PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.23.352096v1?rss=1

Authors: Gralinska, E., Vingron, M.

Abstract:

In molecular biology, just as in many other fields of science, data often come in the form of matrices or contingency tables with many measurements (rows) for a set of variables (columns). While projection methods like Principal Component Analysis or Correspondence Analysis can be applied for obtaining an overview of such data, in cases where the matrix is very large the associated loss of information upon projection into two or three dimensions may be dramatic. However, when the set of variables can be grouped into clusters, this opens up a new angle on the data. We focus on the question which measurements are associated to a cluster and distinguish it from other clusters. Correspondence Analysis employs a geometry geared towards answering this question. We exploit this feature in order to introduce Association Plots for visualizing cluster-specific measurements in complex data. Association Plots are two-dimensional, independent of the size of data matrix or cluster, and depict the measurements associated to a cluster of variables. We demonstrate our method first on a small data set and then on a genomic example comprising more than 10,000 conditions. We will show that Association Plots can clearly highlight those measurements which characterize a cluster of variables.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.23.350462v1?rss=1

Authors: Blasco, T., Perez-Burillo, S., Balzerani, F., Lerma, A., Hinojosa-Nogueira, D., Pastoriza, S., Gosalbes, M. J., Jimenez-Hernandez, N., Francino, M. P., Rufian-Henares, J. A., Apaolaza, I., Planes, F. J.

Abstract:

Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex question. However, when applied to nutritional questions, a major issue in existing reconstructions is the lack of information about degradation pathways of relevant nutrients in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of the human gut microbiota metabolism for personalized nutrition. AGREDA includes the degradation pathways of 231 nutrients present in the human diet and allows us to more comprehensively simulate the interplay between food and gut microbiota. We show that AGREDA is more accurate than existing reconstructions in predicting output metabolites of the gut microbiota. Finally, using AGREDA, we established relevant metabolic differences among clinical subgroups of Spanish children: lean, obese, allergic to foods and celiac.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.23.350736v1?rss=1

Authors: Zervou, M. A., Doutsi, E., Pavlidis, P., Tsakalides, P.

Abstract:

Protein structure prediction is one of the most significant problems in bioinformatics, as it has a prominent role in understanding the function and evolution of proteins. Designing an efficient and accurate computational prediction method remains a pressing issue, especially for sequences that we cannot obtain a sufficient amount of homologous information from existing protein sequence databases. Several studies demonstrate the potential of utilizing chaos game representation (CGR) along with time series analysis tools such as recurrence quantification analysis (RQA), complex networks, horizontal visibility graphs (HVG) and others. However, the majority of existing works involve a large amount of features and they require an exhaustive, time consuming search of the optimal parameters. To address the aforementioned problems, this work adopts the generalized multidimensional recurrence quantification analysis (GmdRQA) as an efficient tool that enables to process concurrently multidimensional time series and reduce the number of features. In addition, two recently proposed data-driven algorithms, namely average mutual information (AMI) and false nearest neighbors (FNN), are utilized to define in a fast yet precise manner the optimal GmdRQA parameters. Finally yet importantly, the classification accuracy is improved by the combination of the aforementioned GmdRQA with the HVG. Experimental evaluation on a real benchmark dataset demonstrates that our methods achieve similar performance with the state-of-the-art but with a smaller computational cost.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.22.328864v1?rss=1

Authors: Caballero Perez, J., de Blas Perez, C., Leza Alvarez, F., Caballero Contreras, J. M.

Abstract:

COVID-19 has had an unprecedented global impact in health and economy affecting millions of persons world-wide. To support and enable a collaborative response from the global research communities, we created a data collection for different public sources for anonymized patient clinical data, imaging datasets, molecular data as nucleotide and protein sequences for the SARS-CoV-2 virus, reports of count of cases and deaths per city/country, and other economic indicators in Databiology Lab (https://www.lab.databiology.net/) where researchers could access these data assets and use the hundreds of available open source bioinformatic applications to analyze them. These data assets are regularly updated and was used in a successful virtual 3-day hackathon organized by Databiology Ltd and Mindstream-AI where hundreds of attendees to work collaboratively to analyze these data collections.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.22.351072v1?rss=1

Authors: Coelho, V. L., Brito, T. F. d., Brito, I. A. d. A., Cardoso, M. A., Berni, M. A., Araujo, H. M. M., Sammeth, M., Pane, A.

