PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.229484v1?rss=1

Authors: Zerihun, M. B., Pucci, F., Schug, A.

Abstract:

Physics-based co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are able to predict protein contact maps with astonishing accuracy. Such contacts can be used as constraints in structure prediction and massively increase prediction accuracy. Unfortunately, the same ML methods cannot readily be applied to RNA as they rely on large structural datasets only available for proteins but not for RNAs. Here, we demonstrate how the small amount of data available for RNA can be used to significantly improve prediction of RNA contact maps. We introduce an algorithm called CoCoNet that is based on a combination of a Coevolutionary model and a shallow Convolutional Neural Network. Despite its simplicity and the small number of trained parameters, the method boosts the contact prediction accuracy by about 70% with respect to straightforward DCA as tested by cross-validation on a dataset of about sixty RNA structures. Both our extensive robustness tests and the limited number of parameters allow the generalization properties of our model. Finally, applications to other RNAs highlight the power of our approach. CoCoNet is freely available and can be found at https://github.com/KIT-MBS/coconet.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.230987v1?rss=1

Authors: Patro, L. P. P., Sathyaseelan, C., Uttamrao, P. P., Rathinavelan, T.

Abstract:

To accelerate the drug and vaccine development against the severe acute respiratory syndrome virus 2 (SARS-CoV-2), a comparative analysis of SARS-CoV-2 proteome has been performed in two phases by considering manually curated 31389 whole genome sequences from 84 countries. Among the 9 mutations that occur at a high significance (T85I-NPS2, L37F-NSP6, P323L-NSP12, D614G-spike, Q57H-ORF3a, G251V-ORF3a, L84S-ORF8, R203K-nucleocapsid and G204R-nucleocapsid), R203K-nucleocapsid and G204R-nucleocapsid are co-occurring mutations and P323L-NSP12 and D614G-spike often appear simultaneously. Other notable variations that appear with a moderate to low significance are, M85-NSP1 deletion, D268-NSP2 deletion, 112 amino acids deletion in ORF8, a phenylalanine insertion amidst F34-F36 (NSP6) and several co-existing substitution/deletion (I559V & P585S in NSP2, P504L & Y541C in NSP13, G82 & H83 deletions in NSP1 and K141, S142 & F143 deletions in NSP2) mutations. P323L-NSP12, D614G-spike, L37F-NSP6, L84S-ORF8 and the sequences deficient of the high significant mutations has led to 4 major SARS-CoV-2 clades. The top 5 countries bearing all the high significant and majority of the moderate significant mutations are: USA, England, Wales, Australia and Scotland. Further, the majority of the significant mutations has evolved in the first phase and has already transmitted around the globe indicating the positive selection pressure. Among the 26 SARS-CoV-2 proteins, nucleocapsid protein, ORF3a, ORF8, RNA dependent RNA polymerase and spike exhibit a higher heterogeneity compared with the rest of the proteins. However, NSP9, NSP10, NSP8, the envelope protein and NSP4 are highly resistant to mutations and can be exploited for drug/vaccine development.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.229096v1?rss=1

Authors: Bai, Y., Qin, Y., Kim, S., Fan, Z., Nam, K., Koldamova, R., Padiath, Q. S., Park, H. J.

Abstract:

Alternative polyadenylation (APA) causes shortening or lengthening of the 3'-untranslated region (3'-UTR), widespread in complex tissues. Bioinformatic tools have been developed to identify dynamic APA in single cell RNA-Seq (scRNA-Seq) data, but with relatively low power and the lack of interpretability for multiple cell types. To address these limitations, we developed a model-based method, scMAPA. scMAPA increases power by building a regression model based on a sensitive quantification of 3'-UTR short and long isoforms. By developing a de novo simulation platform, we demonstrated that scMAPA shows a markedly better sensitivity (9.5% on average) than the previous method with a negligible loss in specificity (0.3% on average). scMAPA improves interpretability by modeling the direction and the degree of dynamic APA for each cell type for each gene, while allowing flexibility to control potential confounders. scMAPA enables to bring a systematic understanding of APA dynamics in complex tissues in human Peripheral Blood Monocellular Cells and mouse brain data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.228270v1?rss=1

Authors: Imakubo, M., Takayama, J., Okada, H., Onami, S.

