eccDB: a comprehensive repository for eccDNA-mediated chromatin contacts in multi-species

Yang, Min; Qiu, Bo; He, Guo-You; Zhou, Jianyuan; Yu, Haojie; Zhang, Yuying; Li, Yan-Shang; Li, Tai-Song; Guo, Jincheng; Li, Xuecang; Xie, Jian‐Jun

doi:10.1093/bioinformatics/btad173

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…EccDNA sequences are available across various databases such as eccDNA Atlas 39 , TeCD 40 , EccBase 41 , EccDB 42 , and EccDNADB 43 . Each database includes extrachromosomal DNA sequences of different species and cells.…”

Section: Methodsmentioning

confidence: 99%

Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns

Abbasi,

Asim,

Ahmed

et al. 2024

Sci Rep

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Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely Homo sapiens (HM), Arabidopsis Thaliana (AT), and Saccharomyces cerevisiae (SC/YS). It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor, more than 140 baseline predictors, and 858 encoder ensembles. The proposed predictor outperforms baseline predictors and encoder ensembles across diverse leccDNA datasets by producing average performance values of 81.09%, 62.2% and 81.08% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available at https://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction. To facilitate the scientific community, a web application for leccDNA identification is available at https://sds_genetic_analysis.opendfki.de/iLEC_DNA/.

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Section: Methodsmentioning

confidence: 99%

Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns

Abbasi,

Asim,

Ahmed

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…EccDNA sequences are available across various databases such as eccDNA Atlas 66 , TeCD 67 , EccBase 68 , EccDB 69 , and EccDNADB 70 . Each database includes extrachromosomal DNA sequences of different species and cells.…”

Section: Methodsmentioning

confidence: 99%

iLEC-DNA: Identifying Long Extra-chromosomal Circular DNA by Fusing Sequence-derived Features of Physicochemical Properties and Nucleotide Distribution Patterns

Abbasi,

Asim,

Dengel

et al. 2023

Preprint

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Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely HM, AT, and YS. It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor and more than 140 baseline predictors. The proposed predictor outperforms baseline predictors across diverse leccDNA datasets by producing average performance values of 80.699%, 61.45% and 80.7% in terms of ACC, MCC and AUC-ROC across all the datasets.

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“…To address this issue, efforts have been made to organize the data and establish databases for information integration. This study has compiled and compared various online databases dedicated to eccDNAs, such as CircleBase ( Zhao et al, 2022 ), eccDNAdb ( Peng et al, 2022 ), eccDNA Atlas ( Zhong et al, 2023 ), EccBase ( Sun et al, 2023 ), and eccDB ( Yang et al, 2023 ), in terms of their design and functionality ( Table 3 ). For instance, CircleBase, as the pioneering eccDNA database, provides comprehensive annotations of 601,036 eccDNAs derived from the human genome.…”

Section: Online Resources For Exploring Eccdnamentioning

confidence: 99%

Characterization, biogenesis model, and current bioinformatics of human extrachromosomal circular DNA

Zhou,

Tang,

et al. 2024

Front. Genet.

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Human extrachromosomal circular DNA, or eccDNA, has been the topic of extensive investigation in the last decade due to its prominent regulatory role in the development of disorders including cancer. With the rapid advancement of experimental, sequencing and computational technology, millions of eccDNA records are now accessible. Unfortunately, the literature and databases only provide snippets of this information, preventing us from fully understanding eccDNAs. Researchers frequently struggle with the process of selecting algorithms and tools to examine eccDNAs of interest. To explain the underlying formation mechanisms of the five basic classes of eccDNAs, we categorized their characteristics and functions and summarized eight biogenesis theories. Most significantly, we created a clear procedure to help in the selection of suitable techniques and tools and thoroughly examined the most recent experimental and bioinformatics methodologies and data resources for identifying, measuring and analyzing eccDNA sequences. In conclusion, we highlighted the current obstacles and prospective paths for eccDNA research, specifically discussing their probable uses in molecular diagnostics and clinical prediction, with an emphasis on the potential contribution of novel computational strategies.

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eccDB: a comprehensive repository for eccDNA-mediated chromatin contacts in multi-species

Cited by 11 publications

References 14 publications

Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns

Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns

iLEC-DNA: Identifying Long Extra-chromosomal Circular DNA by Fusing Sequence-derived Features of Physicochemical Properties and Nucleotide Distribution Patterns

Characterization, biogenesis model, and current bioinformatics of human extrachromosomal circular DNA

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