@inbook{8074066d61ca4c91a2e17fdc6ce48c8e,
title = "CRM discovery beyond model insects",
abstract = "Although the number of sequenced insect genomes numbers in the hundreds, little is known about gene regulatory sequences in any species other than the well-studied Drosophila melanogaster. We provide here a detailed protocol for using SCRMshaw, a computational method for predicting cis-regulatory modules (CRMs, also “enhancers”) in sequenced insect genomes. SCRMshaw is effective for CRM discovery throughout the range of holometabolous insects and potentially in even more diverged species, with true-positive prediction rates of 75\% or better. Minimal requirements for using SCRMshaw are a genome sequence and training data in the form of known Drosophila CRMs; a comprehensive set of the latter can be obtained from the SCRMshaw download site. For basic applications, a user with only modest computational know-how can run SCRMshaw on a desktop computer. SCRMshaw can be run with a single, narrow set of training data to predict CRMs regulating a specific pattern of gene expression, or with multiple sets of training data covering a broad range of CRM activities to provide an initial rough regulatory annotation of a complete, newly-sequenced genome.",
keywords = "Enhancer prediction, Genome annotation, Non-model insects, Regulatory genomics, Transcriptional gene regulation",
author = "Majid Kazemian and Halfon, \{Marc S.\}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2019",
doi = "10.1007/978-1-4939-8775-7\_10",
language = "English",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "117--139",
booktitle = "Methods in Molecular Biology",
}