Bottom-up g
lycoproteomics by
liquid chromatography–mass spectrometry (LC–MS) is an estab
lished approach for assessing g
lycosy
lation in a protein- and site-specific manner. Consequent
ly, too
ls are needed to automatica
lly a
lign, ca
librate, and integrate LC–MS g
lycoproteomics data. We deve
loped a modu
lar software package designed to tack
le the individua
l aspects of an LC–MS experiment, ca
lled LaCyToo
ls. Targeted a
lignment is performed using user defined
m/
z and retention time (
tr) combinations. Subsequent
ly, sum spectra are created for each user defined ana
lyte group. Quantitation is performed on the sum spectra, where each user defined ana
lyte can have its own
tr, minimum, and maximum charge states. Consequent
ly, LaCyToo
ls dea
ls with mu
ltip
le charge states, which gives an output per charge state if desired, and offers various ana
lyte and spectra qua
lity criteria. We compared throughput and performance of LaCyToo
ls to combinations of avai
lab
le too
ls that dea
l with individua
l processing steps. LaCyToo
ls yie
lded re
lative quantitation of equa
l precision (re
lative standard deviation &
lt;0.5%) and higher trueness due to the use of MS peak area instead of MS peak intensity. In conc
lusion, LaCyToo
ls is an accurate automated data processing too
l for high-throughput ana
lysis of LC–MS g
lycoproteomics data. Re
leased under the Apache 2.0
license, it is free
ly avai
lab
le on GitHub (
ls" class="extLink">https://github.com/Tarskin/LaCyTools).