ORCID Profile
0000-0003-2331-7011
Current Organisation
The University of Hong Kong
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Epigenetics (incl. Genome Methylation and Epigenomics) | Genetics
Publisher: Springer Science and Business Media LLC
Date: 24-06-2021
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 06-2023
DOI: 10.1161/HYPERTENSIONAHA.122.20752
Abstract: Sex differences in the pathogenesis of hypertension exist. While gut microbiota (GM) has been associated with hypertension, it is unclear whether there are sex-linked differences in the association between GM and hypertension. We conducted a cross-sectional study to investigate the sex differences in associations between GM characterized by shotgun sequencing, GM-derived short-chain fatty acids, and 24-hour ambulatory blood pressure in 241 Hong Kong Chinese (113 men and 128 women mean age, 54±6 years). The hypertensive group was associated with GM alterations however, significant differences in β- ersity and GM composition in hypertensive versus normotensive groups were only observed in women and not in men under various statistical models adjusting for the following covariates: age, sex, body mass index, sodium intake estimated by spot urine analysis, blood glucose, triglycerides, low- and high-density lipoprotein cholesterol, smoking, menopause, and fatty liver status. Specifically, Ruminococcus gnavus , Clostridium bolteae , and Bacteroides ovatus were significantly more abundant in the hypertensive women, whereas Dorea formicigenerans was more abundant in the normotensive women. No bacterial species were found to be significantly associated with hypertension in men. Furthermore, total plasma short-chain fatty acids and propionic acid were independent predictors of systolic and diastolic blood pressure in women but not men. GM dysregulation was strongly associated with 24-hour ambulatory blood pressure in women but not men, which may be mediated through propionic acid. Our work suggests that sex differences may be an important consideration while assessing the role of GM in the development and treatment of hypertension.
Publisher: Wiley
Date: 08-06-2010
Publisher: World Scientific Pub Co Pte Lt
Date: 02-2020
Publisher: Oxford University Press (OUP)
Date: 19-09-2017
DOI: 10.1093/NAR/GKX837
Publisher: Auckland University of Technology (AUT) Library
Date: 04-05-2016
DOI: 10.24135/TEACHERSWORK.V12I2.181
Abstract: Innovative Learning Environments (ILE) with their origins in OECD literature, propose to revolutionise Education as we know it. ILEs draw on a large body of literature: constructivist learning theory distributed leadership personalised 21st century learning blended learning (digital) and, future focused Education. Despite an increasing body of research in the area, there appears to be confusion around the concept of ILEs in Aotearoa/New Zealand Schools. This article reports on survey research with 126 questionnaire respondents. These principals and teachers, drawn from a random s le of New Zealand schools, commented on the implications of ILEs for teaching and learning in their contexts. This article explores the theoretical framework that educators apply to this concept. Four themes emerged from the responses: lack of clarity the significance of material spaces pedagogical implications and, the politics around ILEs. The authors pose the question: are ILEs just another neoliberal ambush on Education or opportunity to innovate the fundamentals of schooling?
Publisher: Springer Science and Business Media LLC
Date: 25-01-2019
Publisher: Springer Science and Business Media LLC
Date: 12-2014
Publisher: Cold Spring Harbor Laboratory
Date: 05-08-2016
DOI: 10.1101/068049
Abstract: The central task of a genome browser is to enable easy visual exploration of large genomic data to gain biological insight. Most existing genome browsers were designed for data exploration by in idual users, while a few allow some limited forms of collaboration among multiple users, such as file sharing and wiki-style collaborative editing of gene annotations. Our work’s premise is that allowing sharing of genome browser views instantaneously in real-time enables the exchange of ideas and insight in a collaborative project, thus harnessing the wisdom of the crowd. PBrowse is a parallel-access real-time collaborative web-based genome browser that provides both an integrated, real-time collaborative platform and a comprehensive file sharing system. PBrowse also allows real-time track comment and has integrated group chat to facilitate interactive discussion among multiple users. Through the Distributed Annotation Server protocol, PBrowse can easily access a wide range of publicly available genomic data, such as the ENCODE data sets. We argue that PBrowse, with the re-designed user management, data management and novel collaborative layer based on Biodalliance, represents a paradigm shift from seeing genome browser merely as a tool of data visualisation to a tool that enables real-time human-human interaction and knowledge exchange in a collaborative setting. PBrowse is available at pbrowse.victorchang.edu.au , and its source code is available via the open source BSD 3 license at github.com/VCCRI/PBrowse . j.ho@victorchang.edu.au Supplementary video demonstrating collaborative feature of pbrowse is available in atch?v=ROvKXZoXiIc .
Publisher: Public Library of Science (PLoS)
Date: 12-06-2012
Publisher: Springer Science and Business Media LLC
Date: 22-07-2019
DOI: 10.1038/S41598-019-47022-W
Abstract: We have previously reported a subpopulation of mesenchymal stromal cells (MSCs) within the platelet-derived growth factor receptor-alpha (PDGFRα)/CD90 co-expressing cardiac interstitial and adventitial cell fraction. Here we further characterise PDGFRα/CD90-expressing cardiac MSCs (PDGFRα + cMSCs) and use human telomerase reverse transcriptase (hTERT) over-expression to increase cMSCs ability to repair the heart after induced myocardial infarction. hTERT over-expression in PDGFRα + cardiac MSCs (hTERT + PDGFRα + cMSCs) modulates cell differentiation, proliferation, survival and angiogenesis related genes. In vivo , transplantation of hTERT + PDGFRα + cMSCs in athymic rats significantly increased left ventricular function, reduced scar size, increased angiogenesis and proliferation of both cardiomyocyte and non-myocyte cell fractions four weeks after myocardial infarction. In contrast, transplantation of mutant hTERT + PDGFRα + cMSCs (which generate catalytically-inactive telomerase) failed to replicate this cardiac functional improvement, indicating a telomerase-dependent mechanism. There was no hTERT + PDGFRα + cMSCs engraftment 14 days after transplantation indicating functional improvement occurred by paracrine mechanisms. Mass spectrometry on hTERT + PDGFRα + cMSCs conditioned media showed increased proteins associated with matrix modulation, angiogenesis, cell proliferation/survival/adhesion and innate immunity function. Our study shows that hTERT can activate pro-regenerative signalling within PDGFRα + cMSCs and enhance cardiac repair after myocardial infarction. An increased understanding of hTERT’s role in mesenchymal stromal cells from various organs will favourably impact clinical regenerative and anti-cancer therapies.
Publisher: Springer Science and Business Media LLC
Date: 23-12-2011
Publisher: Springer Science and Business Media LLC
Date: 21-09-2022
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 03-2018
DOI: 10.1161/CIRCGEN.117.001978
Abstract: Congenital heart disease (CHD)—structural abnormalities of the heart that arise during embryonic development—is the most common inborn malformation, affecting ≤1% of the population. However, currently, only a minority of cases can be explained by genetic abnormalities. The goal of this study was to identify disease-causal genetic variants in 30 families affected by CHD. Whole-exome sequencing was performed with the DNA of multiple family members. We utilized a 2-tiered whole-exome variant screening and interpretation procedure. First, we manually curated a high-confidence list of 90 genes known to cause CHD in humans, identified predicted damaging variants in genes on this list, and rated their pathogenicity using American College of Medical Genetics and Genomics-Association for Molecular Pathology guidelines. In 3 families (10%), we found pathogenic variants in known CHD genes TBX5 , TFAP2B , and PTPN11 , explaining the cardiac lesions. Second, exomes were comprehensively analyzed to identify additional predicted damaging variants that segregate with disease in CHD candidate genes. In 10 additional families (33%), likely disease-causal variants were uncovered in PBX1 , CNOT1 , ZFP36L2 , TEK , USP34 , UPF2 , KDM5A , KMT2C , TIE1 , TEAD2 , and FLT4 . The pathogenesis of CHD could be explained using our high-confidence CHD gene list for variant filtering in a subset of cases. Furthermore, our unbiased screening procedure of family exomes implicates additional genes and variants in the pathogenesis of CHD, which suggest themselves for functional validation. This 2-tiered approach provides a means of (1) identifying clinically actionable variants and (2) identifying additional disease-causal genes, both of which are essential for improving the molecular diagnosis of CHD.
Publisher: Springer Science and Business Media LLC
Date: 05-2018
Publisher: Springer Science and Business Media LLC
Date: 02-2022
Publisher: Springer Science and Business Media LLC
Date: 08-07-2020
DOI: 10.1186/S13059-020-02071-7
Abstract: High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3 ′ UTR shortening in cardiac fibroblasts. Sierra is available at github.com/VCCRI/Sierra .
Publisher: Springer Science and Business Media LLC
Date: 25-04-2022
DOI: 10.1038/S41467-022-29874-5
Abstract: The genome-editing Cas9 protein uses multiple amino-acid residues to bind the target DNA. Considering only the residues in proximity to the target DNA as potential sites to optimise Cas9’s activity, the number of combinatorial variants to screen through is too massive for a wet-lab experiment. Here we generate and cross-validate ten in silico and experimental datasets of multi-domain combinatorial mutagenesis libraries for Cas9 engineering, and demonstrate that a machine learning-coupled engineering approach reduces the experimental screening burden by as high as 95% while enriching top-performing variants by ∼7.5-fold in comparison to the null model. Using this approach and followed by structure-guided engineering, we identify the N888R/A889Q variant conferring increased editing activity on the protospacer adjacent motif-relaxed KKH variant of Cas9 nuclease from Staphylococcus aureus (KKH-SaCas9) and its derived base editor in human cells. Our work validates a readily applicable workflow to enable resource-efficient high-throughput engineering of genome editor’s activity.
