PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.245928v1?rss=1

Authors: Huber, F., Ridder, L., Rogers, S., van der Hooft, J. J.

Abstract:

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses, such as library matching and molecular networking. This is based upon the assumption that spectral similarity is a good proxy for structural similarity. Although weaknesses in the relationship between common spectral similarity scores and the true structural similarities have been pointed out, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm -- Word2Vec. Where Word2Vec learns relationships between words in sentences, Spec2Vec does so for mass fragments and neutral losses in MS/MS spectra. The spectral similarity score is based on spectral embeddings learnt from the fragmental relationships within a large set of spectral data. Using a dataset derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores are more proportional to structural similarity of molecules than the commonly used cosine score and its derivative, the modified cosine score. We also demonstrate the advantages of Spec2Vec in library searching for both exact matches and analogues as well as in molecular networking. Finally, Spec2Vec is also computationally more scalable allowing us to search for structural analogues in a large database within seconds.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.245993v1?rss=1

Authors: Gruca, A., Ziemska-Legiecka, J., Jarnot, P., Sarnowska, E., Sarnowski, T., Grynberg, M.

Abstract:

The rapid spread of the COVID-19 demands immediate response from the scientific communities. Appropriate countermeasures mean thoughtful and educated choice of viral targets (epitopes). There are several articles that discuss such choices in the SARS-CoV-2 proteome, other focus on phylogenetic traits and history of the Coronaviridae genome/proteome. However none consider viral protein low complexity regions (LCRs). Recently we created the first methods that are able to compare such fragments. We show that five low complexity regions (LCRs) in three proteins (nsp3, S and N) encoded by the SARS-CoV-2 genome are highly similar to regions from human proteome. As many as 21 predicted T-cell epitopes and 27 predicted B-cell epitopes overlap with the five SARS-CoV-2 LCRs similar to human proteins. Interestingly, replication proteins encoded in the central part of viral RNA are devoid of LCRs. Similarity of SARS-CoV-2 LCRs to human proteins may have implications on the ability of the virus to counteract immune defenses. The vaccine targeted LCRs may potentially be ineffective or alternatively lead to autoimmune diseases development. These findings are crucial to the process of selection of new epitopes for drugs or vaccines which should omit such regions.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.246041v1?rss=1

Authors: Praznikar, J.

Abstract:

The application of graph theory in structural biology offers an alternative means of studying 3D models of large macromolecules, such as proteins. However, basic structural parameters still play an important role in the description of macromolecules. For example, the radius of gyration, which scales with exponent ~0.4, provides quantitative information about the compactness of the protein structure. In this study, we combine two proven methods, the graph-theoretical and the fundamental scaling laws, to study 3D protein models. This study shows that the mean node degree of the protein graphs, which scales with exponent 0.038, is scale-invariant. In addition, proteins that differ in size have a highly similar node degree distribution, which peaks at node degree 7, and additionally conforms to the same statistical properties at any scale. Linear regression analysis showed that the graph parameters (radius, diameter and mean eccentricity) can explain up to 90% of the total radius of gyration variance. Thus, the graph parameters of radius, diameter and mean eccentricity scale with the same exponent as the radius of gyration. The main advantage of graph eccentricity compared to the radius of gyration is that it can be used to analyse the distribution of the central and peripheral amino acids/nodes of the macromolecular structure. The central nodes are hydrophobic amino acids (Val, Leu, Ile, Phe), which tend to be buried, while the peripheral nodes are more hydrophilic residues (Asp, Glu, Lys). Furthermore, it has been shown that the number of central and peripheral nodes is more related to the fold of the protein than to the protein length.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.245951v1?rss=1

Authors: Crisp, D., Parent, R., Nakatani, M., Murphy, G. G., Stacey, W. C.

Abstract:

Optimizing antiepileptic drug therapy is very challenging due to the absence of a reliable method to assess how brain activity changes between seizures. This work uses the Taxonomy of Seizure Dynamics (Saggio et al., 2020) to investigate how anticonvulsants influence seizure onset dynamotypes. The no Mg2+/high K+ mouse brain-slice seizure model (N = 92) was used to generate consistent epileptiform onsets. We compared the onset bifurcations of controls with slices treated with either GABA or carbamazepine. Each anticonvulsant uniquely changed the types of bifurcations in the slices. This experiment provides proof-of-concept evidence that brain states exist on a "map" of seizure dynamics, and that antiepileptic drugs with different mechanisms can change the positioning of the brain states on the map.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.232520v1?rss=1

Authors: Fu, Y., Pistolozzi, M., Yang, X., Lin, Z.

Abstract:

In this work, we present a unified and robust classification scheme for coronaviruses based on concatenated protein clusters. This subsequently allowed us to infer the apparent 'horizontal gene transfer' events via reconciliation with the corresponding gene trees, which we argue can serve as a marker for cross-host transmissions. The cases of SARS-CoV, MERS-CoV, and SARS-CoV-2 are discussed. Our study provides a possible technical route to understand how coronaviruses evolve and are transmitted to humans.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.09.243444v1?rss=1

Authors: Gu, H., Yuan, G.

