PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.30.271395v1?rss=1

Authors: Mori, T., Shinwari, N., Fujibuchi, W.

Abstract:

Single-cell RNA-seq (scRNA-seq) analysis is widely used to characterize cell types or detect heterogeneity of cell states at much higher resolutions than ever before. Here we introduce scMontage (https://scmontage.stemcellinformatics.org), a gene expression similarity search server dedicated to scRNA-seq data, which can rapidly compare a query with thousands of samples within a few seconds. The scMontage search is based on Spearman's rank correlation coefficient and its robustness is ensured by introducing Fisher's Z-transformation and Z-test. Furthermore, search results are linked to a human cell database SHOGoiN (http://shogoin.stemcellinformatics.org), which enable users to fast access to additional cell-type specific information. The scMontage is available not only as a web server but also as a stand-alone application for user's own data, and thus it enhances the reliability and throughput of cell analysis and helps users gain new insights into their research.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.29.273722v1?rss=1

Authors: Ye, T., Ma, W.

Abstract:

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 inaccurate modeling of diploid genome architecture. Here, we present ASHIC, a hierarchical Bayesian framework to model allele-specific chromatin organizations in diploid genomes. We developed two models under this 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 our methods outperformed existing approaches, especially under low coverage and low SNP density conditions. Additionally, we applied ASHIC-ZIPM to a published diploid mouse Hi-C data and studied the active/inactive X chromosomes and the H19/Igf2 imprinting region. In both cases, our 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.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.30.273839v1?rss=1

Authors: Turki, T., Taguchi, Y.-h.

Abstract:

Analyzing single-cell pancreatic data would play an important role in understanding various metabolic diseases and health conditions. Due to the sparsity and noise present in such single-cell gene expression data, analyzing various functions related to the inference of gene regulatory networks, derived from single-cell data, remains difficult, thereby posing a barrier to the deepening of understanding of cellular metabolism. Since recent studies have led to the reliable inference of single-cell gene regulatory networks (SCGRNs), the challenge of discriminating between SCGRNs has now arisen. By accurately discriminating between SCGRNs (e.g., distinguishing SCGRNs of healthy pancreas from those of T2D pancreas), biologists would be able to annotate, organize, visualize, and identify common patterns of SCGRNs for metabolic diseases. Such annotated SCGRNs could play an important role in speeding up the process of building large data repositories. In this study, we aimed to contribute to the development of a novel deep learning (DL) application. First, we generated a dataset consisting of 224 SCGRNs belonging to both T2D and healthy pancreas and made it freely available. Next, we chose seven DL architectures, including VGG16, VGG19, Xception, ResNet50, ResNet101, DenseNet121, and DenseNet169, trained each of them on the dataset, and checked prediction based on a test set. We evaluated the DL architectures on an HP workstation platform with a single NVIDIA GeForce RTX 2080Ti GPU. Experimental results on the whole dataset, using several performance measures, demonstrated the superiority of VGG19 DL model in the automatic classification of SCGRNs, derived from the single-cell pancreatic data.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.29.273698v1?rss=1

Authors: Liu, H., Ma, W.

Abstract:

Recent advances in Hi-C techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures, thereby shedding light on the principles of genome architecture and functions. However, statistical methods for detecting changes in large-scale chromatin organization such as topologically-associating domain (TAD) are still lacking. We proposed a new statistical method, DiffGR, for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps. We utilized the stratum-adjusted correlation coefficient (SCC) to measure similarity of local TAD regions. We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions. Through simulation studies, we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions. Furthermore, we successfully revealed cell type-specific changes in genomic interacting regions using real Hi-C datasets.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.28.271981v1?rss=1

Authors: He, J., Huang, S.-Y.

Abstract:

Motivation and Results: Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-EM maps. However, building accurate models for the EM maps at 3-5 [A] resolution remains challenging and time-consuming. Here, we present a fully automatic de novo structure determination method using a deep learning-based framework, named as DeepMM, which automatically builds atomic-accuracy all-atom models from cryo-EM maps at near-atomic resolution. In our method, the main-chain and C positions as well as their amino acid and secondary structure types are predicted in the EM map using Densely Connected Convolutional Networks. DeepMM was extensively validated on 40 simulated maps at 5 [A] resolution and 30 experimental maps at 2.6-4.8 [A] resolution as well as an EMDB-wide data set of 2931 experimental maps at 2.6-4.9 [A] resolution. DeepMM built correct models for >60% of the cases, and it outperformed existing state-of-the-art algorithms including RosettaES, MAINMAST, and Phenix. Availability: http://huanglab.phys.hust.edu.cn/DeepMM/ Note: This manuscript has been submitted to Nature Communications on June 17, 2020.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.27.270967v1?rss=1

Authors: Quinn, T. P., Nguyen, D., Nguyen, P., Gupta, S., Venkatesh, S.

Abstract:

In most biological studies, prediction is used primarily to validate the model; the real quest is to understand the underlying phenomenon. Therefore, interpretable deep models for biological studies are required. Here, we propose HyperXPair (the Hyper-parameter eXplainable Motif Pair framework), a new architecture that learns biological motifs and their distance-dependent context through explicitly interpretable parameters that are immediately understood by a biologist. This makes HyperXPair more than a decision- support tool; it is also a hypothesis-generating tool designed to advance knowledge in the field. We demonstrate the utility of our model by learning distance-dependent motif interactions for two biological problems: transcription initiation and RNA splicing.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.28.271585v1?rss=1

Authors: Nunn, A., Otto, C., Stadler, P. F., Langenberger, D.

