PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.10.290973v1?rss=1

Authors: Wei, J., Zhou, T., Zhang, X., Tian, T.

Abstract:

One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. In this work we devise a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. This method consists of two major steps: namely a new dimension reduction method (i.e. Bhattacharyya kernel feature decomposition (BKFD)) and a novel approach, named Reverse Searching on kNN Graph (RSKG), to identify the underlying multi-branching processes of cellular differentiations. In BKFD we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm and then propose a new distance metric for calculating pseudo-times of single-cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of the new method with two state-of-the-art methods. Simulation results suggest that our proposed method has superior accuracy and strong robustness properties for constructing pseudo-time trajectories. Availability: DTFLOW is implemented in Python and available at https://github.com/statway/DTFLOW.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.10.291203v1?rss=1

Authors: Friedrichs, K., Spranger, M., Palaniappan, S.

Abstract:

The rapid growth of scientific publications every year makes it infeasible to keep pace with and survey manually, even for a specific field. Keeping up with literature and gaining a birds-eye view in a timely manner is crucial to the pursuit of scientific discovery and innovation. To help gain a clearer understanding of the state and progress of science and the nature of discovery, one can encode key information from these publications and represent them as a network. Observations on the structural evolution of these graphs can offer valuable insights on the dynamics at play. This work describes the construction and analyses the temporal evolution of a knowledge network of keywords (specifically focusing on genes/proteins, diseases and chemicals) from publications in the biomedical sciences domain. We compare and contrast the representations and evolution of these keyword networks types and find significant differences in the network growth, largely corresponding to our intuition. Furthermore, we focus on the formation and evolution of new links, which we argue corresponds to new scientific discoveries. Our findings suggest that these links are progressively formed in short network distance, leading to clusters of extensively studied keywords. This strategy, however, seems to impede ground-breaking innovation, which could be beneficial for research progress.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.10.291377v1?rss=1

Authors: Quinlan, A., Cormier, M., Belyeu, J., Pedersen, B. S., Brown, J., Koster, J.

Abstract:

Genomics research is complicated by the inherent difficulty of collecting, transforming, and integrating the numerous datasets and annotations germane to one's research. Furthermore, these data exist in disparate sources, and are stored in numerous, often abused formats from multiple genome builds. Since these complexities waste time, inhibit reproducibility, and curtail research creativity, we developed Go Get Data (GGD; https://gogetdata.github.io/) as a fast, reproducible approach to installing standardized data recipes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.10.291781v1?rss=1

Authors: Bonnardel, F., Haslam, S. M., Dell, A., Feizi, T., Liu, Y., Tajadura-Ortega, V., Akune, Y., Sykes, L., Bennett, P. R., MacIntyre, D. A., Lisacek, F., Imberty, A.

Abstract:

Lectins, such as adhesins and toxins, are carbohydrate-binding proteins that recognise glycans of cells and their secretions. While mediation of microbe-microbe and microbe-host interactions by lectins has long been recognised in the lung and gut, little is known about those in the vagina, where such interactions are implicated in health and various disease states. These include sexually transmitted infections, cervical cancer and poor pregnancy outcomes such as preterm birth. In this study, the curated UniLectin3D database was used to establish a lectin classification based primarily on taxonomy and protein 3D structure. The resulting 109 lectin classes were characterised by specific Hidden Markov Model (HMM) profiles. Screening of microbial genomes in the UniProt and NCBI NR sequence databases resulted in identification of >100 000 predicted bacterial lectins available at unilectin.eu/bacteria. Screening of the complete genomes of 90 isolates from 21 vaginal bacterial species showed that the predicted lectomes (ensemble of predicted lectins) of Lactobacilli associated with vaginal health are substantially less diverse than those of pathogens and pathobionts. Both the number of predicted bacterial lectins, and their specificities for carbohydrates correlated with pathogenicity. This study provides new insights into potential mechanisms of commensal and pathogen colonisation of the reproductive tract that underpin health and disease states.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.11.292730v1?rss=1

Authors: Newman, T., Chang, H. F. K., Jabbari, H.

