scholarly journals DASAF: An R Package for Deep Sequencing-Based Detection of Fetal Autosomal Abnormalities from Maternal Cell-Free DNA

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Baohong Liu ◽  
Xiaoyan Tang ◽  
Feng Qiu ◽  
Chunmei Tao ◽  
Junhui Gao ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Genome Mapping ◽  
Massively Parallel Sequencing ◽  
R Package ◽  
Detection Methods ◽  
Sequencing Data ◽  
Internal Reference ◽  
Cell Free Dna ◽  
Maternal Cell ◽  
Free Dna

Background. With the development of massively parallel sequencing (MPS), noninvasive prenatal diagnosis using maternal cell-free DNA is fast becoming the preferred method of fetal chromosomal abnormality detection, due to its inherent high accuracy and low risk. Typically, MPS data is parsed to calculate a risk score, which is used to predict whether a fetal chromosome is normal or not. Although there are several highly sensitive and specific MPS data-parsing algorithms, there are currently no tools that implement these methods.Results. We developed an R package, detection of autosomal abnormalities for fetus (DASAF), that implements the three most popular trisomy detection methods—the standardZ-score (STDZ) method, the GC correctionZ-score (GCCZ) method, and the internal referenceZ-score (IRZ) method—together with one subchromosome abnormality identification method (SCAZ).Conclusions. With the cost of DNA sequencing declining and with advances in personalized medicine, the demand for noninvasive prenatal testing will undoubtedly increase, which will in turn trigger an increase in the tools available for subsequent analysis. DASAF is a user-friendly tool, implemented in R, that supports identification of whole-chromosome as well as subchromosome abnormalities, based on maternal cell-free DNA sequencing data after genome mapping.

scholarly journals DISMIR: a deep learning-based cancer-detection method by integrating DNA sequence and methylation information of individual cell-free DNA reads

2021 ◽  
Author(s):  
Jiaqi Li ◽  
Lei Wei ◽  
Xianglin Zhang ◽  
Wei Zhang ◽  
Haochen Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Cancer Detection ◽  
High Throughput Sequencing ◽  
Detection Method ◽  
Low Cost ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Free Dna ◽  
Novel Approach

ABSTRACTDetecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel non-invasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise prediction with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as “switching region” to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state, and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultra-low sequencing depths. Analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites’ methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.

scholarly journals Non-Invasive Prenatal Diagnosis (NIPD) of Sickle-Cell Disease By Massively Parallel Sequencing of Cell-Free Fetal DNA in Maternal Serum

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2085-2085
Author(s):  
Yvonne Daniel ◽  
Julia Van Campen ◽  
Lee Silcock ◽  
Michael Yau ◽  
Joo Wook Ahn ◽  
...  
Keyword(s):  
Prenatal Diagnosis ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Massively Parallel Sequencing ◽  
Plasma Samples ◽  
Cell Disease ◽  
Cell Free Dna ◽  
Non Invasive ◽  
Free Dna ◽  
Cell Free Fetal Dna

Sickle cell disease (SCD) is the most common genetic haematological disorder worldwide. Around 300.000 affected infants are born every year, including at least 1000 in the United States. Prenatal diagnosis is currently carried out using amniotic fluid or chorionic villus sampling. These invasive procedures are perceived to have a small risk of miscarriage. The availability of non-invasive prenatal diagnosis (NIPD) is predicted to increase uptake of prenatal diagnosis for SCD, as it has no perceived miscarriage risk. NIPD may also be more readily implemented than invasive prenatal diagnosis in the low-resource countries in which SCD is the most prevalent. However, accurate NIPD of autosomal recessive disorders such as sickle cell disease has proven challenging as this requires detection of fetal inheritance of a maternal allele from a mixed maternal-fetal pool of cell-free DNA. We report the development of a targeted massively parallel sequencing assay for the NIPD of fetal SCD using cell-free fetal DNA from maternal plasma. No paternal or previous offspring samples were required. 44 clinical samples were analysed, including 37 plasma samples from pregnant SCD carriers and 7 plasma samples from women with SCD due to Hb SC. We used a relative mutation dosage based approach for the 37 samples from maternal SCD carriers (Hb AS or Hb AC), integrating Unique Molecular Identifiers (UMIs) into the analysis to improve the accuracy of wildtype and mutant allele counts. We used a separate wildtype allele detection approach for the 7 samples from women with compound heterozygous SCD, in whom the detection of wildtype cell-free DNA indicates the presence of a carrier fetus. The success of the assay was evaluated by comparing results with the established fetal sickle status as determined through either invasive prenatal diagnosis or newborn screening. During development, two key factors improved the accuracy of the results: i) Selective analysis of only smaller cell-free DNA fragments enhanced the fetal fraction for all samples, with greater effects observed in samples from earlier gestations. This approach improved diagnostic accuracy: for 3 out of 44 samples, the genotype was inconclusive or incorrect before size selection, but correct after size selection. ii) Modifications to DNA fragment hybridisation capture optimised the diversity of Unique Molecular Identifier-tagged molecules analysed. This led to improvements in the results obtained for 5 samples, with 3 previously inconclusive samples correctly called and 2 previously discrepant results moved into the inconclusive range. In total, 37 results were concordant with the established fetal sickle status; this included 30/37 samples from carrier women and 7/7 samples from women with sickle cell disease due to Hb SC. The remaining 7 carrier samples gave an inconclusive result, which for 3 samples was attributed to a low fetal fraction. Samples from as early as 8 weeks gestation were successfully genotyped. There were no false positive or false negative results. This study is the largest to use NGS-based NIPD on clinical plasma samples from pregnancies at risk of SCD. Efforts to validate the assay on a larger sample cohort and to reduce the inconclusive rate are warranted. This study shows that NIPD for SCD is approaching clinical utility and has the potential to provide increased choice to women with pregnancies at risk of sickle cell disease. Disclosures Silcock: Nonacus Ltd.: Employment.

