PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.01.321067v1?rss=1

Authors: Saha, S., Wang, Z., Rajasekaran, S.

Abstract:

Widespread availability of next-generation sequencing (NGS) technologies has prompted a recent surge in interest in the microbiome. As a consequence, metagenomics is a fast-growing field in bioinformatics and computational biology. An important problem in analyzing metagenomic sequenced data is to identify the microbes present in the sample and figure out their relative abundances. In this article we propose a highly efficient algorithm dubbed as Hybrid Metagenomic Sequence Classifier (HMSC) to accurately detect microbes and their relative abundances in a metagenomic sample. The algorithmic approach is fundamentally different from other state-of-the-art algorithms currently existing in this domain. HMSC judiciously exploits both alignment-free and alignment-based approaches to accurately characterize metagenomic sequenced data. To demonstrate the effectiveness of HMSC we used 8 metagenomic sequencing datasets (2 mock and 6 in silico bacterial communities) produced by 3 different sequencing technologies (e.g., HiSeq, MiSeq, and NovaSeq) with realistic error models and abundance distribution. Rigorous experimental evaluations show that HMSC is indeed an effective, scalable, and efficient algorithm compared to the other state-of-the-art methods in terms of accuracy, memory, and runtime.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.01.322164v1?rss=1

Authors: Karasikov, M., Mustafa, H., Danciu, D., Zimmermann, M., Barber, C., Ratsch, G., Kahles, A.

Abstract:

The amount of biological sequencing data available in public repositories is growing exponentially, forming an invaluable biomedical research resource. Yet, making all this sequencing data searchable and easily accessible to life science and data science researchers is an unsolved problem. We present MetaGraph, a versatile framework for the scalable analysis of extensive sequence repositories. MetaGraph efficiently indexes vast collections of sequences to enable fast search and comprehensive analysis. A wide range of underlying data structures offer different practically relevant trade-offs between the space taken by an index and its query performance. Achieving compression ratios of up to 1,000-fold over the already compressed raw input data, MetaGraph indexes can represent the content of large sequencing archives in the working memory of a single compute server. We demonstrate our framework's scalability by indexing over 1.4 million whole genome sequencing (WGS) records from NCBI's Sequence Read Archive, representing a total input of more than three petabases. MetaGraph provides a flexible methodological framework allowing for index construction to be scaled from consumer laptops to distribution onto a cloud compute cluster for processing terabases to petabases of input data. Notably, processing of data sets ranging from 1 TB of raw WGS reads to 20 TB of human RNA-sequencing data results in indexes whose memory footprints are small enough to host on standard desktop workstations. Besides demonstrating the utility of MetaGraph indexes on key applications, such as experiment discovery, sequence alignment, error correction, and differential assembly, we make a wide range of indexes available as a community resource, including indexes of over 450,000 microbial WGS records, more than 110,000 fungi WGS records, and more than 40,000 whole metagenome sequencing records. A subset of these indexes is made available online for interactive queries. All indexes will be available for download and in the cloud. In total, indexes comprising more than 1 million sequencing records are available for download. As an example of our indexes' integrative analysis capabilities, we introduce the concept of differential assembly, which allows for the extraction of sequences present in a foreground set of samples but absent in a given background set. We apply this technique to differentially assemble contigs to identify pathogenic agents transfected via human kidney transplants. In a second example, we indexed more than 20,000 human RNA-Seq records from the TCGA and GTEx cohorts and use them to extract transcriptome features that are hard to characterize using a classical linear reference. We discovered over 200 trans-splicing events in GTEx and found broad evidence for tissue-specific non-A-to-I RNA-editing in GTEx and TCGA.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.320994v1?rss=1

Authors: Ausmees, K., Nettelblad, C.

Abstract:

Dimensionality reduction is a data transformation technique widely used in various fields of genomics research, with principal component analysis one of the most frequently employed methods. Application of principal component analysis to genotype data is known to capture genetic similarity between individuals, and is used for visualization of genetic variation, identification of population structure as well as ancestry mapping. However, the method is based on a linear model that is sensitive to characteristics of data such as correlation of single-nucleotide polymorphisms due to linkage disequilibrium, resulting in limitations in its ability to capture complex population structure. Deep learning models are a type of nonlinear machine learning method in which the features used in data transformation are decided by the model in a data-driven manner, rather than by the researcher, and have been shown to present a promising alternative to traditional statistical methods for various applications in omics research. In this paper, we propose a deep learning model based on a convolutional autoencoder architecture for dimensionality reduction of genotype data. Using a highly diverse cohort of human samples, we demonstrate that the model can identify population clusters and provide richer visual information in comparison to principal component analysis, and also yield a more accurate population classification model. We also discuss the use of the methodology for more general characterization of genotype data, showing that models of a similar architecture can be used as a genetic clustering method, comparing results to the ADMIXTURE software frequently used in population genetic studies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.02.322917v1?rss=1

Authors: Green, A. J., Mohlenkamp, M. J., Das, J., Chaudhari, M., Truong, L., Tanguay, R. L., Reif, D. M.

