Glycan CFG encoding and graph encoding.

<p>On the left hand side a glycan structure encoded with CFG nomenclature is presented, while the right hand side shows the same structure translated into a graph. Each monosaccharide or substituent becomes a node and each glycosidic bond becomes an edge in the graph. Avoiding any loss of information all the properties of each monosaccharide or substituent are converted in node properties whereas glycosidic bond properties are translated in edge properties. To be more clear the colour code associate with the monosaccharide type is preserved among the images.</p

Ontology overview.

<p>Overview of the ontology developed for translating glycan structures into RDF/semantic triples. The figure shows all the predicates and the entities used for defining a glycan structures into the RDF triple store.</p

Average query time.

<p>The mean value calculated on the response times of each query in both sets is shown in two bar charts. Panel (A) shows the mean query times for the first set and panel (B) contains the values for the second set. The column assign to Virtuoso in the second set of query is empty because we could not record any data due to a problem in running large and very large queries.</p

Query building example.

<p>A. Example of use of the RDF model to build a SPARQL query from a glycan substructure focussing on the translation process. The prefix part of the query is omitted but further detailed examples are provided in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144578#pone.0144578.s001" target="_blank">S1 File</a>. B. The same example is shown with building a Cypher query, the native language in Neo4J. Similarly, additional examples are provided in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144578#pone.0144578.s001" target="_blank">S1 File</a>.</p

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 <i>de novo</i> algorithm for sequencing glycan structures based on MS/MS spectra. Glycoforest was tested on two data sets (human gastric and salmon mucosa <i>O</i>-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