New insight into HCV E1/E2 region of genotype 4a

Hussein, Nehal; Zekri, Abdel‐Rahman N.; Abouelhoda, Mohamed; El-Din, Hanaa M Alam; Ghamry, Ahmed Abdelwahab; Amer, Medhat; Sherif, Ghada M.; Bahnassy, Abeer A.

doi:10.1186/s12985-014-0231-y

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…Phylogenetic trees were constructed according to the Maximum Likelihood method based on the Tamura-Nei model [ 23 ] using MEGA 6.0. We used the same approach in our previous studies [ 20 , 24 ] and the superiority of Tamura-Nei model for the analysis of HVR1 was reported by others [ 25 ]. The robustness of tree topology was estimated by the bootstrapping method (resampling 1000 data sets) using MEGA 6.0.…”

Section: Methodsmentioning

confidence: 99%

Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center

et al. 2018

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Molecular characterization of early hepatitis C virus (HCV) infection remains rare. Ten out of 78 patients of a hematology/oncology center were found to be HCV RNA positive two to four months after hospitalization. Only two of the ten patients were anti-HCV positive. HCV hypervariable region 1 (HVR1) was amplified in seven patients (including one anti-HCV positive) and analyzed by next generation sequencing (NGS). Genetic variants were reconstructed by Shorah and an empirically established 0.5% variant frequency cut-off was implemented. These sequences were compared by phylogenetic and diversity analyses. Ten unrelated blood donors with newly acquired HCV infection detected at the time of donation (HCV RNA positive and anti-HCV negative) served as controls. One to seven HVR1 variants were found in each patient. Sequences intermixed phylogenetically with no evidence of clustering in individual patients. These sequences were more similar to each other (similarity 95.4% to 100.0%) than to those of controls (similarity 64.8% to 82.6%). An identical predominant variant was present in four patients, whereas other closely related variants dominated in the remaining three patients. In five patients the HCV population was limited to a single variant or one predominant variant and minor variants of less than 10% frequency. In conclusion, NGS analysis of a cluster of HCV infections acquired in the hospital setting revealed the presence of low diversity, very closely related variants in all patients, suggesting an early-stage infection with the same virus. NGS combined with phylogenetic analysis and classical epidemiological analysis could help in tracking of HCV outbreaks.

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Section: Methodsmentioning

confidence: 99%

Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center

et al. 2018

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“…Two HCV populations observed in patients in previous studies are used as simulated viral populations. The first, denoted as HCV 1, consists of 36 HCV strains from E1/E2 region and are of length 1672 bps [33] . The second, denoted as HCV 2, consists of 44 HCV strains from the E1/E2 regions of the HCV genome with lengths 1734 bps [8] , [17] .…”

Section: Resultsmentioning

confidence: 99%

A random forest classifier for detecting rare variants in NGS data from viral populations

Malhotra

Jha

Poss

et al. 2017

Computational and Structural Biotechnology Journal

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We propose a random forest classifier for detecting rare variants from sequencing errors in Next Generation Sequencing (NGS) data from viral populations. The method utilizes counts of varying length of k-mers from the reads of a viral population to train a Random forest classifier, called MultiRes, that classifies k-mers as erroneous or rare variants. Our algorithm is rooted in concepts from signal processing and uses a frame-based representation of k-mers. Frames are sets of non-orthogonal basis functions that were traditionally used in signal processing for noise removal. We define discrete spatial signals for genomes and sequenced reads, and show that k-mers of a given size constitute a frame.We evaluate MultiRes on simulated and real viral population datasets, which consist of many low frequency variants, and compare it to the error detection methods used in correction tools known in the literature. MultiRes has 4 to 500 times less false positives k-mer predictions compared to other methods, essential for accurate estimation of viral population diversity and their de-novo assembly. It has high recall of the true k-mers, comparable to other error correction methods. MultiRes also has greater than 95% recall for detecting single nucleotide polymorphisms (SNPs) and fewer false positive SNPs, while detecting higher number of rare variants compared to other variant calling methods for viral populations. The software is available freely from the GitHub link https://github.com/raunaq-m/MultiRes.

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“…Two HCV populations observed in patients in previous studies are used as simulated viral populations. The first, denoted as HCV 1, consists of 36 HCV strains from E1/E2 region and are of length 1672 bps [14]. The second, denoted as HCV 2, consists of 44 HCV strains from the E1/E2 regions of the HCV genome with lengths 1734 bps [24,31].…”

Section: Robustness: Testing Multires On a Hepatitis C Virus Datasetmentioning

confidence: 99%

A frame-based representation of genomic sequences for removing errors and rare variant detection in NGS data

Malhotra,

Mukhopadhyay,

Poss

et al. 2016

Preprint

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We propose a frame-based representation of k-mers for detecting sequencing errors and rare variants in next generation sequencing data obtained from populations of closely related genomes. Frames are sets of non-orthogonal basis functions, traditionally used in signal processing for noise removal. We define a frame for genomes and sequenced reads to consist of discrete spatial signals of every k-mer of a given size. We show that each k-mer in the sequenced data can be projected onto multiple frames and these projections are maximized for spatial signals corresponding to the k-mer's substrings. Our proposed classifier, MultiRes, is trained on the projections of k-mers as features used for marking k-mers as erroneous or true variations in the genome. We evaluate MultiRes on simulated and real viral population datasets and compare it to other error correction methods known in the literature. MultiRes has 4 to 500 times less false positives k-mer predictions compared to other methods, essential for accurate estimation of viral population diversity and their de-novo assembly. It has high recall of the true k-mers, comparable to other error correction methods. MultiRes also has greater than 95% recall for detecting single nucleotide polymorphisms (SNPs), fewer false positive SNPs, while detecting higher number of rare variants compared to other variant calling methods for viral populations. The software is freely available from the GitHub link https://github.com/raunaq-m/ MultiRes.

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New insight into HCV E1/E2 region of genotype 4a

Cited by 5 publications

References 40 publications

Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center

Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center

A random forest classifier for detecting rare variants in NGS data from viral populations

A frame-based representation of genomic sequences for removing errors and rare variant detection in NGS data

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