PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.16.299925v1?rss=1

Authors: Ziegler, J., Hechtman, J. F., Ptashkin, R., Jayakumaran, G., Middha, S., Chavan, S. S., Vanderbilt, C., DeLair, D., Casanova, J., Shia, J., DeGroat, N., Benayed, R., Ladanyi, M., Berger, M. F., Fuchs, T. J., Zehir, A.

Abstract:

Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Recent work has demonstrated that next-generation sequencing data can be used to identify samples with MSI-high phenotype. However, low tumor purity, as frequently observed in routine clinical samples, poses a challenge to the sensitivity of existing algorithms. To overcome this critical issue, we developed MiMSI, an MSI classifier based on deep neural networks and trained using a dataset that included low tumor purity MSI cases in a multiple instance learning framework. On a challenging yet representative set of cases, MiMSI showed higher sensitivity (0.940) and auROC (0.988) than MSISensor(sensitivity: 0.57; auROC: 0.911), an open-source software previously validated for clinical use at our institution using MSK-IMPACT large panel targeted NGS data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.17.297358v1?rss=1

Authors: Gunturkun, M. H., Flashner, E., Wang, T., Mulligan, M. K., Williams, R. W., Prins, P., Chen, H.

Abstract:

Interpreting and integrating results from omics studies on addiction phenotypes require a comprehensive survey of the extant literature. Most often this is conducted by ad hoc queries of the PubMed database. Here, we introduce RatsPub, a literature mining web service that searches user-provided gene symbols in conjunction with a set of systematically curated keywords related to addiction, as well as results from human genome-wide association studies (GWAS). We have organized over 300 keywords into six categories forming an ontology. The literature search is conducted by querying the NIH PubMed server using a programmatic interface. Abstracts are retrieved from a local copy of the PubMed archive. The main results presented to the user are sentences containing the gene symbol and at least one keyword. These sentences are presented in the browser through an interactive graphical interface or using tables. Results are linked to the source PubMed records. GWAS results are displayed using a similar method. We wrote a natural language processing module that uses deep convolutional neural networks to distinguish sentences describing systemic stress vs cellular stress. The automated and comprehensive search strategy provided by RatsPub facilitates the integration of new discoveries from addiction omic studies with existing literature and improves analysis and modeling in the field of addiction biology. RatsPub is free and open source software. The source code of RatsPub and the link to a running instance is available at https://githhub.com/chen42/ratspub.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.16.300632v1?rss=1

Authors: Oh, S., Abdelnabi, J., Al-Dulaimi, R., Davis, S. R., Riester, M., Waldron, L. D.

Abstract:

Gene symbols are recognizable identifiers for gene names but are unstable and error-prone due to aliasing, manual entry, and unintentional conversion by spreadsheets to date format. Official gene symbol resources such as HUGO Gene Nomenclature Committee (HGNC) for human genes and the Mouse Genome Informatics project (MGI) for mouse genes provide authoritative sources of valid, aliased, and outdated symbols, but lack a programmatic interface and correction of symbols converted by spreadsheets. We present HGNChelper, an R package that identifies known aliases and outdated gene symbols based on the HGNC human and MGI mouse gene symbol databases, in addition to common mislabeling introduced by spreadsheets, and provides corrections where possible. HGNChelper identified invalid gene symbols in the most recent Molecular Signatures Database (mSigDB 7.0) and in platform annotation files of the Gene Expression Omnibus, with prevalence ranging from ~3% in recent platforms to 30-40% in the earliest platforms from 2002-03. HGNChelper is installable from CRAN, with open development and issue tracking on GitHub and an associated pkgdown site https://waldronlab.io/HGNChelper/.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.18.303131v1?rss=1

Authors: Gussler, J. W., Campo, D. S., Dimitrova, Z., Skums, P., Khudyakov, Y.

