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Article ; Online: Copy Number Variation Detection by Single-Cell DNA Sequencing with SCOPE.

Wang, Rujin / Jiang, Yuchao

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2493, Page(s) 279–288

Abstract: Whole-genome single-cell DNA sequencing (scDNA-seq) enables the characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ... ...

Abstract	Whole-genome single-cell DNA sequencing (scDNA-seq) enables the characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we describe SCOPE, a normalization and copy number estimation method for scDNA-seq data. We give an overview of the methodology and illustrate SCOPE with step-by-step demonstrations.
MeSH term(s)	Algorithms ; DNA Copy Number Variations ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Neoplasms/genetics ; Sequence Analysis, DNA/methods ; Whole Genome Sequencing
Language	English
Publishing date	2022-06-25
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2293-3_18
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Article: Genome-wide analysis of transcription-coupled repair reveals novel transcription events in

Kose, Cansu / Lindsey-Boltz, Laura A / Sancar, Aziz / Jiang, Yuchao

bioRxiv : the preprint server for biology

2024

Abstract: Bulky DNA adducts such as those induced by ultraviolet light are removed from the genomes of multicellular organisms by nucleotide excision repair, which occurs through two distinct mechanisms, global repair, requiring the DNA damage recognition-factor ... ...

Abstract	Bulky DNA adducts such as those induced by ultraviolet light are removed from the genomes of multicellular organisms by nucleotide excision repair, which occurs through two distinct mechanisms, global repair, requiring the DNA damage recognition-factor XPC (xeroderma pigmentosum complementation group C), and transcription-coupled repair (TCR), which does not. TCR is initiated when elongating RNA polymerase II encounters DNA damage, and thus analysis of genome-wide excision repair in XPC-mutants only repairing by TCR provides a unique opportunity to map transcription events missed by methods dependent on capturing RNA transcription products and thus limited by their stability and/or modifications (5'-capping or 3'-polyadenylation). Here, we have performed the eXcision Repair-sequencing (XR-seq) in the model organism
Language	English
Publishing date	2024-03-29
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.10.12.562083
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Article: Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing.

Weideman, Ann Marie K / Wang, Rujin / Ibrahim, Joseph G / Jiang, Yuchao

bioRxiv : the preprint server for biology

2024

Abstract: Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into ... ...

Abstract	Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.
Language	English
Publishing date	2024-03-19
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.18.585595
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Article ; Online: TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions.

Agarwal, Aman / Zhao, Fengdi / Jiang, Yuchao / Chen, Li

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 2

Abstract: Motivation: Small insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for non-coding sindel (nc-sindel) to have an impact on human diseases and phenotypes is through the regulation of ... ...

Abstract	Motivation: Small insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for non-coding sindel (nc-sindel) to have an impact on human diseases and phenotypes is through the regulation of gene expression. Nevertheless, current sequencing experiments may lack statistical power and resolution to pinpoint the functional sindel due to lower minor allele frequency or small effect size. As an alternative strategy, a supervised machine learning method can identify the otherwise masked functional sindels by predicting their regulatory potential directly. However, computational methods for annotating and predicting the regulatory sindels, especially in the non-coding regions, are underdeveloped. Results: By leveraging labeled nc-sindels identified by cis-expression quantitative trait loci analyses across 44 tissues in Genotype-Tissue Expression (GTEx), and a compilation of both generic functional annotations and large-scale epigenomic profiles, we develop TIssue-specific Variant Annotation for Non-coding indel (TIVAN-indel), which is a supervised computational framework for predicting non-coding regulatory sindels. As a result, we demonstrate that TIVAN-indel achieves the best prediction performance in both with-tissue prediction and cross-tissue prediction. As an independent evaluation, we train TIVAN-indel from the 'Whole Blood' tissue in GTEx and test the model using 15 immune cell types from an independent study named Database of Immune Cell Expression. Lastly, we perform an enrichment analysis for both true and predicted sindels in key regulatory regions such as chromatin interactions, open chromatin regions and histone modification sites, and find biologically meaningful enrichment patterns. Availability and implementation: https://github.com/lichen-lab/TIVAN-indel. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Humans ; Quantitative Trait Loci ; Epigenomics ; Regulatory Sequences, Nucleic Acid ; Chromatin ; INDEL Mutation
Chemical Substances	Chromatin
Language	English
Publishing date	2023-01-08
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad060
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Article ; Online: Joint gene network construction by single‐cell RNA sequencing data

Dong, Meichen / He, Yiping / Jiang, Yuchao / Zou, Fei

Biometrics. 2023 June, v. 79, no. 2 p.915-925

2023

Abstract: In contrast to differential gene expression analysis at the single‐gene level, gene regulatory network (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases ... ...

