PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.14.250720v1?rss=1

Authors: Zhu, A., Matoba, N., Wilson, E., Tapia, A. L., Li, Y., Ibrahim, J. G., Stein, J. L., Love, M. I.

Abstract:

Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci displaying allelic heterogeneity, that is, containing multiple LD-independent eQTLs. MRLocus makes use of a colocalization step applied to each eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five causal candidate genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus' estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.250076v1?rss=1

Authors: Zhang, D., Lu, J.

Abstract:

The COVID-19 pandemic has exposed global inadequacies in therapeutic options against both the COVID-19-causing SARS-CoV-2 virus and other newly emerged respiratory viruses. In this study, we present the VirusSi computational pipeline, which facilitates the rational design of siRNAs to target existing and future respiratory viruses. Mode A of VirusSi designs siRNAs against an existing virus, incorporating considerations on siRNA properties, off-target effects, viral RNA structure and viral mutations. It designs multiple siRNAs out of which the top candidate targets >99% of SARS-CoV-2 strains, and the combination of the top four siRNAs is predicted to target all SARS-CoV-2 strains. Additionally, we develop Greedy Algorithm with Redundancy (GAR) and Similarity-weighted Greedy Algorithm with Redundancy (SGAR) to support the Mode B of VirusSi, which pre-designs siRNAs against future emerging viruses based on existing viral sequences. Time-simulations using known coronavirus genomes as early as 10 years prior to the COVID-19 outbreak show that at least three SARS-CoV-2-targeting siRNAs are among the top 30 pre-designed siRNAs. Before-the-outbreak pre-design is also possible against the MERS-CoV virus and the 2009-H1N1 swine flu virus. Our data support the feasibility of pre-designing anti-viral siRNA therapeutics prior to viral outbreaks. We propose the development of a collection of pre-designed, safety-tested, and off-the-shelf siRNAs that could accelerate responses toward future viral diseases.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.14.251496v1?rss=1

Authors: Ong, E., Huang, X., Pearce, R., Zhang, Y., He, Y.

Abstract:

The current COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and thousands of deaths globally. Extensive efforts and progress have been made to develop effective and safe vaccines against COVID-19. A primary target of these vaccines is the SARS-CoV-2 spike (S) protein, and many studies utilized structural vaccinology techniques to either stabilize the protein or fix the receptor-binding domain at certain states. In this study, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants without perturbing the surface conformation and B cell epitopes of the S protein. We then evaluated the mutated S protein candidates based on predicted MHC-II T cell promiscuous epitopes as well as the epitopes' similarity to human peptides. The presented strategy aims to improve the S protein's immunogenicity and antigenicity by inducing stronger CD4 T cell response while maintaining the protein's native structure and function. The top EvoDesign S protein candidate (Design-10705) recovered 31 out of 32 MHC-II T cell promiscuous epitopes in the native S protein, in which two epitopes were present in all seven human coronaviruses. This newly designed S protein also introduced nine new MHC-II T cell promiscuous epitopes and showed high structural similarity to its native conformation. The proposed structural vaccinology method provides an avenue to rationally design the antigen's structure with increased immunogenicity, which could be applied to the rational design of new COVID-19 vaccine candidates.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.14.251306v1?rss=1

Authors: Levy, J., Chen, Y., Azizgolshani, N., Petersen, C. L., Titus, A. J., Moen, E. L., Vaickus, L. J., Salas, L. A., Christensen, B.

Abstract:

DNA methylation (DNAm) alterations are implicated with aging and diseases by regulating gene expression. DNAm deep-learning approaches can capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. We present deep-learning software, MethylCapsNet and MethylSPWNet, that group CpGs into user-specified or predefined biologically relevant groupings (eg. gene promoter or CpG island context) related to diagnostic and prognostic outcomes. We train our models on a cohort (n=3,897) to classify central nervous system tumors and compare to existing approaches. Our methodology presents opportunities to increase interpretability of disease mechanisms through utilization of biologically relevant annotations.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.14.250712v1?rss=1

Authors: Zhang, N. L., Zou, Y.