Abstract:

Investigation of outbreaks to identify the primary case is crucial for the interruption and prevention of transmission of infectious diseases. These individuals may have a higher risk of participating in near future transmission events when compared to the other patients in the outbreak, so directing more transmission prevention resources towards these individuals is a priority. Genetic characterization of intra-host viral populations, although highly efficient in the identification of transmission clusters, is not as efficient in routing transmissions during outbreaks, owing to complexity of viral evolution. Here, we present a new computational framework, PYCIVO: primarycase inference in viral outbreaks. This framework expands upon our earlier work in development of QUENTIN, which builds a probabilistic disease transmission tree based on simulation of evolution of intra-host hepatitis C virus (HCV) variants between cases involved in direct transmission during an outbreak. PYCIVO improves upon QUENTIN by also implementing a custom heterogeneity index which empowers PYCIVO to make the important No primary case prediction. One or more samples, possibly including the primary case, may have not been sampled, and this designation is meant to account for these scenarios. These approaches were validated using a set of 105 sequence samples from 11 distinct HCV transmission clusters identified during outbreak investigations, in which the primary case was epidemiologically verified. Both models can detect the correct primary case in 9 out of 11 transmission clusters (81.8%). However, while QUENTIN issues erroneous predictions on the remaining 2 transmission clusters, PYCIVO issues a null output for these clusters, giving it an effective prediction accuracy of 100%. To further evaluate accuracy of the inference, we created 10 modified transmission clusters in which the primary case had been removed. In this scenario, PYCIVO was able to correctly identify that there was no primary case in 8/10 (80%) of these modified clusters. This model was validated with HCV; however, this approach may be applicable to other microbial pathogens.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.16.300491v1?rss=1

Authors: Moyer, D. C., Pacheco, A. R., Bernstein, D. B., Segre, D.

Abstract:

Uncovering the general principles that govern the architecture of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries, in silico representations of chemical reaction networks arising from a defined set of mathematical rules, can help address this challenge by enabling the exploration of alternative chemical universes and the possible metabolic networks that could emerge within them. Here we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We study string chemistries using tools borrowed from the field of stoichiometric constraint-based modeling of organismal metabolic networks, through a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET). In addition to exploring the complexity and connectivity properties of different string chemistries, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental molecules within the string chemistry framework. We found that the identities of the metabolites in the biomass reaction wield much more influence over the structure of the minimal metabolic networks than the identities of the nutrient metabolites - a notion that could help us better understand the rise and evolution of biochemical organization. Our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.16.300822v1?rss=1

Authors: Kaymaz, Y., Ganglberger, F., Tang, M., Fernandez-Albert, F., Lawless, N., Sackton, T. B.

Abstract:

The emergence of single-cell RNA sequencing (scRNA-seq) has led to an explosion in novel methods to study biological variation among individual cells, and to classify cells into functional and biologically meaningful categories. Here, we present a new cell type projection tool, HieRFIT (Hierarchical Random Forest for Information Transfer), based on hierarchical random forests. HieRFIT uses a priori information about cell type relationships to improve classification accuracy, taking as input a hierarchical tree structure representing the class relationships, along with the reference data. We use an ensemble approach combining multiple random forest models, organized in a hierarchical decision tree structure. We show that our hierarchical classification approach improves accuracy and reduces incorrect predictions especially for inter-dataset tasks which reflect real life applications. We use a scoring scheme that adjusts probability distributions for candidate class labels and resolves uncertainties while avoiding the assignment of cells to incorrect types by labeling cells at internal nodes of the hierarchy when necessary. Using HieRFIT, we re-analyzed publicly available scRNA-seq datasets showing its effectiveness in cell type cross-projections with inter/intra-species examples. HieRFIT is implemented as an R package and it is available at (https://github.com/yasinkaymaz/HieRFIT/releases/tag/v1.0.0)

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.15.299156v1?rss=1

Authors: Sanchez Reyes, L. L., Kandziora, M., McTavish, E. J.

Abstract:

1. Phylogenies are a key part of research in many areas of biology. Tools that automate some parts of the process of phylogenetic reconstruction, mainly molecular character matrix assembly, have been developed for the advantage of both specialists in the field of phylogenetics and nonspecialists. However, interpretation of results, comparison with previously available phylogenetic hypotheses, and selection of one phylogeny for downstream analyses and discussion still impose difficulties to one that is not a specialist either on phylogenetic methods or on a particular group of study. 2. Physcraper is a command-line Python program that automates the update of published phylogenies by adding public DNA sequences to underlying alignments of previously published phylogenies. It also provides a framework for straightforward comparison of published phylogenies with their updated versions, by leveraging upon tools from the Open Tree of Life project to link taxonomic information across databases. 3. Physcraper can be used by the nonspecialist, as a tool to generate phylogenetic hypotheses based on publicly available expert phylogenetic knowledge. Phylogeneticists and taxonomic group specialists will find it useful as a tool to facilitate molecular dataset gathering and comparison of alternative phylogenetic hypotheses (topologies). 4. The Physcraper workflow demonstrates the benefits of doing open science for phylogenetics, encouraging researchers to strive for better sharing practices. Physcraper can be used with any OS and is released under an open-source license. Detailed instructions for installation and use are available at https://physcraper.readthedocs.io

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.17.301234v1?rss=1

Authors: Sowdhamini, R., Manikkaraja, C., Bhavika, M., Singh, R., Nagarathinam, B., George, G., Gulyani, A., Archunan, G.

