Table_1_The Current State and Future of CRISPR-Cas9 gRNA Design Tools.DOCX

<p>Recent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing the features of the target site. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations and future considerations. We discuss new trends around personalized health by taking genomic variants into account when predicting target sites as well as discussing other governing factors that can improve prediction accuracy.</p

The effects of biological replicates on the differential expression analysis.

<p>The numbers of differentially expressed genes identified by each of three tools under different numbers of biological replicates based on the K_N dataset (A) and the LCL2 dataset (B).</p

The effects of balanced and unbalanced depths of reads for detecting DEGs based on ROC curve.

<p>ROC curves for evaluating the performance of Cuffdiff2, DESeq and edgeR on balanced and on unbalanced depths of reads based on the K_N dataset (A–C) and the LCL3 simulated dataset (D–F).</p

The performance of the three tools.

<p>ROC curves of the three tools are shown based on the MAQC (A), K_N (B) and LCL2 (C) datasets. Venn diagrams are used to show the intersection of the numbers of the differentially expressed genes identified by three tools compared with the benchmarks based on MAQC (D), K_N (E) and LCL2 (F).</p

The effects of sequencing depth on the differential expression analysis.

<p>The numbers of differentially expressed genes identified by Cuffdiff2, DESeq and edgeR are shown based on the K_N subsets (A) and the LCL3 simulated dataset (B).</p

The effects of sequencing depth for detecting DEGs.

<p>ROC curves for evaluating the performance of Cuffdiff2, DESeq and edgeR with different sequencing depths based on the K_N subsets (A–C) and the LCL3 simulated dataset (D–F).</p

The effects of replicates for detecting DEGs based on ROC curves.

<p>ROC curves for evaluating the performance of Cuffdiff2, DESeq and edgeR on 1 to 2 technical replicates based on the MAQC dataset (A–C), 1 to 4 biological replicates based on the K_N dataset (D–F), and 1 to 20 biological replicates based on the LCL2 dataset (H–I).</p