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Any two unrelated individuals differ by about 10,000 single amino acid variants (SAVs). Do these impact molecular function? Experimental answers cannot answer comprehensively, while state-of-the-art prediction methods can. We predicted the functional impacts of SAVs within human and for variants between human and other species. Several surprising results stood out. Firstly, four methods (CADD, PolyPhen-2, SIFT, and SNAP2) agreed within 10 percentage points on the percentage of rare SAVs predicted with effect. However, they differed substantially for the common SAVs: SNAP2 predicted, on average, more effect for common than for rare SAVs. Given the large ExAC data sets sampling 60,706 individuals, the differences were extremely significant (p-value

Advances in high throughput sequencing have deposited a vast amount of microbial genetic data into public databases. This data describes the proteins that microbes are able to synthesize and molecular functions they facilitate. Detailed understanding microbial functionality, in turn, provides for a better understanding of organism interactions with each other and their surroundings. Moreover, as microbial, and particularly bacterial, phylogeny-driven taxonomy is a dynamic and hotly debated field, I postulate in this work that standardized functional assessment of organisms can provide for a more stable and meaningful way for organism classification. Fortschritte in high-throughput Sequenzierung haben dazu geführt, dass massive Mengen von mikrobiellen genetischen Daten in öffentlichen Datenbanken eingepflegt wurden. Diese Daten beschrieben die Proteine die Mikroben in der Lage sind zu synthetisieren, sowie die funktionellen Eigenschaften, die diese Proteine zur Verfügung stellen. Ein detailliertes Verständnis der Funktionalität stellt wiederum ein besseres Verständnis der Interaktionen der Organismen untereinander sowie mit deren Umgebung dar. Außerdem ist die mikrobielle, und insbesondere bakterielle, phylogenetisch getriebene Taxonomie, ein dynamisches und heiß debattiertes Feld. Ich argumentiere in dieser Arbeit, dass ein standardisiertes, funktionsbasiertes Verfahren Organismen zu analysieren, eine weitaus stabilere, und bedeutsamere Art der Klassifizierung von Organismen darstellen kann.

Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease., Author Summary The variations in the genetic sequence between individuals affect the gene-product, i.e. the protein differently. Some variants have no measurable effect (are neutral), while others affect protein function. Some of those effects are so severe they cause so called monogenic Mendelian diseases, i.e. diseases triggered by a single letter change. Some in silico methods predict the molecular impact of sequence variation. However, both experimental and computational analyses struggle to generalize from the effect upon molecular protein function to the effect upon the organism such as a disease. Here, we confirmed that methods predicting molecular effects correctly capture the type of effects causing Mendelian diseases in human and introduced a data set for animal diseases that was also captured by predictions methods. Predicted effects were less when in silico testing human variants in an animal model (here mouse). This is important to know because “mouse models” are common to study human diseases. Overall, we provided some evidence for a link between the molecular level and some type of disease.

Additional file 2. The ROC and PR curves of the eight city predictors in raw-select model.

Additional file 3. The ROC and PR curves of the eight city predictors in balance-select model.

Additional file 1. The ROC and PR curves of the eight city predictors in raw-full model.

Additional file 2. The ROC and PR curves of the eight city predictors in raw-select model.

Additional file 1. The ROC and PR curves of the eight city predictors in raw-full model.