PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.247890v1?rss=1

Authors: Zaheri, M., Salamin, N.

Abstract:

The mechanistic models of codon evolution rely on some simplistic assumptions in order to reduce the computational complexity of estimating the high number of parameters of the models. This paper is an attempt to investigate how much these simplistic assumptions are misleading when they violate the nature of the biological dataset in hand. We particularly focus on three simplistic assumptions made by most of the current mechanistic codon models including: 1) only single substitutions between nucleotides within codons in the codon transition rate matrix are allowed. 2) mutation is homogeneous across nucleotides within a codon. 3) assuming HKY nucleotide model is good enough at the nucleotide level. For this purpose, we developed a framework of mechanistic codon models, each model in the framework hold or relax some of the mentioned simplifying assumptions. Holding or relaxing the three simplistic assumptions results in total to eight different mechanistic models in the framework. Through several experiments on biological datasets and simulations we show that the three simplistic assumptions are unrealistic for most of the biological datasets and relaxing these assumptions lead to accurate estimation of evolutionary parameters such as selection pressure.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.248914v1?rss=1

Authors: LYu, X., Zou, Y., Li, L.

Abstract:

Cysteine S-sulphenylation (CSO), as a novel post-translational modification (PTM), has emerged as a potential mechanism to regulate protein functions and affect signal networks. Because of its functional significance, several prediction approaches have been developed. Nevertheless, they are based on a limited dataset from Homo sapiens and there is a lack of prediction tools for the CSO sites of other species. Recently, this modification has been investigated at the proteomics scale for a few species and the number of identified CSO sites has significantly increased. Thus, it is essential to explore the characteristics of this modification across different species and construct prediction models with better performances based on the enlarged dataset. In this study, we constructed a few classifiers and fond that the long short-term memory model with the word-embedding encoding approach, dubbed LSTMWE, 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 values for LSTMWE ranged from 0.82 to 0.85 for different organisms, which is superior to the reposted CSO predictors. Moreover, we developed the general model based on the integrated data from different species and it showed great universality and effectiveness. We provided the on-line prediction service called DeepCSO that included both species-specific and general models, which is accessible through http://www.bioinfogo.org/DeepCSO.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.247312v1?rss=1

Authors: Zhang, X., Luo, J., Cai, Y., Zhu, W., Yang, X., Cai, H., Lin, Z.

Abstract:

Deep learning has been increasingly used in protein tertiary structure prediction, a major goal in life science. However, all the algorithms developed so far mostly use protein sequences as input, whereas the vast amount of protein tertiary structure information available in the Protein Data Bank (PDB) database remains largely unused, because of the inherent complexity of 3D data computation. In this study, we propose Protein Structure Camera (PSC) as an approach to convert protein structures into images. As a case study, we developed a deep learning method incorporating PSC (DeepPSC) to reconstruct protein backbone structures from alpha carbon traces. DeepPSC outperformed all the methods currently available for this task. This PSC approach provides a useful tool for protein structure representation, and for the application of deep learning in protein structure prediction and protein engineering.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.247577v1?rss=1

Authors: Rodriguez Horta, E., Weigt, M.

Abstract:

Coevolution-based contact prediction, either directly by coevolutionary couplings resulting from global statistical sequence models or using structural supervision and deep learning, has found widespread application in protein-structure prediction from sequence. However, one of the basic assumptions in global statistical modeling is that sequences form an at least approximately independent sample of an unknown probability distribution, which is to be learned from data. In the case of protein families, this assumption is obviously violated by phylogenetic relations between protein sequences. It has turned out to be notoriously difficult to take phylogenetic correlations into account in coevolutionary model learning. Here, we propose a complementary approach: we develop two strategies to randomize or resample sequence data, such that conservation patterns and phylogenetic relations are preserved, while intrinsic (i.e. structure- or function-based) coevolutionary couplings are removed. An analysis of these data shows that the strongest coevolutionary couplings, i.e. those used by Direct Coupling Analysis to predict contacts, are only weakly influenced by phylogeny. However, phylogeny-induced spurious couplings are of similar size to the bulk of coevolutionary couplings, and dissecting functional from phylogeny-induced couplings might lead to more accurate contact predictions in the range of intermediate-size couplings.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.247734v1?rss=1

Authors: Howe, K., Chow, W., Collins, J., Pelan, S., Pointon, D.-L., Sims, Y., Torrance, J., Tracey, A., Wood, J.

