2019
DOI: 10.1002/pmic.201800300
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hmSEEKER: Identification of hmSILAC Doublets in MaxQuant Output Data

Abstract: Heavy methyl Stable Isotope Labeling with Amino acids in Cell culture (hmSILAC) is a metabolic labeling strategy employed in proteomics to increase the confidence of global identification of methylated peptides by MS. However, to this day, the automatic and robust identification of heavy and light peak doublets from MS‐raw data of hmSILAC experiments is a challenging task, for which the choice of computational methods is very limited. Here, hmSEEKER, a software designed to work downstream of a MaxQuant analysi… Show more

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Cited by 13 publications
(21 citation statements)
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References 12 publications
(15 reference statements)
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“…Identification of high confidence methyl-sites was carried out with an in-house developed, Perl-based pipeline, named hmSEEKER, which identifies doublets of heavy and light hmSILAC peptides from MaxQuant output tables (Massignani et al, 2019). hmSEEKER performs the following steps: methyl-peptides identified in the msms file are first filtered to remove: (i) all contaminants and decoy peptides, (ii) all peptides with single charge and (iii) all peptides bearing simultaneous heavy and light modifications.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Identification of high confidence methyl-sites was carried out with an in-house developed, Perl-based pipeline, named hmSEEKER, which identifies doublets of heavy and light hmSILAC peptides from MaxQuant output tables (Massignani et al, 2019). hmSEEKER performs the following steps: methyl-peptides identified in the msms file are first filtered to remove: (i) all contaminants and decoy peptides, (ii) all peptides with single charge and (iii) all peptides bearing simultaneous heavy and light modifications.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, MS023 elicited a specific effect on the methylation of proteins involved in translation (Figures 6E-6G). Most of the methyl-peptides responding to GSK591 (70%) and MS023 (89%) were orthogonally validated through the intersection with a high-quality methyl-proteome dataset annotated through the heavy methyl SILAC labeling strategy (Massignani et al, 2019;Ong et al, 2004) (Figure S5C; Table S4).…”
Section: Global Profiling Of Prmt Substrates At Single-site Resolution By Quantitative Liquid Chromatography-tandem Mass Spectrometrymentioning
confidence: 99%
“…The automated and confident identification of hmSILAC doublets from raw MS data was achieved through an in-house developed algorithm called hmSEEKER that, starting from the MaxQuantexecuted methyl peptide search output, automatically associates the light and heavy forms and retrieves hmSILAC doublets ( fig. S2 and table S2) (36). This tool has been designed to reconstruct hmSILAC pairs, also when the identification of either the light or the heavy peak is not available.…”
Section: Orthogonal Validation Of Gsk591-regulated Methyl Peptides Bymentioning
confidence: 99%
“…Identification of high-confidence methyl sites was carried out with an in-house developed, Perl-based pipeline, named hmSEEKER (36). hmSEEKER identifies doublets of heavy and light hmSILAC peptides from MaxQuant output tables.…”
Section: Hmseeker: a Perl-based Pipeline For High-confidence Assignmementioning
confidence: 99%
“…Nonetheless, the comprehensive study of R-methylation has been hindered by several technical challenges, thus delaying the global analysis of this modification compared to others, such as serine threonine/tyrosine (ST/Y)-phosphorylation, lysine (K)-ubiquitination, and acetylation (Wang et al, 2017). The recent optimization of biochemical workflows for the affinity enrichment of R-methylated peptides from total protein extracts, coupled to stateof-the-art liquid chromatography-tandem mass spectrometry (LC-MS/MS), as well as automated pipelines for MS-data analysis, has led to the significant expansion of the annotated human R-methyl proteome (Larsen et al, 2016;Massignani et al, 2019;Musiani et al, 2019;Sylvestersen et al, 2014).…”
Section: Introductionmentioning
confidence: 99%