Biology subjects

Jiang, J.-Y.

Publications and source records attributed to Jiang, J.-Y..

4 recordsLinked to original sources

bioRxiv2020

JEDI: Circular RNA Prediction based on Junction Encoders and Deep Interaction among Splice Sites

Circular RNA is a novel class of endogenous non-coding RNAs that have been largely discovered in eukaryotic transcriptome. The circular structure arises from a non-canonical splicing process, where the donor site backsplices to an upstream acceptor site. These circular form of RNAs are conserved across species, and often show tissue or cell-specific expression. Emerging evidences have suggested its vital roles in gene regulation, which are further associated with various types of diseases. As the fundamental effort to elucidate its function and mechanism, numerous efforts have been devoted to predicting circular RNA from its primary sequence. However, statistical learning methods are constrained by the information presented with explicit features, and the existing deep learning approach falls short on fully exploring the positional information of the splice sites and their deep interaction. We present an effective and robust end-to-end framework, JEDI, for circular RNA prediction using only the nucleotide sequence. Our framework first leverages the attention mechanism to encode each junction site based on deep bidirectional recurrent neural networks and then presents the novel cross-attention layer to model deep interaction among these sites for backsplicing. Finally, JEDI is capable of not only addressing the task of circular RNA prediction but also interpreting the relationships among splice sites to discover the hotspots for backsplicing within a gene region. Experimental evaluations demonstrate that JEDI significantly outperforms several state-of-the-art approaches in circular RNA prediction on both isoform-level and gene-level. Moreover, JEDI also shows promising results on zero-shot backsplicing discovery, where none of the existing approaches can achieve. The implementation of our framework is available at https://github.com/hallogameboy/JEDI.

bioinformatics↗

bioRxiv2019

Mutation effect estimation on protein-protein interactions using deep contextualized representation learning

The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein-protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations are commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations to PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR, to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wildtype protein pair and its mutation pair. Multiple-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that MuPIPR outperforms various state-of-the-art systems on the change of binding affinity prediction and the buried surface area prediction. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR

bioinformatics↗

bioRxiv2019

Grass Carp Reovirus (GCRV) Giving Its All to Suppress IFN Production by Countering MAVS-TBK1 Activation .

As a crucial signaling pathway for interferon (IFN) production, the RIG-I-like receptor (RLR) axis is usually the host target of viruses to enhance viral infection. To date, though immune evasion methods to contrapose IFN production have been characterized for a series of terrestrial viruses, the strategies employed by fish viruses remain unclear. Here, we report that all grass carp reovirus (GCRV) proteins encoded by segments S1 to S11 interact with fish RLR factors, specifically for mitochondrial antiviral signaling protein-TANK-binding kinase 1 (MAVS-TBK1) signaling transduction, leading to decreased IFN expression. First, the GCRV viral proteins blunted the MAVS-induced expression of IFN but had little effect on TBK1-induced IFN expression. Subsequently, interestingly, co-immunoprecipitation experiments demonstrated that all GCRV viral proteins interacted with several RLR cascades, especially with TBK1. To further illustrate the mechanisms of these interactions between GCRV viral proteins and host RLRs, two of the viral proteins, NS79 (S4) and VP3 (S3), were selected as representative proteins for the study. The obtained data demonstrated that NS79 did not affect the stability of the host RLR protein, but was phosphorylated by gcTBK1, leading to the reduction of host substrate gcIRF3/7 phosphorylation. On the other hand, VP3 degraded gcMAVS and the degradation was significantly reversed by 3-MA. The biological effects of both NS79 and VP3 were consistently found to be related to the suppression of IFN expression and the promotion of viral evasion. Our findings shed light on the special evasion mechanism utilized by fish virus through IFN regulation, which might differ between fish and mammals.\n\nAuthor summaryThe RLR signaling pathway is crucial for IFN induction when host cells are infected with virus and RLR factors are targeted by virus. To date, the evasion mechanisms of fish viruses remain mysterious. In this study, we reveal that all 11 GCRV proteins interact with fish RLR factors and suppress the activation of MAVS-TBK1 signaling transduction, leading to the reduction of IFN expression. Two viral proteins were employed as examples to investigate the different evasion mechanisms of GCRV. These findings reveal the novel countermeasures used by fish virus to avoid the host IFN response.

immunology↗

bioRxiv2017

TahcoRoll: An Efficient Approach for Signature Profiling in Genomic Data through Variable-Length k-mers

k-mer profiling has been one of the trending approaches to analyze read data generated by high-throughput sequencing technologies. The tasks of k-mer profiling include, but are not limited to, counting the frequencies and determining the occurrences of short sequences in a dataset. The notion of k-mer has been extensively used to build de Bruijn graphs in genome or transcriptome assembly, which requires examining all possible k-mers presented in the dataset. Recently, an alternative way of profiling has been proposed, which constructs a set of representative k-mers as genomic markers and profiles their occurrences in the sequencing data. This technique has been applied in both transcript quantification through RNA-Seq and taxonomic classification of metagenomic reads. Most of these applications use a set of fixed-size k-mers since the majority of existing k-mer counters are inadequate to process genomic sequences with variable-length k-mers. However, choosing the appropriate k is challenging, as it varies for different applications. As a pioneer work to profile a set of variable-length k-mers, we propose TahcoRoll in order to enhance the Aho-Corasick algorithm. More specifically, we use one bit to represent each nucleotide, and integrate the rolling hash technique to construct an efficient in-memory data structure for this task. Using both synthetic and real datasets, results show that TahcoRoll outperforms existing approaches in either or both time and memory efficiency without using any disk space. In addition, compared to the most efficient state-of-the-art k-mer counters, such as KMC and MSBWT, TahcoRoll is the only approach that can process long read data from both PacBio and Oxford Nanopore on a commodity desktop computer. The source code of TahcoRoll is implemented in C++14, and available at https://github.com/chelseaju/TahcoRoll.git.

bioinformatics↗