PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.17.253484v1?rss=1

Authors: Oulas, A., Zanti, M., Tomazou, M., Zachariou, M., Minadakis, G., Bourdakou, M. M., Pavlidis, P., Spyrou, G. M.

Abstract:

This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize, genetic data from all strains available from GISAID and countries' regional information such as deaths and cases per million as well as covid-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.18.255570v1?rss=1

Authors: Banerjee, R., Basak, K., Ghosh, A., Rajachandran, V., Sureka, K., Ganguly, D., Chattopadhyay, S.

Abstract:

The dire need of effective preventive measures and treatment approaches against SARS CoV 2 virus, causing COVID 19 pandemic, calls for an in-depth understanding of its evolutionary dynamics with attention to specific geographic locations, since lockdown and social distancing to prevent the virus spread could lead to distinct localized dynamics of virus evolution within and between countries owing to different environmental and host specific selection pressures. To decipher any correlation between SARS CoV 2 evolution and its epidemiology in India, we studied the mutational diversity of spike glycoprotein, the key player for the attachment, fusion and entry of virus to the host cell. For this, we analyzed the sequences of 630 Indian isolates as available in GISAID database till June 07, 2020, and detected the spike protein variants to emerge from two major ancestors, Wuhan-Hu-1/2019 and its D614G variant. Average stability of the docked spike protein host receptor (SR) complexes for these variants correlated strongly (R2=0.96) with the fatality rates across Indian states. However, while more than half of the variants were found unique to India, 67% of all variants showed lower stability of SR complex than the respective ancestral variants, indicating a possible fitness loss in recently emerged variants, despite a continuous increase in mutation rate. These results conform to the sharply declining fatality rate countrywide (>7 fold during April 11 and June 28, 2020). Altogether, while we propose the potential of SR complex stability to track disease severity, we urge an immediate need to explore if SARS CoV 2 is approaching mutational meltdown in India.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.17.254839v1?rss=1

Authors: Reese, J. T., Unni, D. R., Callahan, T. J., Cappelletti, L., Ravanmehr, V., Carbon, S., Fontana, T., Blau, H., Matentzoglu, N., Harris, N. L., Munoz-Torres, M. C., Robinson, P. N., Joachimiak, M. P., Mungall, C. J.

Abstract:

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.16.252635v1?rss=1

Authors: Mo, S., Li, J., Li, B., Yu, R., Nie, S., Zhang, Z., Kashif, M., He, S., Liao, J., Jiang, Q., Shen, P., Yan, B., Jiang, C.

Abstract:

Motivation Sulfate reduction is an important process in the sulfur cycle. However, the relationship between this process and the genotype of microorganisms involved in subtropical mangrove ecosystems is poorly understood. Genotyping can identify and is crucial for sulfate reduction in mangrove ecosystems with high sulfur concentrations. A professional and efficient gene integration database of sulfur metabolism has not been established yet. Results This work aimed to evaluate sulfate reduction by microorganisms in mangroves by using a sulfur metabolism gene integrative database (SMDB) that was mainly constructed to analyze the sulfur cycle (sub) gene families quickly and accurately. The database achieved high coverage, fast retrieval, and low false positives. Relative enrichment of sulfate adenylyltransferase indicated that environmental factors select for a partial dissimilatory sulfate reduction process. Furthermore, the sulfate reduction community compensates by producing certain sulfate-reduction genes in response to high-sulfur environments in mangrove sediments. Taxonomic assignment of dissimilatory sulfate-reduction genes revealed that Crenarchaeota and Halobacterota are completely responsible for this process. Sulfite reductase can help the community cope with the toxic sulfite produced by these Archaea phyla. Collectively, these findings suggested that Halobacterota and Crenarchaeota play essential roles in dissimilatory sulfate reduction. Availability and implementation SMDB is freely available under http://smdb.gxu.edu.cn/ and https://github.com/taylor19891213/sulfur-metabolism-gene-database. Supplementary information Supplementary data are available at Bioinformatics online.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.16.252791v1?rss=1

Authors: Garreta, L. E., Ceron-Souza, I., Palacio, M. R., Reyes, P.

Abstract:

The Genome-Wide Association Studies (GWAS) are essential to determine the genetic bases of either ecological or economic phenotypic variation across individuals within populations of model and non-model organisms. For this research question, current practice is the replication of the GWAS testing different parameters and models to validate the reproducibility of results. However, straightforward methodologies that manage both replication and tetraploid data are still missing. To solve this problem, we designed the MultiGWAS, a tool that does GWAS for diploid and tetraploid organisms by executing in parallel four software, two for polyploid data (GWASpoly and SHEsis) and two for diploids data (PLINK and TASSEL). MultiGWAS has several advantages. It runs either in the command line or in an interface. It manages different genotype formats, including VCF. It executes both the full and naive models using several quality filters. Besides, it calculates a score to choose the best gene action model across GWASPoly and TASSEL. Finally, it generates several reports that facilitate the identification of false associations from both the significant and the best-ranked association SNP among the four software. We tested MultiGWAS with tetraploid potato data. The execution demonstrated that the Venn diagram and the other companion reports (i.e., Manhattan and QQ plots, heatmaps for associated SNP profiles, and chord diagrams to trace associated SNP by chromosomes) were useful to identify associated SNP shared among different models and parameters. Therefore, we confirmed that MultiGWAS is a suitable wrapping tool that successfully handles GWAS replication in both diploid and tetraploid organisms.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.14.251512v1?rss=1

Authors: Tackenberg, M. C., Hughey, J. J.

