Biology subjects

Kindlova, M.

Publications and source records attributed to Kindlova, M..

2 recordsLinked to original sources

bioRxiv2023

An allele-resolved nanopore-guided tour of the human placental methylome

The placenta is a temporary organ present during pregnancy that is responsible for coordinating all aspects of pregnancy between the mother and fetus. It has a distinct epigenetic, transcriptomic, and mutational landscape with low levels of methylation, high numbers of transcribed loci, and a high mutational burden relative to somatic tissues. We present this landscape through the application of nanopore sequencing technology to provide a more comprehensive picture of female placental genomics and methylomics along with integrated haplotype-resolved transcriptomic analyses across eight trios. Whole genome sequencing of trios allows robust phasing, permitting comprehensive genome-wide investigation of parent-of-origin methylation and transcription. This enhanced view facilitates identifications of many new differentially methylated regions (DMRs), both conserved and differing between individuals, as well as novel imprinted genes including ILDR2 and RASA1 which are potentially important for healthy placental and fetal development.

genomics↗

bioRxiv2021

Methylartist: Tools for Visualising Modified Bases from Nanopore Sequence Data

Methylartist is a consolidated suite of tools for processing, visualising, and analysing nanopore methylation data derived from modified basecalling methods. All detectable methylation types (e.g. 5mCpG, 5hmC, 6mA) are supported, enabling integrated study of base pairs when modified naturally or as part of an experimental protocol. BackgroundCovalent modification of nucleobases is an important component of genomic regulatory regimes across all domains of life [1-3] and is harnessed by various genomic footprinting assays, including DamID[4], SMAC-seq[5], and NOMe-seq[6]. Nanopore sequencing offers comprehensive assessment of base modifications from arbitrarily long sequence reads through analysis of electrical current profiles, generally through machine learning models trained to discriminate between modified and unmodified bases [7]. An increasing number of computational tools have been developed or enhanced for calling modified bases [8], including nanopolish [7], megalodon [9], and guppy [10], along with an increasing number of available pre-trained models.

bioinformatics↗