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Biology subjects

Helen Lindsay

Publications and source records attributed to Helen Lindsay.

2 recordsLinked to original sources

CrispRVariants: precisely charting the mutation spectrum in genome engineering experiments

CRISPR-Cas9 and related technologies efficiently alter genomic DNA at targeted positions and have far-reaching implications for functional screening and therapeutic gene editing. Understanding and unlocking this potential requires accurate evaluation of editing efficiency. We show that methodological decisions for analyzing sequencing data can significantly affect mutagenesis efficiency estimates and we provide a comprehensive R-based toolkit, CrispRVariants and accompanying web tool CrispRVariantsLite, that resolves and localizes individual mutant alleles with respect to the endonuclease cut site. CrispRVariants-enabled analyses of newly generated and existing genome editing datasets underscore how careful consideration of the full variant spectrum gives insight toward effective guide and amplicon design as well as the mutagenic process.

Bioinformatics

MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions

DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.

Bioinformatics