PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.25.313908v1?rss=1

Authors: Li, X., Duan, C., Li, R., Wang, D.

Abstract:

To reduce the reproductive loss caused by semen quality and provide theoretical guidance for the eradication of human male infertility, differential analysis of the bovine transcriptome among round spermatids, elongated spermatids, and epididymal sperm was carried out with the reference of the mouse transcriptome, and the homology trends of gene expression to the mouse were also analysed. First, to explore the physiological mechanism of spermiogenesis that profoundly affects semen quality, homological trends of differential genes were compared during spermiogenesis in dairy cattle and mice. Next, the Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein-protein interaction network (PPI network), and bioinformatics analysis uncovered the regulation network of acrosome formation during the transition from round to elongated spermatids. In addition, processes that regulate gene expression during spermiogenesis from elongated spermatid to epididymal sperm, such as ubiquitination, acetylation, deacetylation, glycosylation, and the functional gene ART3 may play an important role during spermiogenesis. Therefore, its localisation in the seminiferous tubule was investigated by immunofluorescent analysis, and its structure and function were also predicted. This study provides important data for revealing the mystery of life during spermiogenesis resulting from acrosome formation, histone replacement, and the fine regulation of gene expression.

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

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310094v1?rss=1

Authors: Vanheer, L., Schiavo, A. A., Van Haele, M., Haesen, T., Janiszewski, A., Chappell, J., Roskams, T., Cnop, M., Pasque, V.

Abstract:

Cellular identity during development is under the control of transcription factors that form gene regulatory networks. However, the transcription factors and gene regulatory networks underlying cellular identity in the human adult pancreas remain largely unexplored. Here, we integrate multiple single-cell RNA sequencing datasets of the human adult pancreas, totaling 7393 cells, and comprehensively reconstruct gene regulatory networks. We show that a network of 142 transcription factors forms distinct regulatory modules that characterize pancreatic cell types. We present evidence that our approach identifies key regulators of cell identity in the human adult pancreas. We predict that HEYL and JUND are active in acinar and alpha cells, respectively, and show that these proteins are present in the human adult pancreas as well as in human induced pluripotent stem cell-derived pancreatic cells. The comprehensive gene regulatory network atlas can be explored interactively online. We anticipate our analysis to be the starting point for a more sophisticated dissection of how transcription factors regulate cell identity in the human adult pancreas. Furthermore, given that transcription factors are major regulators of embryo development and are often perturbed in diseases, a comprehensive understanding of how transcription factors work will be relevant in development and disease biology.

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

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.25.310078v1?rss=1

Authors: Taiwo, I. A., Adeleye, N., Anwoju, F. O., Adeyinka, A., Uzoma, I. C., Bankole, T. T.

Abstract:

Background: Coronaviruses are a group of viruses that belong to the Family Coronaviridae, Genus Betacoronavirus. In December 2019, a new coronavirus disease (COVID-19) characterized by severe respiratory symptoms was discovered. The causative pathogen was a novel coronavirus known as 2019-nCoV and later as SARS-CoV-2. Within two months of its discovery, COVID-19 became a pandemic causing widespread morbidity and mortality. Methodology: Whole genome sequence data of SARS-CoV-2 isolated from Nigerian COVID-19 cases were retrieved by downloading from GISAID database. A total of 18 sequences that satisfied quality assurance (length > 29700 nts and number of unknown bases denoted as N < 5%) were used for the study. Multiple sequence alignment (MSA) was done in MAFFT (Version 7.471) while SNP calling was implemented in DnaSP (Version 6.12.03) respectively and then visualized in Jalview (Version 2.11.1.0). Phylogenetic analysis was with MEGA X software. Results: Nigerian SARS-CoV-2 had 99.9% genomic similarity with four large conserved genomic regions. A total of 66 SNPs were identified out of which 31 were informative. Nucleotide diversity assessment gave Pi = 0.00048 and average SNP frequency of 2.22 SNPs per 1000 nts. Non-coding genomic regions particularly 5 UTR and 3 UTR had a SNP density of 3.77 and 35.4 respectively. The region with the highest SNP density was ORF10 with a frequency of 8.55 SNPs/1000 nts). Majority (72.2%) of viruses in Nigeria are of L lineage with preponderance of D614G mutation which accounted for 11 (61.1%) out of the 18 viral sequences. Nigeria SARS-CoV-2 revealed 3 major clades namely Oyo, Ekiti and Osun on a maximum likelihood phylogenetic tree. Conclusion and Recommendation: Nigerian SARS-CoV-2 reveals high mutation rate together with preponderance of L lineage and D614G mutants. Implication of these mutations for SARS-CoV-2 virulence and the need for more aggressive testing and treatment of COVID-19 in Nigeria is discussed. Additionally, attempt to produce testing kits for COVID-19 in Nigeria should consider the conserved regions identified in this study. Strict adherence to COVID-19 preventive measure is recommended in view of Nigerian SARS-CoV-2 phylogenetic clustering pattern, which suggests intensive community transmission possibly rooted in communal culture characteristic of many ethnicities in Nigeria.

