PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.235366v1?rss=1

Authors: Wen, Y. Q., Wu, L. l., Yang, X. X., Yan, B. W., He, S., Bo, X. C.

Abstract:

The synthetic lethality (SL) relationship arises when a combination of deficiencies in two genes leads to cell death, whereas a deficiency in either one of the two genes does not. The survival of the mutant tumor cells depends on the SL partner genes of the mutant gene, so the cancer cells could be selectively killed by inhibiting the SL partners of the oncogenic genes but normal cells not. Therefore, developing SL pairs identification methods is increasingly needed for cancer targeted therapy. In this paper, we proposed a new approach based on similarity fusion to predict SL pairs. Multiple types of gene similarity measures are integrated and k-NN algorithm are applied to achieve the similarity-based classification task between gene pairs. As a similarity-based method, our method demonstrated excellent performance in multiple experiments. Besides the effectiveness of our method, the ease of use and expansibility can also make our method more widely used in practice.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.234989v1?rss=1

Authors: Boyarsky, A.

Abstract:

The receptor binding domain (RBD) of the spike protein of the Covid-19 virus is responsible for attachment to human ACE2. A number of recent articles have studied monoclonal antibody blocking and peptide inhibitors of the Covid-19 virus. Here we report virtual ligand-based screening that targets pockets on each side of an important binding site with residues 502-504 on the RBD that contact residues 353-357 of hACE2. These ligands are intended as pre-exposure therapy for Covid-19 infection.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.229575v1?rss=1

Authors: Wang, M. F. Z., Mantri, M., Chou, S.-P., Scuderi, G. J., McKellar, D., Butcher, J. T., Danko, C. G., De Vlaminck, I.

Abstract:

Single-cell RNA sequencing (scRNA-seq) enables the study of cell biology with high resolution. scRNA-seq expression analyses rely on the availability of a high quality annotation of genes in the genome. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, gene annotations often fail to cover the full transcriptome of every cell type at every stage of development, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant information beyond the scope of the best available gene annotation. This is achieved by performing single-cell expression analysis on any region in the genome for which transcriptional products are detected. Our routine identifies transcriptionally active regions (TARs) using a hidden Markov model, generates a matrix of expression levels for all TARs across all cells in a dataset, performs single-cell TAR expression analysis to identify TARs that are biologically significant, and then annotates biologically significant TARs using gene homology analysis. This procedure leverages single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts in complex tissues and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.230763v1?rss=1

Authors: Zhao, Y., Zheng, K., Guan, B., Guo, M., Song, L., Gao, J., Qu, H., Wang, Y., Zhang, Y., Shi, D.

Abstract:

To elucidate novel molecular mechanism of known drugs, efficient and feasible computational methods for predicting potential drug-target interactions (DTI) would be of great importance. A novel calculation model called DLDTI was generated, for predicting DTI based on network representation learning and convolutional neural networks. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources, and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning low-dimensional and rich depth features of drugs and proteins. Low-dimensional feature vectors were used to train DLDTI to obtain optimal mapping space and infer new DTIs by ranking DTI candidates based on their proximity to optimal mapping space. DLDTI achieves promising performance under 5-fold cross-validation with AUC values of 0.9172, which was higher than that of the method based on different classifiers or different feature combination technique. Moreover, biomedical experiments were also completed to validate the performance of DLDTI. Consistent with the predicted result, tetramethylpyrazine, an amide analogous monomer, ameliorated atherosclerosis progression and inhibited signal transduction in platelets, via PI3K/Akt, cAMP and calcium signaling pathways. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.230599v1?rss=1

Authors: Miller, R., Willighagen, E., Evelo, C., Kutmon, M., Waagmeester, A., Bohler, A.

Abstract:

Background To grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of biological pathways, such as signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format that is easier for humans to interpret. It has already been established that these diagrams can be represented in machine readable formats, as done in KEGG, Reactome, and WikiPathways. However, while humans are good at interpreting the message of the creator of such a diagram, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles, and therefore needs to harmonize this to offer semantically enriched access via the Resource Description Framework format. Particularly challenging in the normalization of diagrams are the interactions between the biological entities, so that we can glean information about the connectivity of the entities represented. These interactions include information about the type of interaction (metabolic conversion, inhibition, etc.), the direction, and the participants. Availability of the interactions in a semantic and harmonized format enables searching the full network of biological interactions and integration with the linked data cloud. Results We here study how the graphically modelled biological knowledge in diagrams can be semantified and harmonized efficiently, and exemplify how the resulting data can be used to programmatically answer biological questions. We find that we can translate graphically modelled biological knowledge to a sufficient degree into a semantic model of biological knowledge and discuss some of the current limitations. Furthermore, we show how this interaction knowledge base can be used to answer specific biological questions. Conclusion This paper demonstrates that most of the graphical biological knowledge from WikiPathways is modelled in the semantic layer of WikiPathways with the semantic information intact and connectivity information preserved. The usability of the WikiPathways pathway and connectivity information has shown to be useful and has been integrated into other platforms. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.02.233569v1?rss=1

Authors: Wang, J., Ma, A., Chang, Y., Gong, J., Jiang, Y., Fu, H., Wang, C., Qi, R., Ma, Q., Xu, D.

