PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.24.218537v1?rss=1

Authors: Cherry, C., Cahan, P., Garmire, L. X., Elisseeff, J. H.

Abstract:

Intercellular communication is crucial for function of cells but current methods for reconstruction of intercellular signaling from single cell data can be challenging to connect with biological outcomes. Here, we present Domino (github.com/chris-cherry/domino), a computational tool which reconstructs intercellular signaling coupled with transcription factor activation. Matching signaling with transcription factor activation allows for immediate connection of results to known biological phenotypes. We first apply Domino to model biomaterial environments in a muscle wound where different chemical compositions produce distinct tissue microenvironments. While traditional single cell RNA sequencing analyses captured few differences between conditions, Domino identified previously unknown signaling pathways distinct to conditions. We then demonstrate Domino on a previously published human Alzheimers data set to discover new signaling pathways associated with health and disease.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.23.217083v1?rss=1

Authors: Victor, M., Das, R., Ghosh, T.

Abstract:

SARS-CoV-2 has caused a global pandemic that has costed enormous human lives in the recent past. The present study is an investigation of the viral codon adaptation, ORF's stability and tRNA co-adaptation with humans. We observed that for the codon usage bias in viral ssRNA, ORFs have near values of folding free energies and codon adaptation index with mRNAs of the human housekeeping CDS. However, the correlation between the stability of the ORFs in ssRNA and CAI is stronger than the mRNA stability and CAI of HKG, suggesting a greater expression capacity of SARS-CoV-2. Mutational analysis reflects polymorphism in the virus for ORF1ab, surface glycoprotein and nucleocapsid phosphoprotein ORFs. Non-synonymous mutations have shown non-polar substitutions. Out of the twelve mutations nine are for a higher t-RNA copy number. Viruses in general have high mutation rates. To understand the chances of survival for the mutated SARS-CoV-2 we did simulation for synonymous mutations. It resulted in 50% ORFs with higher stability than their native equivalents. Thus, considering only the synonymous mutations the virus can exhibit a lot of polymorphism. Collectively our data provides new insights for SARS-CoV-2 mutations and the human t-RNA compatibility.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.22.215855v1?rss=1

Authors: Luo, H., Bu, D., Shao, L., Li, Y., Sun, L., Wang, C., Wang, J., Yang, W., Yang, X., Dong, J., Zhao, Y., Li, F.

Abstract:

The development of new therapeutic targets for cancer immunotherapies and the development of new biomarkers require deep understanding of T cells. To date, the complete landscape and systematic characterization of long noncoding RNAs (lncRNAs) in T cells in cancer immunity are lacking. Here, by systematically analyzing full-length single-cell RNA sequencing (scRNA-seq) data of more than 20,000 T cell libraries across three cancer types, we provide the first comprehensive catalog and the functional repertoires of lncRNAs in human T cells. Specifically, we developed a custom pipeline for de novo transcriptome assembly obtaining 9,433 novel lncRNA genes that increased the number of current human lncRNA catalog by 16% and nearly doubled the number of lncRNAs expressed in T cells. We found that a portion of expressed genes in single T cells were lncRNAs which have been overlooked by the majority of previous studies. Based on metacell maps constructed by MetaCell algorithm that partition scRNA-seq datasets into disjointed and homogenous groups of cells (metacells), 154 signature lncRNAs associated with effector, exhausted, and regulatory T cell states are identified, 84 of which are functionally annotated based on co-expression network, indicating that lncRNAs might broadly participate in regulation of T cell functions. Our findings provide a new point of view and resource for investigating the mechanisms of T cell regulation in cancer immunity as well as for novel cancer-immune biomarker development and cancer immunotherapies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.22.216275v1?rss=1

Authors: Campbell, E. M., Boyles, A. A., Shankar, A., Kim, J., Knyazev, S., Switzer, W. M.

Abstract:

Motivation Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions. Results We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. Using publicly available HIV sequences and other data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020. Availability and Implementation MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully-operational without an internet connection. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. The source code is available at https://github.com/cdcgov/microbetrace.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.22.216580v1?rss=1

Authors: Lind, A. L., Pollard, K. S.

Abstract:

Microbial eukaryotes are found alongside bacteria and archaea in natural microbial systems, including host-associated microbiomes. While microbial eukaryotes are critical to these communities, they are often not included in metagenomic analyses. Here we present EukDetect, a bioinformatics approach based on universal eukaryotic marker genes, to identify eukaryotes in shotgun metagenomic sequencing data. EukDetect is accurate, sensitive, has a broad taxonomic coverage of microbial eukaryotes, and is resilient against bacterial contamination in eukaryotic genomes. This enables us to identify and make observations about eukaryotes in public human and plant microbiome datasets. Using EukDetect, we describe the spatial distribution of eukaryotes in the human gut, finding that fungi and protists are present in the lumen and mucosa throughout the large intestine. We find that there is a succession of eukaryotes that colonize the human gut during the first years of life, similar to patterns of succession observed in bacteria in the developing gut. By comparing DNA and RNA sequencing of paired samples from the human gut, we find that many eukaryotes continue active transcription after passage through the gut, while others do not, suggesting they are dormant or nonviable. Finally, we identify eukaryotes in Arabidopsis leaf samples, many of which are not identifiable from public protein databases. EukDetect is an accurate and sensitive pipeline to routinely characterize eukaryotes in shotgun sequencing datasets from diverse microbiomes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.22.216812v1?rss=1

Authors: Allenby, M. C., Liang, E. S., Harvey, J., Woodruff, M. A., Prior, M., Winter, C. D., Alonso-Caneiro, D.