Abstract	In contrast to differential gene expression analysis at the single‐gene level, gene regulatory network (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases and traits. Recent advances in single‐cell RNA sequencing (scRNA‐seq) allow constructing GRNs at a much finer resolution than bulk RNA‐seq and microarray data. However, scRNA‐seq data are inherently sparse, which hinders the direct application of the popular Gaussian graphical models (GGMs). Furthermore, most existing approaches for constructing GRNs with scRNA‐seq data only consider gene networks under one condition. To better understand GRNs across different but related conditions at single‐cell resolution, we propose to construct Joint Gene Networks with scRNA‐seq data (JGNsc) under the GGMs framework. To facilitate the use of GGMs, JGNsc first proposes a hybrid imputation procedure that combines a Bayesian zero‐inflated Poisson model with an iterative low‐rank matrix completion step to efficiently impute zero‐inflated counts resulted from technical artifacts. JGNsc then transforms the imputed data via a nonparanormal transformation, based on which joint GGMs are constructed. We demonstrate JGNsc and assess its performance using synthetic data. The application of JGNsc on two cancer clinical studies of medulloblastoma and glioblastoma gains novel insights in addition to confirming well‐known biological results.
Keywords	Bayesian theory ; RNA ; gene expression regulation ; gene regulatory networks ; genes ; glioblastoma ; humans ; hybrids ; microarray technology ; models ; sequence analysis ; transcriptomics
Language	English
Dates of publication	2023-06
Size	p. 915-925.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	213543-7
ISSN	0099-4987 ; 0006-341X
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13645
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Identification of Parkinson's disease subtypes with distinct brain atrophy progression and its association with clinical progression.

Pan, Guoqing / Jiang, Yuchao / Zhang, Wei / Zhang, Xuejuan / Wang, Linbo / Cheng, Wei

Psychoradiology

2024 Volume 4, Page(s) kkae002

Abstract: Background: Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear.: Objective: This study aims to identify PD subtypes with ... ...

Abstract	Background: Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear. Objective: This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression. Methods: This study included 107 PD patients (mean age: 60.06 ± 9.98 years, 70.09% male) with baseline and ≥ 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes. Results: Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part Ⅰ, Conclusion: The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.
Language	English
Publishing date	2024-02-24
Publishing country	England
Document type	Journal Article
ISSN	2634-4416
ISSN (online)	2634-4416
DOI	10.1093/psyrad/kkae002
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Article ; Online: EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing.

Wang, Rujin / Lin, Dan-Yu / Jiang, Yuchao

PLoS genetics

2022 Volume 18, Issue 6, Page(s) e1010251

Abstract: More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- ... ...

Abstract	More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.
MeSH term(s)	Diabetes Mellitus, Type 2/genetics ; Genome-Wide Association Study/methods ; Humans ; Multifactorial Inheritance ; Phenotype ; Polymorphism, Single Nucleotide/genetics ; Sequence Analysis, RNA
Language	English
Publishing date	2022-06-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2186725-2
ISSN	1553-7404 ; 1553-7390
ISSN (online)	1553-7404
ISSN	1553-7390
DOI	10.1371/journal.pgen.1010251
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Article ; Online: Joint gene network construction by single-cell RNA sequencing data.

Dong, Meichen / He, Yiping / Jiang, Yuchao / Zou, Fei

Biometrics

2022 Volume 79, Issue 2, Page(s) 915–925

Abstract: In contrast to differential gene expression analysis at the single-gene level, gene regulatory network (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases ... ...

