PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.04.236208v1?rss=1

Authors: Jackson, M., Wallace, E., Kavoussanakis, K.

Abstract:

Workflow management systems represent, manage, and execute multi-step computational analyses and offer many benefits to bioinformaticians. They provide a common language for describing analysis workflows, contributing to reproducibility and to building libraries of reusable components. They can support both incremental build and re-entrancy - the ability to selectively re-execute parts of a workflow in the presence of additional inputs or changes in configuration and to resume execution from where a workflow previously stopped. Many workflow management systems enhance portability by supporting the use of containers, high-performance computing systems and clouds. Most importantly, workflow management systems allow bioinformaticians to delegate how their workflows are run to the workflow management system and its developers. This frees the bioinformaticians to focus on the content of these workflows, their data analyses, and their science. RiboViz is a package to extract biological insight from ribosome profiling data to help advance understanding of protein synthesis. At the heart of RiboViz is an analysis workflow, implemented in a Python script. To conform to best practices for scientific computing which recommend the use of build tools to automate workflows and to re-use code instead of rewriting it, the authors reimplemented this workflow within a workflow management system. To select a workflow management system, a rapid survey of available systems was undertaken, and candidates were shortlisted: Snakemake, cwltool and Toil (implementations of the Common Workflow Language) and Nextflow. An evaluation of each candidate, via rapid prototyping of a subset of the RiboViz workflow, was performed and Nextflow was chosen. The selection process took 10 person-days, a small cost for the assurance that Nextflow best satisfied the authors' requirements. This use of rapid prototyping can offer a low-cost way of making a more informed selection of software to use within projects, rather than relying solely upon reviews and recommendations by others.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.04.236224v1?rss=1

Authors: Soto, C. J., Bryner, D., Neretti, N., Srivastava, A.

Abstract:

The study of the 3-dimensional (3D) structure of chromosomes -- the largest macromolecules in biology -- is one of the most challenging to date in structural biology. Here, we develop a novel representation of chromosomes, as sequences of shape letters from a finite shape alphabet, which provides a compact and efficient way to analyze ensembles of chromosome shape data, akin to the analysis of texts in a language by using letters. We construct a Chromosome Shape Alphabet (CSA) from an ensemble of chromosome 3D structures inferred from Hi-C data -- via SIMBA3D or other methods -- by segmenting curves based on topologically associating domains (TADs) boundaries, and by clustering all TADs' 3D structures into groups of similar shapes. The median shapes of these groups, with some pruning and processing, form the Chromosome Shape Letters (CSLs) of the alphabet. We provide a proof-of-concept for these CSLs by reconstructing independent test curves using only CSLs (and corresponding transformations) and comparing these reconstructions with the original curves. Finally, we demonstrate how CSLs can be used to summarize the variability of shapes in an ensemble of chromosome 3D structures using generalized sequence logos.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.05.237560v1?rss=1

Authors: Li, Z., Song, T., Yong, J., Kuang, R.

Abstract:

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. One major limitation of in-situ capturing is the high dropout rate of mRNAs that fail the capture or the amplification, which leads to incomplete profiling of the gene expressions. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x,y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed several best performing single-cell RNAseq data imputation methods. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions highly relevant to three different kinds of tissues in mouse kidney.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.234120v1?rss=1

Authors: Anh, V. V., Nguyen, H. T., Craig, A., Tran, E., Wang, Y.

Abstract:

This paper investigates the cause and detection of power-law scaling of brain wave activity due to the heterogeneity of the brain cortex, considered as a complex system, and the initial condition such as the alert or fatigue state of the brain. Our starting point is the construction of a mathematical model of global brain wave activity based on EEG measurements on the cortical surface. The model takes the form of a stochastic delay-differential equation (SDDE). Its fractional diffusion operator and delay operator capture the responses due to the heterogeneous medium and the initial condition. The analytical solution of the model is obtained in the form of a Karhunen-Loeve expansion. A method to estimate the key parameters of the model and the corresponding numerical schemes are given. Real EEG data on driver fatigue at 32 channels measured on 50 participants are used to estimate these parameters. Interpretation of the results is given by comparing and contrasting the alert and fatigue states of the brain. The EEG time series at each electrode on the scalp display power-law scaling, as indicated by their spectral slopes in the low-frequency range. The diffusion of the EEG random field is non-Gaussian, reflecting the heterogeneity of the brain cortex. This non-Gaussianity is more pronounced for the alert state than the fatigue state. The response of the system to the initial condition is also more significant for the alert state than the fatigue state. These results demonstrate the usefulness of global SDDE modelling complementing the time series approach for EEG analysis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.234476v1?rss=1

Authors: Gerard, D.

