PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.17.254359v1?rss=1

Authors: Khan, H., Hussain, T., Kataria, M., SETH, A., Malik, M. Z., Dash, A., Chand, S., KHAN, M. A.

Abstract:

Hypertension is one of a major reason of mortality and morbidity and it is associated with heart and renal disease. The aim of this study is to find out the antihypertensive role of bioactive compounds from selected medicinal plants targeting ACE molecule which so far is not known. The plants taken in this study were Moringa oleifera, Azadirachta indica, and Hibiscus sabdariffa. The nitric oxide and superoxide scavenging property vary from 39.50% to 68% and 37.67 % to 75.50 %. respectively. The inhibition of ACE activity was found maximally in methanolic extract of A. indica (74 %), followed by H. sabdariffa (73.4%), and least in M. oleifera (71.8 %). The bioactive chloroform fraction was characterized for the presence of compound using standard techniques such as LCMS and NMR (13C-NMR 1H-NMR). The results revealed the presence of beta-sitosterol in M. oleifera, azadiradionolide in A. indica and hibiscitrin in H. sabdariffa. The compounds have shown significant low binding energy for hibiscitrin (-12.3kcal/mol), beta-sitosterol (-11.2kcal/mol) and azadiradionolide (-11.3kcal/mol) indicating the high efficacy of binding on the enzyme. While, binding energy of drug captopril was -5.6kcal/mol & enalpril -8.1kcal/mol in the same pocket of the ACE molecule. Upon subjecting molecular dynamic simulation results indicated that beta sitosterol complex provided more compactness than the hibiscitrin and azadiradionolide compounds. The current study delivers a new perspective for the drug development against systolic blood pressure regulation and also opens new horizons for considering alternate highly potent drug target for hypertension.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.18.256735v1?rss=1

Authors: Gu, H., Yuan, G.

Abstract:

There is an urgent need to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) that leads to COVID-19 and respiratory failure. Our study is to discover differentially expressed genes (DEGs) and biological signaling pathways by using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE150819 datasets were originally produced using an Illumina NextSeq 500 (Homo sapiens). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) were utilized to identify functional categories and significant pathways. KEGG and GO results suggested that the Cytokine-cytokine receptor interaction, P53 signaling pathway, and Apoptosis are the main signaling pathways in SARS-CoV-2 infected human bronchial organoids (hBOs). Furthermore, NFKBIA, C3, and CCL20 may be key genes in SARS-CoV-2 infected hBOs. Therefore, our study provides further insights into the therapy of COVID-19.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.18.256776v1?rss=1

Authors: Dominguez Andres, A., Feng, Y., Campos, A. R., Yin, J., Yang, C.-C., James, B., Murad, R., Kim, H., Deshpande, A. J., Gordon, D. E., Krogan, N. J., Pippa, R., Ronai, Z. A.

Abstract:

Disrupted antiviral immune responses are associated with severe COVID-19, the disease caused by SAR-CoV-2. Here, we show that the 73-amino-acid protein encoded by ORF9c of the viral genome contains a putative transmembrane domain, interacts with membrane proteins in multiple cellular compartments, and impairs antiviral processes in a lung epithelial cell line. Proteomic, interactome, and transcriptomic analyses, combined with bioinformatic analysis, revealed that expression of only this highly unstable small viral protein impaired interferon signaling, antigen presentation, and complement signaling, while inducing IL-6 signaling. Furthermore, we showed that interfering with ORF9c degradation by either proteasome inhibition or inhibition of the ATPase VCP blunted the effects of ORF9c. Our study indicated that ORF9c enables immune evasion and coordinates cellular changes essential for the SARS-CoV-2 life cycle.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.18.255752v1?rss=1

Authors: dos Santos, F. R. C., Guardia, G. D. A., dos Santos, F. F., Galante, P. A. F.

Abstract:

Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to carry out these analyses. Here, we present Reboot, an original and efficient algorithm to find genes and splicing isoforms associated with cancer patient survival, disease progression, or other clinical endpoints. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests for supporting the findings, which are automatically plotted. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients' prognosis, even in conjunction with other well-established clinical parameters. Similarly, Reboot was able to find a seven-splicing isoforms signature (CENPF-201; MLKL-202; NUP54-201; MCF2L-201; TFDP1-207; BBS1-206; HTT-202) related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive, and straightforward way for finding genes or splicing isoforms (transcripts) signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated sets of cancer-related genes. Reboot effectively runs on either servers or personal computers and it is freely available at github.com/galantelab/reboot.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.17.240838v1?rss=1

Authors: Kautsar, S. A., van der Hooft, J. J. J., de Ridder, D., Medema, M. H.

Abstract:

Genome mining for Biosynthetic Gene Clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools suffer from a bottleneck caused by the expensive network-based approach used to group these BGCs into Gene Cluster Families (GCFs). Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs) within ten days on a typical 36-cores CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration. BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global, searchable interconnected network of BGCs. As more genomes get sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-lab/bigslice.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.17.254730v1?rss=1

Authors: Liu, Q., Chen, S., Jiang, R., Wong, W. H.

