A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing.

Oxford Nanopore sequencing can detect DNA methylations from ionic current signal of single molecules, offering a unique advantage over conventional methods. Additionally, adaptive sampling, a software-controlled enrichment method for targeted sequencing, allows reduced representation methylation sequencing that can be applied to CpG islands or imprinted regions. Here we present DeepMod2, a comprehensive deep-learning framework for methylation detection using ionic current signal from Nanopore sequencing. DeepMod2 implements both a bidirectional long short-term memory (BiLSTM) model and a Transformer model and can analyze POD5 and FAST5 signal files generated on R9 and R10 flowcells. Additionally, DeepMod2 can run efficiently on central processing unit (CPU) through model pruning and can infer epihaplotypes or haplotype-specific methylation calls from phased reads. We use multiple publicly available and newly generated datasets to evaluate the performance of DeepMod2 under varying scenarios. DeepMod2 has comparable performance to Guppy and Dorado, which are the current state-of-the-art methods from Oxford Nanopore Technologies that remain closed-source. Moreover, we show a high correlation (r = 0.96) between reduced representation and whole-genome Nanopore sequencing. In summary, DeepMod2 is an open-source tool that enables fast and accurate DNA methylation detection from whole-genome or adaptive sequencing data on a diverse range of flowcell types.

DELongSeq for efficient detection of differential isoform expression from long-read RNA-seq data.

Conventional gene expression quantification approaches, such as microarrays or quantitative PCR, have similar variations of estimates for all genes. However, next-generation short-read or long-read sequencing use read counts to estimate expression levels with much wider dynamic ranges. In addition to the accuracy of estimated isoform expression, efficiency, which measures the degree of estimation uncertainty, is also an important factor for downstream analysis. Instead of read count, we present DELongSeq, which employs information matrix of EM algorithm to quantify uncertainty of isoform expression estimates to improve estimation efficiency. DELongSeq uses random-effect regression model for the analysis of DE isoform, in that within-study variation represents variable precision in isoform expression estimation and between-study variation represents variation in isoform expression levels across samples. More importantly, DELongSeq allows 1 case versus 1 control comparison of differential expression, which has specific application scenarios in precision medicine (such as before versus after treatment, or tumor versus stromal tissues). Through extensive simulations and analysis of several RNA-Seq datasets, we show that the uncertainty quantification approach is computationally reliable, and can improve the power of differential expression (DE) analysis of isoforms or genes. In summary, DELongSeq allows for efficient detection of differential isoform/gene expression from long-read RNA-Seq data.

BepiTBR: T-B reciprocity enhances B cell epitope prediction.

The ability to predict B cell epitopes is critical for biomedical research and many clinical applications. Investigators have observed the phenomenon of T-B reciprocity, in which candidate B cell epitopes with nearby CD4+ T cell epitopes have higher chances of being immunogenic. To our knowledge, existing B cell epitope prediction algorithms have not considered this interesting observation. We developed a linear B cell epitope prediction model, BepiTBR, based on T-B reciprocity. We showed that explicitly including the enrichment of putative CD4+ T cell epitopes (predicted HLA class II epitopes) in the model leads to significant enhancement in the prediction of linear B cell epitopes. Curiously, the positive impact on B cell epitope generation is specific to the enrichment of DQ allele binders. Overall, our work provides interesting mechanistic insights into the generation of B cell epitopes and points to a new avenue to improve B cell epitope prediction for the field.