PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.04.283234v1?rss=1

Authors: Schau, G., Burlingame, E. A., Chang, Y. H.

Abstract:

Deep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domain-specific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.04.283929v1?rss=1

Authors: Lu, A. X., Zhang, H., Ghassemi, M., Moses, A. M.

Abstract:

Pretrained embedding representations of biological sequences which capture meaningful properties can alleviate many problems associated with supervised learning in biology. We apply the principle of mutual information maximization between local and global information as a self-supervised pretraining signal for protein embeddings. To do so, we divide protein sequences into fixed size fragments, and train an autoregressive model to distinguish between subsequent fragments from the same protein and fragments from random proteins. Our model, CPCProt, achieves comparable performance to state-of-the-art self-supervised models for protein sequence embeddings on various downstream tasks, but reduces the number of parameters down to 0.9% to 8.9% of benchmarked models. Further, we explore how downstream assessment protocols affect embedding evaluation, and the effect of contrastive learning hyperparameters on empirical performance. We hope that these results will inform the development of contrastive learning methods in protein biology and other modalities.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.06.284976v1?rss=1

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

Abstract:

The global public health is endangered due to COVID-19 pandemic, which is caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Despite having similar pathology to MERS and SARS-CoV, the infection fatality rate of SARS-CoV-2 is likely lower than 1%. SARS-CoV-2 has been reported to be uniquely characterized by the accessory protein ORF10, which contains eleven cytotoxic T lymphocyte (CTL) epitopes of nine amino acids length each, across various human leukocyte antigen (HLA) subtypes. In this study, all missense mutations found in sequence databases were examined across twnety-two unique SARS-CoV-2 ORF10 variants that could possibly alter viral pathogenicity. Some of these mutations decrease the stability of ORF10, e.g. I4L and V6I were found in the MoRF region of ORF10 which may also possibly contribute to Intrinsic protein disorder. Furthermore, a physicochemical and structural comparative analysis was carried out on SARS-CoV-2 and Pangolin-CoV ORF10 proteins, which share 97.37% amino acid homology. The high degree of physicochemical and structural similarity of ORF10 proteins of SARS-CoV-2 and Pangolin-CoV open questions about the architecture of SARS-CoV-2 due to the disagreement of these two ORF10 proteins over their sub-structure (loop/coil region), solubility, antigenicity and change from the strand to coil at amino acid position 26, where tyrosine is present. Altogether, SARS-CoV-2 ORF10 is a promising pharmaceutical target and a protein which should be monitored for changes which correlate to change pathogenesis and clinical course of COVID-19 infection.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.06.285023v1?rss=1

Authors: Garg, A., Kumar, G., Sinha, S.

Abstract:

nCOVID-19 virus makes cellular entry using its spike protein protruding out on its surface. Angiotensin converting enzyme 2 receptor has been identified as a receptor that mediates the viral entry by binding with the receptor binding motif of spike protein. In the present study, we elucidate the significance of N-terminal domain of spike protein in spike-receptor interactions. Recent clinical reports indicate a link between nCOVID-19 infections with patient comorbidities. The underlying reason behind this relationship is not clear. Using molecular docking, we study the affinity of the nCOVID-19 spike protein with cell receptors overexpressed under disease conditions. Our results suggest that certain cell receptors such as DC/L-SIGN, DPP4, IL22R and ephrin receptors could act as potential receptors for the spike protein. The receptor binding domain of nCOVID-19 is more flexible than that of SARS-COV and has a high propensity to undergo phase separation. Higher flexibility of nCOVID-19 receptor binding domain might enable it to bind multiple receptor partners. Further experimental work on the association of these receptors with spike protein may help us to explain the severity of nCOVID-19 infection in patients with comorbidities.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.04.279554v1?rss=1

Authors: Liu, N., Sadlon, T., Wong, Y. Y., Pederson, S. M., Breen, J., Barry, S. C.

Abstract:

Background: Genome-wide association and fine-mapping studies have enabled the discovery of single nucleotide polymorphisms (SNPs) and other variants that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the SNPs lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. Methods: Autoimmunity results from a failure of immune tolerance, suggesting that regulatory T cells (Treg) are likely a significant point of impact for this genetic risk, as Treg are critical for immune tolerance. Focusing on T1D as a model of defective function of Treg in autoimmunity, we designed a SNPs filtering workflow called 3 Dimensional Functional Annotation of Accessible Cell Type Specific SNPs (3DFAACTS-SNP) that utilises overlapping profiles of Treg-specific epigenomic data (ATAC-seq, Hi-C and FOXP3-ChIP) to identify regulatory elements potentially driving the effect of variants associated with T1D, and the gene(s) that they control. Results: Using 3DFAACTS-SNP we identified 36 SNPs with plausible Treg-specific mechanisms of action contributing to T1D from 1,228 T1D fine-mapped variants, identifying 119 novel interacting regions resulting in the identification of 51 candidate target genes. We further demonstrated the utility of the workflow by applying it to three other fine-mapped/meta-analysed SNP autoimmune datasets, identifying 17 Treg-centric candidate variants and 35 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of any genetic variation using all common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune research. Conclusions: We demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function and illustrate the power of using cell type specific multi-omics datasets to determine disease mechanisms. The 3DFAACTS-SNP workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.04.283812v1?rss=1

Authors: Zhao, E., Stone, M. R., Ren, X., Pulliam, T., Nghiem, P., Bielas, J. H., Gottardo, R.

