PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.02.279380v1?rss=1

Authors: Kinugawa, K., Luginbühl, J., Matsui, T. K., Eura, N., Sakaguchi, Y. M., Shin, J. W., Sugie, K., Schwamborn, J. C., Mori, E.

Abstract:

Human brain organoids provide us the means to investigate human brain development and neurological diseases, and single-cell RNA-sequencing (scRNA-seq) technologies allow us to identify homologous cell types and the molecular heterogeneity between individual cells. Previously established human brain organoids of brainstem (hBSOs) and midbrain (hMBOs) were analyzed by scRNA-seq, but the difference in cellular composition between these organoids remains unclear. Here, we integrated and compared the single-cell transcriptome of hBSOs and hMBOs. Our analysis demonstrated that the hBSOs and hMBOs contain some unique cell types, including inflammatory and mesenchymal cells. Further comparison of the hBSOs and hMBOs with publicly available scRNA-seq dataset of human fetal midbrain (hMB) showed high similarity in their neuronal components. These results provide new insights into human brain organoid technologies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.02.279521v1?rss=1

Authors: Abi Nader, C., Ayache, N., Frisoni, G. B., Robert, P., Lorenzi, M.

Abstract:

Recent failures of clinical trials in Alzheimer's Disease underline the critical importance of identifying optimal intervention time to maximize cognitive benefit. While several models of disease progression have been proposed, we still lack quantitative approaches simulating the effect of treatment strategies on the clinical evolution. In this work, we present a data-driven method to model dynamical relationships between imaging and clinical biomarkers. Our approach allows simulating intervention at any stage of the pathology by modulating the progression speed of the biomarkers, and by subsequently assessing the impact on disease evolution. When applied to multi-modal imaging and clinical data from the Alzheimer's Disease Neuroimaging Initiative our method enables to generate hypothetical scenarios of amyloid lowering interventions. Our results show that in a study with 1000 individuals per arm, accumulation should be completely arrested at least 5 years before Alzheimer's dementia diagnosis to lead to statistically powered improvement of clinical endpoints.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.02.280396v1?rss=1

Authors: Zhu, Y., Yang, X., Wu, J., Tang, H., Wang, Q., Guan, J., Xie, W., Chen, S., Chen, Y., Wang, M., Lan, C., Wei, L., Sun, C., Zhang, Z.

Abstract:

The sequence upstream of antibody variable region (Antibody Upstream Sequence, or AUS) consists of 5' untranslated region (5' UTR) and two leader regions, L-PART1 and L-PART2. The sequence variations in AUS affect the efficiency of PCR amplification, mRNA translation, and subsequent PCR-based antibody quantification as well as antibody engineering. Despite their importance, the diversity of AUSs has long been neglected. Utilizing the rapid amplification of cDNA ends (5'RACE) and high-throughput antibody repertoire sequencing (Rep-Seq) technique, we acquired full-length AUSs for human, rhesus macaque (RM), cynomolgus macaque (CM), mouse, and rat. We designed a bioinformatics pipeline and discovered 2,957 unique AUSs, corresponding to 2,786 and 1,159 unique sequences for 5' UTR and leader, respectively. Comparing with the leader records in the international ImMunoGeneTics (IMGT), while 529 were identical, 313 were with single nucleotide polymorphisms (SNPs), 280 were totally new, and 37 updated the incomplete records. The diversity of AUSs' impact on related antibody biology was also probed. Taken together, our findings would facilitate Rep-Seq primer design for capturing antibodies comprehensively and efficiently as well as provide a valuable resource for antibody engineering and the studies of antibody at the molecular level.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.02.279208v1?rss=1

Authors: Sahlin, K., Makinen, V.

Abstract:

Long-read RNA sequencing techniques are quickly establishing themselves as the primary sequencing technique to study the transcriptome landscape. Many such analyses are dependent upon splice alignment of reads to the genome. However, the error rate and sequencing length of long-read technologies create new challenges for accurately aligning these reads. We present an alignment method uLTRA that, on simulated and synthetic data, shows higher accuracy over state-of-the-art with substantially higher accuracy for small exons. We show several examples on biological data where uLTRA aligns to known and novel isoforms with exon structures that are not detected with other aligners. uLTRA is available at https://github.com/ksahlin/ultra.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.02.279349v1?rss=1

Authors: Freeman, T., Horsewell, S., Patir, A., Harling-Lee, J., Regan, T., Shih, B. B., Prendergast, J., Hume, D. A., Angus, T.