Abstract:

Background Genome sequence assemblies provide the basis for our understanding of biology. Generating error-free assemblies is therefore the ultimate, but sadly still unachieved goal of a multitude of research projects. Despite the ever-advancing improvements in data generation, assembly algorithms and pipelines, no automated approach has so far reliably generated near error-free genome assemblies for eukaryotes. Results Whilst working towards improved data sets and fully automated pipelines, assembly evaluation and curation is actively employed to bridge this shortcoming and significantly reduce the number of assembly errors. In addition to this increase in product value, the insights gained from assembly curation are fed back into the automated assembly strategy and contribute to notable improvements in genome assembly quality. Conclusions We describe our tried and tested approach for assembly curation using gEVAL, the genome evaluation browser. We outline the procedures applied to genome curation using gEVAL and also our recommendations for assembly curation in an gEVAL-independent context to facilitate the uptake of genome curation in the wider community.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.247361v1?rss=1

Authors: Wang, G., Xia, B., Zhou, M., Lv, J., Zhao, D., Li, Y., Bu, Y., Wang, X., Cooke, J. P., Cao, Q., Lee, M., Zhang, L., Chen, K.

Abstract:

Numerous studies of relationship between epigenomic features have focused on their strong correlation across the genome, likely because such relationship can be easily identified by many established methods for correlation analysis. However, two features with little correlation may still colocalize at many genomic sites to implement important functions. There is no bioinformatic tool for researchers to specifically identify such feature pair. Here, we develop a method to identify feature pair in which two features have maximal colocalization but minimal correlation (MACMIC) across the genome. By MACMIC analysis of 3,385 feature pairs in 15 cell types, we reveal a dual role of CTCF in epigenetic regulation of cell identity genes. Although super-enhancers are associated with activation of target genes, only a subset of super-enhancers colocalized with CTCF regulate cell identity genes. At super-enhancers colocalized with CTCF, the CTCF is required for the active marker H3K27ac in cell type requiring the activation, and also required for the repressive marker H3K27me3 in other cell types requiring the repression. Our work demonstrates the biological utility of the MACMIC analysis and reveals a key role for CTCF in epigenetic regulation of cell identity.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.248963v1?rss=1

Authors: Hediyeh-zadeh, S., Webb, A. I., Davis, M. J.

Abstract:

Recent developments in mass spectrometry (MS) instruments and data acquisition modes have aided multiplexed, fast, reproducible and quantitative analysis of proteome profiles, yet missing values remain a formidable challenge for proteomics data analysis. The stochastic nature of sampling in Data Dependent Acquisition (DDA), suboptimal preprocessing of Data Independent Acquisition (DIA) runs and dynamic range limitation of MS instruments impedes the reproducibility and accuracy of peptide quantification and can introduce systematic patterns of missingness that impact downstream analyses. Thus, imputation of missing values becomes an important element of data analysis. We introduce msImpute, an imputation method based on low-rank approximation, and compare it to six alternative imputation methods using public DDA and DIA datasets. We evaluate the performance of methods by determining the error of imputed values and accuracy of detection of differential expression. We also measure the post-imputation preservation of structures in the data at different levels of granularity. We develop a visual diagnostic to determine the nature of missingness in datasets based on peptides with high biological dropout rate and introduce a method to identify such peptides. Our findings demonstrate that msImpute performs well when data are missing at random and highlights the importance of prior knowledge about nature of missing values in a dataset when selecting an imputation technique.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.248732v1?rss=1

Authors: Eskier, D., Suner, A., Oktay, Y., Karakülah, G.