Publisher: MDPI AG
Date: 29-04-2021
Abstract: Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (p 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for in idualized long-term survival.
Publisher: Springer Science and Business Media LLC
Date: 12-08-2022
Publisher: Cold Spring Harbor Laboratory
Date: 16-12-2022
DOI: 10.1101/2022.12.14.520176
Abstract: PD-L1 (programmed death-ligand 1) serves as a pivotal immune checkpoint in both the innate and adaptive immune systems. PD-L1 is expressed in macrophages in response to interferon-gamma (IFNγ). We examined whether PD-L1 might regulate macrophage development. We established PD-L1 -/- human pluripotent stem cells, differentiated them into macrophages, and observed a 60% reduction of CD11B + CD45 + macrophages in PD-L1 -/- , orthogonally verified with PD-L1 inhibitor BMS-1166 reduced macrophages to the same fold. Single-cell RNA sequencing further confirmed the 60% reduction of macrophages as well as the down-regulation of macrophage-defining transcription factors SPI1, KLF6 , and MAFB . Further, PD-L1 -/- macrophages reduced the level of inflammatory signals such as NFκB, TNF, and chemokines CXCL and CCL families. Whilst anti-inflammatory TGF-β was upregulated. Finally, we identified that PD-L1 -/- macrophages significantly down-regulated interferon-stimulated genes (ISGs) despite IFNγ in differentiation media. Mechanistically, PD-L1 -/- macrophages reduced IFNGR1 expression explaining that cells could not respond to IFNγ. These data suggest that PD-L1 regulates inflammatory macrophage development by maintaining the IFNγ signal.
Publisher: Springer Science and Business Media LLC
Date: 2009
Publisher: Cold Spring Harbor Laboratory
Date: 29-03-2021
DOI: 10.1101/2021.03.27.437331
Abstract: Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available via ingle-cell-genetics/MQuad .
Publisher: Cold Spring Harbor Laboratory
Date: 04-2013
Abstract: Histone modifications are now well-established mediators of transcriptional programs that distinguish cell states. However, the kinetics of histone modification and their role in mediating rapid, signal-responsive gene expression changes has been little studied on a genome-wide scale. Vascular endothelial growth factor A (VEGFA), a major regulator of angiogenesis, triggers changes in transcriptional activity of human umbilical vein endothelial cells (HUVECs). Here, we used chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) to measure genome-wide changes in histone H3 acetylation at lysine 27 (H3K27ac), a marker of active enhancers, in unstimulated HUVECs and HUVECs stimulated with VEGFA for 1, 4, and 12 h. We show that sites with the greatest H3K27ac change upon stimulation were associated tightly with EP300, a histone acetyltransferase. Using the variation of H3K27ac as a novel epigenetic signature, we identified transcriptional regulatory elements that are functionally linked to angiogenesis, participate in rapid VEGFA-stimulated changes in chromatin conformation, and mediate VEGFA-induced transcriptional responses. Dynamic H3K27ac deposition and associated changes in chromatin conformation required EP300 activity instead of altered nucleosome occupancy or changes in DNase I hypersensitivity. EP300 activity was also required for a subset of dynamic H3K27ac sites to loop into proximity of promoters. Our study identified thousands of endothelial, VEGFA-responsive enhancers, demonstrating that an epigenetic signature based on the variation of a chromatin feature is a productive approach to define signal-responsive genomic elements. Further, our study implicates global epigenetic modifications in rapid, signal-responsive transcriptional regulation.
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 2018
Publisher: American Medical Association (AMA)
Date: 10-2021
Publisher: Springer Science and Business Media LLC
Date: 21-01-2019
Publisher: Cold Spring Harbor Laboratory
Date: 17-08-2018
DOI: 10.1101/394189
Abstract: Flow cytometry is a popular technology for quantitative single-cell profiling of cell surface markers. It enables expression measurement of tens of cell surface protein markers in millions of single cells. It is a powerful tool for discovering cell sub-populations and quantifying cell population heterogeneity. Traditionally, scientists use manual gating to identify cell types, but the process is subjective and is not effective for large multidimensional data. Many clustering algorithms have been developed to analyse these data but most of them are not scalable to very large data sets with more than ten million cells. Here, we present a new clustering algorithm that combines the advantages of density-based clustering algorithm DBSCAN with the scalability of grid-based clustering. This new clustering algorithm is implemented in python as an open source package, FlowGrid. FlowGrid is memory efficient and scales linearly with respect to the number of cells. We have evaluated the performance of FlowGrid against other state-of-the-art clustering programs and found that FlowGrid produces similar clustering results but with substantially less time. For ex le, FlowGrid is able to complete a clustering task on a data set of 23.6 million cells in less than 12 seconds, while other algorithms take more than 500 seconds or get into error. FlowGrid is an ultrafast clustering algorithm for large single-cell flow cy-tometry data. The source code is available at github.com/VCCRI/FlowGrid .
Publisher: Elsevier BV
Date: 06-2019
DOI: 10.1016/J.COMPBIOLCHEM.2019.03.014
Abstract: There exists over 2.5 million publicly available gene expression s les across 101,000 data series in NCBI's Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO's free text metadata to annotate the experimental type and s le type, this database remains difficult to harness computationally without significant manual intervention. In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control s les and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network. GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at github.com/VCCRI/GEOracle.
Publisher: Springer Science and Business Media LLC
Date: 30-07-2017
Publisher: Elsevier BV
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 09-07-2021
DOI: 10.1038/S41598-021-93719-2
Abstract: Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9–95.8% Sensitivity: 55.5–77.8% Specificity: 91.5–98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.
Publisher: BMJ
Date: 29-09-2022
DOI: 10.1136/GUTJNL-2021-324321
Abstract: Hepatocellular carcinoma (HCC) has high intratumoral heterogeneity, which contributes to therapeutic resistance and tumour recurrence. We previously identified Prominin-1 (PROM1)/CD133 as an important liver cancer stem cell (CSC) marker in human HCC. The aim of this study was to investigate the heterogeneity and properties of Prom1+ cells in HCC in intact mouse models. We established two mouse models representing chronic fibrotic HCC and rapid steatosis-related HCC. We performed lineage tracing post-HCC induction using Prom1 Prom1 in HCC tumours marks proliferative tumour-propagating cells with CSC-like properties. Lineage tracing demonstrated that these cells display clonal expansion in situ in primary tumours. Labelled Prom1+ cells exhibit increasing tumourigenicity in 3D culture and allotransplantation, as well as potential to form cancers of differential lineages on transplantation. Depletion of Prom1+ cells impedes tumour growth and reduces malignant cancer hallmarks in both HCC models. scRNA-seq analysis highlighted the heterogeneity of Prom1+ HCC cells, which follow a trajectory to the dedifferentiated status with high proliferation and stem cells traits. Conserved gene signature of Prom1 linage predicts poor prognosis in human HCC. The activated oxidant detoxification underlies the protective mechanism of dedifferentiated transition and lineage propagation. Our study combines in vivo lineage tracing and scRNA-seq to reveal the heterogeneity and dynamics of Prom1+ HCC cells, providing insights into the mechanistic role of malignant CSC-like cells in HCC progression.
Publisher: Elsevier BV
Date: 2021
Publisher: Informa UK Limited
Date: 12-01-2021
Publisher: Cold Spring Harbor Laboratory
Date: 16-06-2017
DOI: 10.1101/150896
Abstract: There exists over 2.5 million publicly available gene expression s les across 101,000 data series in NCBI’s Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO’s free text metadata to annotate the experimental type and s le type, this database remains difficult to harness computationally without significant manual intervention. In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control s les and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network. GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at github.com/VCCRI/GEOracle .
Publisher: Springer Science and Business Media LLC
Date: 2009
Publisher: American Chemical Society (ACS)
Date: 03-2007
Publisher: Springer Science and Business Media LLC
Date: 26-06-2023
DOI: 10.1186/S13059-023-02980-3
Abstract: Differential composition analysis — the identification of cell types that have statistically significant changes in abundance between multiple experimental conditions — is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of flexible experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintains high sensitivity and specificity compared to state-of-the-art methods.
Publisher: Wiley
Date: 11-09-2022
DOI: 10.1002/RCR2.1039
Abstract: Blind NGT insertion can lead to tube misplacement into the respiratory tract. This can lead to multiple pulmonary complications including lung injury, pneumothorax, pneumonia, empyema and diaphragmatic injury. We present the case of an 80 year‐old female who required an NGT insertion for severe oropharyngeal dysphagia from an acute stroke. Her admission was complicated by multiple pneumothoraces from repeated insertions, as well as the unusual complication of a recurrent nasogastric feeding‐related empyema requiring multiple intercostal chest drain insertions for drainage. This case highlights the importance of careful NGT insertion and placement with imaging confirmation before use.
