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Article ; Online: Fast, Free, and Flexible Peptide and Protein Quantification with FlashLFQ.

Millikin, Robert J / Shortreed, Michael R / Scalf, Mark / Smith, Lloyd M

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2426, Page(s) 303–313

Abstract: The rapid and accurate quantification of peptides is a critical element of modern proteomics that has become increasingly challenging as proteomic data sets grow in size and complexity. We present here FlashLFQ, a computer program for high-speed label- ... ...

Abstract	The rapid and accurate quantification of peptides is a critical element of modern proteomics that has become increasingly challenging as proteomic data sets grow in size and complexity. We present here FlashLFQ, a computer program for high-speed label-free quantification of peptides and proteins following a search of bottom-up mass spectrometry data. FlashLFQ is approximately an order of magnitude faster than established label-free quantification methods and can quantify data-dependent analysis (DDA) search results from any proteomics search program. It is available as a graphical user interface program, a command line tool, a Docker image, and integrated into the MetaMorpheus search software.
MeSH term(s)	Proteomics/methods ; Proteins/chemistry ; Peptides/chemistry ; Software ; Mass Spectrometry/methods
Chemical Substances	Proteins ; Peptides
Language	English
Publishing date	2022-11-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1967-4_13
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: An Algorithm to Improve the Speed of Semi and Non-Specific Enzyme Searches in Proteomics.

Rolfs, Zach / Millikin, Robert J / Smith, Lloyd M

Current bioinformatics

2020 Volume 15, Issue 9, Page(s) 1065–1074

Abstract: Background: The identification of non-specifically cleaved peptides in proteomics and peptidomics poses a significant computational challenge. Current strategies for the identification of such peptides are typically time consuming and hinder routine ... ...

Abstract	Background: The identification of non-specifically cleaved peptides in proteomics and peptidomics poses a significant computational challenge. Current strategies for the identification of such peptides are typically time consuming and hinder routine data analysis. Objective: We aimed to design an algorithm that would improve the speed of semi- and non-specific enzyme searches and could be applicable to existing search programs. Method: We developed a novel search algorithm that leverages fragment-ion redundancy to simultaneously search multiple non-specifically cleaved peptides at once. Briefly, a theoretical peptide tandem mass spectrum is generated using only the fragment-ion series from a single terminus. This spectrum serves as a proxy for several shorter theoretical peptides sharing the same terminus. After database searching, amino acids are removed from the opposing terminus until the observed and theoretical precursor masses match within a given mass tolerance. Results: The algorithm was implemented in the search program MetaMorpheus and found to perform an order of magnitude faster than the traditional MetaMorpheus search and produce superior results. Conclusion: We report a speedy non-specific enzyme search algorithm which is open-source and enables search programs to utilize fragment-ion redundancy to achieve a notable increase in search speed.
Language	English
Publishing date	2020-05-05
Publishing country	United Arab Emirates
Document type	Journal Article
ISSN	1574-8936
ISSN	1574-8936
DOI	10.2174/1574893615999200429123334
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Hybrid Spectral Library and Protein Sequence Database Search Strategy for Bottom-Up and Top-Down Proteomic Data Analysis.

Dai, Yuling / Millikin, Robert J / Rolfs, Zach / Shortreed, Michael R / Smith, Lloyd M

Journal of proteome research

2022 Volume 21, Issue 11, Page(s) 2609–2618

Abstract: Tandem mass spectrometry (MS/MS) is widely employed for the analysis of complex proteomic samples. While protein sequence database searching and spectral library searching are both well-established peptide identification methods, each has shortcomings. ... ...

Abstract	Tandem mass spectrometry (MS/MS) is widely employed for the analysis of complex proteomic samples. While protein sequence database searching and spectral library searching are both well-established peptide identification methods, each has shortcomings. Protein sequence databases lack fragment peak intensity information, which can result in poor discrimination between correct and incorrect spectrum assignments. Spectral libraries usually contain fewer peptides than protein sequence databases, which limits the number of peptides that can be identified. Notably, few post-translationally modified peptides are represented in spectral libraries. This is because few search engines can both identify a broad spectrum of PTMs and create corresponding spectral libraries. Also, programs that generate spectral libraries using deep learning approaches are not yet able to accurately predict spectra for the vast majority of PTMs. Here, we address these limitations through use of a hybrid search strategy that combines protein sequence database and spectral library searches to improve identification success rates and sensitivity. This software uses Global PTM Discovery (G-PTM-D) to produce spectral libraries for a wide variety of different PTMs. These features, along with a new spectrum annotation and visualization tool, have been integrated into the freely available and open-source search engine MetaMorpheus.
MeSH term(s)	Databases, Protein ; Proteomics/methods ; Tandem Mass Spectrometry/methods ; Data Analysis ; Software ; Peptides/analysis ; Peptide Library ; Algorithms
Chemical Substances	Peptides ; Peptide Library
Language	English
Publishing date	2022-10-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.2c00305
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Enhanced Proteomic Data Analysis with MetaMorpheus.

