PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.290585v1?rss=1

Authors: Tang, Z., Yu, Y., Ng, K., Sow, D., Hu, J., Mei, J.

Abstract:

As Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real world evidence. In this study, a graphical disease network, named progressive cardiovascular disease network (progCDN), was built based on EHR data from 14.3 million patients to delineate the progression profiles of cardiovascular diseases (CVD). The network depicted the dominant diseases in CVD development, such as the heart failure and coronary arteriosclerosis. Novel progression relationships were also discovered, such as the progression path from long QT syndrome to major depression. In addition, three age-group progCDNs identified a series of age-associated disease progression paths and important successor diseases with age bias. Furthermore, we extracted a list of salient features to build a series of disease risk models based on the progression pairs in the disease network. The progCDN network can be further used to validate or explore novel disease relationships in real world data. Features with sufficient abundance and high correlation can be widely applied to train disease risk models when using EHR data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.289793v1?rss=1

Authors: Hu, Y., Fang, L., Chen, X., Zhong, J. F., Li, M., Wang, K.

Abstract:

Long-read RNA sequencing (RNA-seq) technologies have made it possible to sequence full-length transcripts, facilitating the exploration of isoform-specific gene expression over conventional short-read RNA-seq. However, long-read RNA-seq suffers from high per-base error rate, presence of chimeric reads and alternative alignments, and other biases, which require different analysis methods than short-read RNA-seq. Here we present LIQA (Long-read Isoform Quantification and Analysis), an Expectation-Maximization based statistical method to quantify isoform expression and detect differential alternative splicing (DAS) events using long-read RNA-seq data. Rather than summarizing isoform-specific read counts directly as done in short-read methods, LIQA incorporates base-pair quality score and isoform-specific read length information to assign different weights across reads, which reflects alignment confidence. Moreover, given isoform usage estimates, LIQA can detect DAS events between conditions. We evaluated LIQA's performance on simulated data and demonstrated that it outperforms other approaches in rare isoform characterization and in detecting DAS events between two groups. We also generated one direct mRNA sequencing dataset and one cDNA sequencing dataset using the Oxford Nanopore long-read platform, both with paired short-read RNA-seq data and qPCR data on selected genes, and we demonstrated that LIQA performs well in isoform discovery and quantification. Finally, we evaluated LIQA on a PacBio dataset on esophageal squamous epithelial cells, and demonstrated that LIQA recovered DAS events on FGFR3 that failed to be detected in short-read data. In summary, LIQA leverages the power of long-read RNA-seq and achieves higher accuracy in estimating isoform abundance than existing approaches, especially for isoforms with low coverage and biased read distribution.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.290411v1?rss=1

Authors: Jones, D. C., Ruzzo, W. L.

Abstract:

The analysis of mRNA transcript abundance with RNA-Seq is a central tool in molecular biology research, but often analyses fail to account for the uncertainty in these estimates, which can be significant, especially when trying to disentangle isoforms or duplicated genes. Preserving uncertainty necessitates a full probabilistic model of the all the sequencing reads, which quickly becomes intractable, as experiments can consist of billions of reads. To overcome these limitations, we propose a new method of approximating the likelihood function of a sparse mixture model, using a technique we call the Polya tree transformation. We demonstrate that substituting this approximation for the real thing achieves most of the benefits with a fraction of the computational costs, leading to more accurate detection of differential transcript expression.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.287771v1?rss=1

Authors: Mohammed, A. A., Elkhalifa, M. E., Elamin, K. E., Mohammed, R. A., Ibrahim, M. E., Dirar, A. I., Migdar, S. H., Hamid, M. A., Elawad, E. H., Abdelsalam, S. O., Hassan, M. A.

