PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.20.347666v1?rss=1

Authors: Johnson, K. R., Mallon, B. S., Fann, Y. C., Chen, K. G.

Abstract:

The ground or naive pluripotent state of human pluripotent stem cells (hPSCs), which was initially established in mouse embryonic stem cells (mESCs), is an emerging and tentative concept. To verify this important concept in hPSCs, we performed a multivariate meta-analysis of major hPSC datasets via the combined analytic powers of percentile normalization, principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and SC3 consensus clustering. This vigorous bioinformatics approach has significantly improved the predictive values of the current meta-analysis. Accordingly, we were able to reveal various similarities between some naive-like hPSCs (NLPs) and their human and mouse in vitro counterparts. Moreover, we also showed numerous fundamental inconsistencies between diverse naive-like states, which are likely attributed to interlaboratory protocol differences. Collectively, our meta-analysis failed to provide global transcriptomic markers that support a bona fide human naive pluripotent state, rather suggesting the existence of altered pluripotent states under current naive-like growth protocols.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.20.346544v1?rss=1

Authors: Kueckelhaus, J., Ehr, J. v., Ravi, V. M., Will, P., Joseph, K. M., Beck, J., Hofmann, U. G., Delev, D., Schnell, O., Heiland, H. D.

Abstract:

Spatial transcriptomic is a technology to provide deep transcriptomic profiling by preserving the spatial organization. Here, we present a framework for SPAtial Transcriptomic Analysis (SPATA, https://themilolab.github.io/SPATA), to provide a comprehensive characterization of spatially resolved gene expression, regional adaptation of transcriptional programs and transient dynamics along spatial trajectories.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.20.347153v1?rss=1

Authors: Wang, X., Yang, Y., Liao, X., Li, K., Li, F., Peng, S.

Abstract:

Predicting potential links in heterogeneous biomedical networks (HBNs) can greatly benefit various important biomedical problem. However, the self-supervised representation learning for link prediction in HBNs has been slightly explored in previous researches. Therefore, this study proposes a two-level self-supervised representation learning, namely selfRL, for link prediction in heterogeneous biomedical networks. The meta path detection-based self-supervised learning task is proposed to learn representation vectors that can capture the global-level structure and semantic feature in HBNs. The vertex entity mask-based self-supervised learning mechanism is designed to enhance local association of vertices. Finally, the representations from two tasks are concatenated to generate high-quality representation vectors. The results of link prediction on six datasets show selfRL outperforms 25 state-of-the-art methods. In particular, selfRL reveals great performance with results close to 1 in terms of AUC and AUPR on the NeoDTI-net dataset. In summary, selfRL provides a general framework that develops self-supervised learning tasks with unlabeled data to obtain promising representations for improving link prediction.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.346445v1?rss=1

Authors: Khomtchouk, B. B., Vand, K. A., Nelson, C. S., Palmisano, S., Grossman, R. L.

Abstract:

Cardiovascular disease (CVD) is the leading cause of death worldwide for both genders and across most racial and ethnic groups. However, different races and ethnicities exhibit different rates of cardiovascular disease and its related cardiorenal and metabolic co-morbidities, suggesting differences in genetic predisposition and risk of onset, as well as socioeconomic and lifestyle factors (diet, exercise, etc.) that act upon an individual's unique underlying genetic background. Here we present HeartBioPortal2.0, a major update to HeartBioPortal, the world's largest CVD genetics data precision medicine platform for harmonized CVD-relevant genetic variants, which now enables search and analysis of human genetic information related to heart disease across ethnically diverse populations and cardiovascular/renal/metabolic quantitative traits pertinent to CVD pathophysiology. HeartBioPortal2.0 is structured as a cloud-based computing platform and knowledge portal that consolidates a multitude of CVD-relevant next-generation sequencing data modalities into a single powerful query and browsing interface between data and user via a user-friendly web application publicly available to the scientific research community. Since its initial release, HeartBioPortal2.0 has added new cardiovascular/renal/metabolic disease relevant gene expression data as well as genetic association data from numerous large-scale genome-wide association study (GWAS) consortiums such as CARDIoGRAMplusC4D, TOPMed, FinnGen, AFGen, MESA, MEGASTROKE, UK Biobank, CHARGE, Biobank Japan, MyCode, among other studies. In addition, HeartBioPortal2.0 now includes support for quantitative traits and ethnically diverse populations, allowing users to investigate the shared genetic architecture of any gene or its variants across the continuous cardiometabolic spectrum from health (e.g., blood pressure traits) to disease (hypertension), facilitating the understanding of CVD trait genetics that inform health-to-disease transitions and endophenotypes. Custom visualizations in the new and improved user interface (UI), including performance enhancements and new security features such as user authentication collectively re-imagine HeartBioPortal's user experience and provide a data commons that co-locates data, storage and computing infrastructure in the context of studying the genetic basis behind the leading cause of global mortality.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.20.347278v1?rss=1

Authors: Lin, A., Plubell, D. L., Keich, U., Noble, W. S.

