Genome duplication is essential for cell proliferation, and DNA synthesis is generally initiated by dedicated replication proteins at specific loci termed origins. In bacteria, the master initiator DnaA binds the chromosome origin ( oriC ) and unwinds the DNA duplex to permit helicase loading. However, despite decades of research it remained unclear how the information encoded within oriC guides DnaA‐dependent strand separation. To address this fundamental question, we took a systematic genetic approach in vivo and identified the core set of essential sequence elements within the Bacillus subtilis chromosome origin unwinding region. Using this information, we then show in vitro that the minimal replication origin sequence elements are necessary and sufficient to promote the mechanical functions of DNA duplex unwinding by DnaA. Because the basal DNA unwinding system characterized here appears to be conserved throughout the bacterial domain, this discovery provides a framework for understanding oriC architecture, activity, regulation and diversity.

Motivation Classification of protein sequences is one big task in bioinformatics and has many applications. Different machine learning methods exist and are applied on these problems, such as support vector machines (SVM), random forests (RF) and neural networks (NN). All of these methods have in common that protein sequences have to be made machine-readable and comparable in the first step, for which different encodings exist. These encodings are typically based on physical or chemical properties of the sequence. However, due to the outstanding performance of deep neural networks (DNN) on image recognition, we used frequency matrix chaos game representation (FCGR) for encoding of protein sequences into images. In this study, we compare the performance of SVMs, RFs and DNNs, trained on FCGR encoded protein sequences. While the original chaos game representation (CGR) has been used mainly for genome sequence encoding and classification, we modified it to work also for protein sequences, resulting in n-flakes representation, an image with several icosagons. Results We could show that all applied machine learning techniques (RF, SVM and DNN) show promising results compared to the state-of-the-art methods on our benchmark datasets, with DNNs outperforming the other methods and that FCGR is a promising new encoding method for protein sequences. Availability and implementation https://cran.r-project.org/. Supplementary information Supplementary data are available at Bioinformatics online.

Efficient assembly of large DNA constructs is a key technology in synthetic biology. One of the most popular assembly systems is the MoClo standard in which restriction and ligation of multiple fragments occurs in a one-pot reaction. The system is based on a smart vector design and type IIs restriction enzymes, which cut outside their recognition site. While the initial MoClo vectors had been developed for the assembly of multiple transcription units of plants, some derivatives of the vectors have been developed over the last years. Here we present a new set of MoClo vectors for the assembly of fragment libraries and insertion of constructs into bacterial chromosomes. The vectors are accompanied by a computer program that generates a degenerate synthetic DNA sequence that excludes "forbidden" DNA motifs. We demonstrate the usability of the new approach by construction of a stable fluorescence repressor operator system (FROS).