Abstract:

Whole genome bisulfite sequencing is currently at the forefront of epigenetic analysis, facilitating the nucleotide-level resolution of 5-methylcytosine (5mC) on a genome-wide scale. Specialised software have been developed to accommodate the unique difficulties in aligning such sequencing reads to a given reference, building on the knowledge acquired from model organisms such as human, or Arabidopsis thaliana. As the field of epigenetics expands its purview to non-model plant species, new challenges arise which bring into question the suitability of previously established tools. Herein, nine short-read aligners are evaluated: Bismark, BS-Seeker2, BSMAP, BWA-meth, ERNE-BS5, GEM3, GSNAP, Last, and segemehl. Precision-recall of simulated alignments, in comparison to real sequencing data obtained from three natural accessions, reveals on-balance that BWA-meth and BSMAP are able to make the best use of the data during mapping. The influence of difficult-to-map regions, characterised by deviations in sequencing depth over repeat annotations, is evaluated in terms of the mean absolute deviation of the resulting methylation calls in comparison to a realistic methylome. Downstream methylation analysis is responsive to the handling of multi-mapping reads relative to mapping quality (MAPQ), and potentially susceptible to bias arising from the increased sequence complexity of densely-methylated reads.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.27.270264v1?rss=1

Authors: Zhou, Y., Sharpee, T.

Abstract:

Understanding the patterns of gene expression is key to elucidating the differences between cell types and across disease conditions. The overwhelmingly large number of genes involved generally makes this problem intractable. Yet, we find that gene expression patterns in five different data datasets can all be described using a small number of variables. These variables describe differences between cells according to a hyperbolic metric. We reach this conclusion by developing methods that, starting with an initial assumption of a Euclidean geometry, can detect the presence of other geometries in the data. The Euclidean metric is used in most of current studies of gene expression, primarily because it is difficult to use other non-linear metrics in high dimensional spaces. The hyperbolic metric is much more suitable for describing data produced by a hierarchically organized network, which is relevant for many biological processes. We find that the hyperbolic effects, but not the space dimensionality, increase with the number of genes that are taken into account. The hyperbolic curvature was the smallest for mouse embryonic stem cells, stronger for mouse kidney, lung and brain cells, and reached the largest value in a set of human cells integrated from multiple sources. We show that taking into account hyperbolic geometry strongly improves the visualization of gene expression data compared to leading visualization methods. These results demonstrate the advantages of knowing the underlying geometry when analyzing high-dimensional data.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.27.270728v1?rss=1

Authors: Xu, P., Storici, F.

Abstract:

Ribonucleoside monophosphate (rNMP) incorporation in DNA is a natural and prominent phenomenon resulting in DNA structural change and genome instability. While DNA polymerases have different rNMP incorporation rates, little is known whether these enzymes incorporate rNMPs following specific sequence patterns. In this study, we analyzed a series of rNMP incorporation datasets, generated from three rNMP mapping techniques, and obtained from Saccharomyces cerevisiae cells expressing wild-type or mutant replicative DNA polymerase and ribonuclease H2 genes. We performed computational analyses of rNMP sites around early and late firing autonomously replicating sequences (ARS's) of the yeast genome, from which bidirectional, leading and lagging DNA synthesis starts. We find the preference of rNMP incorporation on the leading strand in wild-type DNA polymerase yeast cells. The leading/lagging-strand ratio of rNMP incorporation changes dramatically within 500 nt from ARS's, highlighting the Pol {delta} - Pol {epsilon} handoff during early leading-strand synthesis. Furthermore, the pattern of rNMP incorporation is markedly distinct between the leading the lagging strand. Overall, our results show the different counts and patterns of rNMP incorporation during DNA replication from ARS, which reflects the different labor of division and rNMP incorporation pattern of Pol {delta} and Pol {epsilon}.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.28.271569v1?rss=1

Authors: Wang, S., Sun, Q., Xu, Y., Pei, J., Lai, L.

Abstract:

The COVID-19 pandemic calls for rapid development of effective treatments. Although various drug repurpose approaches have been used to screen the FDA-approved drugs and drug candidates in clinical phases against SARS-CoV-2, the coronavirus that causes this disease, no magic bullets have been found until now. We used directed message passing neural network to first build a broad-spectrum anti-beta-coronavirus compound prediction model, which gave satisfactory predictions on newly reported active compounds against SARS-CoV-2. Then we applied transfer learning to fine-tune the model with the recently reported anti-SARS-CoV-2 compounds. The fine-tuned model was applied to screen a large compound library with 4.9 million drug-like molecules from ZINC15 database and recommended a list of potential anti-SARS-CoV-2 compounds for further experimental testing. As a proof-of-concept, we experimentally tested 7 high-scored compounds that also demonstrated good binding strength in docking study against the 3C-like protease of SARS-CoV-2 and found one novel compound that inhibited the enzyme with an IC50 of 37.0 M. Our model is highly efficient and can be used to screen large compound databases with billions or more compounds to accelerate the drug discovery process for the treatment of COVID-19.

Copy rights belong to original authors. Visit the link for more info