Abstract	In contrast to differential gene expression analysis at the single-gene level, gene regulatory network (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases and traits. Recent advances in single-cell RNA sequencing (scRNA-seq) allow constructing GRNs at a much finer resolution than bulk RNA-seq and microarray data. However, scRNA-seq data are inherently sparse, which hinders the direct application of the popular Gaussian graphical models (GGMs). Furthermore, most existing approaches for constructing GRNs with scRNA-seq data only consider gene networks under one condition. To better understand GRNs across different but related conditions at single-cell resolution, we propose to construct Joint Gene Networks with scRNA-seq data (JGNsc) under the GGMs framework. To facilitate the use of GGMs, JGNsc first proposes a hybrid imputation procedure that combines a Bayesian zero-inflated Poisson model with an iterative low-rank matrix completion step to efficiently impute zero-inflated counts resulted from technical artifacts. JGNsc then transforms the imputed data via a nonparanormal transformation, based on which joint GGMs are constructed. We demonstrate JGNsc and assess its performance using synthetic data. The application of JGNsc on two cancer clinical studies of medulloblastoma and glioblastoma gains novel insights in addition to confirming well-known biological results.
MeSH term(s)	Humans ; Gene Regulatory Networks ; Sequence Analysis, RNA/methods ; Bayes Theorem ; RNA-Seq ; Gene Expression Profiling/methods ; Glioblastoma ; RNA/genetics
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2022-04-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	213543-7
ISSN	1541-0420 ; 0099-4987 ; 0006-341X
ISSN (online)	1541-0420
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13645
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Article ; Online: Single-Cell Allele-Specific Gene Expression Analysis.

Dong, Meichen / Jiang, Yuchao

Methods in molecular biology (Clifton, N.J.)

2019 Volume 1935, Page(s) 155–174

Abstract: Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average gene expression across cells. Single-cell RNA sequencing (scRNA-seq) allows the comparison of expression distribution between the two alleles of a diploid ... ...

Abstract	Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average gene expression across cells. Single-cell RNA sequencing (scRNA-seq) allows the comparison of expression distribution between the two alleles of a diploid organism, and characterization of allele-specific bursting. Here we describe SCALE, a bioinformatic and statistical framework for allele-specific gene expression analysis by scRNA-seq. SCALE estimates genome-wide bursting kinetics at the allelic level while accounting for technical bias and other complicating factors such as cell size. SCALE detects genes with significantly different bursting kinetics between the two alleles, as well as genes where the two alleles exhibit non-independent bursting processes. Here, we illustrate SCALE on a mouse blastocyst single-cell dataset with step-by-step demonstration from the upstream bioinformatic processing to the downstream biological interpretation of SCALE's output.
MeSH term(s)	Algorithms ; Alleles ; Animals ; Blastocyst/physiology ; Computational Biology/methods ; Diploidy ; Gene Expression/genetics ; Gene Expression Profiling/methods ; High-Throughput Nucleotide Sequencing/methods ; Mice ; RNA/genetics ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Software
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2019-04-22
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-4939-9057-3_11
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Multi-scale Logo detection algorithm based on convolutional neural network

JIANG Yuchao, JI Lixin, GAO Chao, LI Shaomei

网络与信息安全学报, Vol 6, Iss 2, Pp 116-

2020 Volume 124

Abstract: Aiming at the requirements for multi-scale Logo detection in natural scene images, a multi-scale Logo detection algorithm based on convolutional neural network was proposed. The algorithm was based on the realization of two-stage object detection. By ... ...

Abstract	Aiming at the requirements for multi-scale Logo detection in natural scene images, a multi-scale Logo detection algorithm based on convolutional neural network was proposed. The algorithm was based on the realization of two-stage object detection. By constructing feature pyramids and adopting layer-by-layer prediction, multi-scale region proposals were generated. The multi-layer feature maps in convolutional neural networks were fused to enhance the feature representation. The experimental results on the FlickrLogos-32 dataset show that compared with the baseline, the proposed algorithm can improve the recall rate of region proposals, and can improve the performance of small Logo detection while ensuring the accuracy of large and middle Logo, proving the superiority of the proposed algorithm.
Keywords	logo detection ; convolutional neural network ; multi-scale ; region proposal network ; feature fusion ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2020-04-01T00:00:00Z
Publisher	POSTS&TELECOM PRESS Co., LTD
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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