Gene loss is a prevalent source of genetic variation in genome evolution. Calling loss events effectively and efficiently is a critical step for systematically characterizing their functional and phylogenetic profiles genome-wide. Here, we developed a novel pipeline integrating orthologous inference and genome alignment. Interestingly, we identified 33 gene loss events that give rise to evolutionarily novel lncRNAs that show distinct expression features and could be associated with various functions related to growth, development, immunity and reproduction, suggesting loss relics as a potential source of functional lncRNAs in humans. Our data also demonstrated that the rates of protein gene loss are variable among different lineages with distinct functional biases.

Deep neural networks equipped with convolutional neural layers have been widely used in omics data analysis. Though highly efficient in data-oriented feature detection, the classical convolutional layer is designed with inter-positional independent filters, hardly modeling inter-positional correlations in various biological data. Here, we proposed Markonv layer (Markov convolutional neural layer), a novel convolutional neural layer with Markov transition matrices as its filters, to model the intrinsic dependence in inputs as Markov processes. Extensive evaluations based on both synthetic and real-world data showed that Markonv-based networks could not only identify functional motifs with inter-positional correlations in large-scale omics sequence data effectively, but also decode complex electrical signals generated by Oxford Nanopore sequencing efficiently. Designed as a drop-in replacement of the classical convolutional layer, Markonv layers enable an effective and efficient identification for inter-positional correlations from various biological data of different modalities. All source codes of a PyTorch-based implementation are publicly available on GitHub for academic usage.