BACKGROUND: One of the main challenges for the CRISPR-Cas9 system is selecting optimal single-guide RNAs (sgRNAs). Recently, deep learning has enhanced sgRNA prediction in eukaryotes. However, the prokaryotic chromatin structure is different from eukaryotes, so models trained on eukaryotes may not apply to prokaryotes. RESULTS: We designed and implemented a convolutional neural network to predict sgRNA activity in Escherichia coli. The network was trained and tested on the recently-released sgRNA activity dataset. Our convolutional neural network achieved excellent performance, yielding average Spearman correlation coefficients of 0.5817, 0.7105, and 0.3602, respectively for Cas9, eSpCas9 and Cas9 with a recA coding region deletion. We confirmed that the sgRNA prediction models trained on prokaryotes do not apply to eukaryotes and vice versa. We adopted perturbation-based approaches to analyze distinct biological patterns between prokaryotic and eukaryotic editing. Then, we improved the predictive performance of the prokaryotic Cas9 system by transfer learning. Finally, we determined that potential off-target scores accumulated on a genome-wide scale affect on-target activity, which could slightly improve on-target predictive performance. CONCLUSIONS: We developed convolutional neural networks to predict sgRNA activity for wild type and mutant Cas9 in prokaryotes. Our results show that the prediction accuracy of our method is improved over state-of-the-art models.

Motivation: Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite a multitude of nucleosome positioning resources available on line including experimental datasets of genome-wide nucleosome occupancy profiles and computational tools to the analysis on these data, the complex language of eukaryotic Nucleosome positioning remains incompletely understood. Results: Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) to understand nucleosome positioning. We combined Inception-like networks with a gating mechanism for the response of multiple patterns and long term association in DNA sequences. We developed the open-source package LeNup based on the CNN to predict nucleosome positioning in Homo sapiens, Caenorhabditis elegans, Drosophila melanogaster as well as Saccharomyces cerevisiae genomes. We trained LeNup on four benchmark datasets. LeNup achieved greater predictive accuracy than previously published methods. Availability and implementation: LeNup is freely available as Python and Lua script source code under a BSD style license from https://github.com/biomedBit/LeNup. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs) that lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases. One of the key problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. As such, the development of computational tools that can identify such nsSNPs would enhance our understanding of genetic diseases and help predict the disease. RESULTS: We propose a method, named Parepro (Predicting the amino acid replacement probability), to identify nsSNPs having either deleterious or neutral effects on the resulting protein function. Two independent datasets, HumVar and NewHumVar, taken from the PhD-SNP server, were applied to train the model and test the robustness of Parepro. Using a 20-fold cross validation test on the HumVar dataset, Parepro achieved a Matthews correlation coefficient (MCC) of 50% and an overall accuracy (Q2) of 76%, both of which were higher than those predicted by the methods, such as PolyPhen, SIFT, and HydridMeth. Further analysis on an additional dataset (NewHumVar) using Parepro yielded similar results. CONCLUSION: The performance of Parepro indicates that it is a powerful tool for predicting the effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data.