scholarly journals Whole genome nucleosome sequencing identifies novel types of forensic markers in degraded DNA samples

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Chun-nan Dong ◽  
Ya-dong Yang ◽  
Shu-jin Li ◽  
Ya-ran Yang ◽  
Xiao-jing Zhang ◽  
...  
Keyword(s):  
Degraded Dna ◽  
Whole Genome ◽  
Forensic Markers

scholarly journals A novel use of random priming-based single-strand library preparation for whole genome sequencing of formalin-fixed paraffin-embedded tissue samples

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Emily A Saunderson ◽  
Ann-Marie Baker ◽  
Marc Williams ◽  
Kit Curtius ◽  
J Louise Jones ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Preparation Method ◽  
Degraded Dna ◽  
Whole Genome ◽  
Library Preparation ◽  
Formalin Fixed Paraffin ◽  
Ffpe Samples ◽  
Formalin Fixed Paraffin Embedded ◽  
Formalin Fixed ◽  
Library Preparation Method

Abstract The desire to analyse limited amounts of biological material, historic samples and rare cell populations has collectively driven the need for efficient methods for whole genome sequencing (WGS) of limited amounts of poor quality DNA. Most protocols are designed to recover double-stranded DNA (dsDNA) by ligating sequencing adaptors to dsDNA with or without subsequent polymerase chain reaction amplification of the library. While this is sufficient for many applications, limited DNA requires a method that can recover both single-stranded DNA (ssDNA) and dsDNA. Here, we present a WGS library preparation method, called ‘degraded DNA adaptor tagging’ (DDAT), adapted from a protocol designed for whole genome bisulfite sequencing. This method uses two rounds of random primer extension to recover both ssDNA and dsDNA. We show that by using DDAT we can generate WGS data from formalin-fixed paraffin-embedded (FFPE) samples using as little as 2 ng of highly degraded DNA input. Furthermore, DDAT WGS data quality was higher for all FFPE samples tested compared to data produced using a standard WGS library preparation method. Therefore, the DDAT method has potential to unlock WGS data from DNA previously considered impossible to sequence, broadening opportunities to understand the role of genetics in health and disease.

scholarly journals Hybridization capture and low-coverage SNP profiling for extended kinship analysis and forensic identification of historical remains

2020 ◽  
Author(s):  
Erin M. Gorden ◽  
Ellen M. Greytak ◽  
Kimberly Sturk-Andreaggi ◽  
Janet Cady ◽  
Timothy P. McMahon ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Forensic Analysis ◽  
Forensic Identification ◽  
Degraded Dna ◽  
Whole Genome ◽  
Kinship Analysis ◽  
Hybridization Capture ◽  
Low Coverage ◽  
Historical Remains

AbstractDNA-assisted identification of historical remains requires the genetic analysis of highly degraded DNA, along with a comparison to DNA from known relatives. This can be achieved by targeting single nucleotide polymorphisms (SNPs) using a hybridization capture and next-generation sequencing approach suitable for degraded skeletal samples. In the present study, two SNP capture panels were designed to target ∼25,000 (25K) and ∼95,000 (95K) autosomal SNPs, respectively, to enable distant kinship estimation (up to 4th degree relatives). Low-coverage SNP data were successfully recovered from 14 skeletal elements 75 years postmortem, with captured DNA having mean insert sizes ranging from 32-170 bp across the 14 samples. SNP comparison with DNA from known family references was performed in the Parabon Fχ Forensic Analysis Platform, which utilizes a likelihood approach for kinship prediction that was optimized for low-coverage sequencing data with DNA damage. The 25K and 95K panels produced 15,000 and 42,000 SNPs on average, respectively allowing for accurate kinship prediction in 17 and 19 of the 21 pairwise comparisons. Whole genome sequencing was not able to produce sufficient SNP data for accurate kinship prediction, demonstrating that hybridization capture is necessary for historical samples. This study provides the groundwork for the expansion of research involving compromised samples to include SNP hybridization capture.Author SummaryOur study evaluates ancient DNA techniques involving SNP capture and Next-Generation Sequencing for use in forensic identification. We utilized bone samples from 14 sets of previously identified historical remains aged 70 years postmortem for low-coverage SNP genotyping and extended kinship analysis. We performed whole genome sequencing and hybridization capture with two SNP panels, one targeting ∼25,000 SNPs and the other targeting ∼95,000 SNPs, to assess SNP recovery and accuracy in kinship estimation. A genotype likelihood approach was utilized for SNP profiling of degraded DNA characterized by cytosine deamination typical of ancient and historical specimens. Family reference samples from known relatives up to 4th degree were genotyped using a SNP microarray. We then utilized the Parabon Fχ Forensic Analysis Platform to perform pairwise comparisons of all bone and reference samples for kinship prediction. The results showed that both capture panels facilitated accurate kinship prediction in more than 80% of the tested relationships without producing false positive matches (or adventitious hits), which were commonly observed in the whole genome sequencing comparisons. We demonstrate that SNP capture can be an effective method for genotyping of historical remains for distant kinship analysis with known relatives, which will support humanitarian efforts and forensic identification.

Comparison of CE- and MPS-based analyses of forensic markers in a single cell after whole genome amplification

2020 ◽  
Vol 45 ◽  
pp. 102211 ◽  
Author(s):  
Man Chen ◽  
Jingjing Zhang ◽  
Jing Zhao ◽  
Tong Chen ◽  
Zhiyong Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Whole Genome Amplification ◽  
Whole Genome ◽  
Genome Amplification ◽  
Forensic Markers

Whole genome scan identifies several chromosomal regions linked to equine sarcoids

2012 ◽  
Vol 154 (1) ◽  
pp. 19-25 ◽  
Author(s):  
V. Jandova ◽  
J. Klukowska-Rötzler ◽  
G. Dolf ◽  
J. Janda ◽  
P. Roosje ◽  
...  
Keyword(s):  
Genome Scan ◽  
Whole Genome ◽  
Whole Genome Scan
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