In a new report on Science Advances, Hui Kwon Kim and interdisciplinary researchers at the departments of Pharmacology, Electrical and Computer Engineering, Medical Sciences, Nanomedicine and Bioinformatics in the Republic of Korea, evaluated the activities of SpCas9; a bacterial RNA-guided Cas9 endonuclease variant (a bacterial enzyme that cuts DNA for genome editing) from Streptococcus pyogenes. They used a high-throughput approach with 12,832 target sequences based on a human cell library to build a deep learning model and predict the activity of SpCas9. The data contained oligonucleotides (nucleotides or building blocks) containing target sequence pairs and a corresponding guide sequence to encode single-guide RNA (sgRNA), which can direct the Cas9 protein to bind and cleave a specific DNA sequence for genome editing. They implemented deep learning-based training on the large dataset of SpCas9-induced indel (insertion or deletion) frequencies to develop an SpCas9 activity predicting model named DeepSpCas9 now available online. When the team tested the software against independently generated datasets, the results showed high generalization performance, i.e. the model could properly adapt to new, previously unseen data.
Coronaviruses (CoV) are enveloped, single-stranded, positive-sense RNA viruses relevant in animal and human health [1, 2]. Historically, CoV infection in humans has been associated with mild upper respiratory tract diseases . However, the identification of the severe acute respiratory syndrome CoV (SARS-CoV) in 2003  and the recently emerged (April 2012) Middle East respiratory syndrome CoV (MERS-CoV) , which has been associated with acute pneumonia, redefined historic perceptions and potentiated the relevance of CoVs as important human pathogens. In this sense, the development of CoV infectious clones provides a valuable molecular tool to study fundamental viral processes, to develop genetically defined vaccines, and to test antiviral drugs. However, the generation of CoV infectious clones has been hampered for a long time due to the huge size of the CoV genome (around 30 kb) and the toxicity of some CoV replicase gene sequences during its propagation in bacteria.
To increase the accuracy of the analysis, deep sequencing data were filtered; target sequences with deep sequencing read counts below 200 and background indel frequencies above 8% were excluded as similarly performed previously (21). DNase-sequencing (DNase-seq) narrow peak data from ENCODE (36) were used to calculate chromatin accessibility as previously described (21). For each target site, 23 bases of the PAM plus protospacer sequence were aligned to the hg19 human reference genome using bowtie (41). Only the target sites that overlapped with DNase-seq narrow peaks were considered as DNase I hypersensitive target sites.
Researchers at Columbia University Medical Center have converted a natural bacterial immune system into a microscopic data recorder. By modifying a strain of the ubiquitous human gut microbe E. coli, the enabled it to not only record their interactions with the environment but also time-stamp the events. Columbia researchers used gene editing to enable e. Coli bacteria to not only record their interactions with the environment but also time-stamp the events. 'Such bacteria, swallowed by a patient, might be able to record the changes they experience through the whole digestive tract, yielding an unprecedented view of previously inaccessible phenomena,' says Harris Wang of CUMC, the senior author of the Science paper. Other applications could include environmental sensing and basic studies in ecology and microbiology, where bacteria could monitor otherwise invisible changes without disrupting their surroundings.