Abstract:

Recently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpace's improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data. In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.05.284539v1?rss=1

Authors: Bao, F., Deng, Y., Wan, S., Wang, B., Dai, Q., Altschuler, S., Wu, L.

Abstract:

Advances in spatial transcriptomics technologies enable optical profiling of morphological and transcriptional modalities from the same cells within tissues. Here, we present multi-modal structured embedding (MUSE), an approach to deeply characterize tissue heterogeneity through analysis of combined image and transcriptional single-cell measurements. We demonstrate that MUSE can discover cellular subpopulations missed by either modality as well as compensate for modality-specific noise. MUSE identified biologically meaningful cellular subpopulations and stereotyped spatial patterning within heterogeneous mouse cortex brain tissues, profiled by seqFISH+ or STARmap technologies. MUSE provides a framework for combining multi-modal single-cell data to reveal deeper insights into the states, functions and organization of cells in complex biological tissues.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.05.284224v1?rss=1

Authors: Koksal, A., Donmez, H., Ozcelik, R., Ozkirimli, E., Ozgur, A.

Abstract:

Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an enormous scientific effort from different domains. Resulting publications formed a gigantic domain-specific collection of text in which finding studies on a biomolecule of interest is quite challenging for general purpose search engines due to terminology-rich characteristics of the publications. Here, we present Vapur, an online COVID-19 search engine specifically designed for finding related protein - chemical pairs. Vapur is empowered with a biochemically related entities-oriented inverted index in order to group studies relevant to a biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.03.282186v1?rss=1

Authors: Stilianoudakis, S., Dozmorov, M. G.

Abstract:

High-throughput chromosome conformation capture technology (Hi-C) revealed extensive DNA looping and folding into discrete 3D domains. These include Topologically Associating Domains (TADs) and chromatin loops, the 3D domains critical for cellular processes like gene regulation and cell differentiation. The relatively low resolution of Hi-C data (regions of several kilobases in size) prevents precise mapping of domain boundaries. However, the high resolution of genomic annotations associated with boundaries, such as CTCF and members of cohesin complex, suggests they can inform the precise location of domain boundaries. Several methods attempted to leverage genome annotation data for predicting domain boundaries; however, they overlooked key characteristics of the data, such as spatial associations between an annotation and a boundary, and a much smaller number of boundaries than the rest of the genome (class imbalance). We developed preciseTAD, an optimized random forest model to improve the location of domain boundaries. Trained on high-resolution genome annotation data and boundaries from low-resolution Hi-C data, the model predicts the location of boundaries at base-level resolution. We investigated several feature engineering and resampling techniques (random over- and undersampling, Synthetic Minority Over-sampling TEchnique (SMOTE)) to select the most optimal data characteristics and address class imbalance. Density-based clustering and scalable partitioning techniques were used to identify the precise location of boundary regions and summit points. We benchmarked our method against the Arrowhead domain caller and a novel chromatin loop prediction algorithm, Peakachu, on the two most annotated cell lines. We found that spatial relationship (distance in the linear genome) between boundaries and annotations has the most predictive power. Transcription factor binding sites outperformed other genome annotation types. Random under-sampling significantly improved model performance. Boundaries predicted by preciseTAD were more enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines, highlighting their higher biological significance. The model pre-trained in one cell line performs well in predicting boundaries in another cell line using only genomic annotations, enabling the detection of domain boundaries in cells without Hi-C data. Our study implements the method and the pre-trained models for precise domain boundary prediction using genome annotation data. The precise identification of domain boundaries will improve our understanding of how genomic regulators are shaping the 3D structure of the genome. preciseTAD R package is available on https://dozmorovlab.github.io/preciseTAD/ and Bioconductor (submitted).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.03.282236v1?rss=1

Authors: Ge, R., Ding, S., Keeling, T., Honer, W., Frangou, S., Vila-Rodriguez, F.

Abstract:

Background and Purpose: Source-based morphometry (SBM) is a data-driven multivariate approach for interrogating covariation in structural brain patterns (SBPs) across subjects and quantifying the subject-specific loading parameters of these patterns. This approach has been used in multi-centre studies pooling magnetic resonance imaging (MRI) data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multi-scanner studies, and evaluated its performance relative to a conventional strategy. Methods: We conducted two simulation experiments. In the first experiment, the SimTB toolbox was used to generate simulation datasets mimicking twenty different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical datasets from two different scanners. Results: The outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters. Conclusions: SS-Detect outperformed the conventional strategy in terms of accurately estimating spatial SBPs and loading parameters both at whole-sample and scanner-specific levels and can be considered advantageous when SBM is applied to a multi-scanner study.

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