Improving the Protein Inference from Bottom-Up Proteomic Data Using Identifications from MS1 Spectra

2021 ◽  
Author(s):  
Mark V. Ivanov ◽  
Elizaveta M. Solovyeva ◽  
Julia A. Bubis ◽  
Mikhail V. Gorshkov
Keyword(s):  
Bottom Up ◽  
Protein Inference ◽  
Proteomic Data

scholarly journals Improved Protein Inference from Multiple Protease Bottom-Up Mass Spectrometry Data

2019 ◽  
Vol 18 (9) ◽  
pp. 3429-3438 ◽  
Author(s):  
Rachel M. Miller ◽  
Robert J. Millikin ◽  
Connor V. Hoffmann ◽  
Stefan K. Solntsev ◽  
Gloria M. Sheynkman ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Bottom Up ◽  
Protein Inference

scholarly journals ProteoClade: a taxonomic toolkit for multi-species and metaproteomic analysis

2019 ◽  
Author(s):  
Arshag D. Mooradian ◽  
Sjoerd van der Post ◽  
Kristen M. Naegle ◽  
Jason M. Held
Keyword(s):  
Open Source ◽  
De Novo ◽  
Protein Sequences ◽  
Protein Inference ◽  
Proteomic Data ◽  
Computational Resources ◽  
Specific Peptide

AbstractWe present ProteoClade, a Python toolkit that performs taxa-specific peptide assignment, protein inference, and quantitation for multi-species proteomics experiments. ProteoClade scales to hundreds of millions of protein sequences, requires minimal computational resources, and is open source, multi-platform, and accessible to non-programmers. We demonstrate its utility for processing quantitative proteomic data derived from patient-derived xenografts and its speed and scalability enable a novel de novo proteomic workflow for complex microbiota samples.

scholarly journals Can we put Humpty Dumpty back together again? What does protein quantification mean in bottom-up proteomics?

2021 ◽  
Author(s):  
Deanna L. Plubell ◽  
Lukas Käll ◽  
Bobbie-Jo Webb-Robertson ◽  
Lisa Bramer ◽  
Ashley Ives ◽  
...  
Keyword(s):  
Rna Splicing ◽  
Large Scale ◽  
Protein Quantification ◽  
Bottom Up ◽  
Protein Coding ◽  
Protein Inference ◽  
Humpty Dumpty ◽  
Peptide Data ◽  
Biological Differences ◽  
Quantitative Response

AbstractBottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. In bottom-up proteomics, protein parsimony and protein inference derived from these measured peptides are important for determining which protein coding genes are present. However, given the complexity of RNA splicing processes, and how proteins can be modified post-translationally, it is overly simplistic to assume that all peptides that map to a singular protein coding gene will demonstrate the same quantitative response. Accordingly, by assuming all peptides from a protein coding sequence are representative of the same protein we may be missing out on detecting important biological differences. To better account for the complexity of the proteome we need to think of new or better ways of handling peptide data.

Psychology and Culture: Top Down Versus Bottom Up?

PsycCRITIQUES ◽  
2005 ◽  
Vol 50 (19) ◽  
Author(s):  
Michael Cole
Keyword(s):  
Top Down ◽  
Bottom Up
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