Abstract:

Background: Lujo virus LUJV is a highly fatal human pathogen belonging to the Arenaviridae family. Lujo virus causes viral hemorrhagic fever VHF. An In silico molecular docking was performed on the GPC domain of Lujo virus in complex with the first CUB domain of neuropilin-2. The aim of this study is to predict effective epitope-based vaccine against glycoprotein GPC precursor of Lujo virus using immunoinformatics approaches. Methods and Materials: glycoprotein GPC precursor of Lujo virus Sequence was retrieved from NCBI. Different prediction tools were used to analyze the nominees epitopes in BepiPred-2.0: Sequential B-Cell Epitope Predictor for B-cell, T-cell MHC class II & I. Then the proposed peptides were docked using Autodock 4.0 software program. Results and Conclusions: The proposed and promising peptides FWYLNHTKL and YMFSVTLCI show a very strong binding affinity to MHC class I & II alleles with high population coverage for the world, South Africa and Sudan. This indicates a strong potential to formulate a new vaccine especially with the peptide YMFSVTLCI which is likely to be the first proposed epitope-based vaccine against glycoprotein GPC of the Lujo virus. This study recommends an in-vivo assessment for the most promising peptides especially FWYLNHTKL, YMFSVTLCI and LPCPKPHRLR.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.290049v1?rss=1

Authors: Cameron, E. S., Schmidt, P. J., Tremblay, B. J.- M., Emelko, M. B., Müller, K. M.

Abstract:

The application of amplicon sequencing in water research provides a rapid and sensitive technique for microbial community analysis in a variety of environments ranging from freshwater lakes to water and wastewater treatment plants. It has revolutionized our ability to study DNA collected from environmental samples by eliminating the challenges associated with lab cultivation and taxonomic identification. DNA sequencing data consist of discrete counts of sequence reads, the total number of which is the library size. Samples may have different library sizes and thus, a normalization technique is required to meaningfully compare them. The process of randomly subsampling sequences to a selected normalized library size from the sample library-rarefying-is one such normalization technique. However, rarefying has been criticized as a normalization technique because data can be omitted through the exclusion of either excess sequences or entire samples, depending on the rarefied library size selected. Although it has been suggested that rarefying should be avoided altogether, we propose that repeatedly rarefying enables (i) characterization of the variation introduced to diversity analyses by this random subsampling and (ii) selection of smaller library sizes where necessary to incorporate all samples in the analysis. Rarefying may be a statistically valid normalization technique, but researchers should evaluate their data to make appropriate decisions regarding library size selection and subsampling type. The impact of normalized library size selection and rarefying with or without replacement in diversity analyses were evaluated herein.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.07.286161v1?rss=1

Authors: Levitsky, L. I., Bubis, J. A., Gorshkov, M. V., Tarasova, I. A.

Abstract:

We report on AA_stat, a bioinformatic approach for panoramic profiling of artificial and post-translational modifications and their localization sites in large-scale proteomics data. Presented version of AA_stat provides validation of ultra-tolerant (open) search results followed by interpretation of the observed mass shifts and recommendation of the optimized sets of fixed and variable modifications for subsequent regular searches. Localization of modification sites is based on relative amino acid frequencies and analysis of tandem mass spectra. AA_stat determines groups of peptide identifications with mass shifts from the validated results of the open search and then scores each possible mass shift location by matching the MS/MS spectrum across the theoretical peptide isoforms. Here we demonstrate the utility of AA_stat for blind scanning of abundant and rare amino acid modifications of both artificial and biological origins and analyze advantages and limitations of open search strategies. AA_stat is implemented as an open-source command line tool available at https://github.com/SimpleNumber/aa_stat.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.07.285924v1?rss=1

Authors: Schoenbachler, J. L., Hughey, J. J.

Abstract:

Summary: PubMed is an invaluable resource for the biomedical community. Although PubMed is freely available, the existing API is not designed for large-scale analyses and the XML structure of the underlying data is inconvenient for complex queries. We developed an R package called pmparser to convert the data in PubMed to a relational database. Our implementation of the database, called PMDB, currently contains data on over 31 million PubMed Identifiers (PMIDs) and is updated regularly. Together, pmparser and PMDB can enable large-scale, reproducible, and transparent analyses of the biomedical literature. Availability and implementation: pmparser is licensed under GPL-2 and available at https://pmparser.hugheylab.org. PMDB is stored in PostgreSQL and compressed dumps are available on Zenodo (https://doi.org/10.5281/zenodo.4008109).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.07.286583v1?rss=1

Authors: Mostavi, M., Chiu, Y.-C., Chen, Y., Huang, Y.

