C9febf25-f6ed-4341-a3c4-661efcb9bb0e - samples
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PROVIDER: EGAD00001010100 | EGA |
REPOSITORIES: EGA
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Project description:We present GCparagon, a two-stage algorithm for computing and correcting GC biases in cell-free DNA (cfDNA) samples. The length of the highly fragmented cfDNAs and the number of GC bases are essential parameters in the calculations. Regions of low mappability, known reference genome assembly errors and regions surrounding assembly gaps are excluded from the bias computation. GCparagon outputs a bias matrix and an optional tagged BAM file with GC bias balance weights as alignment tags. Parallelization allows calculation of a GC bias estimate in less than 2 minutes per sample with between 99.0% and 99.9% of fragments already corrected. We propose that GCparagon can help standardize cfDNA applications and evaluate the impact of GC bias on algorithms used in the analysis of liquid biopsy data.
| EGAS00001006963 | EGA
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels
Project description:Cell free DNA (cfDNA) in human plasma carries abundant information on physiological condition, especially in cancer patients such as esophageal cancer. Next-generation sequencing (NGS) as a rapidly developed technology could decode the information effectively. As a key step of NGS, using existing methods to construct cfDNA sequencing libraries is limited by several shortcomings. In this study, we developed a new NGS library construction method for highly degraded DNA, cfDNA as example, based on Single strand Adaptor Library Preparation (SALP). With the high ligation efficiency of single strand adaptor (SSA) which overhangs 3 random bases at 3' end of a double-strand DNA, the new method could construct the sequencing library with high sensitivity without using specific enzymes except T4 DNA ligase and Taq polymerase. With the special designed barcode T adaptor (BTA), multiple libraries constructed from different samples can be amplified in unbiased strategy and facility to compare. Using this method, this study successfully sequenced and compared totally 20 cfDNA samples derived from esophageal cancer patients and healthy people in whole genome scale. This study also compared the chromatin state between cancer patients and healthy people using cfDNA, identified the significant difference between different health condition. Our findings extend the application of cfDNA beyond the analysis with degraded DNA fragments itself, to the transcription regulation level, which also provide an important clue for biopsy of esophageal cancer and other diseases.
2019-04-12 | GSE116345 | GEO
Project description:We performed all stimulatory experiments using THP1 cell line as a representative of primary human monocytes to show fundamental role of the cfDNA in healthy organisms. The experiments were conducted in duplicates using plasma containing cfDNA (NP) and the reference one with cfDNA removed by DNase (TP) to recognize unequivocally the effect of plasma cfDNA on transcriptome and proteome of monocytes. We used native human plasma samples obtained from healthy volunteers with no animal serum addition to cultivation medium in order to avoid the presence of uncharacterized animal cfDNA in the experiments.
2021-05-18 | PXD017285 | Pride
Project description:5-Hydroxymethylcytosine (5hmC) is an important mammalian DNA epigenetic modification that has been linked to gene regulation and cancer pathogenesis. Here we explored the diagnostic potential of 5hmC in cell-free DNA (cfDNA), the circulating DNA found in human plasma which represents a noninvasive window into the health status of the body. We showed that the genome-wide 5hmC distribution in cfDNA can be reliably sequenced by chemical labeling-based 5hmC enrichment. We sequenced cell-free 5hmC from 49 patients of seven different cancer types and found distinct features that can be used for monitoring disease status and progression. Specifically, we discovered that lung cancer leads to a stage-dependent hypohydroxymethlation in cfDNA, whereas hepatocellular carcinoma (HCC) and pancreatic cancer lead to disease-specific changes in the cell-free hydroxymethylome. Our results demonstrate that cell-free 5hmC can be used not only to track the stage of cancer but also to identify tissue of origin in some solid tumors.
