PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.09.375394v1?rss=1

Authors: Du, H., Chen, F., Liu, H., Hong, P.

Abstract:

COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of under-utilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and their hosts' proteins. More in-depth and more comprehensive analyses of that knowledge and data can shed new insight into the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. A machine learning-based method, termed Infection Mechanism and Spectrum Prediction (IMSP), was developed to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2, which deserved public health attention, and eight highly possible interactions between SARS-CoV-2 proteins and human proteins. Given a new virus, IMSP can utilize existing knowledge and data about other highly relevant viruses to predict multi-scale interactions between the new virus and potential hosts.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.11.377713v1?rss=1

Authors: Choudhury, A., Das, N. C., Patra, R., Bhattacharya, M., Mukherjee, S.

Abstract:

The worldwide outbreak of COVID-19 pandemic caused by SARS-CoV-2 leads to loss of mankind and global economic stability. The continuous spreading of the disease and its pathogenesis takes millions of lives of peoples and the unavailability of appropriate therapeutic strategy makes it much more severe. Toll-like receptors (TLRs) are the crucial mediators and regulators of host immunity. The role of several TLRs in immunomodulation of host by SARS-CoV-2 is recently demonstrated. However, the functionality of human intracellular TLRs including TLR3,7,8 and 9 is still being untested for sensing of viral RNA. This study is hoped to rationalize the comparative binding and sensing of SARS-CoV-2 mRNA towards the intracellular TLRs, considering the solvent-based force-fields operational in the cytosolic aqueous microenvironment that predominantly drive these reactions. Our in-silico study on the binding of all mRNAs with the intracellular TLRs shown that the mRNA of NSP10, S2, and E proteins of SARS-CoV-2 are potent enough to bind with TLR3, TLR9, and TLR7 and trigger downstream cascade reactions, and may be used as an option for validation of therapeutic option and immunomodulation against COVID-19.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.09.374694v1?rss=1

Authors: Walker, C. R., Scally, A., De Maio, N., Goldman, N.

Abstract:

Many complex genomic rearrangements arise through template switch errors, which occur in DNA replication when there is a transient polymerase switch to an alternate template nearby in three-dimensional space. While typically investigated at kilobase-to-megabase scales, the genomic and evolutionary consequences of this mutational process are not well characterised at smaller scales, where they are often interpreted as clusters of independent substitutions, insertions and deletions. Here we present an improved statistical approach using pair hidden Markov models, and use it to detect and describe short-range template switches underlying clusters of mutations in the multi-way alignment of hominid genomes. Using robust statistics derived from evolutionary genomic simulations, we show that template switch events have been widespread in the evolution of the great apes' genomes and provide a parsimonious explanation for the presence of many complex mutation clusters in their phylogenetic context. Larger-scale mechanisms of genome rearrangement are typically associated with structural features around breakpoints, and accordingly we show that atypical patterns of secondary structure formation and DNA bending are present at the initial template switch loci. Our methods improve on previous non-probabilistic approaches for computational detection of template switch mutations, allowing the statistical significance of events to be assessed. By specifying realistic evolutionary parameters based on the genomes and taxa involved, our methods can be readily adapted to other intra- or inter-species comparisons.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.09.375550v1?rss=1

Authors: Wu, K. E., Yost, K. E., Chang, H., Zou, J.

Abstract:

Simultaneous profiling of multi-omic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility - for example generating paired measurements of scRNA-seq and scATAC-seq - wide-spread application of joint profiling is challenging due to the experimental complexity, noise, and cost. Here we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging a novel interoperable neural network model, BABEL can generate scRNA- seq directly from a cell's scATAC-seq, and vice versa. This makes it possible to computationally synthesize paired multi-omic measurements when only one modality is experimentally available. Across several paired scRNA-seq and scATAC-seq datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to new biological contexts not seen during training. For example, starting from scATAC-seq of patient derived basal cell carcinoma (BCC), BABEL generated scRNA-seq that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of the experimental BCC scRNA-seq data. We further show that BABEL can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.09.375659v1?rss=1

Authors: Yang, Y., Li, G., Xie, Y., Wang, L., Yang, Y., Liu, J., Qian, L., Li, Y.

Abstract:

Batch effect correction is an essential step in the integrative analysis of multiple single cell RNA-seq (scRNA-seq) data. One state-of-the-art strategy for batch effect correction is via unsupervised or supervised detection of mutual nearest neighbors (MNNs). However, both two kinds of methods only detect MNNs across batches on the top of uncorrected data, where the large batch effect may affect the MNN search. To address this issue, we presented iSMNN, a batch effect correction approach via iterative supervised MNN refinement across data after correction. Our benchmarking on both simulation and real datasets showed the advantages of the iterative refinement of MNNs on the performance of correction. Compared to the popular methods MNNcorrect and Seurat v3, our iSMNN is able to better mix the cells of the same cell type across batches. In addition, iSMNN can also facilitate the identification of DEGs relevant to the biological function of certain cell types. These results indicated that iSMNN will be a valuable method for integrating multiple scRNA-seq datasets that can facilitate biological and medical studies at single-cell level.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.09.375626v1?rss=1

Authors: Cao, X., Fan, R., Zeng, W.

