PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.08.331926v1?rss=1

Authors: Vergetis, V., Liaropoulos, G., Georganaki, M., Dimakakos, A., Skaltsas, D., Gorgoulis, V. G., Tsirigos, A.

Abstract:

Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk - the probability that a drug will eventually receive regulatory approval - has been notoriously hard given the complexities of drug biology and clinical trials. This inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources, and, as a result, an overall reduction in R&D productivity. We propose a Machine Learning (ML) approach that provides a more accurate and unbiased estimate of drug development risk than traditional models.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.08.331090v1?rss=1

Authors: Lefort, G., Liaubet, L., Marty-Gasset, N., Canlet, C., Vialaneix, N., Servien, R.

Abstract:

Metabolomics is a promising approach to characterize phenotypes or to identify biomarkers. It is also easily accessible through NMR, which can provide a comprehensive understanding of the metabolome of any living organisms. However, the analysis of 1H NMR spectrum remains difficult, mainly due to the different problems encountered to perform automatic identification and quantification of metabolites in a reproducible way. In addition, methods that perform automatic identification and quantification of metabolites often do it for one given complex mixture spectrum. Hence, when a set of complex mixture spectra coming from the same experiment has to be processed, the approach is simply repeated independently for every spectrum, despite their resemblance. Here, we present a new method that is the first to identify and quantify metabolites by integrating information coming from several complex spectra of the same experiment. The performances of this new method are then evaluated on both simulated and real datasets. The results show an improvement in the metabolite identification and in the accuracy of metabolite quantifications, especially when the concentration is low. This joint procedure is available in version 2.0 of ASICS package.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.08.332288v1?rss=1

Authors: Heiser, C. N., Wang, V. M., Chen, B., Hughey, J. J., Lau, K. S.

Abstract:

A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in datasets with disparate library sizes confounded by high technical noise (i.e. batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining dataset-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against a conventional thresholding approach and EmptyDrops, a popular computational method, demonstrating greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low and high-background datasets that dropkick's weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to dataset-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell analysis Python packages.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.08.332346v1?rss=1

Authors: Cai, T., Lim, H., Abbu, K. A., Qiu, Y., Nussinov, R., Xie, L.

Abstract:

Molecular interaction is the foundation of biological process. Elucidation of genome-wide binding partners of a biomolecule will address many questions in biomedicine. However, ligands of a vast number of proteins remain elusive. Existing methods mostly fail when the protein of interest is dissimilar from those with known functions or structures. We develop a new deep learning framework DISAE that incorporates biological knowledge into self-supervised learning techniques for predicting ligands of novel unannotated proteins on a genome-scale. In the rigorous benchmark studies, DISAE outperforms state-of-the-art methods by a significant margin. The interpretability analysis of DISAE suggests that it learns biologically meaningful information. We further use DISAE to assign ligands to human orphan G-Protein Coupled Receptors (GPCRs) and to cluster the human GPCRome by integrating their phylogenetic and ligand relationships. The promising results of DISAE open an avenue for exploring the chemical landscape of entire sequenced genomes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.08.332452v1?rss=1

Authors: Hassan, S. S., Ghosh, S., Attrish, D., Pal Choudhury, P., Uversky, V. N., Uhal, B., Lundstrom, K., Rezaei, N., Aljabali, A. A. A., Seyran, M., Pizzol, D., Adadi, P., Soares, A., Abd El-Aziz, T. M., Kandimalla, R., Tambuwala, M., Azad, G. K., Sherchan, S. P., Baetas-da-Cruz, W., Takayama, K., Serrano-Aroca, A., Chauhan, G., Palu, G., Brufsky, A.

Abstract:

Angiotensin-converting enzyme 2 (ACE2) is the cellular receptor for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that is engendering the severe coronavirus disease 2019 (COVID-19) pandemic. The spike (S) protein receptor-binding domain (RBD) of SARS-CoV-2 binds to the three sub-domains viz. amino acids (aa) 22-42, aa 79-84, and aa 330-393 of ACE2 on human cells to initiate entry. It was reported earlier that the receptor utilization capacity of ACE2 proteins from different species, such as cats, chimpanzees, dogs, and cattle, are different. A comprehensive analysis of ACE2 receptors of nineteen species was carried out in this study, and the findings propose a possible SARS-CoV-2 transmission flow across these nineteen species.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.08.318535v1?rss=1

Authors: Xu, Z., Mai, Y., Liu, D., He, W., Lin, X., Xu, C., Zhang, L., Meng, X., Mafofo, J., Zaher, W. A., Li, Y., Qiao, N.

Abstract:

Oxford Nanopore Technologies (ONT) is a promising sequencing technology that could generate relatively longer sequencing reads compared to the next generation sequencing (NGS) technology. The base calling process is very important for TGS. It translates the original electrical signals from the sequencer to the nucleotide sequence. By doing that, the base calling could significantly influence the accuracy of downstream analysis. Bonito is a recently developed basecaller based on deep neuron network, the neuron network architecture of which is composed of a single convolutional layer followed by three stacked bidirectional GRU layers. Although Bonito achieved the state-of-the-art accuracy, its speed is so slow that it is not likely to be used in production. We therefore implement Fast-Bonito, which introduces systematic optimization to speed up Bonito. Fast-Bonito archives 53.8% faster than the original version on NVIDIA V100 and could be further speed up by HUAWEI Ascend 910 NPU, achieving 565% faster than the original version. The accuracy of Fast-Bonito is also slightly higher than the original Bonito.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.08.329920v1?rss=1

Authors: Fan, J., Huang, S., Chorlton, S. D.

