DeCoDe: degenerate codon design for complete protein-coding DNA libraries.

MOTIVATION: High-throughput protein screening is a critical technique for dissecting and designing protein function. Libraries for these assays can be created through a number of means, including targeted or random mutagenesis of a template protein sequence or direct DNA synthesis. However, mutagenic library construction methods often yield vastly more nonfunctional than functional variants and, despite advances in large-scale DNA synthesis, individual synthesis of each desired DNA template is often prohibitively expensive. Consequently, many protein-screening libraries rely on the use of degenerate codons (DCs), mixtures of DNA bases incorporated at specific positions during DNA synthesis, to generate highly diverse protein-variant pools from only a few low-cost synthesis reactions. However, selecting DCs for sets of sequences that covary at multiple positions dramatically increases the difficulty of designing a DC library and leads to the creation of many undesired variants that can quickly outstrip screening capacity. RESULTS: We introduce a novel algorithm for total DC library optimization, degenerate codon design (DeCoDe), based on integer linear programming. DeCoDe significantly outperforms state-of-the-art DC optimization algorithms and scales well to more than a hundred proteins sharing complex patterns of covariation (e.g. the lab-derived avGFP lineage). Moreover, DeCoDe is, to our knowledge, the first DC design algorithm with the capability to encode mixed-length protein libraries. We anticipate DeCoDe to be broadly useful for a variety of library generation problems, ranging from protein engineering attempts that leverage mutual information to the reconstruction of ancestral protein states. AVAILABILITY AND IMPLEMENTATION: github.com/OrensteinLab/DeCoDe. CONTACT: yaronore@bgu.ac.il. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BET-seq: Binding energy topographies revealed by microfluidics and high-throughput sequencing.

Biophysical models of transcriptional regulation rely on energetic measurements of the binding affinities between transcription factors (TFs) and target DNA binding sites. Historically, assays capable of measuring TF-DNA binding affinities have been relatively low-throughput (measuring ~103 sequences in parallel) and have required significant specialized equipment, limiting their use to a handful of laboratories. Recently, we developed an experimental assay and analysis pipeline that allows measurement of binding energies between a single TF and up to 106 DNA species in a single experiment (Binding Energy Topography by sequencing, or BET-seq) (Le et al., 2018). BET-seq employs the Mechanically Induced Trapping of Molecular Interactions (MITOMI) platform to purify DNA bound to a TF at equilibrium followed by high coverage sequencing to reveal relative differences in binding energy for each sequence. While we have previously used BET-seq to refine the binding affinity landscapes surrounding high-affinity DNA consensus target sites, we anticipate this technique will be applied in future work toward measuring a wide variety of TF-DNA landscapes. Here, we provide detailed instructions and general considerations for DNA library design, performing BET-seq assays, and analyzing the resulting data.

An Open-Source, Programmable Pneumatic Setup for Operation and Automated Control of Single- and Multi-Layer Microfluidic Devices.

Microfluidic technologies have been used across diverse disciplines (e.g. high-throughput biological measurement, fluid physics, laboratory fluid manipulation) but widespread adoption has been limited in part due to the lack of openly disseminated resources that enable non-specialist labs to make and operate their own devices. Here, we report the open-source build of a pneumatic setup capable of operating both single and multilayer (Quake-style) microfluidic devices with programmable scripting automation. This setup can operate both simple and complex devices with 48 device valve control inputs and 18 sample inputs, with modular design for easy expansion, at a fraction of the cost of similar commercial solutions. We present a detailed step-by-step guide to building the pneumatic instrumentation, as well as instructions for custom device operation using our software, Geppetto, through an easy-to-use GUI for live on-chip valve actuation and a scripting system for experiment automation. We show robust valve actuation with near real-time software feedback and demonstrate use of the setup for high-throughput biochemical measurements on-chip. This open-source setup will enable specialists and novices alike to run microfluidic devices easily in their own laboratories.

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding.

Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.

A Powerful New Quantitative Genetics Platform, Combining Caenorhabditis elegans High-Throughput Fitness Assays with a Large Collection of Recombinant Strains.

The genetic variants underlying complex traits are often elusive even in powerful model organisms such as Caenorhabditis elegans with controlled genetic backgrounds and environmental conditions. Two major contributing factors are: (1) the lack of statistical power from measuring the phenotypes of small numbers of individuals, and (2) the use of phenotyping platforms that do not scale to hundreds of individuals and are prone to noisy measurements. Here, we generated a new resource of 359 recombinant inbred strains that augments the existing C. elegans N2xCB4856 recombinant inbred advanced intercross line population. This new strain collection removes variation in the neuropeptide receptor gene npr-1, known to have large physiological and behavioral effects on C. elegans and mitigates the hybrid strain incompatibility caused by zeel-1 and peel-1, allowing for identification of quantitative trait loci that otherwise would have been masked by those effects. Additionally, we optimized highly scalable and accurate high-throughput assays of fecundity and body size using the COPAS BIOSORT large particle nematode sorter. Using these assays, we identified quantitative trait loci involved in fecundity and growth under normal growth conditions and after exposure to the herbicide paraquat, including independent genetic loci that regulate different stages of larval growth. Our results offer a powerful platform for the discovery of the genetic variants that control differences in responses to drugs, other aqueous compounds, bacterial foods, and pathogenic stresses.

COPASutils: an R package for reading, processing, and visualizing data from COPAS large-particle flow cytometers.

The R package COPASutils provides a logical workflow for the reading, processing, and visualization of data obtained from the Union Biometrica Complex Object Parametric Analyzer and Sorter (COPAS) or the BioSorter large-particle flow cytometers. Data obtained from these powerful experimental platforms can be unwieldy, leading to difficulties in the ability to process and visualize the data using existing tools. Researchers studying small organisms, such as Caenorhabditis elegans, Anopheles gambiae, and Danio rerio, and using these devices will benefit from this streamlined and extensible R package. COPASutils offers a powerful suite of functions for the rapid processing and analysis of large high-throughput screening data sets.