PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.28.317313v1?rss=1

Authors: Subramanian, R., Sahoo, D.

Abstract:

The retina is a complex tissue containing multiple cell types that is essential for vision. Understanding the gene expression patterns of various retinal cell types has potential applications in regenerative medicine. Retinal organoids (optic vesicles) derived from pluripotent stem cells have begun to yield insights into the transcriptomics of developing retinal cell types in humans through single cell RNA-sequencing studies. Previous methods of gene reporting have relied upon techniques in vivo using microarray data, or correlational and dimension reduction methods for analyzing single cell RNA-sequencing data in silico. Here, we present a bioinformatic approach using Boolean implication to discover retinal cell type-specific genes. We apply this approach to previously published retina and retinal organoid datasets and improve upon previously published correlational methods. Our method improves the prediction accuracy and reproducibility of marker genes of retinal cell types and discovers several new high confidence cone and rod-specific genes. Furthermore, our method is general and can impact all areas of gene expression analyses in cancer and other human diseases.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.01.322024v1?rss=1

Authors: Lee, H., Seo, P. J.

Abstract:

Genome-wide chromosome conformation capture (3C)-based high-throughput sequencing (Hi-C) has enabled identification of genome-wide chromatin loops. Because the Hi-C map with restriction fragment resolution is intrinsically associated with sparsity and stochastic noise, Hi-C data are usually binned at particular intervals; however, the binning method has limited reliability, especially at high resolution. Here, we describe a new method called HiCORE, which provides simple pipelines and algorithms to overcome the limitations of single-layered binning and predict core chromatin regions with 3D physical interactions. In this approach, multiple layers of binning with slightly shifted genome coverage are generated, and interacting bins at each layer are integrated to infer narrower regions of chromatin interactions. HiCORE predicts chromatin looping regions with higher resolution and contributes to the identification of the precise positions of potential genomic elements.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.01.322206v1?rss=1

Authors: Neukamm, J., Peltzer, A., Nieselt, K.

Abstract:

In ancient DNA research, the authentication of ancient samples based on specific features remains a crucial step in data analysis. Because of this central importance, researchers lacking deeper programming knowledge should be able to run a basic damage authentication analysis. Such software should be user-friendly and easy to integrate into an analysis pipeline. Here, we present DamageProfiler, a Java based, stand-alone software to determine damage patterns in ancient DNA. The results are provided in various file formats and plots for further processing. DamageProfiler has an intuitive graphical as well as command line interface that allows the tool to be easily embedded into an analysis pipeline.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.321596v1?rss=1

Authors: Medeiros, I. G., Khayat, A. S., Stransky, B., dos Santos, S. E. B., Assumpcao, P. P., de Souza, J. E. S.

Abstract:

Coronavirus disease 2019 (COVID-19) rapidly transformed into a global pandemic, for which a demand for developing antivirals capable of targeting the SARS-CoV-2 RNA genome and blocking the activity of its genes has emerged. In this work, we propose a database of SARS-CoV-2 targets for siRNA approaches, aiming to speed the design process by providing a broad set of possible targets and siRNA sequences. Beyond target sequences, it also displays more than 170 features, including thermodynamic information, base context, target genes and alignment information of sequences against the human genome, and diverse SARS-CoV-2 strains, to assess whether siRNAs targets bind or not off-target sequences. This dataset is available as a set of four tables in a single spreadsheet file, each table corresponding to sequences of 18, 19, 20, and 21 nucleotides length, respectively, aiming to meet the diversity of technology and expertise among labs around the world concerning siRNAs design of varied sizes, more specifically between 18 and 21nt length. We hope that this database helps to speed the development of new target antivirals for SARS-CoV-2, contributing to more rapid and effective responses to the COVID-19 pandemic.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.29.319046v1?rss=1

Authors: Ahmadi, M., Jahed-Motlagh, M. R., Asgari, E., Torkaman Rahmani, A., McHardy, A. C.

Abstract:

Venom is a mixture of substances produced by a venomous organism aiming at preying, defending, or intraspecific competing resulting in certain unwanted conditions for the target organism. Venom sequences are a highly divergent class of proteins making their machine learning-based and homology-based identification challenging. Prominent applications in drug discovery and healthcare, while having scarcity of annotations in the protein databases, made automatic identification of venom an important protein informatics task. Most of the existing machine learning approaches rely on engineered features, where the predictive model is trained on top of those manually designed features. Recently, transfer learning and representation learning resulted in significant advancements in many machine learning problem settings by automatically learning the essential features. This paper proposes an approach, called ToxVec, for automatic representation learning of protein sequences for the task of venom identification. We show that pre-trained language model-based representation outperforms the existing approaches in terms of the F1 score of both positive and negative classes achieving a macro-F1 of 0.89. We also show that an ensemble classifier trained over multiple training sets constructed from multiple down-samplings of the negative class instances can substantially improve a macro-F1 score to 0.93, which is 7 percent higher than the state-of-the-art performance.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.320119v1?rss=1

Authors: Nair, A., Rao, A. S.

