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Andersen, J. D.

Publications and source records attributed to Andersen, J. D..

3 recordsLinked to original sources

Discovery and characterization of highly polymorphic ultra-short STRs for human identification via shotgun sequencing

Shotgun sequencing (SGS) enables simultaneous interrogation of a broad range of loci across the human genome, even from low-template and highly degraded DNA samples. While human identification traditionally relies on short tandem repeats (STRs) due to their high polymorphism, standard forensic STRs (100-450 bp) are poorly suited for the short read ([~]150 bp) constraint of SGS. The purpose of this study was to evaluate the analysis limitations of standard forensic STRs in SGS data and to identify a novel panel of STRs optimised for short-read genomic data. First, we benchmarked four STR genotyping software tools (STRait Razor, GangSTR, STRinNGS, and HipSTR) by analysing 53 standard forensic STRs in SGS data. HipSTR showed the best performance but achieved only a call rate of 64.5% and an accuracy of 83.8%, and its performance was strongly affected by STR allele length and read depth. To overcome these constraints, we screened the population-wide 1000 Genomes Project dataset and identified a panel of 265 autosomal ultra-short (< 50 bp) STRs with an effective number of alleles (Ae) ranging from 3.0 to 7.5. As few as seven of these loci were sufficient to achieve a Mean Match Probability (MMP) below 1 x 10-6. To validate these findings, we developed a custom PCR-based amplicon sequencing panel targeting 97 of the most polymorphic ultra-short STRs and evaluated these in 41 blood samples from Danish individuals. The polymorphic nature of the selected loci was confirmed (Aeranged from 2.4 to 7.2). Our results furthermore demonstrated high concordance between the amplicon panel and SGS-derived genotypes, which substantiates that these ultra-short STRs provide a robust and highly polymorphic alternative for human identification in SGS data. Author summaryShotgun sequencing (SGS) methods are increasingly being adopted in fields such as forensic genetics. SGS yields large amounts of genetic information by reading short fragments across the entire genome, enabling a wide range of analyses that may be exploited as leads in a police investigation. Human identification has traditionally been based on STR loci with a PCR amplicon length of 100-450 base pairs. However, these loci are often longer than the reads generated by SGS data, which makes them difficult to analyse in a reliable way. In this study, we evaluated four software tools designed to genotype STRs and confirmed the limited ability to genotype traditional forensic STRs in SGS data. To address this limitation, we identified a new set of highly polymorphic ultra-short STRs (less than 50 base pairs in length) that enable robust human identification using SGS data. Despite their shorter length, these loci retain the multi-allelic nature inherent to traditional STRs. This ensures a low random match probability that is comparable with the standard forensic STR panels. The ultra-short STRs may be genotyped from highly degraded DNA and may provide the possibility for complex mixture analysis and multi-donor deconvolution, which makes the STRs uniquely suited for forensic casework.

genomics↗

ECHO: a nanopore sequencing-based workflow for (epi)genetic profiling of the human repeatome

SummaryThe human genome is dominated by repetitive DNA, whose genetic and epigenetic variation plays a key role in gene regulation, genome stability, and disease. Recent advances in long-read sequencing now enable large-scale, haplotype-resolved, and DNA methylation-informative analysis of the human genome, including on previously inaccessible complex and repetitive regions. However, the comprehensive, simultaneous characterisation of the "human repeatome" remains challenging, largely due to the lack of comprehensive tools integrated in a single pipeline that can capture the full spectrum of variation across diverse types of DNA repeats. Here, we present ECHO, a user-friendly, Snakemake-based pipeline for the "(Epi)genomic Characterisation of Human Repetitive Elements using Oxford Nanopore Sequencing". ECHO provides a reproducible and scalable framework for end-to-end analysis of whole-genome nanopore sequencing data, enabling integrative but also tailored (epi)genetic analyses of the human repeatome. Availability and implementationECHO is freely available at Github: https://github.com/leenput/ECHO-pipeline, with the archived version at Zenodo: https://zenodo.org/records/19068468 Contactathina.vidaki@mumc.nl; athina.vidaki@maastrichtuniversity.nl

bioinformatics↗

SNP calling for the Illumina Infinium Omni5-4 SNP BeadChip kit using the butterfly method

We introduce the "butterfly method" for SNP calling with the Illumina Infinium Omni5-4 BeadChip kit without the use of Illumina GenomeStudio software. The method is a within-sample method and does not use other samples nor population frequencies to call SNPs. The butterfly method is based on a three-component mixture of normal distributions, in which parameters are easily found using the open-source statistical software R. This makes the method transparent, straight-forward to change parameters according to the users needs, and easy to analyse the data within R after the SNPs have been called. We contribute with two open-source R packages that make SNP calling easy by helping with bookkeeping and by giving easy access to meta-information about the SNPs on the Illumina Infinium Omni5-4 BeadChip Kit (including chromosome, probe type, and SNP bases). We test our method on > 4 mio. SNPs and compare the results with those obtained with the GenTrain method used by Illumina GenomeStudio as well as SNPs obtained by PCR-free whole genome sequencing (WGS). We demonstrate two variants of our method: one where we account for potential probe type bias by estimating a separate model for each probe type (type I and type II) and another that uses a general model such that the models parameter estimates do not depend on the sample that is being analysed. We focused on varying the no-call rate and show how it changed the concordance with that of WGS. This is done by using a threshold on the a posteriori probability of belonging to a SNP cluster and by using the number of beads to adjust the stringency of the no-call mechanism. With the butterfly method, we achieve a SNP call rate of around 99% and a SNP concordance of around 99% with the WGS data. By lowering the a posteriori probability threshold for no-calls, we can get a higher call rate fraction than the GenomeStudio and by using a higher a posteriori probability threshold, we can achieve a higher concordance with the WGS data than the GenomeStudio.

bioinformatics↗