SUMMARY: The simplicity and precision of CRISPR/Cas9 system has brought in a new era of gene editing. Screening for desired clones with CRISPR-mediated genomic edits in a large number of samples is made possible by next generation sequencing (NGS) due to its multiplexing. Here we present CRISPR-DAV (CRISPR Data Analysis and Visualization) pipeline to analyze the CRISPR NGS data in a high throughput manner. In the pipeline, Burrows-Wheeler Aligner and Assembly Based ReAlignment are used for small and large indel detection, and results are presented in a comprehensive set of charts and interactive alignment view. AVAILABILITY AND IMPLEMENTATION: CRISPR-DAV is available at GitHub and Docker Hub repositories: https://github.com/pinetree1/crispr-dav.git and https://hub.docker.com/r/pinetree1/crispr-dav/. CONTACT: [email protected].

MOTIVATION: Gene expression profiling is an important tool for gaining insight into biology. Novel strategies are required to analyze the growing archives of microarray data and extract useful information from them. One area of interest is in the construction of gene association networks from collections of profiling data. Various approaches have been proposed to construct gene networks using profiling data, and these networks have been used in functional inference as well as in data visualization. Here, we investigated a non-parametric approach to translate profiling data into a gene network. We explored the characteristics and utility of the resulting network and investigated the use of network information in analysis of variance models and hypothesis testing. RESULTS: Our work is composed of two parts: gene network construction and partitioning and hypothesis testing using sub-networks as groups. In the first part, multiple independently collected microarray datasets from the Gene Expression Omnibus data repository were analyzed to identify probe pairs that are positively co-regulated across the samples. A co-expression network was constructed based on a reciprocal ranking criteria and a false discovery rate analysis. We named this network Reference Gene Association (RGA) network. Then, the network was partitioned into densely connected sub-networks of probes using a multilevel graph partitioning algorithm. In the second part, we proposed a new, MANOVA-based approach that can take individual probe expression values as input and perform hypothesis testing at the sub-network level. We applied this MANOVA methodology to two published studies and our analysis indicated that the methodology is both effective and sensitive for identifying transcriptional sub-networks or pathways that are perturbed across treatments.