Abstract:

Background: As the use of nanopore sequencing for metagenomic analysis increases, tools capable of performing long-read taxonomic classification in a fast and accurate manner are needed. Existing tools were either designed for short-read data (eg. Centrifuge) or take days to analyse modern sequencer outputs (eg. MetaMaps). Results: We present BugSeq, a novel, highly accurate metagenomic classifier for nanopore reads. BugSeq (F1=0.91-0.95) offers better read classification than MetaMaps (F1=0.89-0.94) in a fraction of the time. BugSeq significantly improves on the accuracy of Centrifuge (F1=0.79-0.93) while offering competitive run times. We apply BugSeq to metagenomic sequencing of 41 samples from patients with lower respiratory tract infections and show that it produces greater concordance with microbiological culture and qPCR compared with "What's In My Pot" analysis. Conclusion: BugSeq is deployed to the cloud for easy and scalable long-read metagenomic analyses. BugSeq is freely available for non-commercial use at https://bugseq.com/free.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.08.327718v1?rss=1

Authors: Jambor, H., Antonietti, A., Alicea, B. J., Audisio, T. L., Auer, S., Bhardwaj, V., Burgess, S. J., Ferling, I., Gazda, M. A., Hoeppner, L. H., Ilangovan, V., Lo, H., Olson, M., Mohamed, S. Y., Sarabipour, S., Varma, A., Walavalkar, K., Wissink, E., Weissgerber, T. L.

Abstract:

Scientists routinely use images to display data. Readers often examine figures first; therefore, it is important that figures are accessible to a broad audience. Many resources discuss fraudulent image manipulation and technical specifications for image acquisition; however, data on the legibility and interpretability of images are scarce. We systematically examined these factors in non-blot images published in the top 15 journals in three fields; plant sciences, cell biology and physiology. Common problems included missing scale bars, misplaced or poorly marked insets, images or labels that were not accessible to colorblind readers, and insufficient explanations of colors, labels, annotations, or the species and tissue or object depicted in the image. Papers that met all good practice criteria examined for all image-based figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). We present detailed descriptions and visual examples to help scientists avoid common pitfalls when publishing images. Our recommendations address image magnification, scale information, insets, annotation, and color and may encourage discussion about quality standards for bioimage publishing.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.06.327080v1?rss=1

Authors: Cantrell, K., Fedarko, M. W., Rahman, G., McDonald, D., Yang, Y., Zaw, T., Gonzalez, A., Janssen, S., Estaki, M., Haiminen, N., Beck, K. L., Zhu, Q., Sayyari, E., Morton, J., Tripathi, A., Gauglitz, J. M., Marotz, C., Matteson, N. L., martino, c., Sanders, J. G., Carrieri, A. P., Song, S. J., Swafford, A. D., Dorrestein, P., Andersen, K. G., PARIDA, L., Kim, H.-C., Vazquez-Baeza, Y., Knight, R.

Abstract:

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive tool for visualizing trees in the context of microbiome, metabolome, etc. community data scalable beyond modern large datasets like the Earth Microbiome Project. EMPress provides novel functionality-including ordination integration and animations-alongside many standard tree visualization features, and thus simplifies exploratory analyses of many forms of -omic data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.07.321927v1?rss=1

Authors: Vicar, T., Gumulec, J., Kolar, R., Kopecna, O., Pagacova, E., Falk, M.

Abstract:

DNA double-strand breaks, marked by Ionizing Radiation-Induced (Repair) Foci (IRIF), are the most serious DNA lesions, dangerous to human health. IRIF quantification based on confocal microscopy represents the most sensitive and gold standard method in radiation biodosimetry and allows research of DSB induction and repair at the molecular and a single cell level. In this study, we introduce DeepFoci - a deep learning-based fully-automatic method for IRIF counting and its morphometric analysis. DeepFoci is designed to work with 3D multichannel data (trained for 53BP1 and {gamma}H2AX) and uses U-Net for the nucleus segmentation and IRIF detection, together with maximally stable extremal region-based IRIF segmentation. The proposed method was trained and tested on challenging datasets consisting of mixtures of non-irradiated and irradiated cells of different types and IRIF characteristics - permanent cell lines (NHDF, U-87) and cell primary cultures prepared from tumors and adjacent normal tissues of head and neck cancer patients. The cells were dosed with 1-4 Gy gamma-rays and fixed at multiple (0-24 h) post-irradiation times. Upon all circumstances, DeepFoci was able to quantify the number of IRIF foci with the highest accuracy among current advanced algorithms. Moreover, while the detection error of DeepFoci remained comparable to the variability between two experienced experts, the software kept its sensitivity and fidelity across dramatically different IRIF counts per nucleus. In addition, information was extracted on IRIF 3D morphometric features and repair protein colocalization within IRIFs. This allowed multiparameter IRIF categorization, thereby refining the analysis of DSB repair processes and classification of patient tumors with a potential to identify specific cell subclones. The developed software improves IRIF quantification for various practical applications (radiotherapy monitoring, biodosimetry, etc.) and opens the door to an advanced DSB focus analysis and, in turn, a better understanding of (radiation) DNA damaging and repair.

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