FinaleDB: a browser and database of cell-free DNA fragmentation patterns

Zheng, Haizi; Zhu, Michelle S; Liu, Yaping

doi:10.1093/bioinformatics/btaa999

Cited by 31 publications

(18 citation statements)

References 8 publications

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“…Due to the enormous commercial interests behind the cfDNA, the access of raw cfDNA NGS data in these repositories usually requires datatransfer agreements that may take several months of negotiations between the two organisations' legal departments. Some initial efforts, such as FinaleDB, have been made recently to establish the cfDNA-fragmentomics database [133][134][135]. However, more community efforts are still needed to collect and uniformly process the comprehensive publicly available cfDNA datasets together with their rich metadata.…”

Section: Discussionmentioning

confidence: 99%

At the dawn: cell-free DNA fragmentomics and gene regulation

Liu

2021

Br J Cancer

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Epigenetic mechanisms play instrumental roles in gene regulation during embryonic development and disease progression. However, it is challenging to non-invasively monitor the dynamics of epigenomes and related gene regulation at inaccessible human tissues, such as tumours, fetuses and transplanted organs. Circulating cell-free DNA (cfDNA) in peripheral blood provides a promising opportunity to non-invasively monitor the genomes from these inaccessible tissues. The fragmentation patterns of plasma cfDNA are unevenly distributed in the genome and reflect the in vivo gene-regulation status across multiple molecular layers, such as nucleosome positioning and gene expression. In this review, we revisited the computational and experimental approaches that have been recently developed to measure the cfDNA fragmentomics across different resolutions comprehensively. Moreover, cfDNA in peripheral blood is released following cell death, after apoptosis or necrosis, mainly from haematopoietic cells in healthy people and diseased tissues in patients. Several cfDNA-fragmentomics approaches showed the potential to identify the tissues-of-origin in cfDNA from cancer patients and healthy individuals. Overall, these studies paved the road for cfDNA fragmentomics to non-invasively monitor the in vivo gene-regulatory dynamics in both peripheral immune cells and diseased tissues.

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Section: Discussionmentioning

confidence: 99%

At the dawn: cell-free DNA fragmentomics and gene regulation

Liu

2021

Br J Cancer

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“…Disease diagnosis can be made based on the signals derived from cfDNA fragmentation pattern, nucleosome positioning, binding of transcription factors, transcription start site regions, cfDNA ended positions, as well as peripheral cellular alterations. The inherent property of information derived from cfDNA like sensitivity and noise and DNA fragment length affect the pattern inference process in the downstream computational analysis [16] .…”

Section: Understanding Cfdna Sources and Featuresmentioning

confidence: 99%

Computational challenges in detection of cancer using cell-free DNA methylation

Sharma

Verma

Kumar

et al. 2022

Computational and Structural Biotechnology Journal

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Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.

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“…We next sought to explore whether or not the cfDNA fragmentation dynamics in the hotspots can reflect the aberrations of gene regulatory elements in early-stage cancer. We collected the publicly available low-coverage cfDNA WGS (~1X/sample) from 90 patients with early-stage hepatocellular carcinoma (HCC, 85 of them are Barcelona Clinic Liver Cancer stage A, 5 of them are stage B) and 32 healthy individuals from the same study [29,30]. Since these cfDNA WGS are all sequenced with low coverage, we estimated the minimum number of fragments required by CRAG (see Supplementary Methods and Fig.…”

Section: Cell-free Dna Fragmentation Hotspots Reveal the Potential Ge...mentioning

confidence: 99%

“…We further extended our study from early-stage HCC to multiple other cancer types. We collected publicly available low-coverage cfDNA WGS data (~1X/sample) from 208 patients across seven different kinds of cancer (88% in stage I-III, colon, breast, lung, gastric, bile duct, ovary, and pancreatic cancer) and 215 healthy controls in the same study [2,30]. We applied a similar strategy as the HCC study above for the hotspot calling (pool the samples in the training dataset).…”

Section: Cell-free Dna Fragmentation Hotspots For the Detection And L...mentioning

confidence: 99%

CRAG:De novocharacterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing

Zhou

Liu

2020

Preprint

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The global variation of cell-free DNA fragmentation patterns is a promising biomarker for cancer diagnosis. However, the characterization of its hotspots and aberrations in early-stage cancer at the fine-scale is still poorly understood. Here, we developed an approach to de novo characterize genome-wide cell-free DNA fragmentation hotspots by integrating both fragment coverage and size from whole-genome sequencing. These hotspots are highly enriched in regulatory elements, such as promoters, and hematopoietic-specific enhancers. Surprisingly, half of the high-confident hotspots are still largely protected by the nucleosome and located near repeats, named inaccessible hotspots, which suggests the unknown origin of cell-free DNA fragmentation. In early-stage cancer, we observed the increases of fragmentation level at these inaccessible hotspots from microsatellite repeats and the decreases of fragmentation level at accessible hotspots near promoter regions, mostly with the silenced biological processes from peripheral immune cells and enriched in CTCF insulators. We identified the fragmentation hotspots from 298 cancer samples across 8 different cancer types (92% in stage I to III), 103 benign samples, and 247 healthy samples. The fine-scale fragmentation level at most variable hotspots showed cancer-specific fragmentation patterns across multiple cancer types and non-cancer controls. Therefore, with the fine-scale fragmentation signals alone in a machine learning model, we achieved 42% to 93% sensitivity at 100% specificity in different early-stage cancer. In cancer positive cases, we further localized cancer to a small number of anatomic sites with a median of 85% accuracy. The results here highlight the significance to characterize the fine-scale cell-free DNA fragmentation hotspot as a novel molecular marker for the screening of early-stage cancer that requires both high sensitivity and ultra-high specificity.

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FinaleDB: a browser and database of cell-free DNA fragmentation patterns

Cited by 31 publications

References 8 publications

At the dawn: cell-free DNA fragmentomics and gene regulation

At the dawn: cell-free DNA fragmentomics and gene regulation

Computational challenges in detection of cancer using cell-free DNA methylation

CRAG:De novocharacterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing

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