Abstract:

Computational approaches for accurate predictions of drug-related interactions such as drug-drug interactions (DDIs) and drug-target interactions (DTIs) are highly demanding for biochemical researchers due to their efficiency and cost-effectiveness. Despite the fact that many methods have been proposed and developed to predict DDIs and DTIs respectively, their success is still limited due to a lack of systematic evaluation of the intrinsic properties embedded in their structure. In this paper, we develop a deep learning framework, named DeepDrug, to overcome these shortcomings by using graph convolutional networks to learn the graphical representations of drugs and proteins such as molecular fingerprints and residual structures in order to boost the prediction accuracy. We benchmark our methods in binary-class DDIs, multi-class DDIs and binary-class DTIs classification tasks using several datasets. We then demonstrate that DeepDrug outperforms other state-of-the-art published methods both in terms of accuracy and robustness in predicting DDIs and DTIs with varying ratios of positive to negative training data. Ultimately, we visualize the structural features learned by DeepDrug, which display compatible and accordant patterns in chemical properties, providing additional evidence to support the strong predictive power of DeepDrug. We believe that DeepDrug is an efficient tool in accurate prediction of DDIs and DTIs and provides a promising path in understanding the underlying mechanism of these biochemical relations. The source code of the DeepDrug can be downloaded from https://github.com/wanwenzeng/deepdrug.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.09.375535v1?rss=1

Authors: Khaki, S., Pham, H., Han, Y., Kuhl, A., Kent, W., Wang, L.

Abstract:

The success of modern farming and plant breeding relies on accurate and efficient collection of data. For a commercial organization that manages large amounts of crops, collecting accurate and consistent data is a bottleneck. Due to limited time and labor, accurately phenotyping crops to record color, head count, height, weight, etc. is severely limited. However, this information, combined with other genetic and environmental factors, is vital for developing new superior crop species that help feed the world's growing population. Recent advances in machine learning, in particular deep learning, have shown promise in mitigating this bottleneck. In this paper, we propose a novel deep learning method for counting on-ear corn kernels in-field to aid in the gathering of real-time data and, ultimately, to improve decision making to maximize yield. We name this approach DeepCorn, and show that this framework is robust under various conditions and can accurately and efficiently count corn kernels. We also adopt a semi-supervised learning approach to further improve the performance of our proposed method. Our experimental results demonstrate the superiority and effectiveness of our proposed method compared to other state-of-the-art methods.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.09.375311v1?rss=1

Authors: Bitard-Feildel, T.

Abstract:

Shedding light on the relationship between protein sequences and their functions is a challenging task with implication in our understanding of protein evolution, diseases, or protein design. However, due to its complexity, protein sequence space is hard to comprehend with potential numerous human bias. Generative models able to learn and recreate the data specificity can help to decipher complex systems. Applied to protein sequences, they can help to pointing out relationships between protein positions and functions or to capture the different sequence patterns associated to functions. In this study, an unsupervised generative approach based on auto-encoder (AE) is proposed to generate and explore new protein sequences with respect to their functions. AE are tested on three protein families known for their multiple functions, of which one has manually curated annotations. Functional labelling of encoded sequences on a two dimensional latent space computed by AE for each family shows a good agreement regarding the ability of the latent space to capture the functional organization and specificity of the sequences. Furthermore, arithmetic between latent spaces and latent space interpolations between encoded sequences are tested as a way to generate new intermediate protein sequences sharing sequential and functional properties of sequences issued of families with different sequences and functions. Using structural homology modelling and assessment, it can be observed that the new protein sequences generated using latent space arithmetic display intermediate physico-chemical properties and energies relatively to the sequences of the families used to generate them. Finally, interpolated protein sequences between data points of the input data set show the ability of the AE to smoothly generalize and to produce meaningful biological sequences from un-charted area of the latent space. Code and data used for this study are freely available at https://github.com/T-B-F/aae4seq.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.09.374579v1?rss=1

Authors: Ryden, M. P. E., Englund, M., Ali, N.

Abstract:

Functional analysis has become a common approach to incorporate biological knowledge into the analysis of omics data, and to explore molecular events that govern a disease state. It is though only one step in a wider analytical pipeline that typically requires use of multiple individual analysis software. There is currently a need for a well-integrated omics analysis tool that performs all the steps. The ProteoMill portal is developed as an R Shiny application and integrates all necessary steps from data-upload, converting identifiers, to quality control, differential expression and network-based functional analysis into a single fast, interactive easy to use workflow. Further, it maintains annotation data sources up to date, overcoming a common problem with use of outdated information, and seamlessly integrates multiple R-packages for an improved user-experience. The functionality provided in this software can benefit researchers by facilitating the exploratory analysis of proteomics data. ProteoMill is available for free at https://proteomill.com.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.376533v1?rss=1

Authors: Gusmao, E. G., Mizi, A., Brant, L., Papantonis, A.

Abstract:

The advent of the chromosome conformation capture (3C) and related technologies has profoundly renewed our understaning of three-dimensional chromatin organization in mammalian nuclei. Alongside these experimental approaches, numerous computational tools for handling, normalizing, visualizing, and ultimately detecting interactions in 3C-type datasets are being developed. Here, we present Bloom, a comprehensive method for the analysis of 3C-type data matrices on the basis of Dirichlet process mixture models that addresses two important open issues. First, it retrieves occult interaction patterns from sparse data, like those derived from single-cell Hi-C experiments; thus, bloomed sparse data can now be used to study interaction landscapes at sub-kbp resolution. Second, it detects enhancer-promoter interactions with high sensitivity and inherently assigns an interaction frequency score (IFS) to each contact. Using enhancer perturbation data of different throughput, we show that IFS accurately quantifies the regulatory influence of each enhancer on its target promoter. As a result, Bloom allows decoding of complex regulatory landscapes by generating functionally-relevant enhancer atlases solely on the basis of 3C-type of data.

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