Abstract:

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus spreading across the world causing the disease COVID-19. The diagnosis of COVID-19 is done by quantitative reverse-transcription polymer chain reaction (qRT-PCR) testing which utilizes different primer-probe sets depending on the assay used. Using in silico analysis we aimed to determine how the secondary structure of the SARS-CoV-2 RNA genome affects the interaction between the reverse primer during qRT-PCR and how it relates to the experimental primer-probe test efficiencies. We introduce the program DinoKnot (Duplex Interaction of Nucleic acids with pseudoKnots) that follows the hierarchical folding hypothesis to predict the secondary structure of two interacting nucleic acid strands (DNA/RNA) of similar or different type. DinoKnot is the first program that utilizes stable stems in both strands as a guide to find the structure of their interaction. Using DinoKnot we predicted the interaction of the reverse primers used in four common COVID-19 qRT-PCR tests with the SARS-CoV-2 RNA genome. In addition, we predicted how 12 mutations in the primer/probe binding region may affect the primer/probe ability and subsequent SARS-CoV-2 detection. While we found all reverse primers are capable of interacting with their target area, we identified partial mismatching between the SARS-CoV-2 genome and some reverse primers. We predicted three mutations that may prevent primer binding, reducing the ability for SARS-CoV-2 detection. We believe our contributions can aid in the design of a more sensitive SARS-CoV-2 test.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.11.292722v1?rss=1

Authors: Mironov, A., Denisov, S., Gress, A., Kalinina, O. V., Pervouchine, D.

Abstract:

Tandem alternative splice sites (TASS) is a special class of alternative splicing events that are characterized by a close tandem arrangement of splice sites. Most TASS lack functional characterization and are believed to arise from splicing noise. Based on the RNA-seq data from the Genotype Tissue Expression project, we present an extended catalogue of TASS in healthy human tissues and analyze their tissue-specific expression. The expression of TASS is usually dominated by one major splice site (maSS), while the expression of minor splice sites (miSS) is at least an order of magnitude lower. Among 73k miSS with sufficient read support, 12k (17%) are significantly expressed above the expected noise level, and among them 2k are expressed tissue-specifically. We found significant correlations between tissue-specific expression of RNA-binding proteins (RBP) and tissue-specific expression of miSS that is consistent with miSS response to RBP inactivation by shRNA. In combination with RBP profiling by eCLIP, this allowed prediction of novel cases of tissue-specific splicing regulation including a miSS in QKI mRNA that is likely regulated by PTBP1. According to the structural annotation of the human proteome, tissue-specific miSS are enriched within disordered regions, and indels induced by miSS are enriched with short linear motifs and post-translational modification sites. Nonetheless, more than 15% of tissue-specific miSS affect structured protein regions and may adjust protein-protein interactions or modify the stability of the protein core. The significantly expressed miSS evolve under the same selection pressure as maSS, while other miSS lack signatures of evolutionary selection and conservation. Using mixture models, we estimated that not more than 10% of maSS and not more than 50% of significantly expressed miSS are noisy, while the proportion of noisy splice sites among not significantly expressed miSS is above 70%.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.11.293472v1?rss=1

Authors: PERRIN, A., ROCHA, E.

Abstract:

The gene repertoires of microbial species, their pangenomes, evolve very fast. Their study facilitates the discrimination between lineages and reveals which genes drive their recent adaptation. It has therefore become a key topic of study in microbial evolution and genomics. Yet, the increase in the number of genomes available to certain species, now reaching many thousands, complicates the establishment of the basic building blocks of comparative genomics. Here, we present PanACoTA, a tool that allows to download all genomes of a species, build a database with those passing quality and redundancy controls, define uniform annotation, and use them to build a pangenome, several variants of core or persistent genomes, their alignments, and a rapid but accurate phylogenetic tree. While many programs have become available in the last few years to build pangenomes, we have focused on a method that tackles all the key steps of the process, from download to phylogenetic inference. This was conceived in a modular way, i.e. while all steps are integrated, they can also be run separately and multiple times to allow rapid and extensive exploration of the space of parameters of interest. The software is built in Python 3 and includes features to facilitate its installation and its future development. We believe PanACoTa is an interesting addition to the current set of bioinformatics software for comparative genomics, since it will accelerate and standardize the more routine parts of the work, allowing microbial genomicists to more quickly tackle their specific questions.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.11.293449v1?rss=1

Authors: Huang, X., Zhang, C., Pearce, R., Omenn, G. S., Zhang, Y.

