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

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

doi:10.1101/2020.08.18.255885

Cited by 7 publications

(12 citation statements)

References 25 publications

(29 reference statements)

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“…These trends were observed in plasma cfDNA derived from healthy individuals as well as from cancer patients in Cohort 1 (Fig. 1A to 1D) as well as in publicly available data 18…”

Section: Observation 1: End-motif Frequency Is Influenced By Local Gc...supporting

confidence: 56%

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identifying cancer patients from GC-patterned fragment ends of cell-free DNA

Curtis¹,

Summers

Cohen³

et al. 2022

Preprint

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One of the most intriguing characteristics of cell-free DNA (cfDNA) from plasma is the sequence at the ends of the fragments. Previous studies have shown that these end-sequences are somewhat different in cancer patients than in healthy individuals. While investigating this characteristic, we noticed that the bases at the 5-prime ends of a double-stranded fragment were highly correlated with the GC content of that particular fragment. This led us to develop a method, called MendSeqS (Modified End-based sequencing System), that incorporates the correlation between end-motifs and GC content into the analysis of shallow (0.5x) whole genome sequencing (WGS). MendSeqS was able to classify patients with a sensitivity of 96% at 98% specificity in a cohort comprised of 107 individuals evaluated in our laboratory (43 with cancer and 64 without). In cohorts evaluated in three other laboratories, comprising a total o4 401 individuals (193 with cancer and 208 without), MendSeqS achieved a sensitivity of 87% at 98% specificity. MendSeqS could in principle be combined with other methods of cfDNA analysis to enhance cancer detection.

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“…These trends were observed in plasma cfDNA derived from healthy individuals as well as from cancer patients in Cohort 1 (Fig. 1A to 1D) as well as in publicly available data 18…”

Section: Observation 1: End-motif Frequency Is Influenced By Local Gc...supporting

confidence: 56%

“…Fragmentation data was downloaded for 352 patients from the FinaleDB database 18 . Data files were downloaded in sorted tsv format and contained fragment-end positioning (chr,start,end) and strand alignment.…”

Section: Analysis Of Finaledb Samplesmentioning

confidence: 99%

identifying cancer patients from GC-patterned fragment ends of cell-free DNA

Curtis¹,

Summers

Cohen³

et al. 2022

Preprint

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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: Resultsmentioning

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: Resultsmentioning

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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“…Notably, achieving this aim requires access to as many as possible published cfDNA datasets to train the models. Few web sites started appearing that allow visualisation and download of a limited number of cfDNA datasets (Yu et al 2020;Zheng et al 2021), but a centralised resource which systematically collects cfDNA datasets from the dozens (and increasing towards hundreds) of currently available cfDNA publications is desperately needed. Here, we have developed such a resource -NucPosDB -which aims to curate all published datasets of sequenced cfDNA, nucleosome positioning maps in vivo and software for nucleosomics analysis.…”

Section: Short-termmentioning

confidence: 99%

NucPosDB: a database of nucleosome positioning in vivo and nucleosomics of cell-free DNA

et al. 2022

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Nucleosome positioning is involved in many gene regulatory processes happening in the cell, and it may change as cells differentiate or respond to the changing microenvironment in a healthy or diseased organism. One important implication of nucleosome positioning in clinical epigenetics is its use in the “nucleosomics” analysis of cell-free DNA (cfDNA) for the purpose of patient diagnostics in liquid biopsies. The rationale for this is that the apoptotic nucleases that digest chromatin of the dying cells mostly cut DNA between nucleosomes. Thus, the short pieces of DNA in body fluids reflect the positions of nucleosomes in the cells of origin. Here, we report a systematic nucleosomics database — NucPosDB — curating published nucleosome positioning datasets in vivo as well as datasets of sequenced cell-free DNA (cfDNA) that reflect nucleosome positioning in situ in the cells of origin. Users can select subsets of the database by a number of criteria and then obtain raw or processed data. NucPosDB also reports the originally determined regions with stable nucleosome occupancy across several individuals with a given condition. An additional section provides a catalogue of computational tools for the analysis of nucleosome positioning or cfDNA experiments and theoretical algorithms for the prediction of nucleosome positioning preferences from DNA sequence. We provide an overview of the field, describe the structure of the database in this context, and demonstrate data variability using examples of different medical conditions. NucPosDB is useful both for the analysis of fundamental gene regulation processes and the training of computational models for patient diagnostics based on cfDNA. The database currently curates ~ 400 publications on nucleosome positioning in cell lines and in situ as well as cfDNA from > 10,000 patients and healthy volunteers. For open-access cfDNA datasets as well as key MNase-seq datasets in human cells, NucPosDB allows downloading processed mapped data in addition to the regions with stable nucleosome occupancy. NucPosDB is available at https://generegulation.org/nucposdb/.

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

Cited by 7 publications

References 25 publications

identifying cancer patients from GC-patterned fragment ends of cell-free DNA

identifying cancer patients from GC-patterned fragment ends of cell-free DNA

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

NucPosDB: a database of nucleosome positioning in vivo and nucleosomics of cell-free DNA

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