Abstract:

Rhodnius prolixus is a Triatominae insect species and a primary vector of Chagas disease. The genome of R. prolixus has been recently sequenced and partially assembled, but few transcriptome analyses have been performed to date. In this study, we describe the stage-specific transcriptomes obtained from previtellogenic stages of oogenesis and from mature eggs. By analyzing ~228 million paired-end RNA-Seq reads, we significantly improved the current genome annotations for 9,206 genes. We provide extended 5' and 3' UTRs, complete Open Reading Frames, and alternative transcript variants. Strikingly, using a combination of genome-guided and de novo transcriptome assembly we found more than two thousand novel genes, thus increasing the number of genes in R. prolixus from 15,738 to 17,864. We used the improved transcriptome to investigate stage-specific gene expression profiles during R. prolixus oogenesis. Our data reveal that 11,127 genes are expressed in the early previtellogenic stage of oogenesis and their transcripts are deposited in the developing egg including key factors regulating germline development, genome integrity, and the maternal-zygotic transition. In addition, GO term analyses show that transcripts encoding components of the steroid hormone receptor pathway, cytoskeleton, and intracellular signaling are abundant in the mature eggs, where they likely control early embryonic development upon fertilization. Our results significantly improve the R. prolixus genome and transcriptome and provide novel insight into oogenesis and early embryogenesis in this medically relevant insect.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.22.349720v1?rss=1

Authors: Cornejo-Granados, F., Kohl, T. A., Sotomayor, F. V., Andres, S., Hernandez-Pando, R., Hurtado-Ramirez, J. M., Utpatel, C., Niemann, S., Maurer, F. P., Ochoa-Leyva, A.

Abstract:

Mycobacterium abscessus (MAB) is a widely disseminated pathogenic non-tuberculous mycobacterium (NTM). Like with M. tuberculosis complex (MTBC), excreted / secreted (ES) proteins play an essential role for its virulence and survival inside the host. ES proteins contain highly immunogenic proteins, which are of interest for novel diagnostic assays and vaccines. Here, we used a robust bioinformatics pipeline to predict the secretome of the M. abscessus ATCC 19977 reference strain and fifteen clinical isolates belonging to all three MAB subspecies, M. abscessus subsp. abscessus, M. abscessus subsp. bolletii, and M. abscessus subsp. massiliense. We found that ~18% of the proteins encoded in the MAB genomes were predicted as secreted and that the three MAB subspecies shared > 85 % of the predicted secretomes. MAB isolates with a rough (R) colony morphotype showed larger predicted secretomes than isolates with a smooth (S) morphotype. Additionally, proteins exclusive to the secretomes of MAB R variants had higher antigenic densities than those exclusive to S variants, independently of the subspecies. For all investigated isolates, ES proteins had a significantly higher antigenic density than non-ES proteins. We identified 337 MAB ES proteins with homologues in previously investigated M. tuberculosis secretomes. Among these, 222 have previous experimental support of secretion, and some proteins showed homology with protein drug targets reported in the DrugBank database. The predicted MAB secretomes showed a higher abundance of proteins related to quorum-sensing and Mce domains as compared to MTBC indicating the importance of these pathways for MAB pathogenicity and virulence. Comparison of the predicted secretome of M. abscessus ATCC 19977 with the list of essential genes revealed that 99 secreted proteins corresponded to essential proteins required for in vitro growth. All predicted secretomes were deposited in the Secret-AAR web-server (http://microbiomics.ibt.unam.mx/tools/aar/index.php).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.21.349605v1?rss=1

Authors: Khan, J., Patro, R.

