Biology subjects

Paluoja, P.

Publications and source records attributed to Paluoja, P..

6 recordsLinked to original sources

bioRxiv2024

Honey bulk DNA metagenomic analysis to identify honey biological composition and monitor honeybee pathogens

Honeys DNA mixture originates from various organismal groups like plants, arthropods, fungi, bacteria, and viruses. Conventional methods like melissopalynological analysis and targeted honey DNA metabarcoding offer a limited view of honeys biological composition. We conducted a honey bulk DNA metagenomic analysis to characterize the honeys taxonomic composition and identify honey bee-related pathogens and parasites based on 266 Estonian and 103 foreign honey samples. 70.4% of the DNA in Estonian honey was derived from green plant families like Brassicaceae, Rosaceae, Fabaceae, and Pinaceae. Geographical distribution analysis revealed distinct botanical compositions between Estonian mainland and island samples. The bacterial family Lactobacillaceae was prevalent overall, reflecting the honey bee microbiota in honey. We detected 12 honey bee pathogens and parasites, including Paenibacillus larvae and Nosema ceranae. In conclusion, the study underscores the potential of bulk DNA-based and non-targeted metagenomic approaches for monitoring honey bee health, environmental quality, and honey composition, origin, and authenticity.

genomics↗

bioRxiv2021

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

MotivationNon-invasive prenatal testing (NIPT) is a powerful screening method for fetal aneuploidy detection, relying on laboratory and computational analysis of cell-free DNA. Although several published computational NIPT analysis tools are available, no comprehensive and direct accuracy comparison of these tools is published. Here, we evaluate and determine the precision of five commonly used computational NIPT aneuploidy analysis tools, considering diverse sequencing depth (coverage) and fetal DNA fraction (FF) on clinically validated NIPT samples. MethodsWe evaluated computational NIPT aneuploidy analysis tools WisecondorX, NIPTeR, NIPTmer, RAPIDR, and GIPseq, on the same set of clinically validated samples, subsampled to different sequencing coverages between 1.25-20M reads per sample (RPS). These clinically validated samples consisted of 423 samples, including 19 samples with fetal chromosome 21 trisomy (T21, Down syndrome), eight trisomy 18 (T18, Edwards syndrome) and three trisomy 13 (T13, Patau syndrome) samples. For each software and sequencing coverage, we determined the number of false-negative and false-positive trisomy/euploidy calls. For a uniform trisomy detection interpretation, we defined a framework based on the percent-point function for determining the cut-off threshold for calling aneuploidy based on the sample Z-score and the reference group Z-score distribution. We also determined the effect of the naturally occurring arbitrary read placement driven uncertainty on T21 detection at very low sequencing coverage and the effect of cell-free fetal DNA fraction (FF) on the accuracy of these computational tools in the case of various sequencing coverages. ResultsThis is the first head-to-head comparison of NIPT aneuploidy detection tools for the low-coverage whole-genome sequencing approach. We determined that, with the currently available software tools, the minimum sequencing coverage with no false-negative trisomic cases was 5M RPS. Secondly, for these compared tools, the number of false-negative trisomic cases could be reduced if the trisomy call cut-off threshold considers the Z-score distribution of euploid reference samples. Thirdly, we observed that in the case of low FF, both aneuploidy Z-score and FF inference was considerably less accurate, especially in NIPT assays with 5M RPS or lower coverage. ConclusionsWe determined that all compared computational NIPT tools were affected by lower sequencing depth, resulting in systematically increasing the proportions of false-negative trisomy results as the sequencing depth decreased. Trisomy detection for lower coverage NIPT samples (e.g. 2.5M RPS) is technically possible but can increase the proportion of false-positive and false-negative trisomic cases, especially in the case of low FF.

bioinformatics↗

bioRxiv2019

DUX4 regulates oocyte to embryo transition in human

During the human oocyte-to-embryo transition, the fertilized oocyte undergoes final maturation and the embryo genome is gradually activated during the first three cell divisions. How this transition is coordinated in humans is largely unknown. We show that the double homeodomain transcription factor DUX4 contributes to this transition. DUX4 knockdown in human zygotes caused insufficient transcriptome reprogramming as observed three days after fertilization. Induced DUX4 expression in human embryonic stem cells activated transcription of thousands of newly identified bi-directional transcripts, including putative enhancers for embryonic genome activation genes such as LEUTX. DUX4 protein interacted with transcriptional modifiers that are known to couple enhancers and promoters. Taken together, our results reveal that DUX4 is a pioneer regulating oocyte-to-embryo transition in human through activation of intergenic genome, especially enhancers, and hence setting the stage for early human embryo development.

