Browsing by Author "Kaplinski, L."

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DNA methylation alterations—potential cause of endometriosis pathogenesis or a reflection of tissue heterogeneity?
(2018) Salumets, A.; Kaplinski, L.; Saare, M.; Krigul, KL.; Laisk-Podar, T.; Ponandai-Srinivasan, S.; Rahmioglu, N.; Lalit Kumar, PG.; Zondervan, K.; Peters, M.
Alterations in the DNA methylation pattern of endometriotic lesions and endometrium of endometriosis patients have been proposed as one potential factor accompanying the endometriosis development. Although many differentially methylated genes have been associated with the pathogenesis of this disease, the overlap between the results of different studies has remained small. Among other potential confounders, the impact of tissue heterogeneity on the outcome of DNA methylation studies should be considered, as tissues are mixtures of different cell types with their own specific DNA methylation signatures. This review focuses on the results of DNA methylation studies in endometriosis from the cellular heterogeneity perspective. We consider both the studies using highly heterogeneous whole-lesion biopsies and endometrial tissue, as well as pure cell fractions isolated from lesions and endometrium to understand the potential impact of the cellular composition to the results of endometriosis DNA methylation studies. Also, future perspectives on how to diminish the impact of tissue heterogeneity in similar studies are provided.
NIPTmer: rapid k-mer-based software package for detection of fetal aneuploidies
(2018) Sauk, M.; Žilina, O.; Kurg, A.; Ustav, EL.; Peters, M.; Paluoja, P.; Roost, AM.; Teder, H.; Palta, P.; Brison, N.; Vermeesch, JR.; Krjutškov, K.; Salumets, A.; Kaplinski, L.
Non-invasive prenatal testing (NIPT) is a recent and rapidly evolving method for detecting genetic lesions, such as aneuploidies, of a fetus. However, there is a need for faster and cheaper laboratory and analysis methods to make NIPT more widely accessible. We have developed a novel software package for detection of fetal aneuploidies from next-generation low-coverage whole genome sequencing data. Our tool – NIPTmer – is based on counting pre-defined per-chromosome sets of unique k-mers from raw sequencing data, and applying linear regression model on the counts. Additionally, the filtering process used for k-mer list creation allows one to take into account the genetic variance in a specific sample, thus reducing the source of uncertainty. The processing time of one sample is less than 10 CPU-minutes on a high-end workstation. NIPTmer was validated on a cohort of 583 NIPT samples and it correctly predicted 37 non-mosaic fetal aneuploidies. NIPTmer has the potential to reduce significantly the time and complexity of NIPT post-sequencing analysis compared to mapping-based methods. For non-commercial users the software package is freely available at http://bioinfo.ut.ee/NIPTMer/.