Abstract:

Unruptured intracranial aneurysms (UIAs) are prevalent neurovascular anomalies which, in rare circumstances, rupture to create a catastrophic subarachnoid haemorrhage. Although surgical management can reduce rupture risk, the majority of IAs exist undiscovered until rupture. Current computer-aided UIA diagnoses sensitively detect and measure UIAs within cranial angiograms, but remain limited to low specificities whose output requires considerable neuroradiologist interpretation not amenable to broad screening efforts. To address these limitations, we propose an analysis which interprets single-voxel morphometry of segmented neurovasculature to identify UIAs. Once neurovascular anatomy of a specified resolution is segmented, interrelationships between voxel-specific morphometries are estimated and spatially-clustered outliers are identified as UIA candidates. Our automated solution detects UIAs within magnetic resonance angiograms (MRA) at unmatched 86% specificity and 81% sensitivity using 3 minutes on a conventional laptop. Our approach does not rely on interpatient comparisons or training datasets which could be difficult to amass and process for rare incidentally discovered UIAs within large MRA files, and in doing so, is versatile to user-defined segmentation quality, to detection sensitivity, and across a range of imaging resolutions and modalities. We propose this method as a unique tool to aid UIA screening, characterisation of abnormal vasculature in at-risk patients, morphometry-based rupture risk prediction, and identification of other vascular abnormalities.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.22.163949v1?rss=1

Authors: Kim, S. Y., Choe, E. K., Shivakumar, M., Kim, D., Sohn, K.-A.

Abstract:

Motivation: To better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. Additionally, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW, and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene-gene graph using pathway information by assigning interactions between genes in multiple layers of networks. Results: As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene-gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets. Availability: iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.23.217869v1?rss=1

Authors: Stoof, R., Grozinger, L., Tas, H., Goni-Moreno, A.

Abstract:

Measuring fluorescence by flow cytometry is fundamental for characterising single-cell performance. While it is known that fluorescence and scattering values tend to positively correlate, the impact of cell volume on fluorescence is typically overlooked. This makes of fluorescence values alone an inaccurate measurement for high-precision characterisations. We developed FlowScatt, an open-source software package that removes volume-dependency in the fluorescence channel. Using FlowScatt, flourescence values are re-calculated based on the unified volume per cell that arises from scattering decomposition. FlowScatt is openly available as a Python package on https://github.com/rstoof/FlowScatt. Experimental data for validation is available online.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.23.214536v1?rss=1

Authors: Bedre, R. H., Avila, C. A., Mandadi, K.

Abstract:

Motivation: Use of high-throughput sequencing (HTS) has become indispensable in life science research. Raw HTS data contains several sequencing artifacts, and as a first step it is imperative to remove the artifacts for reliable downstream bioinformatics analysis. Although there are multiple stand-alone tools available that can perform the various quality control steps separately, availability of an integrated tool that can allow one-step, automated quality control analysis of HTS datasets will significantly enhance handling large number of samples parallelly. Results: Here, we developed HTSeqQC, a stand-alone, flexible, and easy-to-use software for one-step quality control analysis of raw HTS data. HTSeqQC can evaluate HTS data quality and perform filtering and trimming analysis in a single run. We evaluated the performance of HTSeqQC for conducting batch analysis of HTS datasets with 322 sample datasets with an average ~ 1M (paired end) sequence reads per sample. HTSeqQC accomplished the QC analysis in ~3 hours in distributed mode and ~31 hours in shared mode, thus underscoring its utility and robust performance. Availability and implementation: HTSeqQC software, Docker image and Nextflow template are available for download at https://github.com/reneshbedre/HTSeqQC and graphical user interface (GUI) is available at CyVerse Discovery Environment (DE) (https://cyverse.org/ ). Documentation available at https://reneshbedre.github.io/blog/htseqqc.html and https://cyverse-htseqqc-cyverse-tutorial.readthedocs-hosted.com/en/latest/ (for CyVerse).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.23.217745v1?rss=1

Authors: Mersmann, S. F., Johns, E., Yong, T., McEwan, W. A., James, L. C., Cohen, E. A. K., Grove, J.

Abstract:

Cellular biology occurs through myriad interactions between diverse molecular components, many of which assemble in to specific complexes. Various techniques can provide a qualitative survey of which components are found in a given complex. However, quantitative analysis of the absolute number of molecules within a complex (known as stoichiometry) remains challenging. Here we provide a novel method that combines fluorescence microscopy and statistical modelling to derive accurate molecular counts. We have devised a system in which a given biomolecule is differentially labelled with spectrally distinct fluorescent dyes (label A or B), which are then mixed such that B-labelled molecules are vastly outnumbered by those with label A. Complexes, containing this component, are then simply scored as either being positive or negative for label B. The frequency of positive complexes is directly related to the stoichiometry of interaction and molecular counts can be inferred by statistical modelling. We demonstrate this method using complexes of Adenovirus particles and monoclonal antibodies, achieving counts that are in excellent agreement with previous estimates. Beyond virology, this approach is readily transferable to other experimental systems and, therefore, provides a powerful tool for quantitative molecular biology.

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