Abstract:

The olfactory system is capable of detecting and distinguishing thousands of environmental odorants that play a key role in reproduction, social behaviours including pheromones influenced classical events. Membrane secretary odorant binding proteins (OBPs) are soluble lipocalins, localized in the nasal membrane of mammals. They bind and carry odorants within the nasal epithelium to putative olfactory transmembrane receptors (ORs). While the existence of OBPs and their significant functions are very well known in insects and laboratory mammals, there is little information about the species-specific OBPs in buffaloes. In fact, the OBP of nasal epithelium has not yet been exploited to develop a suitable technique to detect estrus which is being reported as a difficult task in buffalo. In the present study, using molecular biology and protein engineering approaches, we have cloned six novel OBP isoforms from buffalo nasal epithelium (bnOBPs). Furthermore, 3D model was developed and molecular-docking, dynamics experiments were performed by In-silico approach. In particular, we found four residues (Phe104, Phe134, Phe69 and Asn118) from OBP1a, which had strong binding affinities towards two sex pheromones, specifically oleic acid and p-cresol. We expressed this protein in Escherichia coli to examine its involvement in the sex pheromone perception from female buffalo urine and validated through fluorescence quenching studies. Interestingly, fluorescence binding experiments also showed similar strong binding affinities of OBP1a to oleic acid and p-cresol. By using structural data, the binding specificity is also verified by site-directed mutagenesis of the four residues followed by in-vitro binding assays. Our results enable to better understand the functions of different nasal epithelium OBPs in buffaloes. They also lead to improved understanding of the interaction between olfactory proteins and odorants to develop highly selective biosensing devices for non-invasive detection of estrus in buffaloes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.17.301226v1?rss=1

Authors: Gan, G. L., Nguyen, M., Willie, E., Lee, B., Chauve, C., Libbrecht, M., Chindelevitch, L.

Abstract:

The efficacy of antibiotic drug treatments in tuberculosis (TB) is significantly threatened by the development of drug resistance. There is a need for a robust diagnostic system that can accurately predict drug resistance in patients. In recent years, researchers have been taking advantage of whole-genome sequencing (WGS) data to infer antibiotic resistance. In this work we investigate the power of machine learning tools in inferring drug resistance from WGS data on three distinct datasets differing in their geographical diversity. We analyzed data from the Relational Sequencing TB Data Platform, which comprises global isolates from 32 different countries, the PATRIC database, containing isolates contributed by researchers around the world, and isolates collected by the British Columbia Centre for Disease Control in Canada. We predicted drug resistance to the first-line drugs: isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin. We focused on the genes which previous evidence suggests are involved in drug resistance in TB. We called single-nucleotide polymorphisms using the Snippy pipeline, then applied different machine learning models. Following best practices, we chose the best parameters for each model via cross-validation on the training set and evaluated the performance via the sensitivity-specificity tradeoffs on the testing set. To the best of our knowledge, our study is the first to predict antibiotic resistance in TB across multiple datasets. We obtained a performance comparable to that seen in previous studies, but observed that performance may be negatively affected when training on one dataset and testing on another, suggesting the importance of geographical heterogeneity in drug resistance predictions. In addition, we investigated the importance of each gene within each model, and recapitulated some previously known biology of drug resistance. This study paves the way for further investigations, with the ultimate goal of creating an accurate, interpretable and globally generalizable model for predicting drug resistance in TB.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.17.301200v1?rss=1

Authors: Baranwal, M., Magner, A., Saldinger, J., Turali-Emre, E. S., Kozarekar, S., Elvati, P., VanEpps, J. S., Kotov, N., Violi, A., Hero, A. O.

Abstract:

Development of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. In this study, we address this problem and describe a PPI analysis method based on a graph attention network, named Struct2Graph , for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a five-fold cross validation average accuracy of 99.42%. Moreover, unsupervised prediction of the interaction sites by Struct2Graph for phenol-soluble modulins are found to be in concordance with the previously reported binding sites for this family.

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