PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.13.295063v1?rss=1

Authors: Wang, Q., Yan, G.

Abstract:

(1) Background: Compared with monotherapy, efficacious drug combinations can increase the therapeutic effect, decrease drug resistance of experimental subjects and the side effects of drugs. Therefore, efficacious drug combinations are widely used in the treatment of complex diseases, such as various cancers. However, compared with the mathematical model and computational method, experimental screening efficacious drug combinations is time-consuming, costly, laborious, and inefficient; (2) Methods: we predicted efficacious drug combinations in cancer based on random walk with restart (ERWR). An efficacious score can be obtained between any two individual drugs by ERWR; (3) Results: As a result, we analyzed the rationality of the efficacious score first. Besides, compared with the other methods by leave-one-out cross-validation, all the Area Under Receiver Operating Characteristic Curves (AUROCs) of ERWR were higher for data sets of breast cancer, colorectal cancer, and lung cancer. Moreover, the case study of breast cancer showed that ERWR could discover potential efficacious drug combinations; (4) Conclusions: These results suggest that ERWR is a novel way to discover efficacious drug combinations in cancer, which provides new prospects for cancer treatment. Furthermore, ERWR is a semi-supervised learning framework that can be used to predict combinations of drugs for other complex diseases.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.12.292821v1?rss=1

Authors: Manukyan, A., Hovhannisyan, A.

Abstract:

Breast cancer resistance protein (BCRP, ABCG2) is one of the ATP binding cassette (ABC) transporter proteins which involved in multi-drug resistance of cancer therapy. BCRP is expressed in various types of human cancer and inhibiting can be a solution to overcome multidrug resistance. Significant effort has been devoted to develop treatment strategies to overcome BCRP-mediated resistance. In addition, BCRP is expressed also in many normal tissues and plays an important role in drug absorption, distribution, and elimination. In order to design an effective cancer treatment strategy, a lot of flavones and their derivatives are used as anticancer drug compounds. One of the most promising flavonols is quercetin and quercetin semisynthetic derivatives which can inhibit the expression of BCRP.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.13.295089v1?rss=1

Authors: Ueda, H.

Abstract:

Advances in Nanopore single-molecule direct RNA sequencing (DRS) have presented the possibility of detecting comprehensive post-transcriptional modifications (PTMs) as an alternative to experimental approaches combined with high-throughput sequencing. It has been shown that the DRS method can detect the change in the raw electric current signal of a PTM; however, the accuracy and reliability still require improvement. Here, we presented a new software, called nanoDoc, for detecting PTMs from DRS data using a deep neural network. Current signal deviations caused by PTMs are analyzed via Deep One-Class Classification with a convolutional neural network. Using a ribosomal RNA dataset, the software archive displayed an area under the curve (AUC) accuracy of 0.96 for the detection of 23 different kinds of modifications in Escherichia coli and Saccharomyces cerevisiae. We also demonstrated a tentative classification of PTMs using unsupervised clustering. Finally, we applied this software to severe acute respiratory syndrome coronavirus 2 data and identified commonly modified sites among three groups. nanoDoc is open source (GPLv3) and available at https://github.com/uedaLabR/nanoDoc .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.11.293977v1?rss=1

Authors: Bittrich, S., Burley, S. K., Rose, A. S.

Abstract:

Biochemical and biological functions of proteins are the product of both the overall fold of the polypeptide chain, and, typically, structural motifs made up of smaller numbers of amino acids constituting a catalytic center or a binding site. Detection of such structural motifs can provide valuable insights into the function(s) of previously uncharacterized proteins. Technically, this remains an extremely challenging problem because of the size of the Protein Data Bank (PDB) archive. Existing methods depend on a clustering by sequence similarity and can be computationally slow. We have developed a new approach that uses an inverted index strategy capable of analyzing >160,000 PDB structures with unmatched speed. The efficiency of the inverted index method depends critically on identifying the small number of structures containing the query motif and ignoring most of the structures that are irrelevant. Our approach (implemented at https://motif.rcsb.org) enables real-time retrieval and superposition of structural motifs, either extracted from a reference structure or uploaded by the user. Herein, we describe the method and present five case studies that exemplify its efficacy and speed for analyzing 3D structures of both proteins and nucleic acids.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.11.293456v1?rss=1

Authors: Okinaga, Y., Kyogoku, D., Kondo, S., Nagano, A. J., Hirose, K.

Abstract:

Motivation: The least absolute shrinkage and selection operator (lasso) and principal component regression (PCR) are popular methods of estimating traits from high-dimensional omics data, such as transcriptomes. The prediction accuracy of these estimation methods is highly dependent on the covariance structure, which is characterized by gene regulation networks. However, the manner in which the structure of a gene regulation network together with the sample size affects prediction accuracy has not yet been sufficiently investigated. In this study, Monte Carlo simulations are conducted to investigate the prediction accuracy for several network structures under various sample sizes. Results: When the gene regulation network was random graph, the simulation indicated that models with high estimation accuracy could be achieved with small sample sizes. However, a real gene regulation network is likely to exhibit a scale-free structure. In such cases, the simulation indicated that a relatively large number of observations is required to accurately predict traits from a transcriptome. Availability and implementation: Source code at https://github.com/keihirose/simrnet Contact: [email protected]

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.11.293092v1?rss=1

Authors: Davies, V., Wandy, J., Weidt, S., van der Hooft, J. J. J., Miller, A., Daly, R., Rogers, S.