Abstract:

SARS-CoV-2 is a betacoronavirus responsible for human cases of COVID-19, a pandemic with global impact that first emerged in late 2019. Since then, the viral genome has shown considerable variance as the disease spread across the world, in part due to the zoonotic origins of the virus and the human host adaptation process. As a virus with an RNA genome that codes for its own genomic replication proteins, mutations in these proteins can significantly impact the variance rate of the genome, affecting both the survival and infection rate of the virus, and attempts at combating the disease. In this study, we analyzed the mutation densities of viral isolates carrying frequently observed mutations for four proteins in the RNA synthesis complex over time in comparison to wildtype isolates. Our observations suggest mutations in nsp14, an error-correcting exonuclease protein, have the strongest association with increased mutation load in both regions without selective pressure and across the genome, compared to nsp7, 8, and 12, which form the core polymerase complex. We propose nsp14 as a priority research target for understanding genomic variance rate in SARS-CoV-2 isolates, and nsp14 mutations as potential predictors for high mutability strains.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.248724v1?rss=1

Authors: Andagalu, B., Lu, P., Onyango, I., Bergmann-Leitner, E., Wasuna, R., Odhiambo, G., Chebone-Bore, L., Ingasia, L. A., Opot, B., Cheruiyot, A., Yeda, R., Okudo, C., Okoth, R., Campo, J., Wallqvist, A., Akala, H., Ochiel, D., Ogutu, B., Chaudhury, S., Kamau, E.

Abstract:

The role of humoral immunity on the efficacy of artemisinin combination therapy (ACT) has not been investigated, yet naturally acquired immunity is key determinant of antimalarial therapeutic response. We conducted a therapeutic efficacy study in high transmission settings of western Kenya, which showed artesunate-mefloquine (ASMQ) and dihydroartemisinin-piperaquine (DP) were more efficacious than artemether-lumefantrine (AL). To investigate the underlying prophylactic mechanism, we compared a broad range of humoral immune responses in cohort I study participants treated with ASMQ or AL, and applied machine-learning (ML) models using immunoprofile data to analyze individual participants treatment outcome. We showed ML models could predict treatment outcome for ASMQ but no AL with high (72-92%) accuracy. Simulated PK profiling provided evidence demonstrating specific humoral immunity confers protection in the presence of sub-therapeutic residual mefloquine concentration. We concluded patient humoral immunity and partner drug interact to provide long prophylactic effect of ASMQ.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.243766v1?rss=1

Authors: Marin-Llao, J., Mubeen, S., Perera-Lluna, A., Hofmann-Apitius, M., Picart-Armada, S., Domingo-Fernandez, D.

Abstract:

High-throughput screening yields vast amounts of biological data which can be highly challenging to interpret. In response, knowledge-driven approaches emerged as possible solutions to analyze large datasets by leveraging prior knowledge of biomolecular interactions represented in the form of biological networks. Nonetheless, given their size and complexity, their manual investigation quickly becomes impractical. Thus, computational approaches, such as diffusion algorithms, are often employed to interpret and contextualize the results of high-throughput experiments. Here, we present MultiPaths, a framework consisting of two independent Python packages for network analysis. While the first package, DiffuPy, comprises numerous state-of-the-art diffusion algorithms applicable to any generic network, the second, DiffuPath, enables the application of these algorithms on multi-layer biological networks. To facilitate its usability, the framework includes a command line interface, reproducible examples, and documentation. To demonstrate the framework, we conducted several diffusion experiments on three independent multi-omics datasets over disparate networks generated from pathway databases, thus, highlighting the ability of multi-layer networks to integrate multiple modalities. Finally, the results of these experiments demonstrate how the generation of harmonized networks from disparate databases can improve predictive performance with respect to individual resources.

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