geNomad processes user-provided nucleotide sequences through two branches. In the sequence branch, the inputs are one-hot encoded fed to an IGLOO neural network, which scores inputs based on the detection of non-local sequence motifs (A1 I). In the marker branch, proteins encoded by the input sequences are annotated using markers that are specific to chromosomes, plasmids or viruses (A1 II). A set of numerical features is then extracted from the annotated proteins and fed to a tree ensemble model, which scores the inputs based on their marker content. Next, the scores provided by both branches are aggregated by weighing the contribution of each branch based on the frequency of markers in the sequence (A2). Aggregated scores can then be calibrated to approximate probabilities in a process that leverages the sample composition inferred from the classification of sequences from the same batch (A3). Lastly, classification results are summarized and presented together with additional data, such as virus taxonomy, gene function and the inferred genetic code (A4).