Abstract:

The COVID-19 pandemic has infected millions of people and overwhelmed many health systems globally. Our study is to identify differentially expressed genes (DEGs) and associated biological processes of COVID-19 using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE152075 datasets were originally produced by using the high-throughput Illumina NextSeq 500. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed to identify functional categories and biochemical pathways. GO and KEGG results suggested that several biological pathways such as Fatty acid metabolism and Cilium morphogenesis are mostly involved in the development of COVID-19. Moreover, several genes are critical for virus invasion and adhesion including FLOC, DYNLL1, FBXL3, and FBXW11 and show significant differences in COVID-19 patients. Thus, our study provides further insights into the underlying pathogenesis of COVID-19.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.244947v1?rss=1

Authors: Levinson, M. A., Niestroy, J., Al Manir, S., Fairchild, K. D., Lake, D. E., Moorman, J. R., Clark, T.

Abstract:

Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis consists of accessible data and software with runtime environment and personnel involved. Evidence graphs - a derivation of argumentation frameworks adapted to biological science - can provide this disclosure as machine-readable metadata resolvable from persistent identifiers for computationally generated graphs, images, or tables, that can be archived and cited in a publication including a persistent ID. We have built a cloud-based, computational research commons for predictive analytics on biomedical time series datasets with hundreds of algorithms and thousands of computations using a reusable computational framework we call FAIRSCAPE. FAIRSCAPE computes a complete chain of evidence on every result, including software, computations, and datasets. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves the provenance graph across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software. FAIRSCAPE is a reusable computational framework, enabling simplified access to modern scalable cloud-based components. It fully implements the FAIR data principles and extends them to provide FAIR Evidence, including provenance of datasets, software and computations, as metadata for all computed results

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.244442v1?rss=1

Authors: Sato, K., Akiyama, M., Sakakibara, Y.

Abstract:

RNA secondary structure prediction is one of the key technologies for unveiling the essential roles of functional non-coding RNAs. Although machine learning-based rich-parametrized models achieved extremely high performance in prediction accuracy, a possibility of the risk of overfitting for such a model has been reported. We propose a new algorithm for predicting RNA secondary structures using deep learning with thermodynamic integrations, which enable us robust predictions. Our algorithm computes folding scores for the nearest neighbor loops using a deep neural network. Similar to our previous work, the folding scores are integrated with the traditional thermodynamic parameters to enable robust predictions. Then, the Zuker-style dynamic programming is employed to find an optimal secondary structure that maximizes the folding score. We also propose a new regularization, called the thermodynamic regularization, to train our deep neural network with avoiding the overfit to training data. Our algorithm (MXfold2) archived the most robust and accurate predictions in the computational experiments assuming the discovery of new non-coding RNAs. The source code of MXfold2 and the datasets used in the experiments are available at https://github.com/keio-bioinformatics/mxfold2/.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.245134v1?rss=1

Authors: Bruna, T., Hoff, K., Stanke, M., Lomsadze, A., Borodovsky, M.

Abstract:

Full automation of gene prediction has become an important bioinformatics task since the advent of next generation sequencing. The eukaryotic genome annotation pipeline BRAKER1 had combined self-training GeneMark ET with AUGUSTUS to generate genes coordinates with support of transcriptomic data. Here, we introduce BRAKER2, a pipeline with GeneMark EP+ and AUGUSTUS externally supported by cross-species protein sequences aligned to the genome. Among the challenges addressed in the development of the new pipeline was generation of reliable hints to the locations of protein-coding exon boundaries from likely homologous but evolutionarily distant proteins. Under equal conditions, the gene prediction accuracy of BRAKER2 was shown to be higher than the one of MAKER2, yet another genome annotation pipeline. Also, in comparison with BRAKER1 supported by a large volume of transcript data, BRAKER2 could produce a better gene prediction accuracy if the evolutionary distances to the reference species in the protein database were rather small. All over, our tests demonstrated that fully automatic BRAKER2 is a fast and accurate method for structural annotation of novel eukaryotic genomes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.245142v1?rss=1

Authors: Mandera, D., Ritz, A.

Abstract:

Predicting the response to a particular drug for specific cancer, despite known genetic mutations, still remains a huge challenge in modern oncology and precision medicine. Today, prescribing a drug for a cancer patient is based on a doctor's analysis of various articles and previous clinical trials; it is an extremely time-consuming process. We developed a machine learning classifier to automatically predict a drug given a carcinogenic gene mutation profile. Using the Breast Invasive Carcinoma Dataset from The Cancer Genome Atlas (TCGA), the method first selects features from mutated genes and then applies K-Fold, Decision Tree, Random Forest and Ensemble Learning classifiers to predict best drugs. Ensemble Learning yielded prediction accuracy of 66% on the test set in predicting the correct drug. To validate that the model is general-purpose, Lung Adenocarcinoma (LUAD) data and Colorectal Adenocarcinoma (COADREAD) data from TCGA was trained and tested, yielding prediction accuracies 50% and 66% respectively. The resulting accuracy indicates a direct correlation between prediction accuracy and cancer data size. More importantly, the results of LUAD and COADREAD show that the implemented model is general purpose as it is able to achieve similar results across multiple cancer types. We further verified the validity of the model by implementing it on patients with unclear recovery status from the COADREAD dataset. In every case, the model predicted a drug that was administered to each patient. This method will offer oncologists significant time-saving compared to their current approach of extensive background research, and offers personalized patient care for cancer patients.

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