Dataset Information

Third-generation Sequencing Reveals Extensive Polycistronism and Transcriptional Overlapping in a Baculovirus.

ABSTRACT: The Autographa californica multiple nucleopolyhedrovirus (AcMNPV) is an insect-pathogen baculovirus. In this study, we applied the Oxford Nanopore Technologies platform for the analysis of the polyadenylated fraction of the viral transcriptome using both cDNA and direct RNA sequencing methods. We identified and annotated altogether 132 novel transcripts and transcript isoforms, including 4 coding and 4 non-coding RNA molecules, 47 length variants, 5 splice isoforms, as well as 23 polycistronic and 49 complex transcripts. All of the identified novel protein-coding genes were 5'-truncated forms of longer host genes. In this work, we demonstrated that in the case of transcript start site isoforms, the promoters and the initiator sequence of the longer and shorter variants belong to the same kinetic class. Long-read sequencing also revealed a complex meshwork of transcriptional overlaps, the function of which needs to be clarified. Additionally, we developed bioinformatics methods to improve the transcript annotation and to eliminate the non-specific transcription reads generated by template switching and false priming.

SUBMITTER: Moldovan N

PROVIDER: S-EPMC5988703 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Third-generation Sequencing Reveals Extensive Polycistronism and Transcriptional Overlapping in a Baculovirus.

Moldován Norbert N Tombácz Dóra D Szűcs Attila A Csabai Zsolt Z Balázs Zsolt Z Kis Emese E Molnár Judit J Boldogkői Zsolt Z

Scientific reports 20180605 1

The Autographa californica multiple nucleopolyhedrovirus (AcMNPV) is an insect-pathogen baculovirus. In this study, we applied the Oxford Nanopore Technologies platform for the analysis of the polyadenylated fraction of the viral transcriptome using both cDNA and direct RNA sequencing methods. We identified and annotated altogether 132 novel transcripts and transcript isoforms, including 4 coding and 4 non-coding RNA molecules, 47 length variants, 5 splice isoforms, as well as 23 polycistronic a ...[more]

PMID: 29872099

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Project description:BackgroundThird-generation sequencing offers some advantages over next-generation sequencing predecessors, but with the caveat of harboring a much higher error rate. Clustering-related sequences is an essential task in modern biology. To accurately cluster sequences rich in errors, error type and frequency need to be accounted for. Levenshtein distance is a well-established mathematical algorithm for measuring the edit distance between words and can specifically weight insertions, deletions and substitutions. However, there are drawbacks to using Levenshtein distance in a biological context and hence has rarely been used for this purpose. We present novel modifications to the Levenshtein distance algorithm to optimize it for clustering error-rich biological sequencing data.ResultsWe successfully introduced a bidirectional frameshift allowance with end-user determined accommodation caps combined with weighted error discrimination. Furthermore, our modifications dramatically improved the computational speed of Levenstein distance. For simulated ONT MinION and PacBio Sequel datasets, the average clustering sensitivity for 3GOLD was 41.45% (S.D. 10.39) higher than Sequence-Levenstein distance, 52.14% (S.D. 9.43) higher than Levenshtein distance, 55.93% (S.D. 8.67) higher than Starcode, 42.68% (S.D. 8.09) higher than CD-HIT-EST and 61.49% (S.D. 7.81) higher than DNACLUST. For biological ONT MinION data, 3GOLD clustering sensitivity was 27.99% higher than Sequence-Levenstein distance, 52.76% higher than Levenshtein distance, 56.39% higher than Starcode, 48% higher than CD-HIT-EST and 70.4% higher than DNACLUST.ConclusionOur modifications to Levenshtein distance have improved its speed and accuracy compared to the classic Levenshtein distance, Sequence-Levenshtein distance and other commonly used clustering approaches on simulated and biological third-generation sequenced datasets. Our clustering approach is appropriate for datasets of unknown cluster centroids, such as those generated with unique molecular identifiers as well as known centroids such as barcoded datasets. A strength of our approach is high accuracy in resolving small clusters and mitigating the number of singletons.

Dataset Information

Third-generation Sequencing Reveals Extensive Polycistronism and Transcriptional Overlapping in a Baculovirus.

Publications

Third-generation Sequencing Reveals Extensive Polycistronism and Transcriptional Overlapping in a Baculovirus.

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