CasCollect: targeted assembly of CRISPR-associated operons from high-throughput sequencing data

Dynamics of CRISPR-mediated virus-host interactions in the human gut microbiome

Arms races between mobile genetic elements and prokaryotic hosts are major drivers of ecological and evolutionary change in microbial communities. Prokaryotic defense systems such as CRISPR-Cas have the potential to regulate microbiome composition by modifying the interactions among bacteria, plasmids, and phages. Here, we used longitudinal metagenomic data from 130 healthy and diseased individuals to study how the interplay of genetic parasites and CRISPR-Cas immunity reflects on the dynamics and composition of the human gut microbiome. Based on the coordinated study of 80 000 CRISPR-Cas loci and their targets, we show that CRISPR-Cas immunity effectively modulates bacteriophage abundances in the gut. Acquisition of CRISPR-Cas immunity typically leads to a decrease in the abundance of lytic phages but does not necessarily cause their complete disappearance. Much smaller effects are observed for lysogenic phages and plasmids. Conversely, phage-CRISPR interactions shape bacterial microdiversity by producing weak selective sweeps that benefit immune host lineages. We also show that distal (and chronologically older) regions of CRISPR arrays are enriched in spacers that are potentially functional and target crass-like phages and local prophages. This suggests that exposure to reactivated prophages and other endemic viruses is a major selective pressure in the gut microbiome that drives the maintenance of long-lasting immune memory.

CRISPRclassify: Repeat-Based Classification of CRISPR Loci

Detection and classification of CRISPR-Cas systems in metagenomic data have become increasingly prevalent in recent years due to their potential for diverse applications in genome editing. Traditionally, CRISPR-Cas systems are classified through reference-based identification of proximate cas genes. Here, we present a machine learning approach for the detection and classification of CRISPR loci using repeat sequences in a cas-independent context, enabling identification of unclassified loci missed by traditional cas-based approaches. Using biological attributes of the CRISPR repeat, the core element in CRISPR arrays, and leveraging methods from natural language processing, we developed a machine learning model capable of accurate classification of CRISPR loci in an extensive set of metagenomes, resulting in an F1 measure of 0.82 across all predictions and an F1 measure of 0.97 when limiting to classifications with probabilities >0.85. Furthermore, assessing performance on novel repeats yielded an F1 measure of 0.96. Although the performance of cas-based identification will exceed that of a repeat-based approach in many cases, CRISPRclassify provides an efficient approach to classification of CRISPR loci for cases in which cas gene information is unavailable, such as metagenomes and fragmented genome assemblies.