Publisher: Oxford University Press (OUP)
Date: 14-02-2017
DOI: 10.1093/BIOINFORMATICS/BTX092
Abstract: DNA binding proteins such as chromatin remodellers, transcription factors (TFs), histone modifiers and co-factors often bind cooperatively to activate or repress their target genes in a cell type-specific manner. Nonetheless, the precise role of cooperative binding in defining cell-type identity is still largely uncharacterized. Here, we collected and analyzed 214 public datasets representing chromatin immunoprecipitation followed by sequencing (ChIP-Seq) of 104 DNA binding proteins in embryonic stem cell (ESC) lines. We classified their binding sites into those proximal to gene promoters and those in distal regions, and developed a web resource called Proximal And Distal (PAD) clustering to identify their co-localization at these respective regions. Using this extensive dataset, we discovered an extensive co-localization of BRG1 and CHD7 at distal but not proximal regions. The comparison of co-localization sites to those bound by either BRG1 or CHD7 alone showed an enrichment of ESC master TFs binding and active chromatin architecture at co-localization sites. Most notably, our analysis reveals the co-dependency of BRG1 and CHD7 at distal regions on regulating expression of their common target genes in ESC. This work sheds light on cooperative binding of TF binding proteins in regulating gene expression in ESC, and demonstrates the utility of integrative analysis of a manually curated compendium of genome-wide protein binding profiles in our online resource PAD. PAD is freely available at pad.victorchang.edu.au/ and its source code is available via an open source GPL 3.0 license at github.com/VCCRI/PAD/ Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 19-01-2009
Abstract: Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguide downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered. We propose to use a novel software testing technique, metamorphic testing (MT), to test a range of bioinformatics programs. Instead of requiring a mechanism to verify whether an in idual test output is correct, the MT technique verifies whether a pair of test outputs conform to a set of domain specific properties, called metamorphic relations (MRs), thus greatly increases the number and variety of test cases that can be applied. To demonstrate how MT is used in practice, we applied MT to test two open-source bioinformatics programs, namely GNLab and SeqMap. In particular we show that MT is simple to implement, and is effective in detecting faults in a real-life program and some artificially fault-seeded programs. Further, we discuss how MT can be applied to test programs from various domains of bioinformatics. This paper describes the application of a simple, effective and automated technique to systematically test a range of bioinformatics programs. We show how MT can be implemented in practice through two real-life case studies. Since many bioinformatics programs, particularly those for large scale simulation and data analysis, are hard to test systematically, their developers may benefit from using MT as part of the testing strategy. Therefore our work represents a significant step towards software reliability in bioinformatics.
Publisher: Cold Spring Harbor Laboratory
Date: 11-02-2021
DOI: 10.1101/2021.02.10.430705
Abstract: Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the ersity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g. cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.
Publisher: Oxford University Press (OUP)
Date: 09-06-2006
DOI: 10.1093/BIOINFORMATICS/BTL283
Abstract: Summary: Most phylogenetic methods assume that the sequences evolved under homogeneous, stationary and reversible conditions. Compositional heterogeneity in data intended for studies of phylogeny suggests that the data did not evolve under these conditions. SeqVis, a Java application for analysis of nucleotide content, reads sequence alignments in several formats and plots the nucleotide content in a tetrahedron. Once plotted, outliers can be identified, thus allowing for decisions on the applicability of the data for phylogenetic analysis. Availability: Contact: lars.jermiin@usyd.edu.au
Publisher: Springer Science and Business Media LLC
Date: 08-03-2022
DOI: 10.1038/S41467-022-28845-0
Abstract: Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, identifying informative mtDNA variants in noisy and sparse single-cell sequencing data is still challenging with few computation methods available. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA, DNA or ATAC sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrate its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution.
Publisher: The Company of Biologists
Date: 2015
DOI: 10.1242/DEV.125591
Abstract: The developing lens constitutes a powerful system for investigating the molecular basis of inductive tissue interactions and for studying cataract, the leading cause of blindness. The formation of tightly controlled cell-cell adhesions and cell-matrix junctions between lens epithelial (LE) cells, between lens fiber (LF) cells, and between these two cell populations enables the vertebrate lens to adopt its highly ordered structure and to acquire its optical transparency. Adhesion molecules are thought to maintain this ordered structure, but little is known about their identity or molecular interactions. Cysteine-rich motor neuron 1 (CRIM1), a type I transmembrane protein, is strongly expressed in the developing lens and its mutation causes ocular disease in both mice and humans. However, how Crim1 regulates lens morphogenesis is not understood. We identified a novel ENU-induced hypomorphic allele of Crim1, Crim1glcr11, which in the homozygous state causes cataract and microphthalmia. Using this allele and two other Crim1 mutant alleles, Crim1null and Crim1cko, we show that the lens defects in Crim1 mutants originate from defective LE cell polarity, proliferation and cell adhesion. The Crim1 adhesive function is likely required for interactions both between LE cells and between LE and LF cells. We further show that Crim1 acts in LE cells where it co-localizes with and regulates the levels of active β1 integrin and of phosphorylated FAK and ERK (pFAK, pERK). Lastly, the RGD and transmembrane motifs of Crim1 are required for the regulation of pFAK. These results identify an important function for Crim1 in the regulation of integrin- and FAK-mediated LE cell adhesion during lens development.
Publisher: Cold Spring Harbor Laboratory
Date: 14-10-2022
DOI: 10.1101/2022.10.12.511862
Abstract: This study develops a versatile cell multiplexing and data analysis platform to gain knowledge gain into mechanisms of cell differentiation. We engineer a cell barcoding system in human cells enabling multiplexed single-cell RNA sequencing for high throughput perturbation of customisable and erse experimental conditions. This is coupled with a new computational analysis pipeline that overcomes the limitations of conventional algorithms by using an unsupervised, genome-wide, orthogonal biological reference point to reveal the cell ersity and regulatory networks in the input scRNA-seq data set. We implement this pipeline by engineering transcribed barcodes into induced pluripotent stem cells and multiplex 62 independent experimental conditions comprising eight differentiation time points and nine developmental signalling perturbations in duplicates. We identify and deconstruct the temporal, signalling, and gene regulatory imperatives of iPSC differentiation into cell types of ectoderm, mesoderm, and endoderm lineages. This study provides a cellular and computational pipeline to study cell differentiation applicable to studies in developmental biology, drug discovery, and disease modelling.
Publisher: Springer Science and Business Media LLC
Date: 20-09-2021
DOI: 10.1038/S41467-021-25773-3
Abstract: Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the ersity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.
Publisher: Cold Spring Harbor Laboratory
Date: 19-02-2018
DOI: 10.1101/267880
Abstract: Cell type identification from an unknown s le can often be done by comparing its gene expression profile against a gene expression database containing profiles of a large number of cell-types. This type of compendium-based cell-type identification strategy is particularly successful for human and mouse s les because a large volume of data exists for these organisms. However, such rich data repositories often do not exist for most non-model organisms. This makes transcriptome-based s le classification in these species challenging. We propose to overcome this challenge by performing a cross-species compendium comparison. The key is to utilise a recently published cross-species gene set analysis (XGSA) framework to correct for biases that may arise due to potentially complex homologous gene mapping between two species. The framework is implemented as an open source R package called C3. We have evaluated the performance of C3 using a variety of public data in NCBI Gene Expression Omnibus. We also compared the functionality and performance of C3 against some similar gene expression profile matching tools. Our evaluation shows that C3 is a simple and effective method for cell type identification. C3 is available at github.com/VCCRI/C3 .
Publisher: Springer Science and Business Media LLC
Date: 13-08-2015
Publisher: Wiley
Date: 02-2010
Abstract: Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has heterogeneous clinical manifestation with erse patterns of organ involvement, autoantibody profiles and varying degrees of severity of disease. Research and clinical experience indicate that different subtypes of SLE patients will likely benefit from more tailored treatment regimes, but we currently lack a fast and objective test with high enough sensitivity to enable us to perform such sub-grouping for clinical use. In this article, we review how proteomic technologies could be used as such an objective test. In particular, we extensively review many leukocyte surface markers that are known to have an association with the pathogenesis of SLE, and we discuss how these markers can be used in the further development of a novel SLE-specific antibody leukocyte capture microarray. In addition, we review some bioinformatics challenges and current methods for using the data generated by these cell-capture microarrays in clinical use. In a broader context, we hope our experience in developing a disease specific cell-capture microarray for clinical application can be a guide to other proteomic practitioners who intend to extend their technologies to develop clinical diagnostic and prognostic tests for complex diseases.
Publisher: Frontiers Media SA
Date: 23-09-2022
DOI: 10.3389/FMEDT.2022.905074
Abstract: The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%–100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.
