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Mark D Robinson

Publications and source records attributed to Mark D Robinson.

6 recordsLinked to original sources
bioRxiv2015

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↗
bioRxiv2015

iCOBRA: open, reproducible, standardized and live method benchmarking

We present iCOBRA, a flexible general-purpose web-based application and accompanying R package to evaluate, compare and visualize the performance of methods for estimation or classification when ground truth is available. iCOBRA is interactive, can be run locally or remotely and generates customizable, publication-ready graphics. To facilitate open, reproducible and standardized method comparisons, expanding as new innovations are made, we encourage the community to provide benchmark results in a standard format.

Bioinformatics↗
bioRxiv2015

Differential transcript usage from RNA-seq data: isoform pre-filtering improves performance of count-based methods

Large-scale sequencing of cDNA (RNA-seq) has been a boon to the quantitative analysis of transcriptomes. A notable application is the detection of changes in transcript usage between experimental conditions. For example, discovery of pathological alternative splicing may allow the development of new treatments or better management of patients. From an analysis perspective, there are several ways to approach RNA-seq data to unravel differential transcript usage, such as annotation-based exon-level counting, differential analysis of the percent spliced in measure or quantitative analysis of assembled transcripts. The goal of this research is to compare and contrast current state-of-the-art methods, as well as to suggest improvements to commonly used workflows.\n\nWe assess the performance of representative workflows using synthetic data and explore the effect of using non-standard counting bin definitions as input to a state-of-the-art inference engine (DEXSeq). Although the canonical counting provided the best results overall, several non-canonical approaches were as good or better in specific aspects and most counting approaches outperformed the evaluated event- and assembly-based methods. We show that an incomplete annotation catalog can have a detrimental effect on the ability to detect differential transcript usage in transcriptomes with few isoforms per gene and that isoform-level pre-filtering can considerably improve false discovery rate (FDR) control.\n\nCount-based methods generally perform well in detection of differential transcript usage. Controlling the FDR at the imposed threshold is difficult, mainly in complex organisms, but can be improved by pre-filtering of the annotation catalog.

Bioinformatics↗
bioRxiv2015

benchmarkR: an R package for benchmarking genome-scale methods

benchmarkR is an R package designed to assess and visualize the performance of statistical methods for datasets that have an independent truth (e.g., simulations or datasets with large-scale validation), in particular for methods that claim to control false discovery rates (FDR). We augment some of the standard performance plots (e.g., receiver operating characteristic, or ROC, curves) with information about how well the methods are calibrated (i.e., whether they achieve their expected FDR control). For example, performance plots are extended with a point to highlight the power or FDR at a user-set threshold (e.g., at a method's estimated 5% FDR). The package contains general containers to store simulation results (SimResults) and methods to create graphical summaries, such as receiver operating characteristic curves (rocX), false discovery plots (fdX) and power-to-achieved FDR plots (powerFDR); each plot is augmented with some form of calibration information. We find these plots to be an improved way to interpret relative performance of statistical methods for genomic datasets where many hypothesis tests are performed. The strategies, however, are general and will find applications in other domains.

Bioinformatics↗
bioRxiv2014

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↗