Abstract:

There are currently 85,000 chemicals registered with the EPA under the Toxic Substances Control Act, but only a small fraction (~5%) have any measured toxicological data. To address this data gap, high-throughput screening (HTS) methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional Generative Adversarial Network (cGAN) and leveraging this large set of toxicity data, plus chemical structure information, we could efficiently predict toxic outcomes of untested chemicals. CAS numbers for each chemical were used to generate textual files containing three-dimensional structural information for each chemical in the Protein Data Bank (PDB) file format. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. The in vivo toxicity and chemical data were used to train two neural network generators. The first used regression to train a generator (Go-ZT) to produce toxicity data while the second utilized cGAN architecture to train a generator (GAN-ZT). Our results showed that both Go-ZT and GAN-ZT models produce similar results, but the cGAN achieved higher sensitivity (SE) value of 85.7% vs 71.4%. Conversely, Go-ZT attained a higher specificity (SP), positive predictive value (PPV), and Kappa results of 67.3%, 23.4%, and 0.21 compared to 24.5%, 14.0%, and 0.03 for the cGAN, respectively. By combining both Go-ZT and GAN-ZT, our consensus model improved the SP, PPV, and Kappa, to 75.5%, 25.0%, and 0.211, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.663. Considering their potential use as efficient prescreening tools, these new models can provide in vivo toxicity predictions and insight into as-yet untested areas of chemical space to prioritize compounds for HT testing.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.01.322768v1?rss=1

Authors: Hossain, S. M. M., Halsana, A. A., Khatun, L., Ray, S., Mukhopadhyay, A.

Abstract:

Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer (PC), late detection of which leads to its therapeutic failure. This study aims to find out key regulatory genes and their impact on the progression of the disease helping the etiology of the disease which is still largely unknown. We leverage the landmark advantages of time-series gene expression data of this disease, and thereby the identified key regulators capture the characteristics of gene activity patterns in the progression of the cancer. We have identified the key modules and predicted gene functions of top genes from the compiled gene association network (GAN). Here, we have used the natural cubic spline regression model (splineTimeR) to identify differentially expressed genes (DEG) from the PDAC microarray time-series data downloaded from gene expression omnibus (GEO). First, we have identified key transcriptomic regulators (TR) and DNA binding transcription factors (DbTF). Subsequently, the Dirichlet process and Gaussian process (DPGP) mixture model is utilized to identify the key gene modules. A variation of the partial correlation method is utilized to analyze GAN, which is followed by a process of gene function prediction from the network. Finally, a panel of key genes related to PDAC is highlighted from each of the analyses performed.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.321125v1?rss=1

Authors: Zhang, Z., Zhang, X.

Abstract:

Trajectory inference methods are used to infer the developmental dynamics of a continuous biological process such as stem cell differentiation and cancer cell development. Most of the current trajectory inference methods infer cell developmental trajectories based on the transcriptome similarity between cells, using single cell RNA-Sequencing (scRNA-Seq) data. These methods are often restricted to certain trajectory structures like trees or cycles, and the directions of the trajectory can only be partly inferred when the root cell is provided. We present CellPaths, a single cell trajectory inference method that infers developmental trajectories by integrating RNA velocity information. CellPaths is able to find multiple high-resolution trajectories instead of one single trajectory from traditional trajectory inference methods, and the trajectory structure is no longer constrained to be of any specific topology. The direction information provided by RNA-velocity also allows CellPaths to automatically detect root cell and differentiation direction. We evaluate CellPaths on both real and synthetic datasets. The result shows that CellPaths finds more accurate and detailed trajectories compared to current state-of-the-art trajectory inference methods.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.319996v1?rss=1

Authors: Lin, J.-L., Hsieh, T.-T., Tung, Y.-A., Chen, X.-J., Hsiao, Y.-C., Yang, C.-L., Liu, T.-L., Chen, C.-Y.

