Indian Ocean temperature anomalies predict long-term global dengue trends.

Despite identifying El Niño events as a factor in dengue dynamics, predicting the oscillation of global dengue epidemics remains challenging. Here, we investigate climate indicators and worldwide dengue incidence from 1990 to 2019 using climate-driven mechanistic models. We identify a distinct indicator, the Indian Ocean basin-wide (IOBW) index, as representing the regional average of sea surface temperature anomalies in the tropical Indian Ocean. IOBW is closely associated with dengue epidemics for both the Northern and Southern hemispheres. The ability of IOBW to predict dengue incidence likely arises as a result of its effect on local temperature anomalies through teleconnections. These findings indicate that the IOBW index can potentially enhance the lead time for dengue forecasts, leading to better-planned and more impactful outbreak responses.

Antipsychotic-induced epigenomic reorganization in frontal cortex of individuals with schizophrenia.

Genome-wide association studies have revealed >270 loci associated with schizophrenia risk, yet these genetic factors do not seem to be sufficient to fully explain the molecular determinants behind this psychiatric condition. Epigenetic marks such as post-translational histone modifications remain largely plastic during development and adulthood, allowing a dynamic impact of environmental factors, including antipsychotic medications, on access to genes and regulatory elements. However, few studies so far have profiled cell-specific genome-wide histone modifications in postmortem brain samples from schizophrenia subjects, or the effect of antipsychotic treatment on such epigenetic marks. Here, we conducted ChIP-seq analyses focusing on histone marks indicative of active enhancers (H3K27ac) and active promoters (H3K4me3), alongside RNA-seq, using frontal cortex samples from antipsychotic-free (AF) and antipsychotic-treated (AT) individuals with schizophrenia, as well as individually matched controls (n=58). Schizophrenia subjects exhibited thousands of neuronal and non-neuronal epigenetic differences at regions that included several susceptibility genetic loci, such as NRG1, DISC1, and DRD3. By analyzing the AF and AT cohorts separately, we identified schizophrenia-associated alterations in specific transcription factors, their regulatees, and epigenomic and transcriptomic features that were reversed by antipsychotic treatment; as well as those that represented a consequence of antipsychotic medication rather than a hallmark of schizophrenia in postmortem human brain samples. Notably, we also found that the effect of age on epigenomic landscapes was more pronounced in frontal cortex of AT-schizophrenics, as compared to AF-schizophrenics and controls. Together, these data provide important evidence of epigenetic alterations in the frontal cortex of individuals with schizophrenia, and remark for the first time on the impact of age and antipsychotic treatment on chromatin organization.

Predicting regional somatic mutation rates using DNA motifs.

How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-related regions can accurately predict cancer types. Interestingly, we found that the same mutation signatures often have different contributions to cancer-related and cancer-independent regions, and we also identified the motifs with the most contribution to each mutation signature.

Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study.

China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73-38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70-76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89-2.92) million hospital admissions and 0.16 (95% CI, 0.13-0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.

Association of vaccination, international travel, public health and social measures with lineage dynamics of SARS-CoV-2.

Continually emerging SARS-CoV-2 variants of concern that can evade immune defenses are driving recurrent epidemic waves of COVID-19 globally. However, the impact of measures to contain the virus and their effect on lineage diversity dynamics are poorly understood. Here, we jointly analyzed international travel, public health and social measures (PHSM), COVID-19 vaccine rollout, SARS-CoV-2 lineage diversity, and the case growth rate (GR) from March 2020 to September 2022 across 63 countries. We showed that despite worldwide vaccine rollout, PHSM are effective in mitigating epidemic waves and lineage diversity. An increase of 10,000 monthly travelers in a single country-to-country route between endemic countries corresponds to a 5.5% (95% CI: 2.9 to 8.2%) rise in local lineage diversity. After accounting for PHSM, natural immunity from previous infections, and waning immunity, we discovered a negative association between the GR of cases and adjusted vaccine coverage (AVC). We also observed a complex relationship between lineage diversity and vaccine rollout. Specifically, we found a significant negative association between lineage diversity and AVC at both low and high levels but not significant at the medium level. Our study deepens the understanding of population immunity and lineage dynamics for future pandemic preparedness and responsiveness.