Abstract:

Background: Oocyte quality decreases with aging, thereby increasing errors in fertilization, chromosome segregation, and embryonic cleavage. Oocyte appearance also changes with aging, suggesting a functional relationship between oocyte quality and appearance. However, no methods are available to objectively quantify age-associated changes in oocyte appearance. Results: We show that statistical image processing of Nomarski differential interference contrast microscopy images can be used to quantify age-associated changes in Caenorhabditis elegans oocyte appearance. Max-min Value (mean difference between the maximum and minimum intensities within each moving window) quantitatively characterized the difference in oocyte cytoplasmic texture between 1- and 3-day-old adults (Day 1 and Day 3 oocytes, respectively). With an appropriate parameter set, the gray level co-occurrence matrix (GLCM)-based texture feature Correlation (COR) more sensitively characterized this difference than the Max-min Value. Manipulating the smoothness of and/or adding irregular structures to the cytoplasmic texture of Day 1 oocyte images reproduced the difference in Max-min Value but not in COR between Day 1 and Day 3 oocytes. Increasing the size of granules in synthetic images recapitulated the age-associated changes in COR. Manual measurements validated that the cytoplasmic granules in oocytes become larger with aging. Conclusions: The Max-min Value and COR objectively quantify age-related changes in C. elegans oocyte in Nomarski DIC microscopy images. Our methods provide new opportunities for understanding the mechanism underlying oocyte aging.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.226993v1?rss=1

Authors: Blumenfeld, A., Gonzaga-Jauregui, C., Sharma, D., Geisinger-Regeneron DiscovEHR Collaboration,, Yadav, A., Khalid, S., Balasubramanian, S., Reid, J. G., Habegger, L., Cantor, M. N., Staples, J.

Abstract:

Objective: Large scale next-generation sequencing of population cohorts paired with patients' electronic health records (EHR) provides an excellent resource for the study of gene-disease associations. To validate those associations, researchers often consult databases that identify relationships between genes of interest and relevant disease phenotypes, which we refer to as simply "phenotypes". However, most of these databases contain phenotypes that are not suited for automated analysis of EHR data, which often captured these phenotypes in the form of International Classification of Diseases (ICD) codes. There is a need for a resource that comprehensively provides gene-phenotype mappings in a format that can be used to evaluate phenotypes from EHR. Methods: We built a directed graph database of genes, medical concepts and ICD codes based on a subset of the National Library of Medicine's Unified Medical Language System (UMLS) and other resources. To obtain associations between genes and ICD codes, we traversed the defined relationships from gene, variant and disease concepts to ICD codes, resulting in a set of mappings that link specific genes and variants to these ICD codes. Results: Our method created 249,764 mappings between genes and ICD codes, including 27,226 "disease" phenotypes and 222,538 "symptom" phenotypes, and provided mappings for 4,456 unique genes. Paths were validated by manual review of a diverse sample of paths. In a cohort of 92,455 samples, we used these mappings to validate gene-phenotype associations in 32,786 samples where a person had a potentially disease-causing genetic mutation and at least one corresponding diagnosis in their EHR. Conclusion: The concepts and relationships in the UMLS can be used to generate gene-ICD phenotype mappings that are not explicit in the source vocabularies. We were able use these mappings to validate gene-disease associations in a large cohort of sequenced exomes paired with EHR.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.227363v1?rss=1

Authors: Palopoli, N., Marchetti, J., Monzon, A. M., Zea, D. J., Tosatto, S. C. E., Fornasari, M. S., Parisi, G.