Publisher: Springer Science and Business Media LLC
Date: 12-2019
DOI: 10.1186/S12864-019-6341-6
Abstract: Read alignment and transcript assembly are the core of RNA-seq analysis for transcript isoform discovery. Nonetheless, current tools are not designed to be scalable for analysis of full-length bulk or single cell RNA-seq (scRNA-seq) data. The previous version of our cloud-based tool Falco only focuses on RNA-seq read counting, but does not allow for more flexible steps such as alignment and read assembly. The Falco framework can harness the parallel and distributed computing environment in modern cloud platforms to accelerate read alignment and transcript assembly of full-length bulk RNA-seq and scRNA-seq data. There are two new modes in Falco: alignment-only and transcript assembly. In the alignment-only mode, Falco can speed up the alignment process by 2.5–16.4x based on two public scRNA-seq datasets when compared to alignment on a highly optimised standalone computer. Furthermore, it also provides a 10x average speed-up compared to alignment using published cloud-enabled tool for read alignment, Rail-RNA. In the transcript assembly mode, Falco can speed up the transcript assembly process by 1.7–16.5x compared to performing transcript assembly on a highly optimised computer. Falco is a significantly updated open source big data processing framework that enables scalable and accelerated alignment and assembly of full-length scRNA-seq data on the cloud. The source code can be found at github.com/VCCRI/Falco .
Publisher: Springer Science and Business Media LLC
Date: 28-03-2017
Publisher: Cold Spring Harbor Laboratory
Date: 16-05-2018
DOI: 10.1101/324004
Abstract: A person’s heart rate profile, which consists of resting heart rate, increase of heart rate upon exercise and recovery of heart rate after exercise, is traditionally measured by electrocardiography during a controlled exercise stress test. A heart rate profile is a useful clinical tool to identify in iduals at risk of sudden death and other cardiovascular conditions. Nonetheless, conducting such exercise stress tests routinely is often inconvenient and logistically challenging for patients. The widespread availability of affordable wearable fitness trackers, such as Fitbit and Apple Watch, provides an exciting new means to collect longitudinal heart rate and physical activity data. We reason that by combining the heart rate and physical activity data from these devices, we can construct a person’s heart rate profile. Here we present an open source R package CardiacProfileR for extraction, analysis and visualisation of heart rate dynamics during physical activities from data generated from common wearable heart rate monitors. This package represents a step towards quantitative deep phenotyping in humans. CardiacProfileR is available via an MIT license at github.com/VCCRI/CardiacProfileR .
Publisher: Cold Spring Harbor Laboratory
Date: 04-05-2017
DOI: 10.1101/134130
Abstract: Single cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence ersity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined. We studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths ( bp), but these featured higher technical variability compared to profiles from longer reads. TCRαβ were detected in 1,027 cells (79%), with a success rate between 81% and 100% for datasets with at least 250,000 (PE) reads of length bp. Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.
Publisher: Wiley
Date: 22-03-2013
Publisher: Oxford University Press (OUP)
Date: 05-03-2014
DOI: 10.1093/NAR/GKU178
Publisher: Cold Spring Harbor Laboratory
Date: 18-03-2020
DOI: 10.1101/2020.03.16.993204
Abstract: Predicting where transcription factors bind in the genome from their in-vitro DNA binding affinity is confounded by the large number of possible interactions with nearby transcription factors. To characterise the binding logic for the Wnt effector transcription factor Tcf7l2, we have developed a high-throughput screening platform in which thousands of 99-bp synthesised DNA sequences are inserted into a specific genomic locus through CRISPR/Cas9-based homology-directed repair, followed by measurement of Tcf7l2 binding by DamID. Using this platform at two genomic loci in mouse embryonic stem cells, we show that while the binding of Tcf7l2 closely follows the in-vitro motif binding strength and is influenced by local chromatin accessibility, it is also strongly affected by the surrounding 99-bp of sequence. The presence of nearby Oct4 and Klf4 motifs promote Tcf7l2 binding, particularly in the adjacent ~20 to 50-bp nearby and oscillating with a 10.8-bp phasing relative to these cofactor motifs, which matches the turn of a DNA helix. This novel high-throughput DamID assay provides a powerful platform to determine local DNA sequence grammars that causally influence transcription factor binding in controlled genomic contexts.
Publisher: Oxford University Press (OUP)
Date: 29-08-2016
DOI: 10.1093/BIOINFORMATICS/BTW428
Abstract: Gene set analysis is a powerful tool for determining whether an experimentally derived set of genes is statistically significantly enriched for genes in other pre-defined gene sets, such as known pathways, gene ontology terms, or other experimentally derived gene sets. Current gene set analysis methods do not facilitate comparing gene sets across different organisms as they do not explicitly deal with homology mapping between species. There lacks a systematic investigation about the effect of complex gene homology on cross-species gene set analysis. In this study, we show that not accounting for the complex homology structure when comparing gene sets in two species can lead to false positive discoveries, especially when comparing gene sets that have complex gene homology relationships. To overcome this bias, we propose a straightforward statistical approach, called XGSA, that explicitly takes the cross-species homology mapping into consideration when doing gene set analysis. Simulation experiments confirm that XGSA can avoid false positive discoveries, while maintaining good statistical power compared to other ad hoc approaches for cross-species gene set analysis. We further demonstrate the effectiveness of XGSA with two real-life case studies that aim to discover conserved or species-specific molecular pathways involved in social challenge and vertebrate appendage regeneration. The R source code for XGSA is available under a GNU General Public License at github.com/VCCRI/XGSA jho@victorchang.edu.au
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Frontiers Media SA
Date: 15-09-2022
DOI: 10.3389/FNINS.2022.1000304
Abstract: Many in iduals experience hearing problems that are hidden under a normal audiogram. This not only impacts on in idual sufferers, but also on clinicians who can offer little in the way of support. Animal studies using invasive methodologies have developed solid evidence for a range of pathologies underlying this hidden hearing loss (HHL), including cochlear synaptopathy, auditory nerve demyelination, elevated central gain, and neural mal-adaptation. Despite progress in pre-clinical models, evidence supporting the existence of HHL in humans remains inconclusive, and clinicians lack any non-invasive biomarkers sensitive to HHL, as well as a standardized protocol to manage hearing problems in the absence of elevated hearing thresholds. Here, we review animal models of HHL as well as the ongoing research for tools with which to diagnose and manage hearing difficulties associated with HHL. We also discuss new research opportunities facilitated by recent methodological tools that may overcome a series of barriers that have h ered meaningful progress in diagnosing and treating of HHL.
Publisher: ACM
Date: 14-05-2016
Publisher: Oxford University Press (OUP)
Date: 21-07-2021
DOI: 10.1093/BIOINFORMATICS/BTAB521
Abstract: Scalable clustering algorithms are needed to analyze millions of cells in single cell RNA-seq (scRNA-seq) data. Here, we present an open source python package called FlowGrid that can integrate into the Scanpy workflow to perform clustering on very large scRNA-seq datasets. FlowGrid implements a fast density-based clustering algorithm originally designed for flow cytometry data analysis. We introduce a new automated parameter tuning procedure, and show that FlowGrid can achieve comparable clustering accuracy as state-of-the-art clustering algorithms but at a substantially reduced run time for very large single cell RNA-seq datasets. For ex le, FlowGrid can complete a one-hour clustering task for one million cells in about five min. olab-hku/FlowGrid. Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 22-01-2009
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D1LC00638J
Abstract: This paper reports the numerical, mathematical, and experimental studies of flow delay through wax valves surrounded by PDMS walls on paper microfluidics.
Publisher: SAGE Publications
Date: 31-07-2021
Abstract: Changes in EEG when moving from an eyes-closed to an eyes-open resting condition result from bottom-up sensory processing and have been referred to as activation. In children, activation is characterized by a global reduction in alpha, frontally present reductions for delta and theta, and a frontal increase for beta. The present study aimed to replicate frontal EEG activation effects using single-channel, dry-sensor EEG, and to extend current understanding by examining developmental change in children. Frontal EEG was recorded using a single-channel, dry-sensor EEG device while 182 children aged 7 to 12 years completed eyes-closed resting (EC), eyes-open resting (EO), and focus (FO) tasks. Results indicated that frontal delta, theta, and alpha power were reduced, and frontal beta power was increased, in the EO compared with the EC condition. Exploratory analysis of a form of top-down activation showed that frontal beta power was increased in the FO compared with to the EO condition, with no differences for other bands. The activation effects were robust at the in idual level. The bottom-up activation effects reduced with age for frontal delta and theta, increased for frontal alpha, with no developmental change for top-down or bottom-up frontal beta activation. These findings contribute further to validation of the single-channel, dry-sensor, frontal EEG and provide support for use in a range of medical, therapeutic, and clinical domains.