Optimization of Population-Level Testing, Contact Tracing, and Isolation in Emerging COVID-19 Outbreaks: a Mathematical Modeling Study - Tonghua City and Beijing Municipality, China, 2021-2022.

Introduction: The transmissibility of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant poses challenges for the existing measures containing the virus in China. In response, this study investigates the effectiveness of population-level testing (PLT) and contact tracing (CT) to help curb coronavirus disease 2019 (COVID-19) resurgences in China. Methods: Two transmission dynamic models (i.e. with and without age structure) were developed to evaluate the effectiveness of PLT and CT. Extensive simulations were conducted to optimize PLT and CT strategies for COVID-19 control and surveillance. Results: Urban Omicron resurgences can be controlled by multiple rounds of PLT, supplemented by CT - as long as testing is frequent. This study also evaluated the time needed to detect COVID-19 cases for surveillance under different routine testing rates. The results show that there is a 90% probability of detecting COVID-19 cases within 3 days through daily testing. Otherwise, it takes around 7 days to detect COVID-19 cases at a 90% probability level if biweekly testing is used. Routine testing applied to the age group 21-60 for COVID-19 surveillance would achieve similar performance to that applied to all populations. Discussion: Our analysis evaluates potential PLT and CT strategies for COVID-19 control and surveillance.

Bidirectional Movement of Emerging H5N8 Avian Influenza Viruses Between Europe and Asia via Migratory Birds Since Early 2020.

Migratory birds play a critical role in the rapid spread of highly pathogenic avian influenza (HPAI) H5N8 virus clade 2.3.4.4 across Eurasia. Elucidating the timing and pattern of virus transmission is essential therefore for understanding the spatial dissemination of these viruses. In this study, we surveyed >27,000 wild birds in China, tracked the year-round migration patterns of 20 bird species across China since 2006, and generated new HPAI H5N8 virus genomic data. Using this new data set, we investigated the seasonal transmission dynamics of HPAI H5N8 viruses across Eurasia. We found that introductions of HPAI H5N8 viruses to different Eurasian regions were associated with the seasonal migration of wild birds. Moreover, we report a backflow of HPAI H5N8 virus lineages from Europe to Asia, suggesting that Europe acts as both a source and a sink in the global HPAI virus transmission network.

The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study.

BACKGROUND: The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and the effect of climate change on malaria transmission over 27 years in Hainan, an island province in China. METHODS: For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984-2010 and the WorldClim dataset. A temperature-dependent R0 model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. FINDINGS: Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from -5·6% (95% CI -4·5 to -6·6) to -9·2% (95% CI -7·6 to -10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. INTERPRETATION: Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. FUNDING: Scientific and Technological Innovation 2030: Major Project of New Generation Artificial Intelligence, National Natural Science Foundation of China, Beijing Natural Science Foundation, National Key Research and Development Program of China, Young Elite Scientist Sponsorship Program by CAST, Research on Key Technologies of Plague Prevention and Control in Inner Mongolia Autonomous Region, and Beijing Advanced Innovation Program for Land Surface Science.

Rare variant association tests for ancestry-matched case-control data based on conditional logistic regression.

