Detecting sequence variants in clinically important protozoan parasites.
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- International Journal for Parasitology, 2020, 50, (1), pp. 1-18
- Issue Date:
- 2020
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
Full metadata record
| Field | Value | Language |
|---|---|---|
| dc.contributor.author | Calarco L | |
| dc.contributor.author | Barratt J | |
| dc.contributor.author | Ellis J | |
| dc.date.accessioned | 2020-11-19T02:38:45Z | |
| dc.date.available | 2021-01-31T18:09:46Z | |
| dc.date.issued | 2020 | |
| dc.identifier.citation | International Journal for Parasitology, 2020, 50, (1), pp. 1-18 | |
| dc.identifier.issn | 0020-7519 | |
| dc.identifier.issn | 1879-0135 | |
| dc.identifier.uri | http://hdl.handle.net/10453/144138 | |
| dc.description.abstract | Second and third generation sequencing methods are crucial for population genetic studies, and variant detection is a popular approach for exploiting this sequence data. While mini- and microsatellites are historically useful markers for studying important Protozoa such as Toxoplasma and Plasmodium spp., detecting non-repetitive variants such as those found in genes can be fundamental to investigating a pathogen's biology. These variants, namely single nucleotide polymorphisms and insertions and deletions, can help elucidate the genetic basis of an organism's pathogenicity, identify selective pressures, and resolve phylogenetic relationships. They also have the added benefit of possessing a comparatively low mutation rate, which contributes to their stability. However, there is a plethora of variant analysis tools with nuanced pipelines and conflicting recommendations for best practise, which can be confounding. This lack of standardisation means that variant analysis requires careful parameter optimisation, an understanding of its limitations, and the availability of high quality data. This review explores the value of variant detection when applied to non-model organisms such as clinically important protozoan pathogens. The limitations of current methods are discussed, including special considerations that require the end-users' attention to ensure that the results generated are reproducible, and the biological conclusions drawn are valid. | |
| dc.format | Print-Electronic | |
| dc.language | eng | |
| dc.publisher | Elsevier | |
| dc.relation.ispartof | International Journal for Parasitology | |
| dc.relation.isbasedon | 10.1016/j.ijpara.2019.10.004 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | 0605 Microbiology, 0608 Zoology, 0707 Veterinary Sciences | |
| dc.subject.classification | Mycology & Parasitology | |
| dc.subject.mesh | Animals | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Parasites | |
| dc.subject.mesh | Toxoplasma | |
| dc.subject.mesh | Plasmodium | |
| dc.subject.mesh | Leishmania | |
| dc.subject.mesh | Trypanosoma cruzi | |
| dc.subject.mesh | Protozoan Infections | |
| dc.subject.mesh | DNA, Protozoan | |
| dc.subject.mesh | Sequence Analysis, DNA | |
| dc.subject.mesh | Computational Biology | |
| dc.subject.mesh | Genetics, Population | |
| dc.subject.mesh | Phylogeny | |
| dc.subject.mesh | Drug Resistance | |
| dc.subject.mesh | Polymorphism, Single Nucleotide | |
| dc.subject.mesh | Genome, Protozoan | |
| dc.subject.mesh | Genetic Variation | |
| dc.subject.mesh | High-Throughput Nucleotide Sequencing | |
| dc.subject.mesh | Animals | |
| dc.subject.mesh | Computational Biology | |
| dc.subject.mesh | DNA, Protozoan | |
| dc.subject.mesh | Drug Resistance | |
| dc.subject.mesh | Genetic Variation | |
| dc.subject.mesh | Genetics, Population | |
| dc.subject.mesh | Genome, Protozoan | |
| dc.subject.mesh | High-Throughput Nucleotide Sequencing | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Leishmania | |
| dc.subject.mesh | Parasites | |
| dc.subject.mesh | Phylogeny | |
| dc.subject.mesh | Plasmodium | |
| dc.subject.mesh | Polymorphism, Single Nucleotide | |
| dc.subject.mesh | Protozoan Infections | |
| dc.subject.mesh | Sequence Analysis, DNA | |
| dc.subject.mesh | Toxoplasma | |
| dc.subject.mesh | Trypanosoma cruzi | |
| dc.title | Detecting sequence variants in clinically important protozoan parasites. | |
| dc.type | Journal Article | |
| utslib.citation.volume | 50 | |
| utslib.location.activity | England | |
| utslib.for | 0605 Microbiology | |
| utslib.for | 0608 Zoology | |
| utslib.for | 0707 Veterinary Sciences | |
| utslib.for | 0605 Microbiology | |
| utslib.for | 0608 Zoology | |
| utslib.for | 0707 Veterinary Sciences | |
| pubs.organisational-group | /University of Technology Sydney/Faculty of Science | |
| pubs.organisational-group | /University of Technology Sydney | |
| pubs.organisational-group | /University of Technology Sydney/Faculty of Science/School of Life Sciences | |
| utslib.copyright.status | open_access | * |
| pubs.consider-herdc | true | |
| dc.date.updated | 2020-11-19T02:38:41Z | |
| pubs.issue | 1 | |
| pubs.publication-status | Published | |
| pubs.volume | 50 | |
| utslib.opus.status | open_access | * |
| utslib.citation.issue | 1 |
Abstract:
Second and third generation sequencing methods are crucial for population genetic studies, and variant detection is a popular approach for exploiting this sequence data. While mini- and microsatellites are historically useful markers for studying important Protozoa such as Toxoplasma and Plasmodium spp., detecting non-repetitive variants such as those found in genes can be fundamental to investigating a pathogen's biology. These variants, namely single nucleotide polymorphisms and insertions and deletions, can help elucidate the genetic basis of an organism's pathogenicity, identify selective pressures, and resolve phylogenetic relationships. They also have the added benefit of possessing a comparatively low mutation rate, which contributes to their stability. However, there is a plethora of variant analysis tools with nuanced pipelines and conflicting recommendations for best practise, which can be confounding. This lack of standardisation means that variant analysis requires careful parameter optimisation, an understanding of its limitations, and the availability of high quality data. This review explores the value of variant detection when applied to non-model organisms such as clinically important protozoan pathogens. The limitations of current methods are discussed, including special considerations that require the end-users' attention to ensure that the results generated are reproducible, and the biological conclusions drawn are valid.
Please use this identifier to cite or link to this item:
Download statistics for the last 12 months
Not enough data to produce graph