Publisher: Elsevier BV
Date: 10-2021
Publisher: Cold Spring Harbor Laboratory
Date: 11-09-2019
DOI: 10.1101/765628
Abstract: New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity. We introduce a highly scalable graph-based clustering algorithm PARC - phenotyping by accelerated refined community-partitioning – for ultralarge-scale, high-dimensional single-cell data ( 1 million cells). Using large single cell mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without sub-s ling of cells, including Phenograph, FlowSOM, and Flock, in terms of both speed and ability to robustly detect rare cell populations. For ex le, PARC can cluster a single cell data set of 1.1M cells within 13 minutes, compared to hours to the next fastest graph-clustering algorithm, Phenograph. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis. github.com/ShobiStassen/PARC
Publisher: Springer Science and Business Media LLC
Date: 04-11-2022
DOI: 10.1186/S40168-022-01366-0
Abstract: Gut microbiota (GM) dysregulation, known as dysbiosis, has been proposed as a crucial driver of obesity associated with “Western” diet (WD) consumption. Gut dysbiosis is associated with increased gut permeability, inflammation, and insulin resistance. However, host metabolic pathways implicated in the pathophysiology of gut dysbiosis are still elusive. Exchange protein directly activated by cAMP (Epac) plays a critical role in cell-cell junction formation and insulin secretion. Here, we used homozygous Epac1-knockout (Epac1 –/– ), Epac2-knockout (Epac2 –/– ), and wild-type (WT) mice to investigate the role of Epac proteins in mediating gut dysbiosis, gut permeability, and inflammation after WD feeding. The 16S rRNA gene sequencing of fecal DNA showed that the baseline GM of Epac2 –/– , but not Epac1 –/– , mice was represented by a significantly higher Firmicutes to Bacteroidetes ratio and significant alterations in several taxa compared to WT mice, suggesting that Epac2 –/– mice had gut dysbiosis under physiological conditions. However, an 8-week WD led to a similar gut microbiome imbalance in mice regardless of genotype. While Epac1 deficiency modestly exacerbated the WD-induced GM dysbiosis, the WD-fed Epac2 –/– mice had a more significant increase in gut permeability than corresponding WT mice. After WD feeding, Epac1 –/– , but not Epac2 –/– , mice had significantly higher mRNA levels of tumor necrosis factor-alpha (TNF-α) and F4/80 in the epididymal white adipose tissue (EWAT), increased circulating lipocalin-2 protein and more severe glucose intolerance, suggesting greater inflammation and insulin resistance in WD-fed Epac1 –/– mice than corresponding WT mice. Consistently, Epac1 protein expression was significantly reduced in the EWAT of WD-fed WT and Epac2 –/– mice. Despite significantly dysregulated baseline GM and a more pronounced increase in gut permeability upon WD feeding, WD-fed Epac2 –/– mice did not exhibit more severe inflammation and glucose intolerance than corresponding WT mice. These findings suggest that the role of gut dysbiosis in mediating WD-associated obesity may be context-dependent. On the contrary, we demonstrate that deficiency of host signaling protein, Epac1, drives inflammation and glucose intolerance which are the hallmarks of WD-induced obesity.
Publisher: Springer Science and Business Media LLC
Date: 06-10-2017
DOI: 10.1038/S41598-017-12989-X
Abstract: Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence ersity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths ( bp), but these featured higher technical variability compared to profiles from longer reads. Successful TCRαβ reconstruction was achieved for 6 datasets (81% − 100%) with at least 0.25 millions (PE) reads of length bp, while it failed for datasets with bp reads. Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.
Publisher: Springer Science and Business Media LLC
Date: 10-12-2015
Publisher: IEEE
Date: 05-2017
DOI: 10.1109/MET.2017.4
Publisher: Wiley
Date: 30-12-2021
DOI: 10.1111/HEL.12871
Abstract: Short‐term antibiotics exposure is associated with alterations in microbiota and antibiotic resistance genes (ARGs) in the human gut. While antibiotics are critical in the successful eradication of Helicobacter pylori , the short‐term and long‐term impacts on the composition and quantity of antibiotics resistance genes after H. pylori eradication are unclear. This study used whole‐genome shotgun metagenomic of stool s les to characterize the gut microbiota and ARGs, before and after H. pylori eradication therapy. Forty‐four H. pylori ‐infected patients were recruited, including 21 treatment naïve patients who received clarithromycin‐based triple therapy (CLA group) and 23 patients who failed previous therapies, in which 10 received levofloxacin‐based quadruple therapy (LEVO group) and 13 received other combinations (OTHER group). Stool s les were collected at baseline (before current treatment), 6 week and 6 month after eradication therapy. At baseline, there was only a slight difference among the three groups on ARGs and gut microbiota. After eradication therapy, there was a transient but significant increase in gut ARGs 6 week post‐therapy, among which the LEVO group had the most significant ARGs alteration compared to other two groups. For treatment naïve patients, those with higher ErmF abundance were prone to fail CLA eradication and gain more ARGs after treatment. For gut microbiota, the bacteria richness decreased at 6 week and there was a significant difference in microbiota community among the three groups at 6 week. Our findings demonstrated the dynamic alterations in gut microbiota and ARGs induced by different eradication therapies, which could influence the choices of antibiotics in eradication therapy.
Publisher: SAGE Publications
Date: 17-12-2008
Abstract: Striatal dopamine (DA) release has been shown during behavioural tasks, but the relative contribution of motor, reward, and cognitive components is unclear. Dopamine release was quantified using [ 11 C]-raclopride in two studies using a triple-scan approach, comprising active task, motor control, and rest. In the first, bolus radiotracer was delivered during a sequential motor learning paradigm in the second, a spatial planning task, bolus plus constant infusion was applied. [ 11 C]-raclopride binding potentials (BP ND s) in striatal functional sub isions were compared across conditions. [ 11 C]-raclopride BP ND was significantly reduced in active task compared with rest in both the sensorimotor and associative striatum in both studies, because of differences between rest and motor control conditions. In both regions, the motor control BP ND fell between the rest and active task in the planning study, but the difference between motor control and active task conditions was not significant. No such changes were observed in the limbic striatum. Using rigorous methodology, this study validates earlier evidence that striatal DA release occurs during behavioural challenges. Increased DA release during movement was reliably detected in the sensorimotor and associative striatum, supporting use of the functional sub ision model in humans. No additional DA release was observed specific to the cognitive component of either task.
Publisher: Springer Science and Business Media LLC
Date: 17-04-2023
DOI: 10.1038/S41467-023-37975-Y
Abstract: Transient gut microbiota alterations have been reported after antibiotic therapy for Helicobacter pylori . However, alteration in the gut virome after H. pylori eradication remains uncertain. Here, we apply metagenomic sequencing to fecal s les of 44 H. pylori -infected patients at baseline, 6-week ( N = 44), and 6-month ( N = 33) after treatment. Following H. pylori eradication, we discover contraction of the gut virome ersity, separation of virome community with increased community difference, and shifting towards a higher proportion of core virus. While the gut microbiota is altered at 6-week and restored at 6-month, the virome community shows contraction till 6-month after the treatment with enhanced phage-bacteria interactions at 6-week. Multiple courses of antibiotic treatments further lead to lower virus community ersity when compared with treatment naive patients. Our results demonstrate that H. pylori eradication therapies not only result in transient alteration in gut microbiota but also significantly alter the previously less known gut virome community.
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Date: 21-03-2012
DOI: 10.1167/IOVS.11-8839
Publisher: American Medical Association (AMA)
Date: 20-10-2022
DOI: 10.1001/JAMANETWORKOPEN.2022.36278
Abstract: This cohort study assesses the incidence of emergency department (ED) visits in Hong Kong, China, for sexual abuse among youth before and during the COVID-19 pandemic.
Publisher: Elsevier BV
Date: 11-2012
Publisher: Springer Science and Business Media LLC
Date: 2018
Publisher: Oxford University Press (OUP)
Date: 13-12-2017
DOI: 10.1093/BIOINFORMATICS/BTW732
Abstract: Single-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designed to efficiently process scRNA-seq data due to their limited scalability. Here we introduce Falco, a cloud-based framework to enable paralellization of existing RNA-seq processing pipelines using big data technologies of Apache Hadoop and Apache Spark for performing massively parallel analysis of large scale transcriptomic data. Using two public scRNA-seq datasets and two popular RNA-seq alignment/feature quantification pipelines, we show that the same processing pipeline runs 2.6–145.4 times faster using Falco than running on a highly optimized standalone computer. Falco also allows users to utilize low-cost spot instances of Amazon Web Services, providing a ∼65% reduction in cost of analysis. Falco is available via a GNU General Public License at github.com/VCCRI/Falco/ Supplementary data are available at Bioinformatics online.
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11618058_44
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11618058_42
Publisher: Elsevier BV
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 12-2019
DOI: 10.1186/S12920-019-0636-Y
Abstract: The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only s les. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed. We have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood s les and 253 tumour-only s les sequenced deeply on Ion Torrent Proton platform across 330 licons. Subsequently, the concordance and performance of the three somatic variant callers were assessed. We have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error. Our results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.
Publisher: Elsevier BV
Date: 09-2011
Publisher: Springer Science and Business Media LLC
Date: 28-07-2020
Publisher: Wiley
Date: 24-09-2020
DOI: 10.1002/RCR2.670
Publisher: Springer Science and Business Media LLC
Date: 09-2012
Publisher: Springer Science and Business Media LLC
Date: 09-01-2019
Publisher: Springer Science and Business Media LLC
Date: 13-06-2019
Publisher: Frontiers Media SA
Date: 23-08-2021
DOI: 10.3389/FCELL.2021.695007
Abstract: A group of circulating microRNAs (miRNAs) have been implicated in the pathogenesis of Parkinson’s disease. However, a comprehensive study of the interactions between pathogenic miRNAs and their downstream Parkinson’s disease (PD)-related target genes has not been performed. Here, we identified the miRNA expression profiles in the plasma and circulating exosomes of Parkinson’s disease patients using next-generation RNA sequencing. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses showed that the miRNA target genes were enriched in axon guidance, neurotrophin signaling, cellular senescence, and the Transforming growth factor-β (TGF-β), mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT) and mechanistic target of rapamycin (mTOR) signaling pathways. Furthermore, a group of aberrantly expressed miRNAs were selected and further validated in in idual patient plasma, human neural stem cells (NSCs) and a rat model of PD. More importantly, the full scope of the regulatory network between these miRNAs and their PD-related gene targets in human neural stem cells was examined, and the findings revealed a similar but still varied downstream regulatory cascade involving many known PD-associated genes. Additionally, miR-23b-3p was identified as a novel direct regulator of alpha-synuclein, which is possibly the key component in PD. Our current study, for the first time, provides a glimpse into the regulatory network of pathogenic miRNAs and their PD-related gene targets in PD. Moreover, these PD-associated miRNAs may serve as biomarkers and novel therapeutic targets for PD.
