Cryptosporidium species
publication ID |
https://doi.org/ 10.1016/j.ijppaw.2024.100971 |
persistent identifier |
https://treatment.plazi.org/id/C80D1C28-FFEF-FFF8-961A-FD92BBD3FA89 |
treatment provided by |
Felipe |
scientific name |
Cryptosporidium species |
status |
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2.7. Molecular characterization of Cryptosporidium species and subtypes
Consensus sequences were assembled and edited manually from the forward and reverse reads of the partial 18S rRNA gene using the Unipro UGENE v49.1 software (Unipro, Novosibirsk 630090, Russia) ( Okonechnikov et al., 2012) with the references (Acces. number MK014785) and a mapping similarity> 60%. The consensus sequences derived were then used to identify species by comparing to data in nucleotide databases using the Basic Local Alignment Search Tool (BLAST) (https://bla st.ncbi .nlm.nih.gov/Blast.cgi; last accessed May 10, 2024). Later, published sequences, including those with the highest similarity, were downloaded from the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/; last accessed May 10, 2024), and multiple sequence alignments were carried out to determine the homology between published isolates and the consensus sequences using the MUSCLE ( Edgar, 2004) function included in MEGA 11 software V.11.0.13 ( Tamura et al., 2021). The best fitting model for the DNA/protein phylogeny was selected for each alignment based on the Bayesian information criterion, and a phylogenetic tree was constructed by the maximum likelihood (ML) algorithm, using the Tamura 3-parameter with discrete Gamma distribution (T92+ G) (5 categories) ( Tamura and Nei, 1993) nucleotide substitution model in MEGA 11. Final tree were edited in Mega 11 using Cryptosporidum muris as out group.
For determination of the Cryptosporidium subgenotype family and subtypes, the sequencing reads (i.e., forward and reverse) of the partial gp60 gene were assessed using the software CryptoGenotyper® ( Yanta et al., 2021).
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