Abstract:

Despite considerable research progress on SARS-CoV-2, the direct zoonotic origin (intermediate host) of the virus remains ambiguous. The most definitive approach to identify the intermediate host would be the detection of SARS-CoV-2-like coronaviruses in wild animals. However, due to the high number of animal species, it is not feasible to screen all the species in the laboratory. Given that the recognition of the binding ACE2 proteins is the first step for the coronaviruses to invade host cells, we proposed a computational pipeline to identify potential intermediate hosts of SARS-CoV-2 by modeling the binding affinity between the Spike receptor-binding domain (RBD) and host ACE2. Using this pipeline, we systematically examined 285 ACE2 variants from mammals, birds, fish, reptiles, and amphibians, and found that the binding energies calculated on the modeled Spike-RBD/ACE2 complex structures correlate closely with the effectiveness of animal infections as determined by multiple experimental datasets. Built on the optimized binding affinity cutoff, we suggested a set of 96 mammals, including 48 experimentally investigated ones, which are permissive to SARS-CoV-2, with candidates from primates, rodents, and carnivores at the highest risk of infection. Overall, this work not only suggested a limited range of potential intermediate SARS-CoV-2 hosts for further experimental investigation; but more importantly, it proposed a new structure-based approach to general zoonotic origin and susceptibility analyses that are critical for human infectious disease control and wildlife protection.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.196220v1?rss=1

Authors: Gogate, N., Lyman, D., Crandall, K. A., Kahsay, R., Natale, D. A., Sen, S., Mazumder, R.

Abstract:

Scientists, medical researchers, and health care workers have mobilized worldwide in response to the outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SCoV2). Preliminary data have captured a wide range of host responses, symptoms, and lingering problems post-recovery within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, co-morbidities, genetics, and other factors. As COVID-19-related data continue to accumulate from disparate groups, the heterogeneous nature of these datasets poses challenges for efficient extrapolation of meaningful observations, hindering translation of information into clinical applications. Attempts to utilize, analyze, or combine biomarker datasets from multiple sources have shown to be inefficient and complicated, without a unifying resource. As such, there is an urgent need within the research community for the rapid development of an integrated and harmonized COVID-19 Biomarker Knowledgebase. By leveraging data collection and integration methods, backed by a robust data model developed to capture cancer biomarker data we have rapidly crowdsourced the collection and harmonization of COVID-19 biomarkers. Our resource currently has 138 unique biomarkers. We found multiple instances of the same biomarker substance being suggested as multiple biomarker types during our extensive cross-validation and manual curation. As a result, our Knowledgebase currently has 265 biomarker type combinations. Every biomarker entry is made comprehensive by bringing in together ancillary data from multiple sources such as biomarker accessions (canonical UniProtKB accession, PubChem Compound ID, Cell Ontology ID, Protein Ontology ID, NCI Thesaurus Code, and Disease Ontology ID), BEST biomarker category, and specimen type (Uberon Anatomy Ontology) unified with ontology standards. Our preliminary observations show distinct trends in the collated biomarkers. Most biomarkers are related to the immune system (SAA, TNF-, and IP-10) or coagulopathies (D-dimer, antithrombin, and VWF) and a few have already been established as cancer biomarkers (ACE2, IL-6, IL-4, and IL-2). These trends align with the proposed hypotheses of clinical manifestations compounding the complexity of COVID-19 pathobiology. We explore these trends as we put forth a COVID-19 biomarker resource that will help researchers and diagnosticians alike. All biomarker data are freely available from https://data.oncomx.org/covid19.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.290577v1?rss=1

Authors: David, R., Menezes, R.-J. D., Klerk, J. D., Castleden, I. R., Hooper, C. M., Carneiro, G., Gilliham, M.

Abstract:

With the advent of increased diversity and scale of molecular data, there has been a growing appreciation for the applications of machine learning and statistical methodologies to gain new biological insights. An important step in achieving this aim is the Relation Extraction process which specifies if an interaction exists between two or more biological entities in a published study. Here, we employed natural-language processing (CBOW) and deep Recurrent Neural Network (bi-directional LSTM) to predict relations between biological entities that describe protein subcellular localisation in plants. We applied our system to 1700 published Arabidopsis protein subcellular studies from the SUBA manually curated dataset. The system was able to extract relevant text and the classifier predicted interactions between protein name, subcellular localisation and experimental methodology. It obtained a final precision, recall rate, accuracy and F1 scores of 0.951, 0.828, 0.893 and 0.884 respectively. The classifier was subsequently tested on a similar problem in crop species (CropPAL) and demonstrated a comparable accuracy measure (0.897). Consequently, our approach can be used to extract protein functional features from unstructured text in the literature with high accuracy. The developed system will improve dissemination or protein functional data to the scientific community and unlock the potential of big data text analytics for generating new hypotheses from diverse datasets.

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