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

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.24.312546v1?rss=1

Authors: Chen, M., Harrison, O., Bratcher, H., Bo, Z., Jolley, K. A., Rodrigues, C. M., Bray, J., Guo, Q., Zhang, X., Chen, M., Maiden, M. C.

Abstract:

The expansion of quinolone-resistant Neisseria meningitidis clone ChinaCC4821-R1-C/B from ST-4821 clonal complex (cc4821) caused a serogroup shift from serogroup A to C in invasive meningococcal disease (IMD) in China. To establish the relationship among globally distributed cc4821 meningococci, we analysed whole genome sequence data from 173 cc4821 meningococci isolated in four continents from 1972-2019. These meningococci clustered into four sub-lineages (1-4), with sub-lineage 1 primarily comprising serogroup C IMD isolates (82%, 41/50). Most isolates from outside China formed a distinct sub-lineage (81.6%, 40/49, the Europe-USA cluster), with the typical strain designation B:P1.17-6,23:F3-36:ST-3200(cc4821) and harbouring mutations in penicillin-binding protein 2. These data show that the quinolone-resistant clone ChinaCC4821-R1-C/B has expanded to other countries. The increasing global distribution of B:cc4821 meningococci raises concern that cc4821 has the potential to cause a global pandemic and, this would be challenging to control though there is indirect evidence that Trumenba vaccine might afford some protection.

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

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310565v1?rss=1

Authors: Chen, A. T., Altschuler, K., Zhan, S. H., Chan, Y. A., Deverman, B. E.

Abstract:

COVID-19 CG is an open resource for tracking SARS-CoV-2 single-nucleotide variations (SNVs) and lineages while filtering by location, date, gene, and mutation of interest. COVID-19 CG provides significant time, labor, and cost-saving utility to diverse projects on SARS-CoV-2 transmission, evolution, emergence, immune interactions, diagnostics, therapeutics, vaccines, and intervention tracking. Here, we describe case studies in which users can interrogate (1) SNVs in the SARS-CoV-2 Spike receptor binding domain (RBD) across different geographic regions to inform the design and testing of therapeutics, (2) SNVs that may impact the sensitivity of commonly used diagnostic primers, and (3) the recent emergence of a dominant lineage harboring an S477N RBD mutation in Australia. To accelerate COVID-19 research and public health efforts, COVID-19 CG will be continually upgraded with new features for users to quickly and reliably pinpoint mutations as the virus evolves throughout the pandemic and in response to therapeutic and public health interventions.

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

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310649v1?rss=1

Authors: Li, W., Gao, Y., Li, L., Amos, C. I.

Abstract:

Alternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an "APA-aware" manner. Here, we introduce scDaPars, a bioinformatics algorithm to accurately quantify APA events at both single-cell and single-gene resolution using standard scRNA-seq data. Validations in both real and simulated data indicate that scDaPars can robustly recover missing APA events caused by the low amounts of mRNA sequenced in single cells. When applied to cancer and human endoderm differentiation data, scDaPars not only revealed cell-type specific APA regulation but also identified cell subpopulations that are otherwise invisible to conventional gene expression analysis. Thus, scDaPars will enable us to understand cellular heterogeneity at the post-transcriptional APA level.

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

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.308932v1?rss=1

Authors: Aevermann, B., Zhang, Y. H., Novotny, M., Lelieveldt, B., Bakken, T. E., Miller, J. A., Hodge, R. D., Lein, E., Scheuermann, R. H.

Abstract:

Single cell genomics is rapidly advancing our knowledge of cell phenotypic types and states. Driven by single cell/nucleus RNA sequencing (scRNA-seq) data, comprehensive atlas projects covering a wide range of organisms and tissues are currently underway. As a result, it is critical that the cell transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell-types by surface protein expression to defining diseases by molecular drivers. Here we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the non-linear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that precisely captures the cell type identity represented in the complete scRNA-seq transcriptional profiles. The marker genes selected provide a barcode of the necessary and sufficient characteristics for semantic cell type definition and serve as useful tools for downstream biological investigation. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and non-coding RNAs in neuronal cell type identity.