With the increasing volume of human sequencing data available, analysis incorporating external controls becomes a popular and cost-effective approach to boost statistical power in disease association studies. To prevent spurious association due to population stratification, it is important to match the ancestry backgrounds of cases and controls. However, rare variant association tests based on a standard logistic regression model are conservative when all ancestry-matched strata have the same case-control ratio and might become anti-conservative when case-control ratio varies across strata. Under the conditional logistic regression (CLR) model, we propose a weighted burden test (CLR-Burden), a variance component test (CLR-SKAT) and a hybrid test (CLR-MiST). We show that the CLR model coupled with ancestry matching is a general approach to control for population stratification, regardless of the spatial distribution of disease risks. Through extensive simulation studies, we demonstrate that the CLR-based tests robustly control type 1 errors under different matching schemes and are more powerful than the standard Burden, SKAT and MiST tests. Furthermore, because CLR-based tests allow for different case-control ratios across strata, a full-matching scheme can be employed to efficiently utilize all available cases and controls to accelerate the discovery of disease associated genes.

Evaluating the effectiveness of control measures in multiple regions during the early phase of the COVID-19 pandemic in 2020.

The number of COVID-19 confirmed cases rapidly grew since the SARS-CoV-2 virus was identified in late 2019. Due to the high transmissibility of this virus, more countries are experiencing the repeated waves of the COVID-19 pandemic. However, with limited manufacturing and distribution of vaccines, control measures might still be the most critical measures to contain outbreaks worldwide. Therefore, evaluating the effectiveness of various control measures is necessary to inform policymakers and improve future preparedness. In addition, there is an ongoing need to enhance our understanding of the epidemiological parameters and the transmission patterns for a better response to the COVID-19 pandemic. This review focuses on how various models were applied to guide the COVID-19 response by estimating key epidemiologic parameters and evaluating the effectiveness of control measures. We also discuss the insights obtained from the prediction of COVID-19 trajectories under different control measures scenarios.

Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study.

BACKGROUND: Until broad vaccination coverage is reached and effective therapeutics are available, controlling population mobility (ie, changes in the spatial location of a population that affect the spread and distribution of pathogens) is one of the major interventions used to reduce transmission of SARS-CoV-2. However, population mobility differs across locations, which could reduce the effectiveness of pandemic control measures. Here we assess the extent to which socioeconomic factors are associated with reductions in population mobility during the COVID-19 pandemic, at both the city level in China and at the country level worldwide. METHODS: In this retrospective, observational study, we obtained anonymised daily mobile phone location data for 358 Chinese cities from Baidu, and for 121 countries from Google COVID-19 Community Mobility Reports. We assessed the intra-city movement intensity, inflow intensity, and outflow intensity of each Chinese city between Jan 25 (when the national emergency response was implemented) and Feb 18, 2020 (when population mobility was lowest) and compared these data to the corresponding lunar calendar period from the previous year (Feb 5 to March 1, 2019). Chinese cities were classified into four socioeconomic index (SEI) groups (high SEI, high-middle SEI, middle SEI, and low SEI) and the association between socioeconomic factors and changes in population mobility were assessed using univariate and multivariable linear regression. At the country level, we compared six types of mobility (residential, transit stations, workplaces, retail and recreation, parks, and groceries and pharmacies) 35 days after the implementation of the national emergency response in each country and compared these to data from the same day of the week in the baseline period (Jan 3 to Feb 6, 2020). We assessed associations between changes in the six types of mobility and the country's sociodemographic index using univariate and multivariable linear regression. FINDINGS: The reduction in intra-city movement intensity in China was stronger in cities with a higher SEI than in those with a lower SEI (r=-0·47, p<0·0001). However, reductions in inter-city movement flow (both inflow and outflow intensity) were not associated with SEI and were only associated with government control measures. In the country-level analysis, countries with higher sociodemographic and Universal Health Coverage indexes had greater reductions in population mobility (ie, in transit stations, workplaces, and retail and recreation) following national emergency declarations than those with lower sociodemographic and Universal Health Coverage indexes. A higher sociodemographic index showed a greater reduction in mobility in transit stations (r=-0·27, p=0·0028), workplaces (r=-0·34, p=0·0002), and areas retail and recreation (rxs=-0·30, p=0·0012) than those with a lower sociodemographic index. INTERPRETATION: Although COVID-19 outbreaks are more frequently reported in larger cities, our analysis shows that future policies should prioritise the reduction of risks in areas with a low socioeconomic level-eg, by providing financial assistance and improving public health messaging. However, our study design only allows us to assess associations, and a long-term study is needed to decipher causality. FUNDING: Chinese Ministry of Science and Technology, Research Council of Norway, Beijing Municipal Science & Technology Commission, Beijing Natural Science Foundation, Beijing Advanced Innovation Program for Land Surface Science, National Natural Science Foundation of China, China Association for Science and Technology.

