Coinciding with the increase in sequenced bacteria, mining of bacterial genomes for biosynthetic gene clusters (BGCs) has become a critical component of natural product discovery. The order Myxococcales, a reputable source of biologically active secondary metabolites, spans three suborders which all include natural product producing representatives. Utilizing the BiG-SCAPE-CORASON platform to generate a sequence similarity network that contains 994 BGCs from 36 sequenced myxobacteria deposited in the antiSMASH database, a total of 843 BGCs with lower than 75% similarity scores to characterized clusters within the MIBiG database are presented. This survey provides the biosynthetic diversity of these BGCs and an assessment of the predicted chemical space yet to be discovered. Considering the mere snapshot of myxobacteria included in this analysis, these untapped BGCs exemplify the potential for natural product discovery from myxobacteria.

Chatbots enable machines to emulate human conversation, and recent developments have resulted in many online systems for the public to use. Although a few studies have investigated how humans interact with such programs, we are not aware of any that have analyzed transcripts in depth. In this study, students interacted with two Web-based chatbots, Rose and Mitsuku, for five minutes and evaluated how well they thought the software emulated human conversation. We reviewed the transcripts and found that students used fairly simple language and made many text errors. There were no significant differences between the two systems in our experimental measures, but we found that Rose tended to change the topic more often and Mitsuku seemed more argumentative.