scholarly journals A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing

2021 ◽  
Vol 8 ◽  
Author(s):  
Bo-Wei Han ◽  
Xu Yang ◽  
Shou-Fang Qu ◽  
Zhi-Wei Guo ◽  
Li-Min Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Nucleosome Occupancy ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Transcription Start Sites ◽  
Free Dna ◽  
Coverage Accuracy ◽  
Moderate Cost ◽  
Coverage Profile ◽  
High Depth

Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.

scholarly journals An online tool for fetal fraction prediction based on direct size distribution analysis of maternal cell-free DNA

BioTechniques ◽  
2020 ◽  
Author(s):  
Luca Bedon ◽  
Josef Vuch ◽  
Simeone Dal Monego ◽  
Germana Meroni ◽  
Vanna Pecile ◽  
...  
Keyword(s):  
Size Distribution ◽  
Prenatal Testing ◽  
Distribution Analysis ◽  
Blood Draw ◽  
Noninvasive Prenatal Testing ◽  
Cell Free Dna ◽  
Maternal Cell ◽  
Fetal Dna ◽  
Free Dna ◽  
Fetal Fraction

The discovery of circulating fetal DNA in the plasma of pregnant women has greatly promoted advances in noninvasive prenatal testing. Screening performance is enhanced with higher fetal fraction and analysis of samples whose fetal DNA fraction is lower than 4% are unreliable. Although current approaches to fetal fraction measurement are accurate, most of them are expensive and time consuming. Here we present a simple and cost-effective solution that provides a quick and reasonably accurate fetal fraction by directly evaluating the size distribution of circulating DNA fragments in the extracted maternal cell-free DNA. The presented approach could be useful in the presequencing stage of noninvasive prenatal testing to evaluate whether the sample is suitable for the test or a repeat blood draw is recommended.

First prenatal case of 48,XXYY syndrome detected by maternal cell-free DNA testing

2019 ◽  
Vol 40 (2) ◽  
pp. 270-272
Author(s):  
Jian Li ◽  
Li Zhen ◽  
Min Pan ◽  
Dong-Zhi Li
Keyword(s):  
Dna Testing ◽  
Cell Free Dna ◽  
Maternal Cell ◽  
Free Dna

Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease

2019 ◽  
Vol 4 (4) ◽  
pp. 663-674 ◽  
Author(s):  
Timothy A. Blauwkamp ◽  
Simone Thair ◽  
Michael J. Rosen ◽  
Lily Blair ◽  
Martin S. Lindner ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Dna Sequencing ◽  
Microbial Cell ◽  
Clinical Validation ◽  
Cell Free Dna ◽  
Free Dna

scholarly journals Genome-wide chromosomal instability by cell-free DNA sequencing predicts survival in patients with metastatic breast cancer

The Breast ◽  
2020 ◽  
Vol 53 ◽  
pp. 111-118
Author(s):  
Hongnan Mo ◽  
Xiaobing Wang ◽  
Fei Ma ◽  
Ziliang Qian ◽  
Xiaoying Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Dna Sequencing ◽  
Chromosomal Instability ◽  
Metastatic Breast ◽  
Cell Free Dna ◽  
Free Dna ◽  
Genome Wide
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