Glycoforest 1.0

Tandem
mass spectrometry, when combined with liquid chromatography
and applied to complex mixtures, produces large amounts of raw data,
which needs to be analyzed to identify molecular structures. This
technique is widely used, particularly in glycomics. Due to a lack
of high throughput glycan sequencing software, glycan spectra are
predominantly sequenced manually. A challenge for writing glycan-sequencing
software is that there is no direct template that can be used to infer
structures detectable in an organism. To help alleviate this bottleneck,
we present Glycoforest 1.0, a partial de novo algorithm
for sequencing glycan structures based on MS/MS spectra. Glycoforest
was tested on two data sets (human gastric and salmon mucosa O-linked glycomes) for which MS/MS spectra were annotated
manually. Glycoforest generated the human validated structure for
92% of test cases. The correct structure was found as the best scoring
match for 70% and among the top 3 matches for 83% of test cases. In
addition, the Glycoforest algorithm detected glycan structures from
MS/MS spectra missing a manual annotation. In total 1532 MS/MS previously
unannotated spectra were annotated by Glycoforest. A portion containing
521 spectra was manually checked confirming that Glycoforest annotated
an additional 50 MS/MS spectra overlooked during manual annotation.