Transcriptome and lipidome profile of human mesenchymal stem cells with reduced senescence and increased trilineage differentiation ability upon drug treatment.

Human Mesenchymal stem cells (hMSCs) are multi-potential cells which are widely used in cell therapy. However, the frequently emerged senescence and decrease of differentiation capabilities limited the broad applications of MSC. Several strategies such as small molecules treatment have been widely studied and used to improve the stem characteristics bypassing the senescence but the exact mechanisms for them to reduce senescence have not been fully studied. In this study, hMSCs were treated by rapamycin, oltipraz, metformin, and vitamin C for the indicated time and these cells were subjected to senescence evaluation and trilineage differentiation. Furthermore, transcriptomics and lipidomics datasets of hMSCs after drug treatment were analyzed to interpret biological pathways responsible for their anti-senescence effects. Although four drugs exhibited significant activities in promoting MSC osteogenic differentiation, metformin is the optimal drug to promote trilineage differentiation. GO terms illustrated that the anti-aging effects of drugs were mainly associated with cellular senescence, mitotic and meiosis process. Biosynthesis of phosphatidylcholines (PC) and phosphatidylethanolamine (PE) were inhibited whereas production of phosphatidylinositols (PIs) and saturated fatty acids (SFA)/ mono-unsaturated fatty acids (MUFA) conversion was activated. Medium free fatty acids (FFA) was increased in hMSCs with different anti-aging phenotypes. Therefore, we established a comprehensive method in assessing drug intervention based on the results of transcriptomics and lipidomics. The method can be used to study different biological phenotypes upon drug intervention in MSC which will extend the clinical application of hMSCs.

A follow-up study shows that recovered patients with re-positive PCR test in Wuhan may not be infectious.

BACKGROUND: Previous studies showed that recovered coronavirus disease 2019 (COVID-19) patients can have a subsequent positive polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after they are discharged from the hospital. Understanding the epidemiological characteristics of recovered COVID-19 patients who have a re-positive test is vital for preventing a second wave of COVID-19. METHODS: This retrospective study analyzed the epidemiological and clinical features of 20,280 COVID-19 patients from multiple centers in Wuhan who had a positive PCR test between December 31, 2019, and August 4, 2020. The RT-PCR test results for 4079 individuals who had close contact with the re-positive cases were also obtained. RESULTS: In total, 2466 (12.16%) of the 20,280 patients had a re-positive SARS-CoV-2 PCR test after they were discharged from the hospital, and 4079 individuals had close contact with members of this patient group. All of these 4079 individuals had a negative SARS-CoV-2 PCR test. CONCLUSIONS: This retrospective study in Wuhan analyzed the basic characteristics of recovered COVID-19 patients with re-positive PCR test and found that these cases may not be infectious.

A Novel Transcriptome Integrated Network Approach Identifies the Key Driver lncRNA Involved in Cell Cycle With Chromium (VI)-Treated BEAS-2B Cells.

