PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.01.274753v1?rss=1

Authors: Sobolev, V., Soboleva, A., Svitich, O., Dvoriankova, E., Piruzyan, A., Mildzikhova, D., Korsunskaya, I., Nesterova, A.

Abstract:

Interactions of genes in intersecting signaling pathways, as well as environmental influences, are required for the development of psoriasis. Peroxisome proliferator-activated receptor gamma (PPAR{gamma}) is a nuclear receptor and transcription factor which inhibits the expression of many proinflammatory genes. We tested the hypothesis that low levels of PPAR{gamma} expression promote the development of psoriatic lesions. We combined experimental results and network functional analysis to reconstruct the model of PPAR{gamma} downregulated signaling in psoriasis. We found that the expression of PPAR{gamma} maybe be slightly downregulated in human psoriatic skin and laser treatment may facilitate it. We tested the reconstructed model and found that at least on mRNA level the expression of IL17, STAT3, FOXP3, and RORC and FOSL1 genes in psoriatic skin before and after laser treatment were correlated with the level of PPAR{gamma} mRNA expression suggesting that genes belong to the same signaling pathway that may regulate the development of psoriasis lesion.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.01.277095v1?rss=1

Authors: Weng, G., Kim, J., Won, K. J.

Abstract:

Motivation: Trajectory inference for single cell RNA sequencing (scRNAseq) data is a powerful approach to understand time-dependent cellular processes such as cell cycle and cellular development. However, it is still not easy to infer the trajectory precisely by which cells differentiate to multiple lineages or exhibit cyclic transitions. Recent development of RNA velocity provides a way to visualize cell state transition without a prior knowledge. Trajectory inference that utilizes the velocity information will be highly useful to understand cellular dynamics. Results: We developed VeTra, a tool to infer the trajectories from scRNAseq data. Uniquely, VeTra can perform grouping of cells that are in the same stream of trajectory. For this, VeTra searches for weakly connected components of the directed graph obtained from RNA velocity. Therefore, VeTra makes it easy to define groups of cells from the origin and to the end of a certain trajectory. VeTra has been tested to infer the streams of cells for pancreatic development, neural development in hippocampus and cell cycle. VeTra is a useful tool to perform pseudo-time analysis from the start to the end of each group.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.01.276089v1?rss=1

Authors: Abel, S., Piccinini, F., Beleon, A., Balassa, T., Varga, I. G., Migh, E., Paavolainen, L., Timonen, S., Banerjee, I., Yamauchi, Y., Ando, I., Peltonen, J., Pietiainen, V., Honti, V., Horvath, P.

Abstract:

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce a novel concept to describe and explore biological data in a continuous manner. We have implemented Regression Plane (RP), the first user-friendly discovery tool enabling class-free phenotypic supervised machine learning.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.31.276212v1?rss=1

Authors: Gillis, S., Roth, A. J. L.

Abstract:

We describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. Our proposed method is 10-100x times faster than existing methods, while providing results which are as accurate. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study. Software implementing our method is freely availablehttps://github.com/Roth-Lab/pyclone-vi.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.28.254623v1?rss=1

Authors: Ou, J., Liu, H., Nirala, N. J., Stukalov, A., Acharya, U., Green, M. R., Zhu, L. J.

Abstract:

Sequence logos have been widely used as graphical representations of conserved nucleic acid and protein motifs. Due to the complexity of the amino acid (AA) alphabet, rich post-translational modification, and diverse subcellular localization of proteins, few versatile tools are available for effective identification and visualization of protein motifs. In addition, various reduced AA alphabets based on physicochemical, structural, or functional properties have been valuable in the study of protein alignment, folding, structure prediction, and evolution. However, there is lack of tools for applying reduced AA alphabets to the identification and visualization of statistically significant motifs. To fill this gap, we developed an R/Bioconductor package dagLogo, which has several advantages over existing tools. First, dagLogo allows various formats for input sets and provides comprehensive options to build optimal background models. It implements different reduced AA alphabets to group AAs of similar properties. Furthermore, dagLogo provides statistical and visual solutions for differential AA (or AA group) usage analysis of both large and small data sets. Case studies showed that dagLogo can better identify and visualize conserved protein sequence patterns from different types of inputs and can potentially reveal the biological patterns that could be missed by other logo generators.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.28.272559v1?rss=1

Authors: Kaushik, A., Dunham, D., He, Z., Manohar, M., Desai, M., Nadeau, K. C., Andorf, S.

Abstract:

For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e. ungated cells, and that current (semi-)automated approaches ignore their modeling resulting in misclassified annotations. Therefore, we introduce CyAnno, a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of gated cell types and the ungated cells. By applying this framework on several CyTOF datasets, we demonstrated that including the ungated cells can lead to a significant increase in the prediction accuracy of the gated cell types. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.28.270652v1?rss=1

Authors: Li, K., Ouyang, Z., Lin, D., Mingueneau, M., Chen, W., Sexton, D., Zhang, B.

