Deep mutational scanning reveals the molecular determinants of RNA polymerase-mediated adaptation and tradeoffs.

RNA polymerase (RNAP) is emblematic of complex biological systems that control multiple traits involving trade-offs such as growth versus maintenance. Laboratory evolution has revealed that mutations in RNAP subunits, including RpoB, are frequently selected. However, we lack a systems view of how mutations alter the RNAP molecular functions to promote adaptation. We, therefore, measured the fitness of thousands of mutations within a region of rpoB under multiple conditions and genetic backgrounds, to find that adaptive mutations cluster in two modules. Mutations in one module favor growth over maintenance through a partial loss of an interaction associated with faster elongation. Mutations in the other favor maintenance over growth through a destabilized RNAP-DNA complex. The two molecular handles capture the versatile RNAP-mediated adaptations. Combining both interaction losses simultaneously improved maintenance and growth, challenging the idea that growth-maintenance tradeoff resorts only from limited resources, and revealing how compensatory evolution operates within RNAP.

QuickDeconvolution: fast and scalable deconvolution of linked-read sequencing data.

Motivation: Recently introduced, linked-read technologies, such as the 10× chromium system, use microfluidics to tag multiple short reads from the same long fragment (50-200 kb) with a small sequence, called a barcode. They are inexpensive and easy to prepare, combining the accuracy of short-read sequencing with the long-range information of barcodes. The same barcode can be used for several different fragments, which complicates the analyses. Results: We present QuickDeconvolution (QD), a new software for deconvolving a set of reads sharing a barcode, i.e. separating the reads from the different fragments. QD only takes sequencing data as input, without the need for a reference genome. We show that QD outperforms existing software in terms of accuracy, speed and scalability, making it capable of deconvolving previously inaccessible data sets. In particular, we demonstrate here the first example in the literature of a successfully deconvoluted animal sequencing dataset, a 33-Gb Drosophila melanogaster dataset. We show that the taxonomic assignment of linked reads can be improved by deconvoluting reads with QD before taxonomic classification. Availability and implementation: Code and instructions are available on https://github.com/RolandFaure/QuickDeconvolution. Supplementary information: Supplementary data are available at Bioinformatics Advances online.