Publisher: Springer Science and Business Media LLC
Date: 28-02-2011
Abstract: Chromatin immunoprecipitation (ChIP) followed by microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) allows genome-wide discovery of protein-DNA interactions such as transcription factor bindings and histone modifications. Previous reports only compared a small number of profiles, and little has been done to compare histone modification profiles generated by the two technologies or to assess the impact of input DNA libraries in ChIP-seq analysis. Here, we performed a systematic analysis of a modENCODE dataset consisting of 31 pairs of ChIP-chip/ChIP-seq profiles of the coactivator CBP, RNA polymerase II (RNA PolII), and six histone modifications across four developmental stages of Drosophila melanogaster . Both technologies produce highly reproducible profiles within each platform, ChIP-seq generally produces profiles with a better signal-to-noise ratio, and allows detection of more peaks and narrower peaks. The set of peaks identified by the two technologies can be significantly different, but the extent to which they differ varies depending on the factor and the analysis algorithm. Importantly, we found that there is a significant variation among multiple sequencing profiles of input DNA libraries and that this variation most likely arises from both differences in experimental condition and sequencing depth. We further show that using an inappropriate input DNA profile can impact the average signal profiles around genomic features and peak calling results, highlighting the importance of having high quality input DNA data for normalization in ChIP-seq analysis. Our findings highlight the biases present in each of the platforms, show the variability that can arise from both technology and analysis methods, and emphasize the importance of obtaining high quality and deeply sequenced input DNA libraries for ChIP-seq analysis.
Publisher: Cold Spring Harbor Laboratory
Date: 17-05-2018
DOI: 10.1101/324855
Abstract: We report a new smartphone-enabled virtual reality (VR) program, starmap ( vccri.github.io/starmap/ ), that enables immersive visualisation of single-cell data for hundreds of thousands of cells using a mobile-enabled web browser and low-cost VR head mount device.
Publisher: Oxford University Press (OUP)
Date: 17-12-2016
DOI: 10.1093/NAR/GKV1466
Publisher: Massachusetts Medical Society
Date: 10-08-2017
Publisher: Oxford University Press (OUP)
Date: 27-02-2015
DOI: 10.1093/BIOINFORMATICS/BTV117
Abstract: Motivation: Genome-wide mapping of chromatin states is essential for defining regulatory elements and inferring their activities in eukaryotic genomes. A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for an in idual genome. To perform a principled comparison of evolutionarily distant epigenomes, we must consider species-specific biases such as differences in genome size, strength of signal enrichment and co-occurrence patterns of histone modifications. Results: Here, we present a new Bayesian non-parametric method called hierarchically linked infinite HMM (hiHMM) to jointly infer chromatin state maps in multiple genomes (different species, cell types and developmental stages) using genome-wide histone modification data. This flexible framework provides a new way to learn a consistent definition of chromatin states across multiple genomes, thus facilitating a direct comparison among them. We demonstrate the utility of this method using synthetic data as well as multiple modENCODE ChIP-seq datasets. Conclusion: The hierarchical and Bayesian non-parametric formulation in our approach is an important extension to the current set of methodologies for comparative chromatin landscape analysis. Availability and implementation: Source codes are available at asohn/hiHMM. Chromatin data are available at ata_sets/chromatin/. Contact: peter_park@harvard.edu or juhan@snu.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 21-10-2010
Publisher: Springer Science and Business Media LLC
Date: 22-07-2021
Publisher: JMIR Publications Inc.
Date: 09-08-2022
Abstract: ealthcare avoidance in the COVID-19 Pandemic has been widely reported. Yet few studies have investigated the dynamics of hospital avoidance behaviour during pandemic waves and inferred its impact on excess non-COVID-19 death toll. o measure the impact of hospital avoidance behaviour on excess mortality using emergency department (ED) patient data from 2016 to 2021, during which Hong Kong experienced a unique COVID-19 pandemic with four distinct waves of case number surges. ur data is taken from the CDARS Hong Kong Hospital Authority administrative database, which oversees all local public hospitals and plays a prominent role in emergency care provision. To estimate excess mortality, two-stage least squares was utilised with daily tallies of ED visit and 28-day mortality. Elderly records were categorised by the residential care home for elderly status (RCHE) and comorbidities were used to explain the demographic and clinical attributes of excess 28-day mortality. ompared with the average in 2016-2019 average there was a reduction in total ED visits in 2020 of 25·4%. During the same period, the 28-day mortality of non-COVID-19 ED deaths increased by 7·82% compared with 2016-2019. The estimated total elderly excess non-COVID 28-day death by reduced ED visits throughout 2020 to 2021 is 1,958 (1,100-2,820, no time lag). The actual excess death in 2020 and 2021 are 3,143 and 4,013 respectively, with 2016-2019 average as the benchmark. Death on Arrival (DOA)/ Death before Arrival (DBA) increased by 35·1% in 2020, while non-DOA/DBA mortalities increased only by a moderate 4·65%. In both DOA/DBA and non-DOA/DBA, the increases were higher during wave periods than in non-wave periods. Moreover, non-RCHE patients saw a greater reduction in ED visit than RCHE residents across all waves by more than 10%. Most of the subset comorbidities demonstrated an annualised reduction in visit in 2020. Renal diseases and severe liver diseases saw a notable death increase. e demonstrated a statistical method to estimate hospital avoidance behaviour during a pandemic, and quantified the consequential excess 28-day mortality, with a focus on elderlies, who had high frequencies of ED visit and deaths. This study serves as an informed alert and possible investigation guideline to healthcare professionals about hospital avoidance behaviour and its consequences.
Publisher: IEEE
Date: 08-2009
DOI: 10.1109/QSIC.2009.26
Publisher: Public Library of Science (PLoS)
Date: 04-11-2014
Publisher: Oxford University Press (OUP)
Date: 22-08-2019
DOI: 10.1093/NAR/GKZ718
Abstract: Circular RNAs (circRNA) are a unique class of transcripts that can only be identified from sequence alignments spanning discordant junctions, commonly referred to as backsplice junctions (BSJ). Canonical splicing is also linked with circRNA biogenesis either from the parental transcript or internal to the circRNA, and is not fully utilized in circRNA software. Here we present Ularcirc, a software tool that integrates the visualization of both BSJ and forward splicing junctions and provides downstream analysis of selected circRNA candidates. Ularcirc utilizes the output of CIRI, circExplorer, or raw chimeric output of the STAR aligner and assembles BSJ count table to allow multi-s le analysis. We used Ularcirc to identify and characterize circRNA from public and in-house generated data sets and demonstrate how it can be used to (i) discover novel splicing patterns of parental transcripts, (ii) detect internal splicing patterns of circRNA, and (iii) reveal the complexity of BSJ formation. Furthermore, we identify circRNA that have potential open reading frames longer than their linear sequence. Finally, we detected and validated the presence of a novel class of circRNA generated from ApoA4 transcripts whose BSJ derive from multiple non-canonical splicing sites within coding exons. Ularcirc is accessed via github.com/VCCRI/Ularcirc.
Publisher: Ivyspring International Publisher
Date: 2021
DOI: 10.7150/THNO.46460
Publisher: Cold Spring Harbor Laboratory
Date: 23-03-2022
DOI: 10.1101/2022.03.21.485232
Abstract: Differential composition analysis – the identification of cell types that have statistically significantly change in abundance between multiple experimental conditions – is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of complex experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintain high sensitively and specificity compared to state-of-the-art methods.
