Genomic signal analysis of HIV variability (original) (raw)
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2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005
The conversion of genomic sequences into digital genomic signals offers the possibility to use signal processing methods for the analysis of genomic information. The study of genomic signals reveals local and global features of chromosomes that would be difficult to identify by using only the symbolic representation used in genomic data bases. The paper presents a study of the HIV Protease (PR) and Reverse Transcriptase (RT) genes by combining standard nucleotide sequence analysis with IT techniques based on the genomic signal approach. Cumulated and unwrapped phases of genomic signals are analyzed to characterize the variability of clade F HIV-1 strains isolated in Romania.
Comparative Analysis of Bioinformatics Tools Used in HIV-1 Studies
The HIV-1 virus is one of most studied viruses because of the incredible ability to morph its genetic structure. Sequencing the virus and generating phylogenetic trees gives us the ability to recognise the evolutionary changes of the virus and possibly help in finding a cure. Bioinformatics offers great choice of tools that are used in the process of generating phylogenetic trees. Different factors contribute in the usage of one tool over another and in order to compare phylogenetic quality of a tree we use the bootstrapping method. In this paper HIV-1 bioinformatics tools used in studies conducted in the Balkan Peninsula are presented, and comparative analysis of those tools is elaborated. The tools utilised in these studies are used in processing random data. The results of their usage is also presented.
A Diagnostic HIV-1 Tropism System Based on Sequence Relatedness
Journal of clinical microbiology, 2015
Key clinical studies for HIV coreceptor antagonists have used the phenotyping-based Trofile test. Meanwhile various simpler-to-do genotypic tests have become available that are compatible with standard laboratory equipment and Web-based interpretation tools. However, these systems typically analyze only the most prominent virus sequence in a specimen. We present a new diagnostic HIV tropism test not needing DNA sequencing. The system, XTrack, uses physical properties of DNA duplexes after hybridization of single-stranded HIV-1 env V3 loop probes to the clinical specimen. Resulting "heteroduplexes" possess unique properties driven by sequence relatedness to the reference and resulting in a discrete electrophoretic mobility. A detailed optimization process identified diagnostic probe candidates relating best to a large number of HIV-1 sequences with known tropism. From over 500 V3 sequences representing all main HIV-1 subtypes (Los Alamos database), we obtained a small set o...
Journal of Clinical Microbiology, 2008
Human immunodeficiency virus type 1 (HIV-1) tropism can be assessed using phenotypic assays, but this is quite laborious, expensive, and time-consuming and can be made only in sophisticated laboratories. More accessible albeit reliable tools for testing of HIV-1 tropism are needed in view of the prompt introduction of CCR5 antagonists in clinical practice. Bioinformatics tools based on V3 sequences might help to predict HIV-1 tropism; however, most of these methods have been designed by taking only genetic information derived from HIV-1 subtype B into consideration. The aim of this study was to evaluate the performances of several genotypic tools to predict HIV-1 tropism in non-B subtypes, as data on this issue are scarce. Plasma samples were tested using a new phenotypic tropism assay (Phenoscript-tropism; Eurofins), and results were compared with estimates of coreceptor usage using eight different genotypic predictor softwares (Support Vector Machine [SVM], C4.5, C4.5 with positions 8 to 12 only, PART, Charge Rule, geno2pheno coreceptor, Position-Specific Scoring Matrix X4R5 [PSSM X4R5 ], and PSSM sinsi ). A total of 150 samples were tested, with 115 belonging to patients infected with non-B subtypes and 35 drawn from subtype B-infected patients, which were taken as controls. When non-B subtypes were tested, the concordances between the results obtained using the phenotypic assay and distinct genotypic tools were as follows: 78.8% for SVM, 77.5% for C4.5, 82.5% for C4.5 with positions 8 to 12 only, 82.5% for PART, 82.5% for Charge Rule, 82.5% for PSSM X4R5 , 83.8% for PSSM sinsi , and 71.3% for geno2pheno. When clade B viruses were tested, the best concordances were seen for PSSM X4R5 (91.4%), PSSM sinsi (88.6%), and geno2pheno (88.6%). The sensitivity for detecting X4 variants was lower for non-B than for B viruses, especially in the case of PSSM sinsi (38.4% versus 100%, respectively), SVM wetcat (46% versus 100%, respectively), and PART (30% versus 90%, respectively). In summary, while inferences of HIV-1 coreceptor usage using genotypic tools seem to be reliable for clade B viruses, their performances are poor for non-B subtypes, in which they particularly fail to detect X4 variants.
Journal of Medical Virology, 2009
Assessment of HIV tropism using bioinformatic tools based on V3 sequences correlates poorly with results provided by phenotypic tropism assays, particularly for recognizing X4 viruses. This may represent an obstacle for the use of CCR5 antagonists. An algorithm combining several bioinformatic tools might improve the correlation with phenotypic tropism results. A total of 200 V3 sequences from HIV-1 subtype B, available in several databases with known phenotypic tropism results, were used to evaluate the sensitivity and specificity of seven different bioinformatic tools (PSSM, SVM, C4.5 decision tree generator and C4.5, PART, Charge Rule, and Geno2pheno). The best predictive bioinformatic tools were identified, and a model combining several of these was built. Using the 200 reference sequences, SVM and geno2-pheno showed the highest sensitivity for detecting X4 viruses (98.8% and 93.7%, respectively); however, their specificity was relatively low (62.5% and 86.6%, respectively). For R5 viruses, PSSM and C4.5 gave the same results and outperformed other bioinformatic tools (95.7% sensitivity, 82% specificity). When results from three out of these four tools were concordant, the sensitivity and specificity, taking as reference the results from phenotypic tropism assays, were over 90% in predicting either R5 or X4 viruses (AUC: 0.9701; 95% CI: 0.9358–0.9889). An algorithm combining four distinct bioinformatic tools (SVM, geno2pheno, PSSM and C4.5), improves the genotypic prediction of HIV tropism, and merits further evaluation, as it might prove useful as a screening strategy in clinical practice. J. Med. Virol. 81:763–767, 2009. © 2009 Wiley-Liss, Inc.
Journal of Virology, 2007
subpopulations of HIV type 1 (HIV-1) develop and, if viral trafficking is restricted between subpopulations, the viruses can follow independent evolutionary histories, i.e., become compartmentalized. This phenomenon is usually detected via comparative sequence analysis and has been reported for viruses isolated from the central nervous system (CNS) and the genital tract. Several approaches have been proposed to study the compartmentalization of HIV sequences, but to date, no rigorous comparison of the most commonly employed methods has been made. In this study, we systematically compared inferences made by six different methods for detecting compartmentalization based on three data sets: (i) a sample of 45 patients with sequences gathered from the CNS, (ii) sequences from the female genital tract of 18 patients, and (iii) a set of simulated sequences. We found that different methods often reached contradictory conclusions. Methods based on the topology of a phylogenetic tree derived from clonal sequences were generally more sensitive in detecting compartmentalization than those that relied solely upon pairwise genetic distances between sequences. However, as the branching structure in a phylogenetic tree is often uncertain, especially for short, low-diversity, or recombinant sequences, tree-based approaches may need to be modified to take phylogenetic uncertainty into account. Given the frequently discordant predictions of different methods and the strengths and weaknesses of each particular methodology, we recommend that a suite of several approaches be used for reliable inference of compartmentalized population structure.