Abstract:

Many tasks in statistical genetics involve pairwise estimation of linkage disequilibrium (LD). The study of LD in diploids is mature. However, in polyploids, the field lacks a comprehensive characterization of LD. Polyploids also exhibit greater levels of genotype uncertainty than diploids, and yet no methods currently exist to estimate LD in polyploids in the presence of such genotype uncertainty. Furthermore, most LD estimation methods do not quantify the level of uncertainty in their LD estimates. Our paper contains three major contributions. (i) We characterize gametic and composite measures of LD in polyploids. These composite measures of LD turn out to be functions of common statistical measures of association. (ii) We derive procedures to estimate gametic and composite LD in polyploids in the presence of genotype uncertainty. We do this by estimating LD directly from genotype likelihoods, which may be obtained from many genotyping platforms. (iii) We derive standard errors of all LD estimators that we discuss. We validate our methods on both real and simulated data. Our methods are implemented in the R package ldsep, available on the Comprehensive R Archive Network https://cran.r-project.org/package=ldsep.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.234187v1?rss=1

Authors: Kratochvil, M., Hunewald, O., Heirendt, L., Verissimo, V., Vondrasek, J., Satagopam, V. P., Schneider, R., Trefois, C., Ollert, M.

Abstract:

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow to easily generate data with hundreds of millions of single-cell data points with more than 40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. Results: We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality-reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community, and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. Conclusions: GigaSOM.jl facilitates utilization of the commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from an massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.233692v1?rss=1

Authors: Yuan, A. E., Shou, W.

Abstract:

Complex systems such as microbial communities play key roles in global processes and human life, yet are often challenging to understand. Although mechanistic knowledge in biology is generally rooted in manipulative experiments, perturbing these systems can encounter practical and ethical barriers. Thus, extensive attempts have been made to infer causal knowledge by analyzing observations of taxon abundance over time. When, and to what extent, does this strategy yield genuine insight? Unfortunately, the literature of causal inference can be formidable and controversial, as it draws from divergent fields such as philosophy, statistics, econometrics, and chaos theory. Most benchmarking papers focus on performance details of causal inference approaches, rather than fundamental issues such as the the underlying assumptions and their reasons, conceptual distinctions, and universal limitations. Here, we provide a synthesis of popular causal inference approaches including pairwise correlation and Reichenbach's common cause principle, Granger causality, and state space reconstruction. We find that each of these requires that certain properties of the data do not change with time (e.g. "IID", "stationarity", "reverting dynamics"). We provide new ways of visualizing key concepts, point out important issues that have been under-emphasized, and in some cases describe novel pathologies of causal inference methods. Although our synthesis is motivated by microbial communities, all arguments apply to other types of dynamic systems. We strive to balance precision with accessibility, and hope that our synthesis will motivate future development on causal inference approaches. To facilitate communication to a broad audience, we have made an accompanying video walkthrough (https://youtu.be/TZvEk3jXQfY).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.234302v1?rss=1

Authors: Liu, W., Guo, Y., Xue, Z., Yuan, R.

Abstract:

With the advance of genomic sequencing techniques, chromatin accessible regions, transcription factor binding sites and epigenetic modifications can be identified at genome-wide scale. Conventional analyses focus on the gene regulation at proximal regions; however, distal regions are usually neglected, largely due to the lack of reliable tools to link the distal regions to coding genes. In this study, we introduce RAD (Region Associated Differentially expressed genes), a user-friendly web tool to identify both proximal and distal region associated differentially expressed genes. RAD maps the up- and down-regulated genes associated with any genomic regions of interest (gROI) and helps researchers to infer the regulatory function of these regions based on the distance of gROI to differentially expressed genes. RAD includes visualization of the results and statistical inference for significance.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.233825v1?rss=1

Authors: Wozniak, T., Sajek, M., Jaruzelska, J., Sajek, M. P.

Abstract:

Motivation The function of RNA molecules is mainly determined by their secondary structure. Addressing that issue requires creation of appropriate bioinformatic tools that enable alignment of multiple RNA molecules to determine functional domains and/or classify RNA families. The existing tools for RNA multiple alignment that use structural information are relatively slow. Therefore, providing a rapid tool for multiple structural alignment may improve classification of the known RNAs and reveal the function of the newly discovered ones. Results Here, we developed an extremely fast Python based RNAlign2D tool. It converts RNA sequence and structure to pseudo-amino acid sequence and uses customizable pseudo-amino acid substitution matrix to align RNA secondary structures and sequences using MUSCLE. It is suitable for RNAs containing modified nucleosides and/or pseudoknots. Our approach is compatible with virtually all protein aligners. Availability and implementation RNAlign2D is available from https://github.com/tomaszwozniakihg/rnalign2d. It has been tested on Linux and MacOSX. Supplementary information Supplementary data are available at Bioinformatics online.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.03.233650v1?rss=1

Authors: Park, M., Vorperian, S., Wang, S., Pisco, A. O.

Abstract:

Single cell RNA sequencing (scRNA-seq) enables detailed examination of a cell's underlying regulatory networks and the molecular factors contributing to its identity. We developed scRFE (single-cell identity definition using random forests and recursive feature elimination, pronounced 'surf') with the goal of easily generating interpretable gene lists that can accurately distinguish observations (single-cells) by their features(genes) given a class of interest. scRFE is an algorithm implemented as a Python package that combines the classical random forest method with recursive feature elimination and cross validation to find the features necessary and sufficient to classify cells in a single-cell RNA-seq dataset by ranking feature importance. The package is compatible with Scanpy, enabling a seamless integration into any single-cell data analysis workflow that aims at identifying minimal transcriptional programs relevant to describing metadata features of the dataset. We applied scRFE to the Tabula Muris Senis and reproduced commonly known aging patterns and transcription factor reprogramming protocols, highlighting the biological value of scRFE's learned features.

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