Biology subjects

Zhong, J. F.

Publications and source records attributed to Zhong, J. F..

3 recordsLinked to original sources

bioRxiv2020

LIQA: Long-read Isoform Quantification and Analysis

Long-read RNA sequencing (RNA-seq) technologies have made it possible to sequence full-length transcripts, facilitating the exploration of isoform-specific gene expression (isoform relative abundance and isoform-level TPM) over conventional short-read RNA-seq. However, long-read RNA-seq suffers from high per-base error rate, presence of chimeric reads or alternative alignments, and other biases, which require different analysis methods than short-read RNA-seq. Here we present LIQA (Long-read Isoform Quantification and Analysis), an Expectation-Maximization based statistical method to quantify isoform expression and detect differential alternative splicing (DAS) events using long-read RNA-seq data. Rather than summarizing isoform-specific read counts directly as done in short-read methods, LIQA incorporates base-pair quality score and isoform-specific read length information to assign different weights across reads, which reflects alignment confidence. Moreover, LIQA can detect DAS events between conditions using isoform usage estimates. We evaluated LIQAs performance on simulated data and demonstrated that it outperforms other approaches in characterizing isoforms with low read coverage and in detecting DAS events between two groups. We also generated one direct mRNA sequencing dataset and one cDNA sequencing dataset using the Oxford Nanopore long-read platform, both with paired short-read RNA-seq data and qPCR data on selected genes, and we demonstrated that LIQA performs well in isoform discovery and quantification. Finally, we evaluated LIQA on a PacBio dataset on esophageal squamous epithelial cells, and demonstrated that LIQA recovered DAS events that failed to be detected in short-read data. In summary, LIQA leverages the power of long-read RNA-seq and achieves higher accuracy in estimating isoform abundance than existing approaches, especially for isoforms with low coverage and biased read distribution. LIQA is freely available at https://github.com/WGLab/LIQA.

bioinformatics↗

bioRxiv2019

Crif1 Promotes Osteoporosis in Mice after Radiation

Radiation induces rapid bone loss and enhances bone resorption and RANKL expression. RANKL provides the crucial signal to induce osteoclast differentiation and plays an important role in bone resorption. However, the mechanisms of radiation-induced osteoporosis are not fully understood. Here, we show that Crif1 expression increases in bone marrow cells after radiation. Conditional Crif1 deletion in bone marrow cells causes decreases in RANKL expression and the RANKL/OPG ratio, and relieves bone loss after radiation in mice. We further demonstrated in vitro that Crif1 promotes RANKL secretion via the cAMP/PKA pathway. Moreover, protein-protein docking screening identified five compounds as Crif1 inhibitors; these compounds dramatically suppressed RANKL secretion and CREB phosphorylation when cells were exposed to forskolin. This study enriches current knowledge of the pathogenesis of osteoporosis and provides insights into potential therapeutic strategies for osteoporosis treatment.

cell biology↗

bioRxiv2018

VirTect: a computational method for detecting virus species from RNA-Seq and its application in head and neck squamous cell carcinoma

Next generation sequencing (NGS) provides an opportunity to detect viral species from RNA-seq data on human tissues, but existing computational approaches do not perform optimally on clinical samples. We developed a bioinformatics method called VirTect for detecting viruses in neoplastic human tissues using RNA-seq data. Here, we used VirTect to analyze RNA-seq data from 363 HNSCC (head and neck squamous cell carcinoma) patients and identified 22 HPV-induced HNSCCs. These predictions were validated by manual review of pathology reports on histopathologic specimens. Compared to two existing prediction methods, VirusFinder and VirusSeq, VirTect demonstrated superior performance with many fewer false positives and false negatives. The majority of HPV carcinogenesis studies thus far have been performed on cervical cancer and generalized to HNSCC. Our results suggest that HPV-induced HNSCC involves unique mechanisms of carcinogenesis, so understanding these molecular mechanisms will have a significant impact on therapeutic approaches and outcomes. In summary, VirTect can be an effective solution for the detection of viruses with NGS data, and can facilitate the clinicopathologic characterization of various types of cancers with broad applications for oncology.\n\nSignificance StatementWe developed a new bioinformatics tool, and reported the new inside of HPV carcinogenesis mechanism in HPV-induced head and neck squamous cell carcinoma (HNSCC). This novel bioin-formatics tool and the new knowledge of HPV-induced HNSCC will facilitate the development of target therapies for treating HNSCC.

bioinformatics↗