Hexavalent chromium [Cr(VI)] is a well-known occupational carcinogen, but the mechanisms contributing to DNA damage and cell cycle alternation have not been fully characterized. To study the dose-response effects of Cr(VI) on transcription, we exposed BEAS-2B cells to Cr(VI) at concentrations of 0.2, 0.6, and 1.8 µmol/L for 24 h. Here, we identified 1,484 differentially expressed genes (DEGs) in our transcript profiling data, with the majority of differentially expressed transcripts being downregulated. Our results also showed that these DEGs were enriched in pathways associated with the cell cycle, including DNA replication, chromatin assembly, and DNA repair. Using the differential expressed genes related to cell cycle, a weighted gene co-expression network was constructed and a key mRNA-lncRNA regulation module was identified under a scale-free network with topological properties. Additionally, key driver analysis (KDA) was applied to the mRNA-lncRNA regulation module to identify the driver genes. The KDA revealed that ARD3 (FDR = 1.46 × 10-22), SND1 (FDR = 5.24 × 10-8), and lnc-DHX32-2:1 (FDR = 1.43 × 10-17) were particularly highlighted in the category of G2/M, G1/S, and M phases. Moreover, several genes we identified exhibited great connectivity in our causal gene network with every key driver gene, including CDK14, POLA1, lnc-NCS1-2:1, and lnc-FOXK1-4:1 (all FDR < 0.05 in those phases). Together, these results obtained using mathematical approaches and bioinformatics algorithmics might provide potential new mechanisms involved in the cytotoxicity induced by Cr.

Differential DNA Methylation of Networked Signaling, Transcriptional, Innate and Adaptive Immunity, and Osteoclastogenesis Genes and Pathways in Gout.

OBJECTIVE: In gout, autoinflammatory responses to urate crystals promote acute arthritis flares, but the pathogeneses of tophi, chronic synovitis, and erosion are less well understood. Defining the pathways of epigenomic immunity training can reveal novel pathogenetic factors and biomarkers. The present study was undertaken to seminally probe differential DNA methylation patterns utilizing epigenome-wide analyses in patients with gout. METHODS: Peripheral blood mononuclear cells (PBMCs) were obtained from a San Diego cohort of patients with gout (n = 16) and individually matched healthy controls (n = 14). PBMC methylome data were processed with ChAMP package in R. ENCODE data and Taiji data analysis software were used to analyze transcription factor (TF)-gene networks. As an independent validation cohort, whole blood DNA samples from New Zealand Maori subjects (n = 13 patients with gout, n = 16 control subjects without gout) were analyzed. RESULTS: Differentially methylated loci clearly separated gout patients from controls, as determined by hierarchical clustering and principal components analyses. IL23R, which mediates granuloma formation and cell invasion, was identified as one of the multiple differentially methylated gout risk genes. Epigenome-wide analyses revealed differential methylome pathway enrichment for B and T cell receptor signaling, Th17 cell differentiation and interleukin-17 signaling, convergent longevity regulation, circadian entrainment, and AMP-activated protein kinase signaling, which are pathways that impact inflammation via insulin-like growth factor 1 receptor, phosphatidylinositol 3-kinase/Akt, NF-κB, mechanistic target of rapamycin signaling, and autophagy. The gout cohorts overlapped for 37 (52.9%) of the 70 TFs with hypomethylated sequence enrichment and for 30 (78.9%) of the 38 enriched KEGG pathways identified via TFs. Evidence of shared differentially methylated gout TF-gene networks, including the NF-κB activation-limiting TFs MEF2C and NFATC2, pointed to osteoclast differentiation as the most strongly weighted differentially methylated pathway that overlapped in both gout cohorts. CONCLUSION: These findings of differential DNA methylation of networked signaling, transcriptional, innate and adaptive immunity, and osteoclastogenesis genes and pathways suggest that they could serve as novel therapeutic targets in the management of flares, tophi, chronic synovitis, and bone erosion in patients with gout.

Inferring Gene-Disease Association by an Integrative Analysis of eQTL Genome-Wide Association Study and Protein-Protein Interaction Data.