Abstract:

Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell-cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimer's disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell-cell relationships. It is also a novel and powerful framework that can be applied to scRNA-Seq analyses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.231662v1?rss=1

Authors: Hu, Y., Zhang, Z.

Abstract:

Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting the genetic architecture of these traits toward ultimate yield improvement. High-throughput phenotyping methods have been developed by analyzing images of kernels. However, segmenting kernels from the image background and noise artifacts or from other kernels positioned in close proximity remain challenges. In this study, we developed a software package, named GridFree, to overcome these challenges. GridFree uses an unsupervised machine learning approach, K-Means, to segment kernels from the background by using principal component analysis on both raw image channels and their color indices. GridFree incorporates users' experiences as a dynamic criterion to set thresholds for a divide-and-combine strategy that effectively segments adjacent kernels. When adjacent multiple kernels are incorrectly segmented as a single object, they form an outlier on the distribution plot of kernel area, length, and width. GridFree uses the dynamic threshold settings for splitting and merging. In addition to counting, GridFree measures kernel length, width, and area with the option of scaling with a reference object. Evaluations against existing software programs demonstrated that GridFree had the smallest error on counting seeds for multiple crops, including alfalfa, canola, lentil, wheat, chickpea, and soybean. GridFree was implemented in Python with a friendly graphical user interface to allow users easily visualize the outcomes and make decisions, which ultimately eliminates time-consuming and repetitive manual labor. GridFree is freely available at the GridFree website (https://zzlab.net/GridFree).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.02.233155v1?rss=1

Authors: Sundar, V., Colwell, L.

Abstract:

Machine learning models that predict which small molecule ligands bind a single protein target report high levels of accuracy for held-out test data. An important challenge is to extrapolate and make accurate predictions for new protein targets. Improvements in drug-target interaction (DTI) models that address this challenge would have significant impact on drug discovery by eliminating the need for high-throughput screening experiments against new protein targets. Here we propose a data augmentation strategy that addresses this challenge to enable accurate prediction in cases where no experimental data is available. To proceed, we first build single protein-ligand binding models and use these models to predict whether additional ligands bind to each protein. We then use these predictions to augment the experimental data, train standard DTI models, and predict interactions between unseen test proteins and ligands. This approach achieves Area Under the Receiver Operator Characteristic (AUC) > 0.9 consistently on test sets consisting exclusively of proteins and ligands for which the model is given no experimental data. We verify that performance improvements extend to held-out test proteins distant from the training set. Our data augmentation framework can be applied to any DTI model, and enhances performance on a range of simple models.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.233866v1?rss=1

Authors: Soratto, T. A. T., Darban, H., Bjerkner, A., Coorens, M., Albert, J., Allander, T., Andersson, B.

Abstract:

Large research efforts are going into characterizing, mapping the spread, and studying the biology and clinical features of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we report four complete SARS-CoV-2 genome sequences obtained from patients confirmed to have the disease in Stockholm, Sweden, in late April. A variant at position 23463 was found for the first time in one genome. It changes an arginine (R) residue to histidine (H) at position 364 in the S1 subunit of the spike protein. The genomes belonged to two different genetic groups, previously reported as two of the three main genetic groups found in Sweden. Three of them are from group B.1/G, corresponding to the Italian outbreak, reported by the Public Health Agency of Sweden to have declined in prevalence by late April, and more investigation is needed in order to ensure that the spread of different types of SARS-CoV-2 is fully characterized.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.230292v1?rss=1

Authors: Kozlov, A. M., Alves, J. M., Stamatakis, A., Posada, D.

Abstract:

We have developed a maximum likelihood framework called CellPhy for inferring phylogenetic trees from single-cell DNA sequencing (scDNA-seq) data, that can be directly applied to somatic cells and clones. CellPhy is based on a finite-site Markov nucleotide substitution model with 10 diploid states, akin to those typically used in statistical phylogenetics. It includes a dedicated error function for single cells that explicitly incorporates amplification/sequencing error and allelic dropout (ADO). Moreover, it can explicitly consider the uncertainty of the variant calling process by using genotype likelihoods as input. We implemented CellPhy in a widely used open-source phylogenetic inference package (RAxML-NG) that provides statistical confidence measurements on the estimated tree and scales particularly well on large phylogenies with hundreds or even thousands of cells. To benchmark CellPhy, we carried out 19,400 coalescent simulations of cell samples from exponentially-growing tumors for which the true phylogeny was known. We evolved single-cell diploid DNA genotypes along the simulated genealogies under different scenarios including infinite- and finite-sites nucleotide mutation models, trinucleotide mutational signatures, sequencing and amplification errors, allele dropouts, and doublet cells. Our simulations suggest that CellPhy is robust to amplification/sequencing errors and to ADO and that it outperforms the state-of-the-art methods under realistic scDNA-seq scenarios both in terms of accuracy and speed. In addition, we sequenced 24 single-cell whole genomes from a colorectal cancer, and together with three published scDNA-seq data sets, analyzed them to illustrate how CellPhy can provide more reliable biological insights than competing methods. CellPhy is freely available at https://github.com/amkozlov/cellphy.

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