Selected publications in 2020

Docking sites inside Cas9 for adenine base editing diversification and RNA off-target elimination.
Li S, Yuan B, Cao J, Chen J, Chen J, Qiu J, Zhao XM, Wang X, Qiu Z, Cheng TL..
Nature communications. (2020)

Here, functional ABE variants with diversified editing scopes and reduced RNA off-target activities are identified using domain insertion profiling inside SpCas9 and with different combinations of TadA variants. Engineered ABE variants in this study display narrowed, expanded or shifted editing scopes with efficient editing activities across protospacer positions 2-16.

Identifying age-specific gene signatures of the human cerebral cortex with joint analysis of transcriptomes and functional connectomes.
Zhao X, Chen J, Xiao P, Feng J, Nie Q, Zhao XM.
Briefings in Bioinformatics. (2020)

Here, with a novel method transcriptome-connectome correlation analysis (TCA), which integrates the brain functional magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood and late adulthood.

nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia.
Yang A, Chen J, Zhao XM.
Briefings in Bioinformatics. (2020)

We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks.

Macrel: antimicrobial peptide screening in genomes and metagenomes.
Santos-Júnior CD, Pan S, Zhao XM, Coelho LP.
PeerJ. (2020)

Here, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime).

Deep learning of brain magnetic resonance images: A brief review.
Zhao X, Zhao XM.
Methods. (2020)

In this survey, we give a brief review of the recent popular deep learning approaches and their applications in brain MRI analysis. Furthermore, popular brain MRI databases and deep learning tools are also introduced. The strength and weaknesses of different approaches are addressed, and challenges as well as future directions are also discussed.

Oxidized Glutathione Increases Delta-Subunit Expressing Epithelial Sodium Channel Activity in Xenopus laevis Oocytes.
Grant GJ, Coca C, Zhao XM, Helms MN.
Emed Res. (2020)

Western blot and PCR analysis show that human small airway epithelial cells (hSAEC) express canonical αβγ-subunits alongside δ-ENaC subunits. Differences in single channel responses to GSSG in hSAECs indicate that airway epithelia redox sensitivity may depend on whether δ- or α- subunits assemble in the membrane.

DeepTL-Ubi: A novel deep transfer learning method for effectively predicting ubiquitination sites of multiple species.
Yu Liu, Ao Li, Zhao XM, Minghui Wang.
Methods. (2020)

In this paper, we propose a novel transfer deep learning method, named DeepTL-Ubi, for predicting ubiquitination sites of multiple species.

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation.
YJin K, Ou-Yang L, Zhao XM, Yan H, Zhang XF.
Bioinformatics. (2020)

In this paper, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model.

GMrepo: a database of curated and consistently annotated human gut metagenomes.
Wu S, Sun C, Li Y, Wang T, Jia L, Lai S, Yang Y, Luo P, Dai D, Yang YQ, Luo Q, Gao NL, Ning K, He LJ, Zhao XM, Chen WH.
Nucleic Acids Res. (2020)

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purpose is to facilitate the reusability and accessibility of the rapidly growing human metagenomic data.

What Is the Link Between Attention-Deficit/Hyperactivity Disorder and Sleep Disturbance? A Multimodal Examination of Longitudinal Relationships and Brain Structure Using Large-Scale Population-Based Cohorts.
Shen C, Luo Q, Chamberlain SR, Morgan S, Romero-Garcia R, Du J, Zhao X, Touchette É, Montplaisir J, Vitaro F, Boivin M, Tremblay RE, Zhao XM, Robaey P, Feng J, Sahakian BJ.
Biol Psychiatry. (2020)

This study indicates that ADHD symptoms and sleep disturbances have common neural correlates, including structural changes of the ventral attention system and frontostriatal circuitry. Leveraging data from large datasets, these results offer new mechanistic insights into this clinically important relationship between ADHD and sleep impairment, with potential implications for neurobiological models and future therapeutic directions.

Host DNA Contents in Fecal Metagenomics as a Biomarker for Intestinal Diseases and Effective Treatment.
Jiang P, Lai S, Wu S, Zhao XM, Chen WH.
BMC Genomics. (2020)

Together, we revealed that association between HDCs and gut dysbiosis, and identified HDC as a novel biomarker from fecal metagenomics for diagnosis and effective treatment of intestinal diseases; our results also suggested that host-derived contents may have greater impact on gut microbiota than previously anticipated.

A Graph Regularized Generalized Matrix Factorization Model for Predicting Links in Biomedical Bipartite Networks.
Zhang ZC, Zhang XF, Wu M, Ou-Yang L, Zhao XM, Li XL.
Bioinformatics. (2020)

In this study, we propose a new link prediction method, named graph regularized generalized matrix factorization (GRGMF), to identify potential links in biomedical bipartite Networks.We conduct extensive experiments on six real datasets. Experiment results show that GRGMF can achieve competitive performance on all these datasets, which demonstrate the effectiveness of GRGMF in prediction potential links in biomedical bipartite networks.