PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.265645v1?rss=1

Authors: Prescott, L.

Abstract:

A novel coronavirus (SARS-CoV-2) has devastated the globe as a pandemic that has killed more than 800,000 people. Effective and widespread vaccination is still uncertain, so many scientific efforts have been directed towards discovering antiviral treatments. Many drugs are being investigated to inhibit the coronavirus main protease, 3CLpro, from cleaving its viral polyprotein, but few publications have addressed this protease's interactions with the host proteome or their probable contribution to virulence. Too few host protein cleavages have been experimentally verified to fully understand 3CLpro's global effects on relevant cellular pathways and tissues. Here, we set out to determine this protease's targets and corresponding potential drug targets. Using a neural network trained on coronavirus proteomes with a Matthews correlation coefficient of 0.983, we predict that a large proportion of the human proteome is vulnerable to 3CLpro, with 4,460 out of approximately 20,000 human proteins containing at least one predicted cleavage site. These cleavages are nonrandomly distributed and are enriched in the epithelium along the respiratory tract, brain, testis, plasma, and immune tissues and depleted in olfactory and gustatory receptors despite the prevalence of anosmia and ageusia in COVID-19 patients. Affected cellular pathways include cytoskeleton/motor/cell adhesion proteins, nuclear condensation and other epigenetics, host transcription and RNAi, coagulation, pattern recognition receptors, growth factor, lipoproteins, redox, ubiquitination, and apoptosis. This whole proteome cleavage prediction demonstrates the importance of 3CLpro in expected and nontrivial pathways affecting virulence, lead us to propose more than a dozen potential therapeutic targets against coronaviruses, and should therefore be applied to all viral proteases and experimentally verified.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.264366v1?rss=1

Authors: Al-okaily, A. A., Tbakhi, A.

Abstract:

Data compression is a fundamental problem in the fields of computer science, information theory, and coding theory. The need for compressing data is to reduce the size of the data so that the storage and the transmission of them become more efficient. Motivated from resolving the compression for DNA data, we introduce a novel encoding algorithm that works for any textual data including DNA data. Moreover, the design of this algorithm paves a novel approach so that researchers can build up on and resolve better the compression problem of DNA or textual data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.264267v1?rss=1

Authors: Zhang, Q., Liu, P., Han, Y., Zhang, Y., Wang, X., Yu, B.

Abstract:

DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.264325v1?rss=1

Authors: Yue, L., Wang, M., Yang, X., Han, Y., Song, L., Yu, B.

Abstract:

The identification of fertility-related proteins plays an essential part in understanding the embryogenesis of germ cell development. Since the traditional experimental methods are expensive and time-consuming to identify fertility-related proteins, the purposes of predicting protein functions from amino acid sequences appeared. In this paper, we propose a fertility-related protein prediction model. Firstly, the model combines protein physicochemical property information, evolutionary information and sequence information to construct the initial feature space 'ALL'. Then, the least absolute shrinkage and selection operator (LASSO) is used to remove redundant features. Finally, light gradient boosting machine (LightGBM) is used as a classifier to predict. The 5-fold cross-validation accuracy of the training dataset is 88.5%, and the independent accuracy of the training dataset is 91.5%. The results show that our model is more competitive for the prediction of fertility-related proteins, which is helpful for the study of fertility diseases and related drug targets.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.264317v1?rss=1

Authors: Macher, T.-H., Beermann, A. J., Leese, F.

Abstract:

DNA metabarcoding is increasingly used in research and application to assess biodiversity. Powerful analysis software exists to process raw data. However, when it comes to the translation of sequence read data into biological information many end users with limited bioinformatic expertise struggle with the downstream analysis and explore data only to a minor extent. Thus, there is a growing need for easy-to-use, graphical user interface (GUI) analysis software to analyse and visualise DNA metabarcoding data. We here present TaxonTableTools (TTT), a new platform independent GUI software that aims to fill this gap by providing simple and reproducible analysis and visualisation workflows. TTT uses a so-called "TaXon table" as input. This format can easily be generated within TTT from two input files: a read table and a taxonomy table that can be obtained by various published metabarcoding pipelines. TTT analysis and visualisation modules include e.g. Venn diagrams to compare taxon overlap among replicates, samples or among different analysis methods. It analyses and visualises basic statistics such as read proportion per taxon as well as more sophisticated visualisation such as interactive Krona charts for taxonomic data exploration. Various ecological analyses such as alpha or beta diversity estimates, and rarefaction analysis ordination plots can be produced directly. Data can be explored also in formats required by traditional taxonomy-based analyses of regulatory bioassessment programs. TTT comes with a manual and tutorial, is free and publicly available through GitHub (https://github.com/TillMacher/TaxonTableTools) and the Python package index (https://pypi.org/project/taxontabletools/).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.264473v1?rss=1

Authors: Widodo, W. T., Sumitro, S. B., Widyarti, S., Santjojo, D. H.

