henhaobio
Sign in

Briefings in BioinformaticsBioinformaticsPeer-reviewed journal article

Accurate Identification of Extrachromosomal Circular DNA from Long-read Sequences.

Visanu WanchaiiD, Piroon JenjaroenpuniD, Thongpan LeangapichartiD, Gerard ArreyiD, Charles M BurnhamiD, Maria Charlotte Tümmler, Jesús Delgado‐CalleiD, Birgitte RegenbergiD, Intawat NookaewiD

PDFDOIPreprint

Abstract

Extrachromosomal circular DNA (eccDNA) of chromosomal origin is found in a range of eukaryotic species and cell type including cancer where eccDNA with oncogenes appear to drive tumorigenesis. Most studies of eccDNA employ short-read sequencing to identify for their identification. However, short-read sequencing cannot resolve the complexity of genomic repeats, which can lead to missing eccDNA identification. An alternative is the long-read sequencing technologies that can potentially be used to construct complete eccDNA. We present a software suite, Construction-based Rolling-circle amplification for eccDNA Sequence Identification and Location (CReSIL) 2.0, to identify and characterize eccDNA from long-read sequences. CReSILs performance in the identification of eccDNA, with a minimum F1 score of 0.98, is superior to the other bioinformatic tools based on simulated data. CReSIL provides many useful features for genomic annotation, which can used to infer eccDNA function and Circos visualization for eccDNA architecture investigation. We demonstrated CReSILs capability in many of the long-read sequencing datasets. This includes datasets enriched for eccDNA as well as whole genome datasets from many cells that contained large eccDNA. CReSIL suite software will be a versatile tool to deeply investigate eccDNA in biological samples.

Citations
46
FWCI
5.28
Field percentile
top 3%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISLi '23Faizrahnemo '26Zhu '24Zhang '25Giurgiu '23Mann '22
77% semantic matchGenomics
Fu‐Yu Li, Wenlong Ming, Wenxiang Lu+4 more·Briefings in Bioinformatics·Sep 22, 2023· 14 citations