Abstract:

As a novel type of post-translational modification, lysine 2-Hydroxyisobutyrylation (Khib) plays an important role in gene transcription and signal transduction. In order to understand its regulatory mechanism, the essential step is the recognition of Khib sites. Thousands of Khib sites have been experimentally verified across five different species. However, there are only a couple traditional machine-learning algorithms developed to predict Khib sites for limited species, lacking a general prediction algorithm. We constructed a deep-learning algorithm based on convolutional neural network with the one-hot encoding approach, dubbed CNNOH. It performs favorably to the traditional machine-learning models and other deep-learning models across different species, in terms of cross-validation and independent test. The area under the ROC curve (AUC) values for CNNOH ranged from 0.82 to 0.87 for different organisms, which is superior to the currently-available Khib predictors. Moreover, we developed the general model based on the integrated data from multiple species and it showed great universality and effectiveness with the AUC values in the range of 0.79 to 0.87. Accordingly, we constructed the on-line prediction tool dubbed DeepKhib for easily identifying Khib sites, which includes both species-specific and general models. DeepKhib is available at http://www.bioinfogo.org/DeepKhib.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.14.250902v1?rss=1

Authors: Bhattacharya, A., Hamilton, A. M., Troester, M. A., Love, M. I.

Abstract:

Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.250118v1?rss=1

Authors: Nishizaki, S. S., Boyle, A. P.

Abstract:

Motivation: Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factors motif. Results: SEMplMe uses ChIP-seq and whole genome bisulfite sequencing to predict effects of methylation within binding sites. SEMplMe validates known methylation sensitive and insensitive positions within a binding motif, identifies cell type specific transcription factor binding driven by methylation, and outperforms SELEX-based predictions. These predictions can be used to identify aberrant sites of DNA methylation contributing to human disease. Availability and Implementation: SEMplMe is available from https://github.com/Boyle-Lab/SEMplMe.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.248393v1?rss=1

Authors: Siddiqui, J. K., Miles, W. O.

Abstract:

Immunotherapy has improved the prognosis for half of the melanoma patients, prompting a need to understand differences between responding and non-responding patients. Gene expression profiling of tumors has focused on deriving primarily immune-related signatures, however these have shown limited predictive power. Recent studies have highlighted the role of RNA editing in modulating resistance to immunotherapy. This has led us to test whether RNA editing activity can be predictive of response in publicly available datasets of immunotherapy-treated melanoma patients. Here, we identified RNA editing signatures that were able to predict with very high accuracy and confidence patient responses and outcomes. Our analysis, however, demonstrates that RNA editing by itself is sufficient as a strong predictive tool for examining sensitivity of melanoma patients to immunotherapy.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.248971v1?rss=1

Authors: Zhu, J., Sabatti, C.

Abstract:

We propose GLISS, a strategy to discover spatially-varying genes by integrating two data sources: (1) spatial gene expression data such as image-based fluorescence in situ hybridization techniques, and (2) dissociated whole-transcriptome single-cell RNA-sequencing (scRNA- seq) data. GLISS utilizes a graph-based association measure to select and link genes that are spatially-dependent in both data sources. GLISS can discover new spatial genes and recover cell locations in scRNA-seq data from landmark genes determined from SGE data. GLISS also offers a new dimension reduction technique to cluster the genes, while accounting for the inferred spatial structure of the cells. We demonstrate the utility of GLISS on simulated and real datasets, including datasets on the mouse olfactory bulb and breast cancer biopsies, and two spatial studies of the mammalian liver and intestine.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.248955v1?rss=1

Authors: Wang, L., Niu, D., Wang, X., Shen, Q., Xue, Y.

Abstract:

Strategies to screen antihypertensive peptides with high throughput and rapid speed will be doubtlessly contributed to the treatment of hypertension. The food-derived antihypertensive peptides can reduce blood pressure without side effects. In present study, a novel model based on Extreme Gradient Boosting (XGBoost) algorithm was developed using the primary structural features of the food-derived peptides, and its performance in the prediction of antihypertensive peptides was compared with the dominating machine learning models. To further reflect the reliability of the method in real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of k-mer peptides cutting from 6 key proteins in bovine milk and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance with the accuracy of 0.9841 and the area under the receiver operating characteristic curve of 0.9428, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using XGBoost algorithm as a novel auxiliary tool is feasible for screening antihypertensive peptide derived from food with high throughput and high efficiency.

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