Miller, Rachel M / Millikin, Robert J / Rolfs, Zach / Shortreed, Michael R / Smith, Lloyd M

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2426, Page(s) 35–66

Abstract: MetaMorpheus is a free and open-source software program dedicated to the comprehensive analysis of proteomic data. In bottom-up proteomics, protein samples are digested into peptides prior to chromatographic separation and tandem mass spectrometric ... ...

Abstract	MetaMorpheus is a free and open-source software program dedicated to the comprehensive analysis of proteomic data. In bottom-up proteomics, protein samples are digested into peptides prior to chromatographic separation and tandem mass spectrometric analysis. The resulting fragmentation spectra are subsequently analyzed with search software programs to obtain peptide identifications and infer the presence of proteins in the samples. MetaMorpheus seeks to maximize the information gleaned from proteomic data through the use of (a) mass calibration, (b) post-translational modification discovery, (c) multiple search algorithms, which aid in the analysis of data from traditional, crosslinking, and glycoproteomic experiments, (d) isotope-based or label-free quantification, (e) multi-protease protein inference, and (f) spectral annotation and data visualization capabilities. This protocol provides detailed descriptions of how use MetaMorpheus and how to customize data analysis workflows using MetaMorpheus tasks to meet the specific needs of the user.
MeSH term(s)	Proteomics/methods ; Data Analysis ; Software ; Tandem Mass Spectrometry/methods ; Peptides/chemistry ; Proteins/chemistry ; Algorithms ; Databases, Protein
Chemical Substances	Peptides ; Proteins
Language	English
Publishing date	2022-11-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1967-4_3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Bayesian Null Interval Hypothesis Test Controls False Discovery Rates and Improves Sensitivity in Label-Free Quantitative Proteomics.

Millikin, Robert J / Shortreed, Michael R / Scalf, Mark / Smith, Lloyd M

Journal of proteome research

2020 Volume 19, Issue 5, Page(s) 1975–1981

Abstract: Statistical significance tests are a common feature in quantitative proteomics workflows. The Student' ... ...

Abstract	Statistical significance tests are a common feature in quantitative proteomics workflows. The Student's
MeSH term(s)	Bayes Theorem ; Humans ; Proteins ; Proteomics ; Software ; Workflow
Chemical Substances	Proteins
Language	English
Publishing date	2020-04-14
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.9b00796
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Article: Improving Proteoform Identifications in Complex Systems Through Integration of Bottom-Up and Top-Down Data

Schaffer, Leah V / Millikin, Robert J / Shortreed, Michael R / Scalf, Mark / Smith, Lloyd M

Journal of proteome research. 2020 June 25, v. 19, no. 8

2020

Abstract: Cellular functions are performed by a vast and diverse set of proteoforms. Proteoforms are the specific forms of proteins produced as a result of genetic variations, RNA splicing, and post-translational modifications (PTMs). Top-down mass spectrometric ... ...

Abstract	Cellular functions are performed by a vast and diverse set of proteoforms. Proteoforms are the specific forms of proteins produced as a result of genetic variations, RNA splicing, and post-translational modifications (PTMs). Top-down mass spectrometric analysis of intact proteins enables proteoform identification, including proteoforms derived from sequence cleavage events or harboring multiple PTMs. In contrast, bottom-up proteomics identifies peptides, which necessitates protein inference and does not yield proteoform identifications. We seek here to exploit the synergies between these two data types to improve the quality and depth of the overall proteomic analysis. To this end, we automated the large-scale integration of results from multiprotease bottom-up and top-down analyses in the software program Proteoform Suite and applied it to the analysis of proteoforms from the human Jurkat T lymphocyte cell line. We implemented the recently developed proteoform-level classification scheme for top-down tandem mass spectrometry (MS/MS) identifications in Proteoform Suite, which enables users to observe the level and type of ambiguity for each proteoform identification, including which of the ambiguous proteoform identifications are supported by bottom-up-level evidence. We used Proteoform Suite to find instances where top-down identifications aid in protein inference from bottom-up analysis and conversely where bottom-up peptide identifications aid in proteoform PTM localization. We also show the use of bottom-up data to infer proteoform candidates potentially present in the sample, allowing confirmation of such proteoform candidates by intact-mass analysis of MS1 spectra. The implementation of these capabilities in the freely available software program Proteoform Suite enables users to integrate large-scale top-down and bottom-up data sets and to utilize the synergies between them to improve and extend the proteomic analysis.
Keywords	RNA splicing ; T-lymphocytes ; automation ; cell lines ; computer software ; genetic variation ; humans ; peptides ; post-translational modification ; proteins ; proteome ; proteomics ; tandem mass spectrometry
Language	English
Dates of publication	2020-0625
Size	p. 3510-3517.
Publishing place	American Chemical Society
Document type	Article
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.0c00332
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Improving Proteoform Identifications in Complex Systems Through Integration of Bottom-Up and Top-Down Data.

