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      A new long-read mitochondrial-genome protocol (PacBio HiFi) for haemosporidian parasites: a tool for population and biodiversity studies

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          Abstract

          Background

          Studies on haemosporidian diversity, including origin of human malaria parasites, malaria's zoonotic dynamic, and regional biodiversity patterns, have used target gene approaches. However, current methods have a trade-off between scalability and data quality. Here, a long-read Next-Generation Sequencing protocol using PacBio HiFi is presented. The data processing is supported by a pipeline that uses machine-learning for analysing the reads.

          Methods

          A set of primers was designed to target approximately 6 kb, almost the entire length of the haemosporidian mitochondrial genome. Amplicons from different samples were multiplexed in an SMRTbell® library preparation. A pipeline (HmtG-PacBio Pipeline) to process the reads is also provided; it integrates multiple sequence alignments, a machine-learning algorithm that uses modified variational autoencoders, and a clustering method to identify the mitochondrial haplotypes/species in a sample. Although 192 specimens could be studied simultaneously, a pilot experiment with 15 specimens is presented, including in silico experiments where multiple data combinations were tested.

          Results

          The primers amplified various haemosporidian parasite genomes and yielded high-quality mt genome sequences. This new protocol allowed the detection and characterization of mixed infections and co-infections in the samples. The machine-learning approach converged into reproducible haplotypes with a low error rate, averaging 0.2% per read (minimum of 0.03% and maximum of 0.46%). The minimum recommended coverage per haplotype is 30X based on the detected error rates. The pipeline facilitates inspecting the data, including a local blast against a file of provided mitochondrial sequences that the researcher can customize.

          Conclusions

          This is not a diagnostic approach but a high-throughput method to study haemosporidian sequence assemblages and perform genotyping by targeting the mitochondrial genome. Accordingly, the methodology allowed for examining specimens with multiple infections and co-infections of different haemosporidian parasites. The pipeline enables data quality assessment and comparison of the haplotypes obtained to those from previous studies. Although a single locus approach, whole mitochondrial data provide high-quality information to characterize species pools of haemosporidian parasites.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12936-024-04961-8.

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          Most cited references64

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

            We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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              IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

              Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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                Author and article information

                Contributors
                Maria.Pacheco@temple.edu
                Ananias.Escalante@temple.edu
                Journal
                Malar J
                Malar J
                Malaria Journal
                BioMed Central (London )
                1475-2875
                4 May 2024
                4 May 2024
                2024
                : 23
                : 134
                Affiliations
                [1 ]Biology Department/Institute of Genomics and Evolutionary Medicine (iGEM), Temple University, ( https://ror.org/00kx1jb78) (SERC - 645), 1925 N. 12 St, Philadelphia, PA 19122-1801 USA
                [2 ]University of Pennsylvania, Wildlife Futures Program, ( https://ror.org/00b30xv10) Kennett Square, Philadelphia, PA 19348 USA
                [3 ]GRID grid.413759.d, ISNI 0000 0001 0725 8379, USDA Wildlife Services, ; Springfield, IL 62711 USA
                [4 ]Department of Animal Sciences, University of Illinois at Urbana-Champaign, ( https://ror.org/047426m28) Champaign, IL 61801 USA
                [5 ]Illinois Natural History Survey-Prairie Research Institute, University of Illinois at Urbana-Champaign, ( https://ror.org/047426m28) Champaign, IL 61820 USA
                [6 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Department of Natural Resources and Environmental Sciences, , University of Illinois at Urbana-Champaign, ; Champaign, IL 61820 USA
                [7 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Department of Pathobiology, College of Veterinary Medicine, , University of Illinois at Urbana-Champaign, ; Champaign, IL 61802 USA
                Article
                4961
                10.1186/s12936-024-04961-8
                11069185
                38704592
                8a9946f6-27dd-4781-8b11-f98968d9a8f2
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 26 February 2024
                : 24 April 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000076, Directorate for Biological Sciences;
                Award ID: NSF-DEB 2146653
                Award ID: NSF-DEB 2146653
                Award ID: NSF-DEB 2146653
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Infectious disease & Microbiology
                plasmodium,haemoproteus,leucocytozoon,machine learning,mitochondrial genome,mixed infection,co-infections

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