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doi: 10.1101/2022.02.08.479579 , 10.1186/s13059-023-02923-y , 10.5281/zenodo.7353885 , 10.5281/zenodo.7303513 , 10.5281/zenodo.7334167 , 10.5167/uzh-237603 , 10.5281/zenodo.6189605 , 10.5281/zenodo.5960320 , 10.5281/zenodo.7329791 , 10.5281/zenodo.5960321 , 10.5281/zenodo.7350402 , 10.5281/zenodo.7777147 , 10.5281/zenodo.6332914
pmid: 37095564
pmc: PMC10123983
doi: 10.1101/2022.02.08.479579 , 10.1186/s13059-023-02923-y , 10.5281/zenodo.7353885 , 10.5281/zenodo.7303513 , 10.5281/zenodo.7334167 , 10.5167/uzh-237603 , 10.5281/zenodo.6189605 , 10.5281/zenodo.5960320 , 10.5281/zenodo.7329791 , 10.5281/zenodo.5960321 , 10.5281/zenodo.7350402 , 10.5281/zenodo.7777147 , 10.5281/zenodo.6332914
pmid: 37095564
pmc: PMC10123983
Abstract Long-read RNA sequencing (lrRNA-seq) produces detailed information about full-length transcripts, including novel and sample-specific isoforms. Furthermore, there is opportunity to call variants directly from lrRNA-seq data. However, most state-of-the-art variant callers have been developed for genomic DNA. Here, there are two objectives: first, we perform a mini-benchmark on GATK, DeepVariant, Clair3, and NanoCaller primarily on PacBio Iso-Seq, data, but also on Nanopore and Illumina RNA-seq data; second, we propose a pipeline to process spliced-alignment files, making them suitable for variant calling with DNA-based callers. With such manipulations, high calling performance can be achieved using DeepVariant on Iso-seq data.
QH301-705.5, Sequence Analysis, RNA, Short Report, High-Throughput Nucleotide Sequencing, QH426-470, 10124 Institute of Molecular Life Sciences, 1307 Cell Biology, 1105 Ecology, Evolution, Behavior and Systematics, 1311 Genetics, Exome Sequencing, Genetics, 570 Life sciences; biology, RNA, RNA-Seq, Biology (General)
QH301-705.5, Sequence Analysis, RNA, Short Report, High-Throughput Nucleotide Sequencing, QH426-470, 10124 Institute of Molecular Life Sciences, 1307 Cell Biology, 1105 Ecology, Evolution, Behavior and Systematics, 1311 Genetics, Exome Sequencing, Genetics, 570 Life sciences; biology, RNA, RNA-Seq, Biology (General)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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| downloads | 7 |

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