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Ivanovic, S.

Publications and source records attributed to Ivanovic, S..

2 recordsLinked to original sources

DNA methylation in clonal Duckweed lineages (Lemna minor L.) reflects current and historical environmental exposures.

While some DNA methylation variants are transgenerationally stable in plants, DNA methylation modifications that are specifically induced by environmental exposure are typically transient and subject to resetting in germ lines, limiting the potential for transgenerational epigenetics stress memory. Asexual reproduction circumvents germlines, and may be more conducive to long-term memory and inheritance of epigenetic marks. This, however, has been poorly explored. Taking advantage of the rapid clonal reproduction of the common duckweed Lemna minor, we tested the hypothesis that a long-term, transgenerational stress memory from exposure to high temperature can be detected in DNA methylation profiles. Using a reduced representation bisulfite sequencing approach (epiGBS), we show that high temperature stress induces DNA hypermethylation at many cytosines in CG and CHG contexts but not in CHH. In addition, a subset of the temperature responsive CHG cytosines, showed differential DNA methylation between in lineages exposed to 30{degrees}C and 24{degrees}C, 3-12 clonal generations after subsequent culturing in a common environment, demonstrating a memory effect of stress that persists over many clonal generations and that is reflected in DNA methylation. Structural annotation revealed that this memory effect in CHG methylation was enriched in TEs. We argue that the observed epigenetic stress memory is likely caused by stable transgenerational persistence of high temperature-induced DNA methylation variants across multiple clonal generations. To the extent that such epigenetic memory has functional consequences for gene expression and phenotypes, this result suggests potential for long-term modulation of stress responses in asexual plants and vegetatively propagated crops.

evolutionary biology↗

UPP2: Fast and Accurate Alignment Estimation of Datasets with Fragmentary Sequences

MotivationMultiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. UPP (Ultra-large multiple sequence alignment using Phylogeny-aware Profiles) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets. ResultsWe present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity, and is among the most accurate otherwise. Availabilityhttps://github.com/gillichu/sepp Contactwarnow@illinois.edu

bioinformatics↗