However, when considering differences in protein expression between tumour subtypes, as exemplified in the breast lineage, many of these changes were no longer recapitulated in the cell line models. Integration of proteomics and transcriptomics data suggested that the level of transcriptional control in cell lines changed significantly depending on their lineage. Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis study including cancer-related proteomics datasets. We anticipate this aggregated dataset will be of significant aid to future studies requiring a reference to baseline protein expression in cancer.

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Section: Introductionmentioning

confidence: 99%

An integrated landscape of protein expression in human cancer

Jarnuczak

Najgebauer

Barzine

et al. 2019

Preprint

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“…Finding missense variants with non-canonical libraries. Most publicly available spectrum libraries [22][23][24] are built by searching millions of spectra against a canonical genome. Our approach facilitates the analysis of sequence variants and splice junctions by simplifying exome-specific library construction, and we demonstrate this by analyzing HeLa-specific missense sequence variants.…”

Section: Resultsmentioning

confidence: 99%

Generating high quality libraries for DIA MS with empirically corrected peptide predictions

et al. 2020

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Data-independent acquisition approaches typically rely on experiment-specific spectrum libraries, requiring offline fractionation and tens to hundreds of injections. We demonstrate a library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid, experiment-specific library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa.

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“…In both cases, each acquisition corresponds to a single experiment analyzing multiple single cells using the tandem mass tag (TMT) 10-plex reagents as described in the original paper [24]. The raw data files were downloaded from MassIVE [25] (ID: MSV000083945) and converted to mzML format using ThermoRawFileParser, with vendor peak-picking enabled.…”

Section: Methodsmentioning

confidence: 99%

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

Fondrie

Noble

2019

Preprint

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1Machine learning methods have proven invaluable for increasing the sensitivity of peptide de-2 tection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, 3 use semi-supervised algorithms to learn models directly from the datasets that they analyze. 4 Although these methods are effective for many proteomics experiments, we suspected that they 5 may be suboptimal for experiments of smaller scale. In this work, we found that the power and 6 consistency of Percolator results was reduced as the size of the experiment was decreased. As 7 an alternative, we propose a different operating mode for Percolator: learn a model with Per-8 colator from a large dataset and use the learned model to evaluate the small-scale experiment. 9 We call this a "static modeling" approach, in contrast to Percolator's usual "dynamic model" 10 that is trained anew for each dataset. We applied this static modeling approach to two settings: 11 small, gel-based experiments and single-cell proteomics. In both cases, static models increased 12 the yield of detected peptides and eliminated the model-induced variability of the standard dy-13 namic approach. These results suggest that static models are a powerful tool for bringing the 14 full benefits of Percolator and other semi-supervised algorithms to small-scale experiments. 15 1 Introduction 16The assignment of peptide sequences to tandem mass spectra is a fundamental task in any pro-17 teomics experiment [1]. This task is most often performed by a database search algorithm, which 18 was first introduced with the SEQUEST search engine in 1994 [2]. Database search algorithms score 19 the theoretical mass spectra of peptides from a selected sequence database against the acquired 20 mass spectra from the experiment, yielding a set of peptide-spectrum matches (PSMs). Although 21 the score functions of individual search engines may differ greatly, the scores reported by each are 22 intended to reflect the quality of PSMs. 23 2 Machine learning strategies to re-score PSMs were first introduced due to their ability to inte-24 grate multiple, orthogonal scores and features from database search engines, thereby improving the 25 sensitivity of peptide detection. Two such methods were proposed simultaneously: one based on 26 linear discriminant analysis [3] and another that used support vector machine (SVM) models to a 27 similar end [4]. Critically, both methods were examples of supervised learning: they relied on fully 28 labeled datasets-where the correct and incorrect PSMs could be determined a priori -to train 29 their respective models. These trained models were then used to predict a new score for the PSMs 30 of a new dataset. We refer to the models used by these methods as static, meaning their parameters 31 do not change when the dataset is changed. Critically, the success of these static models depended 32 on the quality of the labeled dataset that was used for training and how well it reflected the datasets 33 that were subsequently analyzed. The origina...

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Assembling the Community-Scale Discoverable Human Proteome

Cited by 144 publications

References 52 publications

An integrated landscape of protein expression in human cancer

An integrated landscape of protein expression in human cancer

Generating high quality libraries for DIA MS with empirically corrected peptide predictions

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

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