Annotating the Insect Regulatory Genome

Abstract

An ever-growing number of insect genomes is being sequenced across the evolutionary spectrum. Comprehensive annotation of not only genes but also regulatory regions is critical for reaping the full benefits of this sequencing. Driven by developments in sequencing technologies and in both empirical and computational discovery strategies, the past few decades have witnessed dramatic progress in our ability to identify cis-regulatory modules (CRMs), sequences such as enhancers that play a major role in regulating transcription. Nevertheless, providing a timely and comprehensive regulatory annotation of newly sequenced insect genomes is an ongoing challenge. We review here the methods being used to identify CRMs in both model and non-model insect species, and focus on two tools that we have developed, REDfly and SCRMshaw. These resources can be paired together in a powerful combination to facilitate insect regulatory annotation over a broad range of species, with an accuracy equal to or better than that of other state-of-the-art methods.

A novel role for trithorax in the gene regulatory network for a rapidly evolving fruit fly pigmentation trait

“…It is possible that some of these sequences function as silencers, and therefore their activity could not be observed in the reporter transgene assays. However, we note that the SCRMshaw training set we used, consisting of only eight CREs, is significantly smaller than the recommended training set size of 20–30 sequences [ 9 ]. These six pCREs may therefore simply represent false-positive predictions, putting the false-positive rate somewhere around 33%.…”

“…Unfortunately, though, for many CREs few if any of their interacting transcription factors are known, nullifying the utility of motif-based approaches. As an alternative, motif-blind approaches have been developed that rely solely on the sequences of a set of known similarly-functioning CREs for predicting new CREs [ 8 , 9 ]. Such in silico approaches offer an opportunity to better resolve the CRE breadth of partially-characterized GRNs, and create a wealth of opportunities to use in vivo methods to reveal the target genes for these new CREs, and to study their evolutionary histories.…”

REDfly: An Integrated Knowledgebase for Insect Regulatory Genomics

“…Su et al [ 52 ] used REDfly data to assess CRM-discovery approaches, which would have been impossible without REDfly. Computational CRM-discovery methods using REDfly for training data also can identify CRMs in diverse insect species [ 53 ] and, as such, provide a powerful tool for annotating insect regulatory genomes [ 54 ].…”

“…We also expect there to be considerable growth in several data categories. These include CRM_segments , as researchers increasingly turn to CRISPR/Cas9-mediated deletion of regulatory sequences, and pCRMs from multiple new species as experimental methods, such as single-cell ATAC-seq [ 73 ] and computational CRM discovery methods, such as SCRMshaw (reviewed by [ 54 ]) continue to be applied to sequenced insect species at a rapid rate.…”

Common Themes and Future Challenges in Understanding Gene Regulatory Network Evolution

“…Promisingly, recent technical advances have made it easier to work with traditionally non-model species and their more highly diverged relatives. ATAC-seq has emerged as an affordable method for CRM detection starting from small cell populations [71,72], and computational CRM discovery approaches such as SCRMshaw have shown effectiveness in predicting putatively homologous CRMs in a cross-species manner [73][74][75]. As more species are sequenced, this increases the ability to find relevant CRMs for the GRNs being studied, although the quality of genome assembly and annotation and the need for robust gene orthology mapping remain important limitations.…”