OBJECTIVES: Genome-wide association studies (GWASs) have revealed many candidate SNPs, but the mechanisms by which these SNPs influence diseases are largely unknown. In order to decipher the underlying mechanisms, several methods have been developed to predict disease-associated genes based on the integration of GWAS and eQTL data (e.g., Sherlock and COLOC). A number of studies have also incorporated information from gene networks into GWAS analysis to reprioritize candidate genes. METHODS: Motivated by these two different approaches, we have developed a statistical framework to integrate information from GWAS, eQTL, and protein-protein interaction (PPI) data to predict disease-associated genes. Our approach is based on a hidden Markov random field (HMRF) model, and we called the resulting computational algorithm GeP-HMRF (a GWAS-eQTL-PPI-based HMRF). RESULTS: We compared the performance of GeP-HMRF with Sherlock, COLOC, and NetWAS methods on 9 GWAS datasets, using the disease-related genes in the MalaCards database as the standard, and found that GeP-HMRF significantly improves the prediction accuracy. We also applied GeP-HMRF to an age-related macular degeneration disease (AMD) dataset. Among the top 50 genes predicted by GeP-HMRF, 7 are reported by the MalaCards database to be AMD-related with an enrichment p value of 3.61 × 10-119. Among the top 20 genes predicted by GeP-HMRF, CFHR1, CGHR3, HTRA1, and CFH are AMD-related in the MalaCards database, and another 9 genes are supported by the literature. CONCLUSIONS: We built a unified statistical model to predict disease-related genes by integrating GWAS, eQTL, and PPI data. Our approach outperforms Sherlock, COLOC, and NetWAS in simulation studies and 9 GWAS datasets. Our approach can be generalized to incorporate other molecular trait data beyond eQTL and other interaction data beyond PPI.

VCNet: vector-based gene co-expression network construction and its application to RNA-seq data.

MOTIVATION: Building gene co-expression network (GCN) from gene expression data is an important field of bioinformatic research. Nowadays, RNA-seq data provides high dimensional information to quantify gene expressions in term of read counts for individual exons of genes. Such an increase in the dimension of expression data during the transition from microarray to RNA-seq era made many previous co-expression analysis algorithms based on simple univariate correlation no longer applicable. Recently, two vector-based methods, SpliceNet and RNASeqNet, have been proposed to build GCN. However, they failed to work when sample size is less than the number of exons. RESULTS: We develop an algorithm called VCNet to construct GCN from RNA-seq data to overcome this dimensional problem. VCNet performs a new statistical hypothesis test based on the correlation matrix of a gene-gene pair using the Frobenius norm. The asymptotic distribution of the new test is obtained under the null model. Simulation studies demonstrate that VCNet outperforms SpliceNet and RNASeqNet for detecting edges of GCN. We also apply VCNet to two expression datasets from TCGA database: the normal breast tissue and kidney tumour tissue, and the results show that the GCNs constructed by VCNet contain more biologically meaningful interactions than existing methods. CONCLUSION: VCNet is a useful tool to construct co-expression network. AVAILABILITY AND IMPLEMENTATION: VCNet is open source and freely available from https://github.com/wangzengmiao/VCNet under GNU LGPL v3. CONTACT: dengmh@pku.edu.cn or nelsontang@cuhk.edu.hk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Estimation of isoform expression in RNA-seq data using a hierarchical Bayesian model.

Estimation of gene or isoform expression is a fundamental step in many transcriptome analysis tasks, such as differential expression analysis, eQTL (or sQTL) studies, and biological network construction. RNA-seq technology enables us to monitor the expression on genome-wide scale at single base pair resolution and offers the possibility of accurately measuring expression at the level of isoform. However, challenges remain because of non-uniform read sampling and the presence of various biases in RNA-seq data. In this paper, we present a novel hierarchical Bayesian method to estimate isoform expression. While most of the existing methods treat gene expression as a by-product, we incorporate it into our model and explicitly describe its relationship with corresponding isoform expression using a Multinomial distribution. In this way, gene and isoform expression are included in a unified framework and it helps us achieve a better performance over other state-of-the-art algorithms for isoform expression estimation. The effectiveness of the proposed method is demonstrated using both simulated data with known ground truth and two real RNA-seq datasets from MAQC project. The codes are available at http://www.math.pku.edu.cn/teachers/dengmh/GIExp/.