Abstract:

Quantitative and qualitative data derived from the analysis of genomes, genes, proteins or metabolites from tissue or cells are currently generated in huge volumes during biomedical research. Graphia is an open-source platform created for the graph-based analysis of such complex data, e.g. transcriptomics, proteomics, genomics data. The software imports data already defined as a network or a similarity matrix and is designed to rapidly visualise very large graphs in 2D or 3D space, providing a wide range of functionality for graph exploration. An extensive range of analysis algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are also available. Graphias core is extensible through the deployment of plugins, supporting rapid development of additional computational analyses and features necessary for a given analysis task or data source. A plugin for correlation network analysis is distributed with the core application, to support the generation of correlation graphs from any tabular matrix of continuous or discrete values. This provides a powerful analysis solution for the interpretation of high-dimensional data from many sources. Several use cases of Graphia are described, to showcase its wide range of applications. Graphia runs on all major desktop operating systems and is freely available to download from https://graphia.app/.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.02.278986v1?rss=1

Authors: Julkunen, H. J., Cichonska, A., Gautam, P., Szedmak, S., Douat, J., Pahikkala, T., Aittokallio, T., Rousu, J.

Abstract:

We present comboFM, a machine learning framework for predicting the responses of drug combinations in preclinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines. The approach enables comboFM to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. We demonstrated high predictive performance of comboFM in various prediction scenarios using data from cancer cell line drug screening. Subsequent experimental validation of a set of previously untested drug combinations further supported the practical and robust applicability of comboFM. For instance, we confirmed a novel synergy between anaplastic lymphoma kinase (ALK) inhibitor crizotinib and proteasome inhibitor bortezomib in lymphoma cells. Overall, our results demonstrate that comboFM provides an effective means for systematic pre-screening of drug combinations to support precision oncology applications.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.03.280701v1?rss=1

Authors: Villmann, T., Abel, J., Bohnsack, K. S., Kaden, M., Weber, M., Leberecht, C.

Abstract:

In this contribution the discrimination between native and mirror models of proteins according to their chirality is tackled based on the structural protein information. This information is contained in the Ramachandran plots of the protein models. We provide an approach to classify those plots by means of an interpretable machine learning classifier - the Generalized Matrix Learning Vector Quantizer. Applying this tool, we are able to distinguish with high accuracy between mirror and native structures just evaluating the Ramachandran plots. The classifier model provides additional information regarding the importance of regions, e.g. $alpha$-helices and $beta$-strands, to discriminate the structures precisely. This importance weighting differs for several considered protein classes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.02.280578v1?rss=1

Authors: Molik, D. C., Tomlinson, D., Davitt, S., Morgan, E. L., Roche, B., Meyers, N., Pfrender, M. E.

Abstract:

Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year--with 180,000 resulting deaths--mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans . We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.03.281600v1?rss=1

Authors: Bereux, S., Delmas, B., Cazals, F.

Abstract:

We introduce Multiple Interface String Alignment (MISA), a visualization tool to display coherently various sequence and structure based statistics at protein-protein interfaces (SSE elements, buried surface area, {Delta}ASA, B factor values, etc). The amino-acids supporting these annotations are obtained from Voronoi interface models. The benefit of MISA is to collate annotated sequences of (homologous) chains found in different biological contexts i.e. bound with different partners or unbound. The aggregated views MISA/SSE, MISA/BSA, MISA/{Delta}ASAetc make it trivial to identify commonalities and differences between chains, to infer key interface residues, and to understand where conformational changes occur upon binding. As such, they should prove of key relevance for knowledge based annotations of protein databases such as the Protein Data Bank. Illustrations are provided on the receptor binding domain (RBD) of coronaviruses, in complex with their cognate partner or (neutralizing) antibodies. MISA computed with a minimal number of structures complement and enrich findings previously reported. The corresponding package is available from the Structural Bioinformatics Library (http://sbl.inria. fr).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.01.278242v1?rss=1

Authors: Moeinizade, S., Han, Y., Pham, H., Hu, G., Wang, L.

Abstract:

Multiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches.

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