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Perchlik, M.

Publications and source records attributed to Perchlik, M..

2 recordsLinked to original sources

Unpacking Chromatin Accessibility with Fiber-seq

Chromatin accessibility has long been used as a marker for regions of DNA with regulatory potential. Fiber-seq detects chromatin accessibility on individual DNA fibers, enabling analyses beyond the identification of the accessible chromatin regions (ACRs). By providing single molecule level high resolution, Fiber-seq provides unprecedented qualitative descriptions, including potential categorizations of ACRs, identification of internal transcription factor footprints and nucleosome positioning within individual DNA fibers. As with all tools, the power of this technique depends on careful experimental design and data analysis -- incorrect usage will result in incorrect conclusions. Here we offer guidelines and flag potential pitfalls when generating and analyzing Fiber-seq data, such as (1) the optimum levels of adenosine methylation per-fiber, (2) the power of per-fiber state inference, (3) the importance of controlling for read depth and methylation rates when comparing across samples, (4) the limitations of long-read sequence mapping, and (5) suggestions for identification of differentially accessible peaks across samples.

genomics↗

Impact of random 50-base sequences inserted into an intron on splicing in Saccharomyces cerevisiae

Intron splicing is a key regulatory step in gene expression in eukaryotes. Three sequence elements required for splicing - 5 and 3 splice sites and a branch point - are especially well- characterized in Saccharomyces cerevisiae, but our understanding of additional intron features that impact splicing in this organism is incomplete, due largely to its small number of introns. To overcome this limitation, we constructed a library in S. cerevisiae of random 50-nucleotide elements (N50) individually inserted into the intron of a reporter gene and quantified canonical splicing and the use of cryptic splice sites by sequencing analysis. More than 70% of approximately 140,000 N50 elements reduced splicing by at least 20% compared to the intron control. N50 features, including higher GC content, presence of GU repeats and stronger predicted secondary structure of its pre-mRNA, correlated with reduced splicing efficiency. A likely basis for the reduced splicing of such a large proportion of variants is the formation of RNA structures that pair N50 bases - such as the GU repeats - with other bases specifically within the reporter pre-mRNA analyzed. However, neither convolutional neural network nor linear models were able to explain more than a small fraction of the variance in splicing efficiency across the library, suggesting that complex non-linear interactions in RNA structures are not accurately captured by RNA structure prediction methods given the limited number of variants. Our results imply that the specific context of a pre-mRNA may determine the bases allowable in an intron to prevent secondary structures that reduce splicing.

synthetic biology↗