Abstract:

Tyramine-Fe complex is a bioinorganic complex that is formed in brotowali. The complex is thought to play a role in the anti-inflammatory activity of brotowali. The mechanism of anti-inflammatory drugs such as aspirin and ibuprofen is to inhibit the reaction of prostaglandin formation from COX2 and arachidonic acid. In this study, a docking simulation was performed between COX2 with single tyramine, tyramine-Fe complex ibuprofen, and aspirin. The results showed that the COX2-single tyramine bond overlapped with COX2-aspirin, while COX2-tyramine-Fe complex bond overlapped with COX2-ibuprofen. The energy required to form COX2-tyramine-Fe complex bond was smaller than that of single tyramine, aspirin, and ibuprofen. The number of bonds of COX2-tyramine-Fe complex bond is higher than that of single tyramine. These results proved that COX2-tyramine-Fe complex bond had anti-inflammatory activity and greater activity than single tyramine.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.23.234237v1?rss=1

Authors: Csaba, G., Berchtold, E., Hadziahmetovic, A., Gruber, M., Ammar, C., Zimmer, R.

Abstract:

While absolute quantification is challenging in high-throughput measurements, changes of features between conditions can often be determined with high precision. Therefore, analysis of fold changes is the standard method, but often, a doubly differential analysis of "changes of changes" is required. Differential alternative splicing is an example of a doubly differential analysis, i.e. fold changes between conditions for different isoforms of a gene. EmpiRe is a quantitative approach for various kinds of omics data based on fold changes for appropriate features of biological objects. Empirical error distributions for these fold changes are estimated from Replicate measurements and used to quantify feature fold changes and their directions. We assess the performance of EmpiRe to detect differentially expressed genes applied to RNA-Seq using simulated data. It achieved higher precision than established tools at nearly the same recall level. Furthermore, we assess the detection of alternatively Spliced genes via changes of isoform fold changes (EmpiReS) on distribution-free simulations and experimentally validated splicing events. EmpiReS achieves the best precision-recall values for simulations based on different biological datasets. We propose EmpiRe(S) as a general, quantitative and fast approach with high reliability and an excellent trade-off between sensitivity and precision in (doubly) differential analyses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.23.262055v1?rss=1

Authors: Alnasir, D. J. J., Shanahan, H. P.

Abstract:

Given the wide variability in the quality of NGS data submitted to public repositories, it is essential to identify methods that can perform quality control on these datasets when additional quality control data, such as mean tile data, is missing. This is particularly important because such datasets are routinely deposited in public archives that now store data at an unprecedented scale. In this paper, we show that correlating counts of reads corresponding to pairs of motifs separated over specific distances on individual exons corresponds to mean tile data in the datasets we analysed, and can therefore be used when mean tile data is not available. As test datasets we use the H. sapiens IVT (in-vitro transcribed) dataset of Lahens et al., and a D. melanogaster dataset comprising wild and mutant types from Aerts et al. The intra-exon motif correlations as a function of both GC content parameters are much higher in the IVT-Plasmids mRNA selection free RNA-Seq sample (control) than in the other RNA-Seq samples that did undergo mRNA selection: both ribosomal depletion (IVT-Only) and PolyA selection (IVT-polyA, wild-type, and mutant). There is considerable degradation of similar correlations in the mutant samples from the D. melanogaster dataset. This matches with the available mean tile data that has been gathered for these datasets. We observe that extremely low correlations are indicative of bias of technical origin, such as flowcell errors.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.21.262147v1?rss=1

Authors: Silva, R., Padovani, K., Goes, F., Alves, R.

Abstract:

Motivation: Microbes perform a fundamental economic, social and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also create a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available which can aid gene annotation process though they lack of handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates. Results: We introduce geneRFinder, a ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar's test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval. Conclusions: We provide geneRFinder, a approach for gene prediction in distinct metagenomic complexities, available at github.com/railorena/geneRFinder, and also we provide a novel, comprehensive benchmark data for gene prediction --- which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions -- avaliable at sourceforge.net/p/generfinder-benchmark .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.23.263467v1?rss=1

Authors: Lin, Y.-W. E., Shnitzer, T., Talmon, R., Villaroel-Espindola, F., Desai, S., Schalper, K., Kluger, Y.

Abstract:

Hyper spectral imaging, sensor networks, spatial multiplexed proteomics, and spatial transcriptomics assays is a representative subset of distinct technologies from diverse domains of science and engineering that share common data structures. The data in all these modalities consist of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and therefore can be analyzed using similar computational methodologies. Furthermore, in many studies practitioners collect datasets consisting of multiple spatial assays of this type, each capturing such data from a single biological sample, patient, or hyper spectral image, etc. Each of these spatial assays could be characterized by several regions of interest (ROIs). The focus of this paper is on a particular application, imaging mass cytometry (IMC), which falls into this problem setup. To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these mdimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 92% accuracy. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intrarelationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image. Importantly, this approach is applicable to other modalities such as spatial transcriptomics.

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