Publisher: Springer Science and Business Media LLC
Date: 15-02-2011
Publisher: Springer Science and Business Media LLC
Date: 07-07-2020
Publisher: BMJ
Date: 25-09-2020
DOI: 10.1136/MEDETHICS-2020-106438
Abstract: Controlled Human Infection Model (CHIM) research involves the infection of otherwise healthy participants with disease often for the sake of vaccine development. The COVID-19 pandemic has emphasised the urgency of enhancing CHIM research capability and the importance of having clear ethical guidance for their conduct. The payment of CHIM participants is a controversial issue involving stakeholders across ethics, medicine and policymaking with allegations circulating suggesting exploitation, coercion and other violations of ethical principles. There are multiple approaches to payment: reimbursement, wage payment and unlimited payment. We introduce a new Payment for Risk Model, which involves paying for time, pain and inconvenience and for risk associated with participation. We give philosophical arguments based on utility, fairness and avoidance of exploitation to support this. We also examine a cross-section of the UK public and CHIM experts. We found that CHIM participants are currently paid variable amounts. A representative s le of the UK public believes CHIM participants should be paid approximately triple the UK minimum wage and should be paid for the risk they endure throughout participation. CHIM experts believe CHIM participants should be paid more than double the UK minimum wage but are ided on the payment for risk. The Payment for Risk Model allows risk and pain to be accounted for in payment and could be used to determine ethically justifiable payment for CHIM participants. Although many research guidelines warn against paying large amounts or paying for risk, our empirical findings provide empirical support to the growing number of ethical arguments challenging this status quo. We close by suggesting two ways (value of statistical life or consistency with risk in other employment) by which payment for risk could be calculated.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 04-2010
DOI: 10.1161/CIRCGENETICS.109.904698
Abstract: Background— Alternative mRNA splicing is an important mechanism for regulation of gene expression. Altered mRNA splicing occurs in association with several types of cancer, and a small number of disease-associated changes in splicing have been reported in heart disease. However, genome-wide approaches have not been used to study splicing changes in heart disease. We hypothesized that mRNA splicing is different in diseased hearts compared with control hearts. Methods and Results— We used the Affymetrix Exon array to globally evaluate mRNA splicing in left ventricular myocardial RNA from controls (n=15) and patients with ischemic cardiomyopathy (n=15). We observed a broad and significant decrease in mRNA splicing efficiency in heart failure, which affected some introns to a greater extent than others. The profile of mRNA splicing separately clustered ischemic cardiomyopathy and control s les, suggesting distinct changes in mRNA splicing between groups. Reverse transcription–polymerase chain reaction validated 9 previously unreported alternative splicing events. Furthermore, we demonstrated that splicing of 4 key sarcomere genes, cardiac troponin T ( TNNT2 ), cardiac troponin I ( TNNI3 ), myosin heavy chain 7 ( MYH7 ), and filamin C, gamma ( FLNC ), was significantly altered in ischemic cardiomyopathy and in dilated cardiomyopathy and aortic stenosis. In aortic stenosis s les, these differences preceded the onset of heart failure. Remarkably, the ratio of minor to major splice variants of TNNT2 , MYH7 , and FLNC classified independent test s les as control or disease with % accuracy. Conclusions— Our data indicate that mRNA splicing is broadly altered in human heart disease and that patterns of aberrant RNA splicing accurately assign s les to control or disease classes.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Cold Spring Harbor Laboratory
Date: 15-05-2018
DOI: 10.1101/318436
Abstract: Circular RNAs (circRNAs) are a unique class of transcripts that can only be identified from sequence alignments spanning discordant junctions, commonly referred to as backsplice junctions (BSJ). The challenges of detecting a BSJ from short read high throughput sequencing (HTS) data has steered software development to focus primarily on algorithmic methods to accurately capture BSJs. Here we present Ularcirc, the first software tool that provides a complete circRNA workflow from detection, integrated visualization, quality filtering of BSJ and forward splicing junctions (FSJ), through to sequence retrieval and downstream functional analysis. More importantly, Ularcirc uses an innovative method to filter out false positive circRNAs coined read alignment distribution (RAD) score which allows detection of circRNAs independent of gene annotations. We used Ularcirc to characterise circRNAs from public and in-house generated data sets and demonstrate how to discover (i) novel splicing patterns of parental transcripts, (ii) internal splicing patterns of circRNA, and (iii) the complexity of BSJ formation. Furthermore, we identify circRNAs that have potential open reading frames longer than their linear sequence. Finally, we have identified and validated the presence of a novel class of circRNA generated from ApoA4 transcripts whose BSJ derive from multiple sites within coding exons. Ularcirc can be accessed via github.com/VCCRI/Ularcirc .
Publisher: Oxford University Press (OUP)
Date: 07-2008
DOI: 10.1093/BIOINFORMATICS/BTN142
Abstract: Motivation: Current microarray analyses focus on identifying sets of genes that are differentially expressed (DE) or differentially coexpressed (DC) in different biological states (e.g. diseased versus non-diseased). We observed that in many human diseases, some genes have a significantincrease or decrease in expression variability (variance). Asthese observed changes in expression variability may be caused by alteration of the underlying expression dynamics, such differential variability (DV) patterns are also biologically interesting. Results: Here we propose a novel analysis for changes in gene expression variability between groups of les, which we call differential variability analysis. We introduce the concept of differential variability (DV), and present a simple procedure for identifying DV genes from microarray data. Our procedure is evaluated with simulated and real microarray datasets. The effect of data preprocessing methods on identification of DV gene is investigated. The biological significance of DV analysis is demonstrated with four human disease datasets. The relationships among DV, DE and DC genes are investigated. The results suggest that changes in expression variability are associated with changes in coexpression pattern, which imply that DV is not merely stochastic noise, but informative signal. Availability: The R source code for differential variability analysis is available from the contact authors upon request. Contact: joshua@it.usyd.edu.au mcharleston@it.usyd.edu.au
Publisher: Springer Science and Business Media LLC
Date: 04-2019
Publisher: Cold Spring Harbor Laboratory
Date: 16-03-2022
DOI: 10.1101/2022.03.15.22272307
Abstract: Survey questions on usual quantity and frequency of alcohol consumption are regularly used in screening tools to identify drinkers requiring intervention. The aim of this study is to examine age-based differences in quantity and frequency of alcohol consumption on the Alcohol Use Disorders Identification Test (AUDIT) and how this relates to the prediction of harmful or dependent drinking Cross sectional survey Australia. Data was taken from 17,399 respondents who reported any alcohol consumption in the last year and were aged 18 and over from the 2016 National Drug Strategy Household Survey, a broadly representative cross-sectional survey on substance use. Respondents were asked about their frequency of consumption, usual quantity per occasion and the other items of the AUDIT. In older drinkers, quantity per occasion (β=0.53 (0.43, 0.64 95%CI in 43-47 year olds as an ex le) is a stronger predictor of dependence than frequency per occasion (β=0.24 (0.17, 0.31). In younger drinkers the reverse was true with frequency a stronger predictor (β=0.54 (0.39, 0.69) in 23-27 year olds) than quantity (β=0.26 (0.18, 0.34) in 23-27 year olds). Frequency of consumption was not a significant predictor of dependence in respondents aged 73 and over (β=-0.03 (−0.08, 0.02)). Similar patterns were found when predicting harmful drinking. Despite this, since frequency of consumption increased steadily with age, the question on frequency was responsible for at least 65% of AUDIT scores in drinkers aged 53 and over. The items with a weaker association with dependent or harmful drinking in younger and older drinkers are the same items with the strongest influence on overall AUDIT scores. Further investigation into age-specific scoring of screening tools is recommended.
Publisher: Cold Spring Harbor Laboratory
Date: 06-12-2022
DOI: 10.1101/867309
Abstract: High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell-types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3’UTR shortening in cardiac fibroblasts. Sierra is available at github.com/VCCRI/Sierra .
Publisher: Cold Spring Harbor Laboratory
Date: 04-09-2020
DOI: 10.1101/2020.09.01.277079
Abstract: Advances in high throughput single-cell and spatial omic technologies have enabled the profiling of molecular expression and phenotypic properties of hundreds of thousands of in idual cells in the context of their two dimensional (2D) or three dimensional (3D) spatial endogenous arrangement. However, current visualisation techniques do not allow for effective display and exploration of the single cell data in their spatial context. With the widespread availability of low-cost virtual reality (VR) gadgets, such as Google Cardboard, we propose that an immersive visualisation strategy is useful. We present starmapVR, a light-weight, cross-platform, web-based tool for visualising single-cell and spatial omic data. starmapVR supports a number of interaction methods, such as keyboard, mouse, wireless controller and voice control. The tool visualises single cells in a 3D space and each cell can be represented by a star plot (for molecular expression, phenotypic properties) or image (for single cell imaging). For spatial transcriptomic data, the 2D single cell expression data can be visualised alongside the histological image in a 2.5D format. The application of starmapVR is demonstrated through a series of case studies. Its scalability has been carefully evaluated across different platforms. starmapVR is freely accessible at holab-hku.github.io/starmapVR , with the corresponding source code available at olab-hku/starmapVR under the open source MIT license. Supplementary data are available at Bioinformatics online.
Publisher: Elsevier BV
Date: 06-2023
Publisher: Wiley
Date: 14-12-2012
Publisher: Springer Science and Business Media LLC
Date: 25-01-2018
DOI: 10.1038/S41598-018-19855-4
Abstract: The benefits of adult stem cells for repair of the heart have been attributed to the repertoire of salutary paracrine activities they appear to exert. We previously isolated human W8B2 + cardiac stem cells (CSCs) and found they powerfully influence cardiomyocytes and endothelial cells to collectively promote cardiac repair and regeneration. Here, the complexity of the W8B2 + CSC secretomes was characterised and examined in more detail. Using ion exchange chromatography to separate soluble proteins based on their net surface charge, the secreted factors responsible for the pro-survival activity of W8B2 + CSCs were found within the low and medium cation fractions. In addition to the soluble proteins, extracellular vesicles generated from W8B2 + CSCs not only exhibited pro-survival and pro-angiogenic activities, but also promoted proliferation of neonatal cardiomyocytes. These extracellular vesicles contain a cargo of proteins, mRNA and primary microRNA precursors that are enriched in exosomes and are capable of modulating collectively many of the cellular pathways involved in protein metabolism, cell growth, as well as cellular responses to stress and organisation of the extracellular matrix. Thus the W8B2 + CSC secretome contains a multitude of bioactive paracrine factors we have now characterised, that might well be harnessed for therapeutic application for cardiac repair and regeneration.
