Search

The recently developed Hi-C technique has been widely applied to map genome-wide chromatin interactions. However, current methods for analyzing diploid Hi-C data cannot fully distinguish between homologous chromosomes. Consequently, the existing diploid Hi-C analyses are based on sparse and inaccurate allele-specific contact matrices, which might lead to incorrect modeling of diploid genome architecture.Here we present ASHIC (Allele-Specific diploid Hi-C modeling), a hierarchical Bayesian framework to model allele-specific chromatin organizations in diploid genomes. We developed two models under the Bayesian framework: the Poisson-multinomial (ASHIC-PM) model and the zero-inflated Poisson-multinomial (ASHIC-ZIPM) model. The proposed ASHIC methods impute allele-specific contact maps from diploid Hi-C data and simultaneously infer allelic 3D structures. Through simulation studies, we demonstrated that ASHIC methods outperformed existing approaches, especially under low coverage and low SNP density conditions. Additionally, in the analyses of diploid Hi-C datasets in mouse and human, our ASHIC-ZIPM method produced fine-resolution diploid chromatin maps and 3D structures and provided insights into the allelic chromatin organizations and functions. To summarize, our work provides a statistically rigorous framework for investigating fine-scale allele-specific chromatin conformations.The ASHIC software is publicly available at https://github.com/wmalab/ASHIC.

Additional file 1: Fig. S1. Attributable fraction (AF) and numbers (AN) of extreme precipitation on outpatient visits for depression. Fig. S2. Sensitivity analysis when altering the degrees of freedom (df = 5–8) for controlling for the long-term trend and seasonality. Fig. S3. Sensitivity analysis when altering the degrees of freedom (df = 3–6) for controlling for mean temperature. Fig. S4. Sensitivity analysis when altering the degrees of freedom (df = 3–6) for controlling for relative humidity. Fig. S5. Sensitivity analysis when altering the degrees of freedom (df = 3–6) for controlling for the sunshine duration. Fig. S6. Relative risk and 95%CI of extreme precipitation on depression outpatient visits by adding other air pollutants in the sensitivity analysis. Fig. S7. Sensitivity analysis by changing the cut-off value of extreme precipitation in the model. Table S1. The AIC values of models for various lag period from lag1 to lag20. Table S2. The single-day effects of extreme precipitation on depression outpatient visits in different subgroups in Suzhou, China, with 95th percentile (13.13 mm) of precipitation relative to no precipitation. Table S3. Single-day and cumulative lag effects of extreme precipitation on depression outpatient visits at various lag days in Suzhou, China, with 95th percentile (13.13 mm) of precipitation relative to no precipitation.

Background: Long noncoding RNAs (lncRNAs) are endogenous noncoding RNAs, arbitrarily longer than 200 nucleotides, that play critical roles in diverse biological processes. LncRNAs exist in different genomes ranging from animals to plants. Objective: PlncRNADB is a searchable database of lncRNA sequences and annotation in plants. Methods: We built a pipeline for lncRNA prediction in plants, providing a convenient utility for users to quickly distinguish potential noncoding RNAs from protein-coding transcripts. Results: More than five thousand lncRNAs are collected from four plant species (Arabidopsis thaliana, Arabidopsis lyrata, Populus trichocarpa and Zea mays) in PlncRNADB. Moreover, our database provides the relationship between lncRNAs and various RNA-binding proteins (RBPs), which can be displayed through a user-friendly web interface. Conclusion: PlncRNADB can serve as a reference database to investigate the lncRNAs and their interaction with RNA-binding proteins in plants. The PlncRNADB is freely available at http://bis.zju.edu.cn/PlncRNADB/.