Abstract:

Almost all of Genome Wide Association Studies (GWAS) hits come from non-coding DNA elements. Data from chromatin interaction analyses suggest a long-range interaction with a putative enhancer upstream of TNFAIP3 . Disrupting the enhancer may impair TNFAIP3 expression and enhance SLE risk. Two variants, rs10499197 and rs58905141 carried on the SLE risk haplotype are situated near this enhancer and could affect its function. Cloning the regulatory region surrounding rs10499197 into an expression plasmid containing a CRISPR-Cas9 backbone, and then performing a genome editing assay, we found that the variant is located near an enhancer. And any changes to the SNP region might impair enhancer and its ability to regulate TNFAIP3 expression.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.29.315374v1?rss=1

Authors: Petrov, P. B., Sustar, V., Awoniyi, L. O., Tolvanen, M., Mattila, P.

Abstract:

Protein-protein interactions govern the cellular decision-making and various functions. Thus, identifying new interactions between proteins can significantly facilitate our understanding on the mechanistic principles of protein functions. Coevolution between proteins is a sign of functional communication and as such provides a powerful approach to search for novel molecular partners. However, evolutionary analyses of large arrays of proteins, in silico, is a highly time-consuming effort, which has prevented the usage of this method beyond protein pairs or narrow protein families, for example. Here, we developed autoCoEv, a user-friendly computational pipeline for the search of coevolution between hundreds and even thousands of proteins. Driving over 10 individual programs, with CAPS2 as a key software for coevolution detection, autoCoEv achieves seamless automation and parallelization of the workflow. In addition, we provide a patch to CAPS2 source code to improve its statistical output, allowing for multiple comparisons correction. We apply the method to inspect coevolution among 297 individual proteins identified to be in close proximity to the B cell receptor (BCR) before and after receptor activation. We successfully detected coevolutionary relations between the proteins, predicting novel partners and clusters of interacting molecules. We conclude that AutoCoEv can be used to predict protein interaction from large datasets of hundreds, and with aid of super-computing recourses even thousands of proteins in a time and cost efficient manner.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.29.318931v1?rss=1

Authors: Nadeau, R., Shahryari Fard, S., Scheer, A., Hashimoto-Roth, E., Nygard, D., Abramchuk, I., Chung, Y.-E., Bennett, S. A. L., Lavallee-Adam, M.

Abstract:

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK293 cells published by Gordon et al. to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on a PPI network generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in the network. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in the network. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.28.317784v1?rss=1

Authors: Tarashansky, A. J., Musser, J., Khariton, M., Li, P., Arendt, D., Quake, S. R., Wang, B.

Abstract:

Comparing single-cell transcriptomic atlases from diverse organisms can provide evolutionary definition of cell types, elucidate the origins of cellular diversity, and transfer cell type knowledge between species. Yet, comparison among distant relatives, especially beyond a single phylum, is hindered by complex gene histories, lineage-specific inventions, and cell type evolutionary diversifications. Here, we develop a method to enable mapping cell atlases throughout Metazoa spanning sponge to mouse. Within phyla, we identify homologous cell types, even between distant species, with some even emerging from distinct germ layers. Across phyla, we find ancient cell type families that form densely interconnected groups, including contractile and stem cells, indicating they likely arose early in animal evolution through hierarchical diversifications. These homologous cell types often substitute paralog expressions at surprising prevalence. Our findings advance the understanding of cell type diversity across the tree of life and the evolution of associated gene expression programs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.28.316901v1?rss=1

Authors: Shi, S.

Abstract:

We proposed a method for data integration and cell type annotation (Dincta) of single cell transcriptomes in a unify framework. The Dincta can handle three cases. In the first case, the data has been annotated the cell type for all cells, Dincta can integrate the the data into a common low dimension embedding space such that cells with different cell types separate while cells from the different batches but in the same cell type cluster together. In the second case, the data was only annotated for part of cells, such as one sample, Dincta can integrate the data into a common low dimension embedding space such that cells with different cell types separate while cells from the different batches but in the same cell type cluster together. Moreover, it can infer the known or novel cell type of the cells with unknown cell type initially. In the third case, there are no cell type information of cells, we can run Dincta in an unsupervised way. It can infer the number of new cell types and annotate the cells into its correspond cell type, and do data integration keeping cells from different cell type separate while removing the batch effects to mix cells in the same cell type. Dincta is simple, accurate and efficient to integrate data, which keeps the cell type information preserved while removes the batch effects, and infers the known or novel cell types of cells.

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