Publisher: Elsevier BV
Date: 05-2019
Publisher: Springer Science and Business Media LLC
Date: 14-07-2020
Publisher: Elsevier BV
Date: 2017
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 06-2022
Publisher: Elsevier BV
Date: 12-2014
DOI: 10.1016/J.JACC.2014.09.048
Abstract: Many genes have been implicated in the development of congenital heart disease (CHD). Next-generation sequencing offers opportunities for genetic testing but is often complicated by logistic and interpretative hurdles. This study sought to apply next-generation sequencing technology to CHD families with multiple affected members using a purpose-designed gene panel to assess diagnostic potential for future clinical applications. We designed a targeted next-generation sequencing gene panel for 57 genes previously implicated in CHD. Probands were screened in 16 families with strong CHD histories and in 15 control subjects. Variants affecting protein-coding regions were classified in silico using prediction programs and filtered according to predicted mode of inheritance, minor allele frequencies, and presence in databases such as dbSNP (Single Nucleotide Polymorphism Database) and ESP (Exome Sequencing Project). Disease segregation studies were conducted in variants identified in CHD cases predicted to be deleterious and with minor allele frequencies <0.1%. Thirteen potential disease-causing variants were identified in 9 families. Of these, 5 variants segregated with disease phenotype, revealing a likely molecular diagnosis in 31% of this cohort. Significant increases in the number of "indels, nonsense, and splice" variants, as well as variants classified as "probably damaging" were identified in CHD cases but not in control subjects. Also, there was a significant increase in the total number of "rare" and "low" frequency variants (minor allele frequencies <0.05) in the CHD cases. When multiple relatives are affected by CHD, a gene panel-based approach may identify its cause in up to 31% of families. Identifying causal variants has implications for clinical care and future family planning.
Publisher: Elsevier BV
Date: 04-2011
Publisher: IEEE
Date: 2006
DOI: 10.1109/CGIV.2006.70
Publisher: Public Library of Science (PLoS)
Date: 26-04-2012
Publisher: MDPI AG
Date: 07-09-2023
Publisher: Elsevier
Date: 2018
Publisher: Public Library of Science (PLoS)
Date: 13-03-2013
Publisher: Public Library of Science (PLoS)
Date: 09-03-2015
Publisher: The Company of Biologists
Date: 15-01-2020
DOI: 10.1242/DEV.179788
Abstract: Single cell RNA-sequencing (scRNA-seq) technology has matured to the point that it is possible to generate large single cell atlases of developing mouse embryos. These atlases allow the dissection of developmental cell lineages and molecular changes during embryogenesis. When coupled with single cell technologies for profiling the chromatin landscape, epigenome, proteome and metabolome, and spatial tissue organisation, these scRNA-seq approaches can now collect a large volume of multi-omic data about mouse embryogenesis. In addition, advances in computational techniques have enabled the inference of developmental lineages of differentiating cells, even without explicitly introduced genetic markers. This Spotlight discusses recent advent of single cell experimental and computational methods, and key insights from applying these methods to the study of mouse embryonic development. We highlight challenges in analysing and interpreting these data to complement and expand our knowledge from traditional developmental biology studies in relation to cell identity, ersity and lineage differentiation.
Publisher: Oxford University Press (OUP)
Date: 18-01-2017
DOI: 10.1093/NAR/GKW1358
Publisher: Informa UK Limited
Date: 12-03-2017
Publisher: American Association for the Advancement of Science (AAAS)
Date: 10-01-2012
DOI: 10.1126/SCISIGNAL.2002414
Abstract: Computational and genetic analyses reveal a key circuit involving Wnt and Bmp in developing teeth in mice.
Publisher: Springer Science and Business Media LLC
Date: 04-2020
DOI: 10.1186/S12866-020-01762-2
Abstract: The human gut microbiome plays a critical role in the carcinogenesis of colorectal cancer (CRC). However, a comprehensive analysis of the interaction between the host and microbiome is still lacking. We found correlations between the change in abundance of microbial taxa, butyrate-related colonic metabolites, and methylation-associated host gene expression in colonic tumour mucosa tissues compared with the adjacent normal mucosa tissues. The increase of genus Fusobacterium abundance was correlated with a decrease in the level of 4-hydroxybutyric acid (4-HB) and expression of immune-related peptidase inhibitor 16 ( PI16 ), Fc Receptor Like A ( FCRLA ) and Lymphocyte Specific Protein 1 ( LSP1 ). The decrease in the abundance of another potentially 4-HB-associated genus, Prevotella 2, was also found to be correlated with the down-regulated expression of metallothionein 1 M ( MT1M ). Additionally, the increase of glutamic acid-related family Halomonadaceae was correlated with the decreased expression of reelin ( RELN ). The decreased abundance of genus Paeniclostridium and genus Enterococcus were correlated with increased lactic acid level, and were also linked to the expression change of Phospholipase C Beta 1 ( PLCB1 ) and Immunoglobulin Superfamily Member 9 ( IGSF9 ) respectively. Interestingly, 4-HB, glutamic acid and lactic acid are all butyrate precursors, which may modify gene expression by epigenetic regulation such as DNA methylation. Our study identified associations between previously reported CRC-related microbial taxa, butyrate-related metabolites and DNA methylation-associated gene expression in tumour and normal colonic mucosa tissues from CRC patients, which uncovered a possible mechanism of the role of microbiome in the carcinogenesis of CRC. In addition, these findings offer insight into potential new biomarkers, therapeutic and/or prevention strategies for CRC.
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2011
DOI: 10.1142/S021972001100563X
Abstract: Many phylogenetic inference programs are available to infer evolutionary relationships among taxa using aligned sequences of characters, typically DNA or amino acids. These programs are often used to infer the evolutionary history of species. However, in most cases it is impossible to systematically verify the correctness of the tree returned by these programs, as the correct evolutionary history is generally unknown and unknowable. In addition, it is nearly impossible to verify whether any non-trivial tree is correct in accordance to the specification of the often complicated search and scoring algorithms. This difficulty is known as the oracle problem of software testing: there is no oracle that we can use to verify the correctness of the returned tree. This makes it very challenging to test the correctness of any phylogenetic inference programs. Here, we demonstrate how to apply a simple software testing technique, called Metamorphic Testing, to alleviate the oracle problem in testing phylogenetic inference programs. We have used both real and randomly generated test inputs to evaluate the effectiveness of metamorphic testing, and found that metamorphic testing can detect failures effectively in faulty phylogenetic inference programs with both types of test inputs.
Publisher: Cold Spring Harbor Laboratory
Date: 13-06-2018
DOI: 10.1101/345876
Abstract: Read alignment is an important step in RNA-seq analysis as the result of alignment forms the basis for further downstream analyses. However, recent studies have shown that published alignment tools have variable mapping sensitivity and do not necessarily align reads which should have been aligned, a problem we termed as the false-negative non-alignment problem. We have developed Scavenger, a pipeline for recovering unaligned reads using a novel mechanism which utilises information from aligned reads. Scavenger performs recovery of unaligned reads by re-aligning unaligned reads against a putative location derived from aligned reads with sequence similarity against unaligned reads. We show that Scavenger can successfully recover unaligned reads in both simulated and real RNA-seq datasets, including single-cell RNA-seq data. The reads recovered contain more genetic variants compared to previously aligned reads, indicating that ergence between personal and reference genomes plays a role in the false-negative non-alignment problem. We also explored the impact of read recovery on downstream analyses, in particular gene expression analysis, and showed that Scavenger is able to both recover genes which were previously non-expressed and also increase gene expression, with lowly expressed genes having the most impact from the addition of recovered reads. We also found that the majority of genes with fold change in expression after recovery are categorised as pseudogenes, indicating that pseudogene expression can be affected by the false-negative non-alignment problem. Scavenger helps to solve the false-negative non-alignment problem through recovery of unaligned reads using information from previously aligned reads. Scavenger is available via an open source license in github.com/VCCRI/Scavenger/ j.ho@victorchang.edu.au
Publisher: Cold Spring Harbor Laboratory
Date: 17-10-2022
DOI: 10.1101/2022.10.12.512003
Abstract: Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here we present TRIAGE-Cluster which uses genome-wide epigenetic data from erse bio-s les to identify genes demarcating cell ersity in scRNA-seq data. TRIAGE-Cluster integrates patterns of repressive chromatin deposited across erse cell types with weighted density estimation to determine cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method that evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell ersification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell ersity and study gene regulation of scRNA-seq data.
Publisher: Springer Science and Business Media LLC
Date: 06-04-2020
Publisher: American Medical Association (AMA)
Date: 10-09-2020
Start Date: 2018
End Date: 2018
Amount: $365,820.00
Funder: Australian Research Council
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