MassIVE MSV000085379

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The power of three in cannabis shotgun proteomics: proteases, databases and search engines.

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Description

Cannabis research has taken of since the relaxation of the legislation, yet proteomics is still lagging behind. Last year we published three proteomics methods aiming at optimizing protein extraction (Vincent et al Molecules 2019), protein digestion (Vincent et al IJMS 2019) for bottom-up and middle-down proteomics, as well as the analysis of intact proteins for top-down proteomics (Vincent et al Proteomes 2019). The database of Cannabis sativa proteins used in these studies was retrieved from the reference repositories for proteins UniProt which is incomplete and therefore underrepresents the genetic diversity of this species. In this fourth study, we remedy this shortcoming by searching larger databases from various sources. We also compare two search engines, the oldest SEQUEST, and the most popular Mascot. This shotgun proteomics experiment is also utilizing the power of parallel digestions with orthogonal proteases of increasing selectivity, namely chymotrypsin, trypsinLys-C and Asp-N. Our results show that, as expected, the larger the databases the greater the list of accessions identified but the longer the duration of the search. Using orthogonal proteases and different search algorithms increases the total number of proteins identified, most of them common among proteases and algorithms, but many of them unique as well. doi:10.3390/proteomes8020013 [doi:10.25345/C5QQ6J] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Cannabis sativa, bottom-up and middle-down proteomics, post-translational modification, missed cleavages, SEQUEST, Mascot, LC-MS, Asp-N, chymotrypsin, trypsin/Lys-C

Contact

Principal Investigators:
(in alphabetical order) 		Delphine Vincent, Agriculture Victoria Research, Australia
Submitting User: delphine_1_2
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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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