Abstract:

We consider cancer classification based on one single gene expression profile. We proposed CancerSiamese, a new one-shot learning model, to predict the cancer type of a query primary or metastatic tumor sample based on a support set that contains only one known sample for each cancer type. CancerSiamese receives pairs of gene expression profiles and learns a representation of similar or dissimilar cancer types through two parallel Convolutional Neural Networks joined by a similarity function. We trained CancerSiamese for both primary and metastatic cancer type predictions using samples from TCGA and MET500. Test results for different N-way predictions yielded an average accuracy improvement of 8% and 4% over the benchmark 1-Nearest Neighbor (1-NN) classifier for primary and metastatic tumors, respectively. Moreover, we applied the guided gradient saliency map and feature selection to CancerSiamese to identify and analyze the marker-gene candidates for primary and metastatic cancers. Our work demonstrated, for the first time, the feasibility of applying one-shot learning for expression-based cancer type prediction when gene expression data of cancer types are limited and could inspire new and ingenious applications of one-shot and few-shot learning solutions for improving cancer diagnosis, treatment planning, and our understanding of cancer.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.289132v1?rss=1

Authors: Sutar, J., Deshpande, S., Mullick, R., Hingankar, N., Patel, V., Bhattacharya, J.

Abstract:

Evolving diversity in globally circulating HIV-1 subtypes presents formidable challenge in defining and developing neutralizing antibodies for prevention and treatment. HIV-1 subtype C is responsible for majority of global HIV-1 infections. Broadly neutralizing antibodies (bnAbs) capable of neutralizing distinct HIV-1 subtypes by targeting conserved vulnerable epitopes on viral envelope protein (Env) are being considered as promising antiviral agents for prevention and treatment. In the present study, we examined the diversity in genetic signatures and attributes that differentiate region-specific global HIV-1 subtype C gp120 sequences associated with virus neutralization outcomes to key bnAbs having distinct epitope specificities. A total of 1814 full length HIV-1 subtype C gp120 sequence from 37 countries were retrieved from Los Alamos National Laboratory HIV database (www.hiv.lanl.gov). The amino acid sequences were assessed for their phylogenetic association, variable loop lengths and prevalence of potential N-linked glycosylation sites (pNLGS). Responses of these sequences to bnAbs were predicted with a machine learning algorithm bNAb-ReP and compared with those reported in the CATNAP database. Phylogenetically, sequences from Asian countries including India clustered together however differed significantly when compared with pan African subtype C sequences. Variable loop lengths within Indian and African clusters were distinct from each other, specifically V1, V2 and V4 loops. Furthermore, V1V2 and V2 alone sequences were also found to vary significantly in their charges. Pairwise analyses at each of the 25 pNLG sites indicated distinct country specific profiles. Highly significant differences (p<0.001***) were observed in prevalence of four pNLGS (N130, N295, N392 and N448) between South Africa and India, having most disease burden associated with subtype C. Our findings highlight that the distinctly evolving clusters within global intra-subtype C gp120 sequences are likely to influence the disparate region-specific sensitivity of circulating HIV-1 subtype C to bnAbs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.286302v1?rss=1

Authors: Ecoffet, A., Poitevin, F., Dao Duc, K.

Abstract:

Motivation: Cryogenic Electron-Microscopy offers the unique potential to capture conformational heterogeneity, by solving multiple 3D classes that co-exist within a single cryo-EM image dataset. To investigate the extent and implications of such heterogeneity, we propose to use an optimal-transport based metric to interpolate barycenters between EM maps and produce morphing trajectories. While standard linear interpolation mostly fails to produce realistic transitions, our method yields continuous trajectories that displace densities to morph one map into the other, instead of blending them. Implementation: Our method is implemented as a plug-in for ChimeraX called MorphOT, which allows the use of both CPU or GPU resources. The code is publicly available on GitHub (https://github.com/kdd-ubc/MorphOT.git), with documentation containing tutorial and datasets.

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