Peter Sarkies @PSarkies@qoto.org

Treatment of parasitic nematode infections in humans and livestock relies on a small arsenal of anthelmintic drugs that have historically reduced parasite burdens. However, anthelmintic resistance (AR) is increasing, and little is known about the molecular and genetic causes of resistance for most drugs. The free-living roundworm Caenorhabditis elegans has proven to be a tractable model to understand AR, where studies have led to the identification of molecular targets of all major anthelmintic drug classes. Here, we used genetically diverse C. elegans strains to perform dose-response analyses across 26 anthelmintic drugs that represent the three major anthelmintic drug classes (benzimidazoles, macrocyclic lactones, and nicotinic acetylcholine receptor agonists) in addition to seven other anthelmintic classes. First, we found that C. elegans strains displayed significant variation in anthelmintic responses across drug classes. Dose-response trends within a drug class showed that the C. elegans strains elicited similar responses within the benzimidazoles but variable responses in the macrocyclic lactones and nicotinic acetylcholine receptor agonists. Next, we compared the effective concentration estimates to induce a 10% maximal response (EC10) and slope estimates of each dose-response curve of each strain to the reference strain, N2, which enabled the identification of anthelmintics with population-wide differences to understand how genetics contribute to AR. Because genetically diverse strains displayed differential susceptibilities within and across anthelmintics, we show that C. elegans is a useful model for screening potential nematicides. Third, we quantified the heritability of responses to each anthelmintic and observed a significant correlation between exposure closest to the EC10 and the exposure that exhibited the most heritable responses. Heritable genetic variation can be explained by strain-specific anthelmintic responses within and across drug classes. These results suggest drugs to prioritize in genome-wide association studies, which will enable the identification of AR genes. ### Competing Interest Statement The authors have declared no competing interest.

Background: Amongst the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code, since the majority of available analysis tools require programming skills. Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results. Results: We have developed RNAlysis, a modular Python-based analysis software for RNA sequencing data. RNAlysis allows users to build customized analysis pipelines suiting their specific research questions, going all the way from raw FASTQ files, through exploratory data analysis and data visualization, clustering analysis, and gene-set enrichment analysis. RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. We demonstrate the use of RNAlysis by analyzing RNA data from different studies using C. elegans nematodes. We note that the software is equally applicable to data obtained from any organism. Conclusions: RNAlysis is suitable for investigating a variety of biological questions, and allows researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. It functions as a gateway into RNA sequencing analysis for less computer-savvy researchers, but can also help experienced bioinformaticians make their analyses more robust and efficient, as it offers diverse tools, scalability, automation, and standardization between analyses. ### Competing Interest Statement The authors have declared no competing interest.

Protein domains that emerged more recently in evolution have higher structural disorder and greater clustering of hydrophobic residues along the primary sequence. It is hard to explain how selection acting via descent with modification could act so slowly as not to saturate over the extraordinarily long timescales over which these trends persist. Here we hypothesize that the trends were created by a higher level of selection which differentially affects the retention probabilities of protein domains with different properties. This hypothesis predicts that loss rates should depend on disorder and clustering trait values. To test this, we inferred loss rates via maximum likelihood for animal Pfam domains, after first performing a set of stringent quality control methods to reduce annotation errors. Intermediate trait values, matching those of ancient domains, are associated with the lowest loss rates, making our results difficult to explain with reference to previously described homology detection biases. Our results support the hypothesis that differential domain loss slowly weeds out those protein domains that have non-optimal levels of disorder and clustering. The same preferences also shape differential diversification of Pfam domains, further impacting proteome composition. ### Competing Interest Statement The authors have declared no competing interest.