Abstract:

Motivation: The construction of the compacted de Bruijn graph from a large collection of reference genomes is a task of increasing interest in genomic analyses. For example, compacted colored reference de Bruijn graphs are increasingly used as sequence indices for the purposes of alignment of short and long reads. Also, as we sequence and assemble a greater diversity of individual genomes, the compacted colored de Bruijn graph can be used as the basis for methods aiming to perform comparative genomic analyses on these genomes. While algorithms have been developed to construct the compacted colored de Bruijn graph from reference sequences, there is still room for improvement, especially in the memory and the runtime performance as the number and the scale of the genomes over which the de Bruijn graph is built grow. Results: We introduce a new algorithm, implemented in the tool Cuttlefish, to construct the colored compacted de Bruijn graph from a collection of one or more genome references. Cuttlefish introduces a novel modeling scheme of the de Bruijn graph vertices as finite-state automata, and constrains the state-space for the automata to enable tracking of their transitioning states with very low memory usage. Cuttlefish is also fast and highly parallelizable. Experimental results demonstrate that the algorithm scales much better than existing approaches, especially as the number and scale of the input references grow. For example, on a typical shared-memory machine, Cuttlefish constructed the compacted graph for 100 human genomes in less than 7 hours, using ~29 GB of memory; no other tested tool successfully completed this task on the testing hardware. We also applied Cuttlefish on 11 diverse conifer plant genomes, and the compacted graph was constructed in under 11 hours, using ~84 GB of memory, while the only other tested tool able to complete this compaction on our hardware took more than 16 hours and ~289 GB of memory. Availability: Cuttlefish is written in C++14, and is available under an open source license at https://github.com/COMBINE-lab/cuttlefish.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.21.348169v1?rss=1

Authors: Yu, G.

Abstract:

Ggtree supports mapping and visualizing associated external data on phylogeny with two general methods. The output of ggtree is a ggtree graphic object that can be rendered as a static image. Most importantly, the input tree and associated data that used in visualization can be extracted from the graphic object, making it an ideal data structure for publishing tree (image, tree and data in one single object) and thus enhance data reuse and analytic reproducibility.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.21.349399v1?rss=1

Authors: Schulze, S., Oltmanns, A., Fufezan, C., Kragenbring, J., Mormann, M., Pohlschroder, M., Hippler, M.

Abstract:

Motivation: Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. Results: Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae. Availability: The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.21.349100v1?rss=1

Authors: Zhao, C., Jiang, T., Ju, J. H., Zhang, S., Tao, J., Fu, Y., Lococo, J., Dockter, J., Powlowski, T., Bilke, S.

Abstract:

Background: As knowledge of mechanisms that drive the development of cancer grows, there has been corresponding growth in therapies specific to a mechanism. While these therapies show improvements in patient outcomes, they can be expensive and are effective only for a subset of patients. These treatments drive interest in research focused on the assignment of cancer therapies based on aberrations in individual genes or biomarkers that assess the broader mutational landscape, including microsatellite instability (MSI) and tumor mutational burden (TMB). Methods: Here we describe the TruSight Oncology 500 (TSO500; Research Use Only) bioinformatics workflow. This tumor-only approach leverages the next-generation sequencing-based assay TSO500 to enable high fidelity determination of DNA variants across 523 cancer-relevant genes, as well as MSI status and TMB in formalin-fixed paraffin-embedded (FFPE) samples. Results: The TSO500 bioinformatic workflow integrates unique molecular identifier (UMI)-based error correction and a dual approach variant filtering strategy that combines statistical modeling of error rates and database annotations to achieve detection of variants with allele frequency approaching 5% with 99.9998% per base specificity and 99% sensitivity in FFPE samples representing a variety of tumor types. TMB determined using the tumor-only workflow of TSO500 correlated well with tumor-normal (N =170, adjusted R2=0.9945) and whole-exome sequencing (N=108, adjusted R2=0.933). Similarly, MSI status determined by TSO500 showed agreement (N=106, 98% agreement) with a MSI-PCR assay. Conclusion: TSO500 is an accurate tumor-only workflow that enables researchers to systematically characterize tumors and identify the next generation of clinical biomarkers.

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