EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM

Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications

Insertions and deletions constitute the second most important source of natural genomic variation. Insertions and deletions make up to 25% of genomic variants in humans and are involved in complex evolutionary processes including genomic rearrangements, adaptation, and speciation. Recent advances in long-read sequencing technologies allow detailed inference of insertions and deletion variation in species and populations. Yet, despite their importance, evolutionary studies have traditionally ignored or mishandled insertions and deletions due to a lack of comprehensive methodologies and statistical models of insertions and deletion dynamics. Here, we discuss methods for describing insertions and deletion variation and modeling insertions and deletions over evolutionary time. We provide practical advice for tackling insertions and deletions in genomic sequences and illustrate our discussion with examples of insertions and deletion-induced effects in human and other natural populations and their contribution to evolutionary processes. We outline promising directions for future developments in statistical methodologies that would allow researchers to analyze insertions and deletion variation and their effects in large genomic data sets and to incorporate insertions and deletions in evolutionary inference.

MSNet-4mC: learning effective multi-scale representations for identifying DNA N4-methylcytosine sites

MOTIVATION

N4-methylcytosine (4mC) is an essential kind of epigenetic modification that regulates a wide range of biological processes. However, experimental methods for detecting 4mC sites are time-consuming and labor-intensive. As an alternative, computational methods that are capable of automatically identifying 4mC with data analysis techniques become a reasonable option. A major challenge is how to develop effective methods to fully exploit the complex interactions within the DNA sequences to improve the predictive capability.

RESULTS

In this work, we propose MSNet-4mC, a lightweight neural network building upon convolutional operations with multi-scale receptive fields to perceive cross-element relationships over both short and long ranges of given DNA sequences. With strong imbalances in the number of candidates in different species in mind, we compute and apply class weights in the cross-entropy loss to balance the training process. Extensive benchmarking experiments show that our method achieves a significant performance improvement and outperforms other state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

The source code and models are freely available for download at https://github.com/LIU-CT/MSNet-4mC, implemented in Python and supported on Linux and Windows.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Context-Dependent Substitution Dynamics in Plastid DNA Across a Wide Range of Taxonomic Groups

The influence of neighboring base composition, or context, on substitution bias at fourfold degenerate coding sites and in intergenic regions in plastid DNA is compared across the angiosperms, gymnosperms, ferns, liverworts, chlorophytes, stramenopiles and rhodophytes. An influence of flanking base G + C content on the relative rates of transitions and transversions is observed in all lineages and extends up to four nucleotides from the site of substitution in some. Despite finding context effects in all lineages, significant differences were observed between lineages. Overall, the data suggest that context is a general factor affecting mutation bias in plastid DNA but that the dynamics of the influence have evolved over time. It is also shown that, although there are similar effects of context on substitution bias at fourfold degenerate coding sites and at sites within intergenic regions, there are also small but significant differences, suggesting that there could be some selection on some of these sites and that there could be some difference in the mutation and/or repair process between coding and noncoding DNA.

Attention-Based Deep Multiple-Instance Learning for Classifying Circular RNA and Other Long Non-Coding RNA

Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture fed with a raw sequence, to learn the sparse features of RNA sequences and to accomplish the circRNAs identification task. The model outperformed the state-of-art models. Moreover, following the validation of the attention mechanism effectiveness by the handwritten digit dataset, the key sequence loci underlying circRNA’s recognition were obtained based on the corresponding attention score. Then, motif enrichment analysis identified some of the key motifs for circRNA formation. In conclusion, we designed deep learning network architecture suitable for learning gene sequences with sparse features and implemented it for the circRNA identification task, and the model has strong representation capability in the indication of some key loci.