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01649nam a2200217Ia 4500 |
| 001 |
10.3233-SHTI220696 |
| 008 |
220718s2022 CNT 000 0 und d |
| 020 |
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|a 18798365 (ISSN)
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| 245 |
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|a Analyzing SARS-CoV-2 Sequence Patterns by Semantic Trajectories
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| 260 |
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|b NLM (Medline)
|c 2022
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.3233/SHTI220696
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|a Since the beginning of the pandemic due to the SARS-CoV-2 emergence, several variants has been observed all over the world. One of the last known, Omicron, caused a large spread of the virus in few days, and several countries reached a record number of contaminations. Indeed, the mutation in the Spike region of the virus played an important role in altering its behavior. Therefore, it is important to understand the virus evolution by extracting and analyzing the virus structure of each variant. In this work we show how patterns sequence could be analyzed and extracted by means of semantic trajectories modeling. To do so, we designed a graph-based model in which the genome organization is handled using nodes and edges to represent respectively the nucleotides and sequence connection (point of interest and routes for trajectories). The modeling choices and pattern extraction from the graph allowed to retrieve a region where a mutation occurred in Omicron (NCBI version:OM011974.1).
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|a Cypher
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|a Graph
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|a Neo4j
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| 650 |
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|a Pattern analysis
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| 650 |
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|a SARS-CoV-2
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| 700 |
1 |
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|a Laddada, W.
|e author
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| 700 |
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|a Soualmia, L.F.
|e author
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|a Zanni-Merk, C.
|e author
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| 773 |
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|t Studies in health technology and informatics
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