SUMMARY: Next-Generation Sequencing is widely used as a tool for identifying and quantifying microorganisms pooled together in either natural or designed samples. However, a prominent obstacle is achieving correct quantification when the pooled microbes are genetically related. In such cases, the outcome mostly depends on the method used for assigning reads to the individual targets. To address this challenge, we have developed Exodus-a reference-based Python algorithm for quantification of genomes, including those that are highly similar, when they are sequenced together in a single mix. To test Exodus' performance, we generated both empirical and in silico next-generation sequencing data of mixed genomes. When applying Exodus to these data, we observed median error rates varying between 0% and 0.21% as a function of the complexity of the mix. Importantly, no false negatives were recorded, demonstrating that Exodus' likelihood of missing an existing genome is very low, even if the genome's relative abundance is low and similar genomes are present in the same mix. Taken together, these data position Exodus as a reliable tool for identifying and quantifying genomes in mixed samples. Exodus is open source and free to use at: https://github.com/ilyavs/exodus. AVAILABILITY AND IMPLEMENTATION: Exodus is implemented in Python within a Snakemake framework. It is available on GitHub alongside a docker containing the required dependencies: https://github.com/ilyavs/exodus. The data underlying this article will be shared on reasonable request to the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The limited success rate of protein-protein docking procedures is generally attributed to structure differences between the bound and unbound states of the molecules. Herein we analyze a large dataset of protein-protein docking results and identify additional parameters that affect the performance of docking procedures. RESULTS: We find that the distinction between nearly correct models (NCMs) and decoys depends on the size of the interface to be predicted thus setting a limit to the prediction ability of docking procedures, particularly those in which the geometric complementarity descriptor is dominant. The geometric complementarity score in grid-based docking carries a large statistical error which further reduces the distinction between NCMs and decoys. We propose a method for correcting the statistical error and show that the distinction is improved when the docking models are ranked by statistically equivalent scores. AVAILABILITY: MolFit can be downloaded from our website http://www.weizmann.ac.il/Chemical_Research_Support/molfit. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.