4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      STRIDE: a command-line HMM-based identifier and sub-classifier of Plasmodium falciparum RIFIN and STEVOR variant surface antigen families

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          RIFINs and STEVORs are variant surface antigens expressed by P. falciparum that play roles in severe malaria pathogenesis and immune evasion . These two highly diverse multigene families feature multiple paralogs, making their classification challenging using traditional bioinformatic methods.

          Results

          STRIDE (STevor and RIfin iDEntifier) is an HMM-based, command-line program that automates the identification and classification of RIFIN and STEVOR protein sequences in the malaria parasite Plasmodium falciparum. STRIDE is more sensitive in detecting RIFINs and STEVORs than available PFAM and TIGRFAM tools and reports RIFIN subtypes and the number of sequences with a FHEYDER amino acid motif, which has been associated with severe malaria pathogenesis.

          Conclusions

          STRIDE will be beneficial to malaria research groups analyzing genome sequences and transcripts of clinical field isolates, providing insight into parasite biology and virulence.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12859-021-04515-8.

          Related collections

          Most cited references13

          • Record: found
          • Abstract: found
          • Article: not found

          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Genome sequence of the human malaria parasite Plasmodium falciparum.

            The parasite Plasmodium falciparum is responsible for hundreds of millions of cases of malaria, and kills more than one million African children annually. Here we report an analysis of the genome sequence of P. falciparum clone 3D7. The 23-megabase nuclear genome consists of 14 chromosomes, encodes about 5,300 genes, and is the most (A + T)-rich genome sequenced to date. Genes involved in antigenic variation are concentrated in the subtelomeric regions of the chromosomes. Compared to the genomes of free-living eukaryotic microbes, the genome of this intracellular parasite encodes fewer enzymes and transporters, but a large proportion of genes are devoted to immune evasion and host-parasite interactions. Many nuclear-encoded proteins are targeted to the apicoplast, an organelle involved in fatty-acid and isoprenoid metabolism. The genome sequence provides the foundation for future studies of this organism, and is being exploited in the search for new drugs and vaccines to fight malaria.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              InterPro in 2017—beyond protein family and domain annotations

              InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.
                Bookmark

                Author and article information

                Contributors
                mtravass@som.umaryland.edu
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                6 January 2022
                6 January 2022
                2022
                : 23
                : 15
                Affiliations
                [1 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Malaria Research Program, Center for Vaccine Development and Global Health, , University of Maryland School of Medicine, ; Baltimore, MD USA
                [2 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Institute for Genome Sciences, , University of Maryland School of Medicine, ; Baltimore, MD USA
                [3 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Program in Personalized and Genomic Medicine, , University of Maryland School of Medicine, ; Baltimore, MD USA
                [4 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Department of Microbiology and Immunology, , University of Maryland School of Medicine, ; Baltimore, MD USA
                Author information
                http://orcid.org/0000-0002-6045-3322
                Article
                4515
                10.1186/s12859-021-04515-8
                8733436
                c729af3b-7745-4c6e-b550-9f2b3010a661
                © The Author(s) 2021

                Open AccessThis 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
                : 21 February 2021
                : 6 December 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 1F30HL146095-01A1
                Award ID: K23AI125720
                Award ID: R01HL146377
                Award ID: R01AI141900
                Award ID: U19AI110820
                Award Recipient :
                Categories
                Software
                Custom metadata
                © The Author(s) 2022

                Bioinformatics & Computational biology
                malaria,plasmodium falciparum,rifin,stevor,bioinformatics,hidden markov models

                Comments

                Comment on this article