Abstract:

Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data Dependent Acquisition (DDA) chooses which ions to fragment based upon intensity observed in MS1 survey scans and typically only fragment a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimise approaches in busy MS facilities. In this work, we firstly provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost efficient development of new DDA acquisition strategies using a previously developed Virtual Metabolomics Mass Spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an instrument application programming interface (API). We demonstrate this framework through the development and optimisation of two new DDA methods which introduce new advanced ion prioritisation strategies. Upon application of the here developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA acquisition strategies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.11.277483v1?rss=1

Authors: Banerjee, S., Velasquez-Zapata, V., Fuerst, G., Elmore, J. M., Wise, R. P.

Abstract:

Mapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate cDNA libraries represent an alternative approach that optimizes scale, cost, and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are assessed to determine whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and it recognized all validated interactions. NGPINT can be used in any organism with an available reference, thus facilitating the discovery of protein-protein interactions in non-model organisms.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.289629v1?rss=1

Authors: Zhang, N., Fan, Z.

Abstract:

Pathogen-associated molecular patterns (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI) are designated as a two-branched system of innate immunity. Although PTI/ETI share a series of downstream molecular events, systematic analysis of convergent and divergent signaling in PTI/ETI is currently lacking. The phytohormones salicylic acid (SA) and jasmonic acid (JA) are considered to constitute the hormonal backbone of plant immunity, are functionally antagonistic, and play essential roles in defending against biotrophic and necrotrophic pathogens, respectively. However, the distinct performance of two phytohormones in PTI/ETI remains unclear. Here, we systemically investigate and validate the reprogramming of molecular networks during PTI and ETI. Using publicly available Arabidopsis RNA sequence data from 560 samples, we construct a co-expression network under Mock conditions and then explore the differential expression/co-expression changes during PTI, ETI, and Pto DC3000 infection. During PTI and ETI, one-third of genes in the Arabidopsis genome exhibit the same directional differential expression in a manner independent of JA/ethylene/PAD4/SA signaling but show differential co-expression patterns. However, the defense phytohormone network is required for defense against Pto DC3000 infection. We also exhibit the use of this network in prioritizing genes that functioned closely with the proteins directly targeted by elicitors.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.10.290924v1?rss=1

Authors: Sweeney, B. A., Hoksza, D., Nawrocki, E. P., Ribas, C. E., Madeira, F., Cannone, J. J., Gutell, R. R., Maddala, A., Meade, C., Williams, L. D., Petrov, A. S., Chan, P. P., Lowe, T. M., Finn, R. D., Petrov, A. I.

Abstract:

Non-coding RNAs (ncRNA) are essential for all life, and the functions of many ncRNAs depend on their secondary (2D) and tertiary (3D) structure. Despite proliferation of 2D visualisation software, there is a lack of methods for automatically generating 2D representations in consistent, reproducible, and recognisable layouts, making them difficult to construct, compare and analyse. Here we present R2DT, a comprehensive method for visualising a wide range of RNA structures in standardised layouts. R2DT is based on a library of 3,632 templates representing the majority of known structured RNAs, from small RNAs to the large subunit ribosomal RNA. R2DT has been applied to ncRNA sequences from the RNAcentral database and produced >13 million diagrams, creating the world's largest RNA 2D structure dataset. The software is freely available at https://github.com/rnacentral/R2DT and a web server is found at https://rnacentral.org/r2dt.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.10.291286v1?rss=1

Authors: Preusser, F., dos Santos, N., Contzen, J., Stachelscheid, H., Costa, E. T., Mergenthaler, P., Preibisch, S.

Abstract:

Three-dimensional stem-cell-derived organoids are a powerful tool for studying cellular processes in tissue-like structures, enabling in vitro experiments in an organ specific context. While organoid research has been closely linked to advances in fluorescent microscopy, capturing cellular structures within their global context in an organoid often remains challenging due to the dense structure and opacity of organoids. The development of optical clearing methods has provided a solution for fixed organoids but optimizing clearing protocols for a given sample type and staining can be challenging. Importantly, quantitative measures for assessing image quality throughout cleared fluorescent samples are missing. Here, we propose the Fourier ring correlation-based quality estimation (FRC-QE) as a new metric for automated 3D image quality estimation in cleared organoids. We show that FRC-QE robustly captures differences in clearing efficiency within an organoid, across replicates and clearing protocols, and also works for different microscopy modalities. FRC-QE is open-source, written in ImgLib2, and provided as an easy-to-use and macro-scriptable plugin for the popular Fiji software. We therefore envision FRC-QE to fill the gap of providing a reliable quality metric for testing, optimizing and comparing optical clearing methods.

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