Schaffer, Leah V / Millikin, Robert J / Shortreed, Michael R / Scalf, Mark / Smith, Lloyd M

Journal of proteome research

2020 Volume 19, Issue 8, Page(s) 3510–3517

Abstract	Cellular functions are performed by a vast and diverse set of proteoforms. Proteoforms are the specific forms of proteins produced as a result of genetic variations, RNA splicing, and post-translational modifications (PTMs). Top-down mass spectrometric analysis of intact proteins enables proteoform identification, including proteoforms derived from sequence cleavage events or harboring multiple PTMs. In contrast, bottom-up proteomics identifies peptides, which necessitates protein inference and does not yield proteoform identifications. We seek here to exploit the synergies between these two data types to improve the quality and depth of the overall proteomic analysis. To this end, we automated the large-scale integration of results from multiprotease bottom-up and top-down analyses in the software program Proteoform Suite and applied it to the analysis of proteoforms from the human Jurkat T lymphocyte cell line. We implemented the recently developed proteoform-level classification scheme for top-down tandem mass spectrometry (MS/MS) identifications in Proteoform Suite, which enables users to observe the level and type of ambiguity for each proteoform identification, including which of the ambiguous proteoform identifications are supported by bottom-up-level evidence. We used Proteoform Suite to find instances where top-down identifications aid in protein inference from bottom-up analysis and conversely where bottom-up peptide identifications aid in proteoform PTM localization. We also show the use of bottom-up data to infer proteoform candidates potentially present in the sample, allowing confirmation of such proteoform candidates by intact-mass analysis of MS1 spectra. The implementation of these capabilities in the freely available software program Proteoform Suite enables users to integrate large-scale top-down and bottom-up data sets and to utilize the synergies between them to improve and extend the proteomic analysis.
MeSH term(s)	Humans ; Protein Processing, Post-Translational ; Proteome/metabolism ; Proteomics ; Software ; Tandem Mass Spectrometry
Chemical Substances	Proteome
Language	English
Publishing date	2020-07-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.0c00332
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models.

Millikin, Robert J / Raja, Kalpana / Steill, John / Lock, Cannon / Tu, Xuancheng / Ross, Ian / Tsoi, Lam C / Kuusisto, Finn / Ni, Zijian / Livny, Miron / Bockelman, Brian / Thomson, James / Stewart, Ron

bioRxiv : the preprint server for biology

2023

Abstract: Background: The PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable ... ...

Abstract	Background: The PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. Results: We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. Conclusions: SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.
Language	English
Publishing date	2023-06-01
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.05.30.542911
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models.

BMC bioinformatics

2023 Volume 24, Issue 1, Page(s) 412

Abstract: Background: The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools ...

Abstract	Background: The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: (1) they identify a relationship but not the type of relationship, (2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, (3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or (4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. Results: We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. Conclusions: SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.
MeSH term(s)	Humans ; Algorithms ; Neoplasms ; PubMed ; Knowledge ; Knowledge Discovery
Language	English
Publishing date	2023-11-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-023-05539-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Binary Classifier for Computing Posterior Error Probabilities in MetaMorpheus.

Shortreed, Michael R / Millikin, Robert J / Liu, Lei / Rolfs, Zach / Miller, Rachel M / Schaffer, Leah V / Frey, Brian L / Smith, Lloyd M

Journal of proteome research

2021 Volume 20, Issue 4, Page(s) 1997–2004

Abstract: MetaMorpheus is a free, open-source software program for the identification of peptides and proteoforms from data-dependent acquisition tandem MS experiments. There is inherent uncertainty in these assignments for several reasons, including the limited ... ...

Abstract	MetaMorpheus is a free, open-source software program for the identification of peptides and proteoforms from data-dependent acquisition tandem MS experiments. There is inherent uncertainty in these assignments for several reasons, including the limited overlap between experimental and theoretical peaks, the
MeSH term(s)	Algorithms ; Databases, Protein ; Peptides ; Probability ; Proteomics ; Software ; Tandem Mass Spectrometry
Chemical Substances	Peptides
Language	English
Publishing date	2021-03-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.0c00838
Database	MEDical Literature Analysis and Retrieval System OnLINE

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