Computational Errors and Biases in Short Read Next Generation Sequencing

Abnizova, Irina; Boekhorst, René te; Orlov, Yuriy L.

doi:10.4172/jpb.1000420

Cited by 37 publications

(45 citation statements)

References 211 publications

(329 reference statements)

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“…Predictions of insertions and deletions were less stable, with only the deletion at loci 2084 and the insertion at 6018 confirmed. The Freebayes analysis detected a deletion at 22 832 in specimen B that was not identified by the first sequence analysis ( appendix p 3 ), but insertion and deletion predictions from short-read alignments are less reliable than are SNV predictions 22 and are merely presented for completeness.…”

Section: Resultsmentioning

confidence: 99%

Genomic evidence for reinfection with SARS-CoV-2: a case study

Tillett

Sevinsky²,

Hartley

et al. 2021

The Lancet Infectious Diseases

729

741

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Background The degree of protective immunity conferred by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently unknown. As such, the possibility of reinfection with SARS-CoV-2 is not well understood. We describe an investigation of two instances of SARS-CoV-2 infection in the same individual.Methods A 25-year-old man who was a resident of Washoe County in the US state of Nevada presented to health authorities on two occasions with symptoms of viral infection, once at a community testing event in April, 2020, and a second time to primary care then hospital at the end of May and beginning of June, 2020. Nasopharyngeal swabs were obtained from the patient at each presentation and twice during follow-up. Nucleic acid amplification testing was done to confirm SARS-CoV-2 infection. We did next-generation sequencing of SARS-CoV-2 extracted from nasopharyngeal swabs. Sequence data were assessed by two different bioinformatic methodologies. A short tandem repeat marker was used for fragment analysis to confirm that samples from both infections came from the same individual.Findings The patient had two positive tests for SARS-CoV-2, the first on April 18, 2020, and the second on June 5, 2020, separated by two negative tests done during follow-up in May, 2020. Genomic analysis of SARS-CoV-2 showed genetically significant differences between each variant associated with each instance of infection. The second infection was symptomatically more severe than the first.Interpretation Genetic discordance of the two SARS-CoV-2 specimens was greater than could be accounted for by short-term in vivo evolution. These findings suggest that the patient was infected by SARS-CoV-2 on two separate occasions by a genetically distinct virus. Thus, previous exposure to SARS-CoV-2 might not guarantee total immunity in all cases. All individuals, whether previously diagnosed with COVID-19 or not, should take identical precautions to avoid infection with SARS-CoV-2. The implications of reinfections could be relevant for vaccine development and application.

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Section: Resultsmentioning

confidence: 99%

Genomic evidence for reinfection with SARS-CoV-2: a case study

Tillett

Sevinsky²,

Hartley

et al. 2021

The Lancet Infectious Diseases

729

741

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“…It allows fast and cost-effective sequencing of whole genomes of many individuals. The downside of sequencing carried out by high-throughput processes are the significant technical (Pfeiffer et al 2018 ; Ma et al 2019 ) and bioinformatics (Abnizova et al 2017 ) error rates. In particular, the very large amounts of genomes sequenced with moderate or low coverage, short-read lengths and individual genetic variation often cause numerous computational problems (Horner et al 2010 ).…”

Section: Introductionmentioning

confidence: 99%

The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines

et al. 2020

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A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing–based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras. Several algorithms were tested: (i) the basic, naïve algorithm, (ii) the naïve algorithm modified by pre-imposing different weights on incorrect and correct SNP class in calculating the loss metric and (iii)–(v) the naïve algorithm modified by random re-sampling (with replacement) of the incorrect SNPs to match 30%/60%/100% of the number of correct SNPs. The training data set was composed of data from three bulls and consisted of 2,227,995 correct (97.94%) and 46,920 incorrect SNPs, while the validation data set consisted of data from one bull with 749,506 correct (98.05%) and 14,908 incorrect SNPs. The results showed that for a rare event classification problem, like incorrect SNP detection in NGS data, the most parsimonious naïve model and a model with the weighting of SNP classes provided the best results for the classification of the validation data set. Both classified 19% of truly incorrect SNPs as incorrect and 99% of truly correct SNPs as correct and resulted in the F1 score of 0.21 — the highest among the compared algorithms. We conclude the basic models were less adapted to the specificity of a training data set and thus resulted in better classification of the independent, validation data set, than the other tested models.

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“…In the future, additional problems of transcription analysis in the human genome should be addressed. Examples are the transposon activity, non-coding gene transcription, complex transcription landscape including antisense transcripts [54], [55], [56].…”

Section: Discussionmentioning

confidence: 99%

Computer Analysis of Glioma Transcriptome Profiling: Alternative Splicing Events

Babenko

Губанова

Bragin

et al. 2017

Journal of Integrative Bioinformatics

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Abstract:Here we present the analysis of alternative splicing events on an example of glioblastoma cell culture samples using a set of computer tools in combination with database integration. The gene expression profiles of glioblastoma were obtained from cell culture samples of primary glioblastoma which were isolated and processed for RNA extraction. Transcriptome profiling of normal brain samples and glioblastoma were done by Illumina sequencing. The significant differentially expressed exon-level probes and their corresponding genes were identified using a combination of the splicing index method. Previous studies indicated that tumor-specific alternative splicing is important in the regulation of gene expression and corresponding protein functions during cancer development. Multiple alternative splicing transcripts have been identified as progression markers, including generalized splicing abnormalities and tumor-and stage-specific events. We used a set of computer tools which were recently applied to analysis of gene expression in laboratory animals to study differential splicing events. We found 69 transcripts that are differentially alternatively spliced. Three cancer-associated genes were considered in detail, in particular: APP (amyloid beta precursor protein), CASC4 (cancer susceptibility candidate 4) and TP53. Such alternative splicing opens new perspectives for cancer research.

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Computational Errors and Biases in Short Read Next Generation Sequencing

Cited by 37 publications

References 211 publications

Genomic evidence for reinfection with SARS-CoV-2: a case study

Genomic evidence for reinfection with SARS-CoV-2: a case study

The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines

Computer Analysis of Glioma Transcriptome Profiling: Alternative Splicing Events

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