Abstract:

Intrinsically disordered proteins (IDPs) lack stable tertiary structure under physiological conditions. The unique composition and complex dynamical behaviour of IDPs make them a challenge for structural biology and molecular evolution studies. Using NMR ensembles, we found that IDPs evolve under a strong site-specific evolutionary rate heterogeneity, mainly originated by different constraints derived from their inter-residue contacts. Evolutionary rate profiles correlate with the experimentally observed conformational diversity of the protein, allowing the description of different conformational patterns possibly related to their structure-function relationships. The correlation between evolutionary rates and contact information improves when structural information is taken not from any individual conformer or the whole ensemble, but from combining a limited number of conformers. Our results suggest that residue contacts in disordered regions constrain evolutionary rates to conserve the dynamic behaviour of the ensemble and that evolutionary rates can be used as a proxy for the conformational diversity of IDPs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.226308v1?rss=1

Authors: Joel, I. y., Adigun, T. O., AJIBOLA, A. O., BANKOLE, O. O., OZOJIOFOR, U. O., REMI-ESAN, I. A., SULAIMON, L. A.

Abstract:

Evading apoptosis is one of the hallmarks of cancer cells, therefore a lot of therapeutic strategies have been developed to induce cell death in these cells. BCL2 family protein governs the intrinsic pathway of cell death. The BCL2 family protein possess both pro and anti-apoptosis members. BCL2 protein, a pioneering member of the anti-apoptosis protein has been implicated in several cancerous cells; with dozens of small-molecule inhibitors developed to inhibit its activity. BCL2 family protein processes BH domains (1-4) for heterodimerization and homodimerization amidst it family members and targeting the BH4 domain is an established new strategy for cancer therapy; however, only BDA366 has been reported so far as a BH4 binding molecule. Using Computer-aided drug discovery techniques (Molecular docking, QM-polarized docking, Induced-fit docking, QM-MM optimization, and QM electronic descriptors) we screened M sample (~ 99,000 compounds) of the SCUBIDOO database to find ligands that interact with the BH4 domain of BCL2 protein with high affinity, we identified 11 putative BH4 specific small molecules with binding affinity ranging from ~ -84kcal/mol to -64kcal/mol with a putative binding hypothesis with BH4 amino acid residues: ASP10, ARG12, GLU13, MET16, LYS17, and HIS20

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.227207v1?rss=1

Authors: BinTayyash, N., Georgaka, S., John, S., Ahmed, S., Boukouvalas, A., Hensman, J., Rattray, M.

Abstract:

Motivation: The negative binomial distribution is a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modeling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics. Results: The GPcounts package implements GP regression methods for modelling counts data using negative binomial likelihood functions. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We also provide the option of modelling additional dropout using a zero-inflated negative binomial likelihood. We validate the method on simulated time course data, showing that it is better able to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to a published GP method with a Gaussian likelihood function. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.226951v1?rss=1

Authors: Ferres, I., Iraola, G.

Abstract:

Pangenome analysis is fundamental to explore evolutionary processes occurring in bacterial populations. However, the lack of standardized methods for handling diverse pangenomic datasets and complex metadata hinders more straightforward and reproducible downstream analyses. To fill this gap, we introduce Pagoo, a new framework that integrates pangenome data, analytical methods and visualization tools in a single object that can be easily stored, shared and responsively queried for improved biological interpretation of bacterial evolution.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.227546v1?rss=1

Authors: Zhang, R., Atwal, G. S., Lim, W.

Abstract:

With the rapid advancement of single-cell RNA-seq (scRNA-seq) technology, many data preprocessing methods have been proposed to address numerous systematic errors and technical variabilities inherent in this technology. While these methods have been demonstrated to be effective in recovering individual gene expression, the suitability to the inference of gene-gene associations and subsequent gene networks reconstruction have not been systemically investigated. In this study, we benchmarked five representative scRNA-seq normalization/imputation methods on human cell atlas bone marrow data with respect to their impact on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data preprocessing steps due to over-smoothing of the raw data. We proposed a model-agnostic noise regularization method that can effectively eliminate the correlation artifacts. The noise regularized gene-gene correlations were further used to reconstruct gene co-expression network and successfully revealed several known immune cell modules.

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