Motivation Transcription factor (TF) DNA-binding is a central mechanism in gene regulation. Biologists would like to know where and when these factors bind DNA. Hence, they require accurate DNA-binding models to enable binding prediction to any DNA sequence. Recent technological advancements measure the binding of a single TF to thousands of DNA sequences. One of the prevailing techniques, high-throughput SELEX, measures protein–DNA binding by high-throughput sequencing over several cycles of enrichment. Unfortunately, current computational methods to infer the binding preferences from high-throughput SELEX data do not exploit the richness of these data, and are under-using the most advanced computational technique, deep neural networks. Results To better characterize the binding preferences of TFs from these experimental data, we developed DeepSELEX, a new algorithm to infer intrinsic DNA-binding preferences using deep neural networks. DeepSELEX takes advantage of the richness of high-throughput sequencing data and learns the DNA-binding preferences by observing the changes in DNA sequences through the experimental cycles. DeepSELEX outperforms extant methods for the task of DNA-binding inference from high-throughput SELEX data in binding prediction in vitro and is on par with the state of the art in in vivo binding prediction. Analysis of model parameters reveals it learns biologically relevant features that shed light on TFs’ binding mechanism. Availability and implementation DeepSELEX is available through github.com/OrensteinLab/DeepSELEX/. Supplementary information Supplementary data are available at Bioinformatics online.
CRISPR/Cas9 system is widely used in a broad range of gene-editing applications. While this editing technique is quite accurate in the target region, there may be many unplanned off-target sites (OTSs). Consequently, a plethora of computational methods have been developed to predict off-target cleavage sites given a guide RNA and a reference genome. However, these methods are based on small-scale datasets (only tens to hundreds of OTSs) produced by experimental techniques to detect OTSs with a low signal-to-noise ratio. Recently, CHANGE-seq, a new in vitro experimental technique to detect OTSs, was used to produce a dataset of unprecedented scale and quality (>200 000 OTS over 110 guide RNAs). In addition, the same study included in cellula GUIDE-seq experiments for 58 of the guide RNAs. Here, we fill the gap in previous computational methods by utilizing these data to systematically evaluate data processing and formulation of the CRISPR OTSs prediction problem. Our evaluations show that data transformation as a pre-processing phase is critical prior to model training. Moreover, we demonstrate the improvement gained by adding potential inactive OTSs to the training datasets. Furthermore, our results point to the importance of adding the number of mismatches between guide RNAs and their OTSs as a feature. Finally, we present predictive off-target in cellula models based on both in vitro and in cellula data and compare them to state-of-the-art methods in predicting true OTSs. Our conclusions will be instrumental in any future development of an off-target predictor based on high-throughput datasets.
CRISPR/Cas9 system is widely used in a broad range of gene-editing applications. While this gene-editing technique is quite accurate in the target region, there may be many unplanned off-target edited sites. Consequently, a plethora of computational methods have been developed to predict off-target cleavage sites given a guide RNA and a reference genome. However, these methods are based on small-scale datasets (only tens to hundreds of off-target sites) produced by experimental techniques to detect off-target sites with a low signal-to-noise ratio. Recently, CHANGE-seq, a new in vitro experimental technique to detect off-target sites, was used to produce a dataset of unprecedented scale and quality (more than 200,000 off-target sites over 110 guide RNAs). In addition, the same study included GUIDE-seq experiments for 58 of the guide RNAs to produce in vivo measurements of off-target sites. Here, we fill the gap in previous computational methods by utilizing these data to perform a systematic evaluation of data processing and formulation of the CRISPR off-target site prediction problem. Our evaluations show that data transformation as a pre-processing phase is critical prior to model training. Moreover, we demonstrate the improvement gained by adding potential inactive off-target sites to the training datasets. Furthermore, our results point to the importance of adding the number of mismatches between the guide RNA and the off-target site as a feature. Finally, we present predictive off-target in vivo models based on transfer learning from in vitro. Our conclusions will be instrumental to any future development of an off-target predictor based on high-throughput datasets.
The Reionization Cluster Survey (RELICS) imaged 41 galaxy clusters with the Hubble Space Telescope (HST), in order to detect lensed and high-redshift galaxies. Each cluster was imaged to about 26.5 AB mag in three optical and four near-infrared bands, taken in two distinct visits separated by varying time intervals. We make use of the multiple near-infrared epochs to search for transient sources in the cluster fields, with the primary motivation of building statistics for bright caustic crossing events in gravitational arcs. Over the whole sample, we do not find any significant (≳ 5σ) caustic crossing events, in line with expectations from semi-analytic calculations but in contrast to what may be naively expected from previous detections of some bright events, or from deeper transient surveys that do find high rates of such events. Nevertheless, we find six prominent supernova (SN) candidates over the 41 fields: three of them were previously reported and three are new ones reported here for the first time. Out of the six candidates, four are likely core-collapse (CC) SNe – three in cluster galaxies, and among which only one was known before, and one slightly behind the cluster at z ∼ 0.6 − 0.7. The other two are likely Ia – both of them previously known, one probably in a cluster galaxy, and one behind it at z ≃ 2. Our study supplies empirical bounds for the rate of caustic crossing events in galaxy cluster fields to typical HST magnitudes, and lays the groundwork for a future SN rate study.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.