Abstract:

To facilitate the process of tailor-making a deep neural network for exploring the dynamics of genomic DNA, we have developed a hands-on package called ezGeno that automates the search process of various parameters and network structure. ezGeno considers three different sets of search spaces, namely, the number of filters, dilation factors, and the connectivity between different layers. ezGeno can be applied to any kind of 1D genomic input such as genomic sequences, histone modifications, DNase feature data and so on. Combinations of multiple abovementioned 1D features are also applicable. Specifically, for the task of predicting TF binding using genomic sequences as the input, ezGeno can consistently return the best performing set of parameters and network structure, as well as highlight the important segments within the original sequences. For the task of predicting tissue-specific enhancer activity using both sequence and DNase feature data as the input, ezGeno also regularly outperforms the hand-designed models. In this study, we demonstrate that ezGeno is superior in efficiency and accuracy when compared to AutoKeras, a general open-source AutoML package. The average AUC of ezGeno is also consistently higher than the result of using a one-layer DeepBind model. With the flexibility of ezGeno, we expect that this package can provide future researchers not only support of model design in their analysis of genomic studies but also more insights into the regulatory landscape.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.321216v1?rss=1

Authors: Dhapola, P., Eldeeb, M., Ugale, A., Olofzon, R., Erlandsson, E., Soneji, S., Bryder, D., Karlsson, G.

Abstract:

Single-cell transcriptomics facilitates innovative approaches to define and identify cell types within tissues and cell populations. An emerging interest in the cancer field is to assess the heterogeneity of transformed cells, including the identification of tumor-initiating cells based on similarities to their normal counterparts. However, such cell mapping is often confounded by the large effects on total gene expression programs introduced by strong perturbations such as an oncogenic event. Here, we present Nabo, a novel computational method that allows mapping of cells from one population to the most similar cells in a reference population, independently of confounding changes to gene expression programs initiated by perturbation. We validated this method on multiple datasets from different sources and platforms and show that Nabo achieves higher rates of accuracy than conventional classification methods. Nabo is available as an integrated toolkit for preprocessing, cell mapping, differential gene expression identification, and visualization of single-cell RNA-Seq data. For exploratory studies, Nabo includes methods to help evaluate the reliability of cell mapping results. We applied Nabo on droplet-based single-cell RNA-Seq data of healthy and oncogene-induced (MLL-ENL) hematopoietic progenitor cells (GMLPs) differentiating in vitro. Despite a substantial cellular heterogeneity resulting from differentiation of GMLPs and the large transcriptional effects induced by the fusion oncogene, Nabo could pinpoint the specific cell stage where differentiation arrest occurs, which included an immunophenotypic definition of the tumor-initiating population. Thus, Nabo allows for relevant comparison between target and control cells, without being confounded by differences in population heterogeneity.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.321661v1?rss=1

Authors: Jing, X., Xu, J.

Abstract:

Motivation: Accurately estimating protein model quality in the absence of experimental structure is not only important for model evaluation and selection, but also useful for model refinement. Progress has been steadily made by introducing new features and algorithms (especially deep neural networks), but accuracy of quality assessment (QA) is still not very satisfactory, especially local QA on hard protein targets. Results: We propose a new single-model-based QA method ResNetQA for both local and global quality assessment. Our method predicts model quality by integrating sequential and pairwise features using a deep neural network composed of both 1D and 2D convolutional residual neural networks (ResNet). The 2D ResNet module extracts useful information from pairwise features such as model-derived distance maps, co-evolution information and predicted distance potential. The 1D ResNet is used to predict local (global) model quality from sequential features and pooled pairwise information generated by 2D ResNet. Tested on the CASP12 and CASP13 datasets, our experimental results show that our method greatly outperforms existing state-of-the-art methods. Our ablation studies indicate that the 2D ResNet module and pairwise features play an important role in improving model quality assessment. Availability and Implementation: https://github.com/AndersJing/ResNetQA

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.320929v1?rss=1

Authors: Clum, A., Huntemann, M., Bushnell, B., Foster, B., Foster, B., Roux, S., Hajek, P., Varghese, N., Mukherjee, S., Reddy, T. B. K., Daum, C., Yoshinaga, Y., Seshadri, R., Kyrpides, N., Eloe-Fadrosh, E., Chen, I.-M. A., Copeland, A., Ivanova, N.

Abstract:

The DOE JGI Metagenome Workflow performs metagenome data processing, including assembly, structural, functional, and taxonomic annotation, and binning of metagenomic datasets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) (https://img.jgi.doe.gov) comparative analysis system (I. Chen, K. Chu, K. Palaniappan, M. Pillay, A. Ratner, J. Huang, M. Huntemann, N. Varghese, J. White, R. Seshadri, et al, Nucleic Acids Rsearch, 2019) and provided for download via the Joint Genome Institute (JGI) Data Portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here we describe the different tools, databases, and parameters used at different steps of the workflow, to help with interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a Workflow Description Language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl.git). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in Genomes OnLine Database (GOLD) (http://www.genomesonline.org/)(S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, H. Katta, A. Mojica, I Chen, and N. Kyrpides, and T. Reddy, Nucleic Acids Research, 2018).

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