developmental biology↗

bioRxiv2018

Computational framework for targeted high-coverage sequencing based NIPT

Non-invasive prenatal testing (NIPT) enables accurate detection of fetal chromosomal trisomies. The majority of existing computational methods for sequencing-based NIPT analyses rely on low-coverage whole-genome sequencing (WGS) data and are not applicable for targeted high-coverage sequencing data from cell-free DNA samples.\n\nHere, we present a novel computational framework for a targeted high-coverage sequencing based NIPT analysis. The developed methods use a hidden Markov model (HMM)-based approach in conjunction with supplemental machine learning methods, such as decision tree (DT) and support vector machine (SVM), to detect fetal trisomy and parental origin of additional fetal chromosomes. These methods were tested with simulated datasets covering a wide range of biologically relevant scenarios with various chromosomal quantities, parental origins of extra chromosomes, fetal DNA fractions and sequencing read depths. Consequently, we determined the functional feasibility and limitations of each proposed approach and demonstrated that read count-based HMM achieved the best overall classification accuracy of 0.89 for detecting fetal euploidies and trisomies. Furthermore, we show that by using the DT and SVM methods on the HMM state classification results, it was possible to increase the final trisomy classification accuracy to 0.98 and 0.99, respectively.\n\nWe demonstrated that read count and allelic ratio-based models can achieve a high accuracy (up to 0.98) for detecting fetal trisomy even if the fetal fraction is as low as 2%. Currently existing methods require at least 4% fetal fraction, which can be an issue in the case of early gestational age (<10 weeks) or elevated maternal body mass index (>35 kg/m2). More accurate detection can be achieved at higher sequencing depth using HMM in conjunction with supplemental methods, which significantly improve the trisomy detection especially in borderline scenarios (e.g., very low fetal fraction) and can enable to perform NIPT even earlier than 10 weeks of pregnancy.

genomics↗

bioRxiv2018

Creating basis for introducing NIPT in the Estonian public health setting

ObjectiveThe study aimed to validate a whole-genome sequencing-based NIPT method and our newly developed NIPTmer analysis software with the potential to integrate the pipeline into prenatal clinical care in Estonia.\n\nMethodIn total, 447 maternal blood samples were included to the study. Analysis pipeline involved whole-genome library preparation and massively parallel sequencing on Illumina NextSeq 500. Aneuploidy status was determined with NIPTmer software, which is based on counting pre-defined per-chromosome sets of unique k-mers from raw sequencing data. To estimate fetal fraction (FF) from total cell-free DNA SeqFF was implemented.\n\nResultsNIPTmer software allowed to identify correctly all samples of non-mosaic T21 (15/15), T18 (9/9) and T13 (4/4) cases. However, one mosaic T18 remained undetected. Six false positive results were observed, including three for T18 (specificity 99.3%) and three for T13 (specificity 99.3%). FF < 4% (2.8-3.99%) was estimated in eight samples, including two samples with T13 and T18. Despite low FF, these two samples were determined as aneuploid with NIPTmer software.\n\nConclusionOur NIPT analysis pipeline proved to perform efficiently in detecting common fetal aneuploidies T21, T18 and T13 and is feasible for implementation into clinical service in Estonia.

genetics↗

bioRxiv2018

TAC-seq: targeted DNA and RNA sequencing for precise biomarker molecule counting

Targeted next-generation sequencing based biomarker detection methods have become essential for biomedical diagnostics. In addition to their sensitivity and high-throughput capacity, absolute molecule counting based on unique molecular identifier (UMI) has high potential to increase biomarker detection accuracy even further through the reduction of systematic technical biases. Here, we present TAC-seq, a simple and cost-effective targeted allele counting by sequencing method that uses UMIs to estimate the original molecule counts of different biomarker types like mRNAs, microRNAs and cell-free DNA. We applied TAC-seq in three different applications and compared the results with standard sequencing technologies. RNA samples extracted from human endometrial biopsies were analyzed using previously described 57 mRNA-based receptivity biomarkers and 49 selected microRNAs at different expression levels. Cell-free DNA aneuploidy testing was based on cell line (47,XX,+21) genomic DNA. TAC-seq mRNA biomarker profiling showed identical clustering results to full transcriptome RNA sequencing, and microRNA detection demonstrated significant reduction in amplification bias, allowing to determine minor expression changes between different samples that remained undetermined by standard sequencing. The mimicking experiment for cell-free DNA fetal aneuploidy analysis showed that TAC-seq can be applied to count highly fragmented DNA, detecting significant (p=4.8x10-11) excess of molecules in case of trisomy 21. Based on three proof-of-principle applications we show that TAC-seq is a highly accurate and universal method for targeted nucleic acid biomarker profiling.

genomics↗