Abstract:

The chi-square periodogram (CSP), developed over 40 years ago, continues to be one of the most popular methods to estimate the period of circadian (circa 24-h) rhythms. Previous work has indicated the CSP is sometimes less accurate than other methods, but understanding of why and under what conditions remains incomplete. Using simulated rhythmic time-courses, we found that the CSP is prone to underestimating the period in a manner that depends on the true period and the length of the time-course. This underestimation bias is most severe in short time-courses (e.g., 3 days), but is also visible in longer simulated time-courses (e.g., 12 days) and in experimental time-courses of mouse wheel-running. The bias is caused by discontinuities in the periodogram that are related to the number of time-points the CSP uses to calculate the observed variance for a given test period. By revising the calculation to avoid discontinuities, we developed a new version, the greedy CSP, that shows reduced bias and improved accuracy. Nonetheless, even the greedy CSP tended to be less accurate on our simulated time-courses than an alternative method, namely the Lomb-Scargle periodogram. Thus, although our study describes a major improvement to a classic method, it also suggests that users should generally avoid the CSP when estimating the period of biological rhythms.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.15.251959v1?rss=1

Authors: Sun, D., Wang, J., Han, Y., Dong, X., Zheng, R., Ge, J., Shi, X., Wang, B., Li, Z., Ren, P., Sun, L., Yan, Y., Zhang, P., Zhang, F., Li, T., Wang, C.

Abstract:

Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors, but also posed computational challenges on how to integrate and utilize the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly two million cells from 76 high-quality tumor datasets across 28 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, malignant cell classification, cell clustering, cell-type annotation, differential expression analysis, and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching, and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.15.250282v1?rss=1

Authors: Yang, A., chen, J., Zhao, X.-M.

Abstract:

Motivation: Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multi-marker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. However, SNPs may also affect genes in a distance, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue-specificity of signals have not been taken into account. Results: We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals, and tissue-specific gene networks. When applied to schizophrenia, nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are reasonably involved in schizophrenia compared to MAGMA and H-MAGMA. Some disease-related functions (e.g. the ATPase pathway in Cortex) tissues are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multi-tissue origins.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.15.252114v1?rss=1

Authors: Prakash, O.

Abstract:

Protocol established and validated for clustering of non-unified protein sequences through memory-map guided deep learning. Data evaluated belongs to the disease causing proteins/genes from human hormonal system. Possibilities for future experiments validation was found for genes as: ACTHR, AGMX1, ATK, BPK, DPDE3, ERBA2, FSHB, GH1, GHSR, GNAS1, GSP, HANF, LCGR, LGR2, LGR3, LHRHR, NR1A2, PKR1, PRKAR1, RNF216, SBP2, SECISBP2, THR1, THRB, TPIT, TRIAD3, TSE1, UBCE7IP1, XAP2, and ZIN. Protocol is recommended for implementation with small to large dataset (protein/ DNA/ RNA sequences of unified or non-unified length) with unclassified data flags.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.250142v1?rss=1

Authors: Abdelmoula, W. M., Lopez, B. G.-C., Randall, E. C., Kapur, T., Sarkaria, J. N., White, F. M., Agar, J. N., Wells, W. M., Agar, N. Y. R.

Abstract:

Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving clinical diagnosis, biomarker discovery, metabolomics research and pharmaceutical applications. The large data size and high dimensional nature of MSI pose computational and memory complexities that hinder accurate identification of biologically-relevant molecular patterns. We propose msiPL, a robust and generic probabilistic generative model based on a fully-connected variational autoencoder for unsupervised analysis and peak learning of MSI data. The method can efficiently learn and visualize the underlying non-linear spectral manifold, reveal biologically-relevant clusters of tumor heterogeneity and identify underlying informative m/z peaks. The method provides a probabilistic parametric mapping to allow a trained model to rapidly analyze a new unseen MSI dataset in a few seconds. The computational model features a memory-efficient implementation using a minibatch processing strategy to enable the analyses of big MSI data (encompassing more than 1 million high-dimensional datapoints) with significantly less memory. We demonstrate the robustness and generic applicability of the application on MSI data of large size from different biological systems and acquired using different mass spectrometers at different centers, namely: 2D Matrix-Assisted Laser Desorption Ionization (MALDI) Fourier Transform Ion Cyclotron Resonance (FT ICR) MSI data of human prostate cancer, 3D MALDI Time-of-Flight (TOF) MSI data of human oral squamous cell carcinoma, 3D Desorption Electrospray Ionization (DESI) Orbitrap MSI data of human colorectal adenocarcinoma, 3D MALDI TOF MSI data of mouse kidney, and 3D MALDI FT ICR MSI data of a patient-derived xenograft (PDX) mouse brain model of glioblastoma.

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