Abstract:

Recent advances in single-cell technologies, including single-cell ATAC-seq (scATAC-seq), have enabled large-scale profiling of the chromatin accessibility landscape at the single cell level. However, the characteristics of scATAC-seq data, including high sparsity and high dimensionality, have greatly complicated the computational analysis. Here, we proposed scDEC, a computational tool for single cell ATAC-seq analysis with deep generative neural networks. scDEC is built on a pair of generative adversarial networks (GANs), and is capable of learning the latent representation and inferring the cell labels, simultaneously. In a series of experiments, scDEC demonstrates superior performance over other tools in scATAC-seq analysis across multiple datasets and experimental settings. In the downstream applications, we demonstrated that the generative power of scDEC helps to infer the trajectory and intermediate state of cells during differentiation and the latent features learned by scDEC can potentially reveal both biological cell types and within-cell-type variations.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.17.254896v1?rss=1

Authors: Cao, Y., Fu, L., Wu, J., Nie, Q., Xie, X.

Abstract:

For many RNA molecules, the secondary structure is essential for the correction function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization. Here we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data without any thermodynamic assumptions. UFold improves substantially upon previous models, with approximately 31% improvement over traditional thermodynamic models and 24.5% improvement over other learning-based methods. It achieves an F1 score of 0.96 on base pair prediction accuracy. An online web server running UFold is publicly available at http://ufold.ics.uci.edu.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.17.255034v1?rss=1

Authors: Dinga, R., Schmaal, L., Penninx, B. W., Veltman, D. J., Marquand, A. F.

Abstract:

Machine learning predictive models are being used in neuroimaging to predict information about the task or stimuli or to identify potentially clinically useful biomarkers. However, the predictions can be driven by confounding variables unrelated to the signal of interest, such as scanner effect or head motion, limiting the clinical usefulness and interpretation of machine learning models. The most common method to control for confounding effects is regressing out the confounding variables separately from each input variable before machine learning modeling. However, we show that this method is insufficient because machine learning models can learn information from the data that cannot be regressed out. Instead of regressing out confounding effects from each input variable, we propose controlling for confounds post-hoc on the level of machine learning predictions. This allows partitioning of the predictive performance into the performance that can be explained by confounds and performance independent of confounds. This approach is flexible and allows for parametric and non-parametric confound adjustment. We show in real and simulated data that this method correctly controls for confounding effects even when traditional input variable adjustment produces false-positive findings.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.17.254185v1?rss=1

Authors: Casanova Ferrer, F., Pascual, M., Hidalgo, M. R., Malmierca-Merlo, P., Guerri, C., Garcia-Garcia, F.

Abstract:

The abuse of alcohol, one of the most popular psychoactive substances, can cause several pathological and psychological consequences, including alcohol use disorder (AUD). An impaired ability to stop or control alcohol intake despite adverse health or social consequences characterize AUD. While AUDs predominantly occur in men, growing evidence suggests the existence of distinct cognitive and biological consequences of alcohol dependence in women. The molecular and physiological mechanisms participating in these differential effects remain unknown. Transcriptomic technology permits the detection of the biological mechanisms responsible for such sex-based differences, which supports the subsequent development of novel personalized therapeutics to treat AUD. We conducted a systematic review and meta-analysis of transcriptomics studies regarding alcohol dependence in humans with representation from both sexes. For each study, we processed and analyzed transcriptomic data to obtain a functional profile of pathways and biological functions and then integrated the resulting data by meta-analysis to characterize any sex-based transcriptomic differences associated with AUD. Global results of the transcriptomic analysis revealed the association of decreased tissue regeneration, embryo malformations, altered intracellular transport, and increased rate of RNA and protein replacement with female AUD patients. Meanwhile, our analysis indicated that increased inflammatory response and blood pressure and a reduction in DNA repair capabilities associated with male AUD patients. In summary, our functional meta-analysis of transcriptomic studies provides evidence for differential biological mechanisms that AUD patients of differing sex.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.17.254615v1?rss=1

Authors: Lee, D.-I., Roy, S.

Abstract:

The three-dimensional (3D) organization of the genome plays a critical role in gene regulation for diverse normal and disease processes. High-throughput chromosome conformation capture (3C) assays, such as Hi-C, SPRITE, GAM, and HiChIP, have revealed higher-order organizational units such as topologically associating domains (TADs), which can shape the regulatory landscape governing downstream phenotypes. Analysis of high-throughput 3C data depends on the sequencing depth, which directly affects the resolution and the sparsity of the generated 3D contact count map. Identification of TADs remains a significant challenge due to the sensitivity of existing methods to resolution and sparsity. Here we present GRiNCH, a novel matrix-factorization-based approach for simultaneous TAD discovery and smoothing of contact count matrices from high-throughput 3C data. GRiNCH TADs are enriched in known architectural proteins and chromatin modification signals and are stable to the resolution, and sparsity of the input data. GRiNCH smoothing improves the recovery of structure and significant interactions from low-depth datasets. Furthermore, enrichment analysis of 746 transcription factor motifs in GRiNCH TADs from developmental time-course and cell-line Hi-C datasets predicted transcription factors with potentially novel genome organization roles. GRiNCH is a broadly applicable tool for the analysis of high throughput 3C datasets from a variety of platforms including SPRITE and HiChIP to understand 3D genome organization in diverse biological contexts.

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