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

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310656v1?rss=1

Authors: Alghamdi, N., Chang, W., Dang, P., Lu, X., Wan, C., Huang, Z., Wang, J., Fishel, M. L., Cao, S., Zhang, C.

Abstract:

The metabolic heterogeneity, and metabolic interplay between cells and their microenvironment have been known as significant contributors to disease treatment resistance. Our understanding of the intra-tissue metabolic heterogeneity and cooperation phenomena among cell populations is unfortunately quite limited, without a mature single cell metabolomics technology. To mitigate this knowledge gap, we developed a novel computational method, namely scFEA (single cell Flux Estimation Analysis), to infer single cell fluxome from single cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a comprehensively reorganized human metabolic map as focused metabolic modules, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multi-layer neural networks to fully capitulate the non-linear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq dataset with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this dataset demonstrated the consistency between predicted flux and metabolic imbalance with the observed variation of metabolites in the matched metabolomics data. We also applied scFEA on publicly available single cell melanoma and head and neck cancer datasets, and discovered different metabolic landscapes between cancer and stromal cells. The cell-wise fluxome predicted by scFEA empowers a series of downstream analysis including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

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

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.24.311316v1?rss=1

Authors: Harbitz, A.

Abstract:

Shape analysis of a closed 2D contour is an important topic within biological shape analysis, where Fourier methods to reproduce the shape with a limited number of parameters have been and still are of vital importance. An example is within marine management research on fish, where shape analysis of otolith (earstone) contours is performed for species identification as well as for stock discrimination purposes. In both cases, it is expected that the fewer parameters that are needed in a method to reproduce the contour sufficiently good, the better. This contribution outlines how a convex contour of any shape can be represented to any wanted accuracy by only one Fourier component. The key idea is to allow a flexible choice of a predetermined number of x -values along an x -axis that goes through the two most distant points of the contour. The y -variable along the perpendicular y -axis is then monotonically transformed to a z -variable so that the minium and maximum z -values on the contour have the same distance from the x -axis. The x -values of the contour points are now chosen so that the corresponding z -values on the contour follows a perfect sinusoid if the x -values were equidistant. The method is illustrated by application to lasso contours of Norwegian Coastal Cod (NCC) and North East Arctic Cod (NEAC) otolith images, where the average new x -positions for the individual otolith contours were applied to all otoliths. The results show that a considerably better fit to the original individual otolith contours were obtained by applying the invers FFT to the new y -values than by the frequently applied 2D EFDs (Elliptical Fourier Descriptors) approach, for the same number, m < 11, of frequency components. A promising classification result was also obtained by the linear Fisher discrimination method and cross validation applied to the individual x -values for the NCC and NEAC otoliths, with 82% score for NCC and 80% score for NEAC with sample sizes 367 and 240, respectively.

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

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.24.311274v1?rss=1

Authors: Gao, S., Wang, F., Niran, J., Li, N., Yin, Y., Yu, C., Jiao, C., Yao, M.

Abstract:

Bacterial spot (BS) disease of pepper, incited by Xanthomonas campestris pv. Vesicatoria ( Xcv ), is one of the most serious diseases. For a comparative analysis of defense response to Xcv infection, we performed a transcriptome analysis of BS -susceptible cultivar ECW and -resistant cultivar VI037601 using the HiSeq TM 2500 sequencing platform. Approximately 140.15 G clean data were generated from eighteen libraries. From the libraries generated, we identified 52,041 genes including 35,336 reference genes, 16,705 novel transcripts, and 4,794 differentially expressed genes (DEGs). There were 1,291, 2,956, 1,795 and 2,448 DEGs in ECW-24h-vs-ECW-0h, ECW-48h-vs-ECW-0h, VI037601-24h-vs-VI037601-0h and VI037601-48h-vs-VI037601-0h groups, respectively. Interestingly, DEGs involved in disease response in the resistant variety were induced at an earlier stage and at higher levels compared with the susceptible variety. Key enriched categories included amino sugar and nucleotide sugar metabolism, sesquiterpenoid and triterpenoid biosynthesis and MAPK signaling pathway. Moreover, 273 DEGs only differentially expressed in VI037601 and 436 overlapping DEGs in ECW and VI037601 post Xcv inoculation, including NBS-LRR genes, oxidoreductase gene, WRKY and NAC transcription factors were identified, which were mainly involved in metabolic process, response to stimulus and biological regulation pathways. Quantitative RT-PCR of sixteen selected DEGs further validated the RNA-seq differential gene expression analysis. Our results will provide a valuable resource for understanding the molecular mechanisms of pepper resistance to Xcv infection and improving pepper resistance cultivars against Xcv .

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