Future Prospects of Spectral Clustering Approaches in Proteomics

Perez‐Riverol, Yasset; Vizcaíno, Juan Antonio; Griss, Johannes

doi:10.1002/pmic.201700454

Cited by 14 publications

(13 citation statements)

References 36 publications

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“…The PSMs reported in PRIDE Archive are quality-filtered using a spectrum clustering approach (29). All the identified spectra coming from the public experiments in PRIDE Archive were clustered using the second iteration of the PRIDE Cluster algorithm, called spectra-cluster (https://github.com/spectra-cluster) (30).…”

Section: Current Status Of Pride Archive and Related Toolsmentioning

confidence: 99%

The PRIDE database and related tools and resources in 2019: improving support for quantification data

Perez‐Riverol

Csordás

Bai

et al. 2018

Nucleic Acids Research

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6,370

5,101

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The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

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Section: Current Status Of Pride Archive and Related Toolsmentioning

confidence: 99%

The PRIDE database and related tools and resources in 2019: improving support for quantification data

Perez‐Riverol

Csordás

Bai

et al. 2018

Nucleic Acids Research

Self Cite

6,370

5,101

View full text Add to dashboard Cite

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“…While these search strategies are better suited to deal with a large number of defined PTMs, they do not ameliorate the fundamental problem of undefined modifications such as random crosslinks between molecules. Upcoming methodologies employing combinatorial spectral libraries [125] or spectral clustering [126] offer a potential solution to this challenge.…”

Section: Summary and Discussionmentioning

confidence: 99%

Irreversible oxidative post-translational modifications in heart disease

Tomin

Schittmayer

Honeder

et al. 2019

Expert Review of Proteomics

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Introduction: Development of specific biomarkers aiding early diagnosis of heart failure is an ongoing challenge. Biomarkers commonly used in clinical routine usually act as readouts of an already existing acute condition rather than disease initiation. Functional decline of cardiac muscle is greatly aggravated by increased oxidative stress and damage of proteins. Oxidative post-translational modifications occur already at early stages of tissue damage and are thus regarded as potential up-coming disease markers. Areas covered: Clinical practice regarding commonly used biomarkers for heart disease is briefly summarized. The types of oxidative post-translational modification in cardiac pathologies are discussed with a special focus on available quantitative techniques and characteristics of individual modifications with regard to their stability and analytical accessibility. As irreversible oxidative modifications trigger protein degradation pathways or cause protein aggregation, both influencing biomarker abundance, a chapter is dedicated to their regulation in the heart.

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“…All these approaches propose to improve peptide identification by benefiting from the aforementioned trade-off: By grouping similar fragmentation spectra into a consensus representation, one clearly reduces the data volume. Moreover, peaks corresponding to random noise should not reinforce between spectra, while on the contrary, small but chemically consistent peaks should [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

CHICKN: extraction of peptide chromatographic elution profiles from large scale mass spectrometry data by means of Wasserstein compressive hierarchical cluster analysis

et al. 2021

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Background The clustering of data produced by liquid chromatography coupled to mass spectrometry analyses (LC-MS data) has recently gained interest to extract meaningful chemical or biological patterns. However, recent instrumental pipelines deliver data which size, dimensionality and expected number of clusters are too large to be processed by classical machine learning algorithms, so that most of the state-of-the-art relies on single pass linkage-based algorithms. Results We propose a clustering algorithm that solves the powerful but computationally demanding kernel k-means objective function in a scalable way. As a result, it can process LC-MS data in an acceptable time on a multicore machine. To do so, we combine three essential features: a compressive data representation, Nyström approximation and a hierarchical strategy. In addition, we propose new kernels based on optimal transport, which interprets as intuitive similarity measures between chromatographic elution profiles. Conclusions Our method, referred to as CHICKN, is evaluated on proteomics data produced in our lab, as well as on benchmark data coming from the literature. From a computational viewpoint, it is particularly efficient on raw LC-MS data. From a data analysis viewpoint, it provides clusters which differ from those resulting from state-of-the-art methods, while achieving similar performances. This highlights the complementarity of differently principle algorithms to extract the best from complex LC-MS data.

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Future Prospects of Spectral Clustering Approaches in Proteomics

Cited by 14 publications

References 36 publications

The PRIDE database and related tools and resources in 2019: improving support for quantification data

The PRIDE database and related tools and resources in 2019: improving support for quantification data

Irreversible oxidative post-translational modifications in heart disease

CHICKN: extraction of peptide chromatographic elution profiles from large scale mass spectrometry data by means of Wasserstein compressive hierarchical cluster analysis

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