2017-07-13 | GSE81314 | GEO
Project description:Difference in RNA content of different cell types introduces bias to gene expression deconvolution methods. If ERCC spike-ins are introduced into samples, predicted proportions of deconvolution methods can be corrected
2019-04-03 | GSE129240 | GEO
Project description:Aims: Cancer is an important public health problem worldwide. In recent years, methods based on the analysis of plasma cfDNA which is as an emerging technology have been largely explored for noninvasive prenatal testing and cancer liquid biopsy. We inferred both epigenetic and genetic biomarkers of esophageal cancer (ESCA) in cfDNA with adapted SALP-seq. Materials & methods: Next Generation Sequencing libraries of cfDNA samples from ESCA patients and healthy people were constructed by using SALP-seq. We performed bioinformatics analysis on our sequencing data to find cancer-specific biomarkers. Results & conclusion: 54 important regulatory elements of ESCA, 49 epigenetic and 37 genetic ESCA-specific genes were inferred in cfDNA with SALP-seq, which may ultimately contribute to the development of effective diagnostic and therapeutic approaches for ESCA.
2022-08-28 | GSE136541 | GEO
Project description:As a non-invasive blood testing, the detection of cell-free DNA (cfDNA) methylation in plasma is raising increasing interest due to its diagnostic and biology applications. Although extensively used in cfDNA methylation analysis, bisulfite sequencing is less cost-effective. Through enriching methylated cfDNA fragments with MeDIP followed by deep sequencing, we aimed to characterize cfDNA methylome in cancer patients. In this study, we investigated the cfDNA methylation patterns in lung cancer patients by MeDIP-seq. MEDIPS package was used for the identification of differentially methylated regions (DMRs) between patients and normal ones. Overall, we identified 330 differentially methylated regions (DMRs) in gene promoter regions, 33 hypermethylation and 297 hypomethylation respectively, by comparing lung cancer patients and healthy individuals as controls. The 33 hypermethylation regions represent 32 genes. Some of the genes had been previously reported to be associated with lung cancers, such as GAS7, AQP10, HLF, CHRNA9 and HOPX. Taken together, our study provided an alternative method of cfDNA methylation analysis in lung cancer patients with potential clinical applications.
2019-03-31 | E-MTAB-7163 | biostudies-arrayexpress
Project description:Spot intensity serves as a proxy for gene expression in dual-label microarray experiments. Dye bias is defined as an intensity difference between samples labeled with different dyes attributable to the dyes instead of the gene expression in the samples. Dye bias that is not removed by array normalization can introduce bias into comparisons between samples of interest. But if the bias is consistent across the samples for the same gene, it can be corrected by proper experimental design and analysis. If the dye bias is not consistent across samples for the same gene, but is different for different samples, then removing the bias becomes more problematic, perhaps indicating a technical limitation to the ability of fluorescent signals to accurately represent gene expression. Thus, it is important to characterize dye bias to determine: (1) whether it will be removed for all genes by array normalization, (2) whether it will not be removed by normalization but can be removed by proper experimental design and analysis and (3) whether dye bias correction is more problematic than either of these and is not easily removable. Keywords: dye swap design
2006-06-06 | GSE4982 | GEO
Project description:Cell-free DNA (cfDNA) contains a composite map of the epigenomes of its cells-of-origin. Tissue-specific TF binding inferred from cfDNA could enable us to track disease states in humans in a non-invasive manner. Here, we present a method to identify the subset of genome-wide transcription factor binding sites that are protected in plasma. We map binding at tissue-specific sites of constitutive factor CTCF and tissue-specific factors PU.1, LYL1, ER, and FOXA1 in plasma cfDNA. Our method also captures the chromatin structure around the factor-bound sites in their cells-of-origin. We define pure tumor TF signatures in an in vivo model by applying our method to estrogen receptor-positive (ER+) breast cancer xenografts. The tumor-specific cfDNA protections of ER-α and FOXA1 reflect TF-specific accessibility across human breast tumors, demonstrating our ability to capture tumor TF binding in plasma. By modeling cfDNA from cancer donors as a mixture of healthy plasma and pure tumor signatures, we can identify tumor TF binding in humans. Thus, our method enables non-invasive mapping of the regulatory phenotype of cancer in humans.
2021-05-24 | GSE171434 | GEO