Abstract:

To meet the growing demands from scientists to effectively extract deep insights from single cell RNA-seq datasets, we developed cellxgene VIP, a frontend interactive visualization plugin to cellxgene framework, which directly interacts with in-memory data to generate a comprehensive set of plots in high resolution, perform advanced analysis, and make data downloadable for further analysis. It makes large scale scRNA-seq data visualization and analysis more accessible and reproducible with the potential to become an ecosystem for the scientific community to contribute even more modules to the Swiss knife of scRNA-seq data exploration tool.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.28.272039v1?rss=1

Authors: Orvis, J., Gottfried, B., Kancherla, J., Adkins, R. S., Song, Y., Dror, A. A., Olley, D., Rose, K., Chrysostomou, E., Kelley, M. C., Milon, B., Matern, M. S., Azaiez, H., Herb, B., Colantuoni, C., Carter, R. L., Ament, S. A., Kelley, M. W., White, O., Bravo, H. C., Mahurkar, A., Hertzano, R.

Abstract:

The gEAR portal (gene Expression Analysis Resource, umgear.org) is an open access community-driven tool for multi-omic and multi-species data visualization, analysis and sharing. The gEAR supports visualization of multiple RNA-seq data types (bulk, sorted, single cell/nucleus) and epigenomics data, from multiple species, time points and tissues in a single-page, user-friendly browsable format. An integrated scRNA-seq workbench provides access to raw data of scRNA-seq datasets for de novo analysis, as well as marker-gene and cluster comparisons of pre-assigned clusters. Users can upload, view, analyze and privately share their own data in the context of previously published datasets. Short, permanent URLs can be generated for dissemination of individual or collections of datasets in published manuscripts. While the gEAR is currently curated for auditory research with over 90 high-value datasets organized in thematic profiles, the gEAR also supports the BRAIN initiative (via nemoanalytics.org) and is easily adaptable for other research domains.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.28.271817v1?rss=1

Authors: Zinger, L., Lionnet, C., Benoiston, A.-S., Donald, J., Mercier, C., Boyer, F.

Abstract:

DNA metabarcoding is becoming the tool of choice for biodiversity studies across taxa and large-scale environmental gradients. Yet, the artefacts present in metabarcoding datasets often preclude a proper interpretation of ecological patterns. Bioinformatic pipelines removing experimental noise have been designed to address this issue. However, these often only partially target produced artefacts, or are marker specific. In addition, assessments of data curation quality and the appropriateness of filtering thresholds are seldom available in existing pipelines, partly due to the lack of appropriate visualisation tools. Here, we present metabaR, an R package that provides a comprehensive suite of tools to effectively curate DNA metabarcoding data after basic bioinformatic analyses. In particular, metabaR uses experimental negative or positive controls to identify different types of artefactual sequences, i.e. reagent contaminants and tag-jumps. It also flags potentially dysfunctional PCRs based on PCR replicate similarities when those are available. Finally, metabaR provides tools to visualise DNA metabarcoding data characteristics in their experimental context as well as their distribution, and facilitate assessment of the appropriateness of data curation filtering thresholds. metabaR is applicable to any DNA metabarcoding experimental design but is most powerful when the design includes experimental controls and replicates. More generally, the simplicity and flexibility of the package makes it applicable any DNA marker, and data generated with any sequencing platform, and pre-analysed with any bioinformatic pipeline. Its outputs are easily usable for downstream analyses with any ecological R package. metabaR complements existing bioinformatics pipelines by providing scientists with a variety of functions with customisable methods that will allow the user to effectively clean DNA metabarcoding data and avoid serious misinterpretations. It thus offers a promising platform for automatised data quality assessments of DNA metabarcoding data for environmental research and biomonitoring.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.30.273870v1?rss=1

Authors: Subramanian, U., Devarajan, B.

Abstract:

Retinal angiogenesis is a common neovascularization mechanism that causes severe irreversible vision loss in the number of retinal diseases worldwide. Patients often do not respond to the current antiangiogenic therapies and have a vision loss. Understanding the various angiogenic pathways and factors involved in the pathogenic mechanism is vital for disease management. In this study, to identify dysregulated angiogenic pathways and specific angiogenic factors involved in vision-threatening diseases namely proliferative diabetic retinopathy (PDR), retinopathy of prematurity (ROP) and neovascular age related macular degeneration (nAMD), we downloaded microarray metadata of samples and obtained the differentially expressed genes (DEGs) in all the disease and each disease samples compared to controls. Subsequently, we performed Gene Set Enrichment (GESA) analysis for pathways, a protein-protein interaction (PPI), and angiome network analysis using R and Cytoscape software. We identified highly enriched dysregulated pathways that were neuroactive ligand receptor interaction and cytokine cytokine receptor interaction. The angiogenic associated DEGs were predominately related to the cytokine cytokine receptor interaction pathway, which we further confirmed with RNA-seq data of PDR samples. Together, our analysis of these data elucidated the molecular mechanisms of retinal angiogenesis and provided potential angiogenic targets for therapeutics.

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