The birth of genes that encode new protein sequences is a major source of evolutionary innovation. However, we still understand relatively little about how these genes come into being and which functions they are selected for. To address these questions, we have obtained a large collection of mammalian-specific gene families that lack homologues in other eukaryotic groups. We have combined gene annotations and de novo transcript assemblies from 30 different mammalian species, obtaining ∼6,000 gene families. In general, the proteins in mammalian-specific gene families tend to be short and depleted in aromatic and negatively charged residues. Proteins which arose early in mammalian evolution include milk and skin polypeptides, immune response components, and proteins involved in reproduction. In contrast, the functions of proteins which have a more recent origin remain largely unknown, despite the fact that these proteins also have extensive proteomics support. We identify several previously described cases of genes originated de novo from noncoding genomic regions, supporting the idea that this mechanism frequently underlies the evolution of new protein-coding genes in mammals. Finally, we show that most young mammalian genes are preferentially expressed in testis, suggesting that sexual selection plays an important role in the emergence of new functional genes.

Skin sensitization potential or potency is an important end point in the safety assessment of new chemicals and new chemical mixtures. Formerly, animal experiments such as the local lymph node assay (LLNA) were the main form of assessment. Today, however, the focus lies on the development of nonanimal testing approaches (i.e., in vitro and in chemico assays) and computational models. In this work, we investigate, based on publicly available LLNA data, the ability of aggregated, Mondrian conformal prediction classifiers to differentiate between non- sensitizing and sensitizing compounds as well as between two levels of skin sensitization potential (weak to moderate sensitizers, and strong to extreme sensitizers). The advantage of the conformal prediction framework over other modeling approaches is that it assigns compounds to activity classes only if a defined minimum level of confidence is reached for the individual predictions. This eliminates the need for applicability domain criteria that often are arbitrary in their nature and less flexible. Our new binary classifier, named Skin Doctor CP, differentiates nonsensitizers from sensitizers with a higher reliability-to-efficiency ratio than the corresponding nonconformal prediction workflow that we presented earlier. When tested on a set of 257 compounds at the significance levels of 0.10 and 0.30, the model reached an efficiency of 0.49 and 0.92, and an accuracy of 0.83 and 0.75, respectively. In addition, we developed a ternary classification workflow to differentiate nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers. Although this model achieved satisfactory overall performance (accuracies of 0.90 and 0.73, and efficiencies of 0.42 and 0.90, at significance levels 0.10 and 0.30, respectively), it did not obtain satisfying class-wise results (at a significance level of 0.30, the validities obtained for nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers were 0.70, 0.58, and 0.63, respectively). We argue that the model is, in consequence, unable to reliably identify strong to extreme sensitizers and suggest that other ternary models derived from the currently accessible LLNA data might suffer from the same problem. Skin Doctor CP is available via a public web service at .

The birth of genes that encode new protein sequences is a major source of evolutionary innovation.However, we still understand relatively little about how these genes come into being and which functions they are selected for. To address these questions we have obtained a large collection of mammalian-specific 5 gene families that lack homologues in other eukaryotic groups. We have combined gene annotations and de novo transcript assemblies from 30 different mamalian species, obtaining about 6,000 gene families. In general, the proteins in mammalian-specific gene families tend to be short and depleted in aromatic and negatively charged residues. Proteins which arose early in mammalian evolution include milk and skin polypeptides, immune response components, and proteins involved in reproduction. In contrast, the 10 functions of proteins which have a more recent origin remain largely unknown, despite the fact that these proteins also have extensive proteomics support. We identify several previously described cases of genes originated de novo from non-coding genomic regions, supporting the idea that this mechanism frequently underlies the evolution of new protein-coding genes in mammals. Finally, we show that most young mammalian genes are preferentially expressed in testis, suggesting that sexual selection plays an important 15 role in the emergence of new functional genes.

The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical ImagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai.

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