Abstract:

The standard proteomics database search strategy involves searching spectra against a peptide database and estimating the false discovery rate (FDR) of the resulting set of peptide-spectrum matches. One assumption of this protocol is that all the peptides in the database are relevant to the hypothesis being investigated. However, in settings where researchers are interested in a subset of peptides, alternative search and FDR control strategies are needed. Recently, two methods were proposed to address this problem: subset-search and all-sub. We show that both methods fail to control the FDR. For subset-search, this failure is due to the presence of "neighbor" peptides, which are defined as irrelevant peptides with a similar precursor mass and fragmentation spectrum as a relevant peptide. Not considering neighbors compromises the FDR estimate because a spectrum generated by an irrelevant peptide can incorrectly match well to a relevant peptide. Therefore, we have developed a new method, "filter then subset-neighbor search" (FSNS), that accounts for neighbor peptides. We show evidence that FSNS properly controls the FDR when neighbors are present and that FSNS outperforms group-FDR, the only other method able to control the FDR relative to a subset of relevant peptides.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.345595v1?rss=1

Authors: Banik, A., Mondal, M. F., Khan, M. M. R., Ahmed, S. R., Hasan, M. M.

Abstract:

The locust problem is a global threat for food security. Locusts can fly and migrate overseas within a zip and creating a large-scale devastation to the diversified agro-ecosystem. GIS based analysis showed the recent movement of locusts, among them Schistocerca gregaria and Locusta migratoria are predominant in Indian subcontinent and are found more notorious and devastating one. This devastation needs to be stopped to save human race from food deprivation. In our study, we screened some commonly used agricultural pesticides and strongly recommended three of them viz. biphenthrin, diafenthiuron and silafluofen which might be potential to control the desert locusts based on their binding affinity towards the locusts survival proteins. Our phylogenetic analysis reveals that these three recommended pesticides might also show potency to the other locust species as well as they are also way safer than the other commercially available pesticides. These proposed pesticides bioactive analogs from fungus and bacteria may also show efficacy as next generation controlling measures of locust as well as different kind of pests. These recommended pesticides are expected to be highly effective against locusts and needs to bring forward by the entomologists by performing experimental field trials.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.346080v1?rss=1

Authors: Lorimer, T., Goodridge, R., Bock, A. K., Agarwal, V., Saberski, E., Sugihara, G., Rifkin, S. A.

Abstract:

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded `attractor' of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach -- defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error -- may be of broad interest and relevance to behavioural researchers working with video-derived time series.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.345983v1?rss=1

Authors: Lange, M., Bergen, V., Klein, M., Setty, M., Reuter, B., Bakhti, M., Lickert, H., Ansari, M., Schniering, J., Schiller, H. B., Pe'er, D., Theis, F. J.

Abstract:

Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.20.347021v1?rss=1

Authors: Basu, S., Mukhopadhyay, S., Das, R., Mukhopadhyay, S., Singh, P. K., Ganguli, S.

Abstract:

The SARS-CoV-2 is a positive stranded RNA virus with a genome size of ~29.9 kilobase pairs which spans 29 open reading frames. Studies have revealed that the genome encodes about 16 non-structural proteins (nsp), four structural proteins, and six or seven accessory proteins. Based on prevalent knowledge on SARS-CoV and other coronaviruses, functions have been assigned for majority of the proteins. While, researchers across the globe are engrossed in identifying a potential pharmacological intervention to control the viral outbreak, none of the work has come up with new antiviral drugs or vaccines yet. One possible approach that has shown some positive results is by treating infected patients with the plasma collected from convalescent COVID-19 patients. Several vaccines around the world have entered their final trial phase in humans and we expect that these will in time be available for application to worldwide population to combat the disease. In this work we analyse the effect of prevalent mutations in the major pathogenesis related proteins of SARS-COV2 and attempt to pinpoint the effects of those mutations on the structural stability of the proteins. Our observations and analysis direct us to identify that all the major mutations have a negative impact in context of stability of the viral proteins under study and the mutant proteins suffer both structural and functional alterations as a result of the mutations. Our binary scoring scheme identifies L84S mutation in ORF8 as the most disruptive of the mutations under study. We believe that, the virus is under the influence of an evolutionary phenomenon similar to Muller s ratchet where the continuous accumulation of these mutations is making the virus less virulent which may also explain the reduction in fatality rates worldwide. Keywords: SARS-COV2, Covid19, Mutations, Structural Analysis

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.18.344697v1?rss=1

Authors: Zhang, Z., Zhang, L.

Abstract:

Due to the complexity of metagenomic community, de novo assembly on next generation sequencing data is commonly unable to produce microbial complete genomes. Metagenomic binning is a crucial task that could group the fragmented contigs into clusters based on their nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Assembly and paired-end graphs can provide the connectedness between contigs, where the linked contigs have high chance to be derived from the same clusters. Results: We developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and paired-end graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends subgraphs. METAMVGL could learn the graphs' weights automatically and predict the contig labels in a uniform multi-view label propagation framework. In the experiments, we observed METAMVGL significantly increased the high-confident edges in the combined graph and linked dead ends to the main graph. It also outperformed with many state-of-the-art binning methods, MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and Graphbin on the metagenomic sequencing from simulation, two mock communities and real Sharon data. Availability and implementation: The software is available at https://github.com/ZhangZhenmiao/METAMVGL.

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