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1. Fundamental gene network rewiring at the second order within and across mammalian systems

   https://doi.org/10.1093/bioinformatics/btab240  

   Sharma, Ruby; Kumar, Sajal; Song, Mingzhou (May 2021, Bioinformatics)

   Kelso, Janet (Ed.)

   Abstract Motivation Genetic or epigenetic events can rewire molecular networks to induce extraordinary phenotypical divergences. Among the many network rewiring approaches, no model-free statistical methods can differentiate gene-gene pattern changes not attributed to marginal changes. This may obscure fundamental rewiring from superficial changes. Results Here we introduce a model-free Sharma-Song test to determine if patterns differ in the second order, meaning that the deviation of the joint distribution from the product of marginal distributions is unequal across conditions. We prove an asymptotic chi-squared null distribution for the test statistic. Simulation studies demonstrate its advantage over alternative methods in detecting second-order differential patterns. Applying the test on three independent mammalian developmental transcriptome datasets, we report a lower frequency of co-expression network rewiring between human and mouse for the same tissue group than the frequency of rewiring between tissue groups within the same species. We also find secondorder differential patterns between microRNA promoters and genes contrasting cerebellum and liver development in mice. These patterns are enriched in the spliceosome pathway regulating tissue specificity. Complementary to previous mammalian comparative studies mostly driven by first-order effects, our findings contribute an understanding of system-wide second-order gene network rewiring within and across mammalian systems. Second-order differential patterns constitute evidence for fundamentally rewired biological circuitry due to evolution, environment, or disease. Availability The generic Sharma-Song test is available from the R package ‘DiffXTables’ at https://cran.r-project.org/package=DiffXTables. Other code and data are described in Methods. Supplementary information Supplementary data are available at Bioinformatics online. 

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2. NBBt-test: a versatile method for differential analysis of multiple types of RNA-seq data

   https://doi.org/10.1038/s41598-022-15762-x  

   Tan, Yuan-De; Guda, Chittibabu (December 2022, Scientific Reports)

   Abstract Rapid development of transcriptome sequencing technologies has resulted in a data revolution and emergence of new approaches to study transcriptomic regulation such as alternative splicing, alternative polyadenylation, CRISPR knockout screening in addition to the regular gene expression. A full characterization of the transcriptional landscape of different groups of cells or tissues holds enormous potential for both basic science as well as clinical applications. Although many methods have been developed in the realm of differential gene expression analysis, they all geared towards a particular type of sequencing data and failed to perform well when applied in different types of transcriptomic data. To fill this gap, we offer a negative beta binomial t-test (NBBt-test). NBBt-test provides multiple functions to perform differential analyses of alternative splicing, polyadenylation, CRISPR knockout screening, and gene expression datasets. Both real and large-scale simulation data show superior performance of NBBt-test with higher efficiency, and lower type I error rate and FDR to identify differential isoforms and differentially expressed genes and differential CRISPR knockout screening genes with different sample sizes when compared against the current very popular statistical methods. An R-package implementing NBBt-test is available for downloading from CRAN ( https://CRAN.R-project.org/package=NBBttest ). 

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3. Large Comparative Analyses of Primate Body Site Microbiomes Indicate that the Oral Microbiome Is Unique among All Body Sites and Conserved among Nonhuman Primates

   https://doi.org/10.1128/spectrum.01643-21  

   Asangba, Abigail E.; Mugisha, Lawrence; Rukundo, Joshua; Lewis, Rebecca J.; Halajian, Ali; Cortés-Ortiz, Liliana; Junge, Randall E.; Irwin, Mitchell T.; Karlson, Johan; Perkin, Andrew; et al (June 2022, Microbiology Spectrum)

   Kormas, Konstantinos Aristomenis (Ed.)

   ABSTRACT The study of the mammalian microbiome serves as a critical tool for understanding host-microbial diversity and coevolution and the impact of bacterial communities on host health. While studies of specific microbial systems (e.g., in the human gut) have rapidly increased, large knowledge gaps remain, hindering our understanding of the determinants and levels of variation in microbiomes across multiple body sites and host species. Here, we compare microbiome community compositions from eight distinct body sites among 17 phylogenetically diverse species of nonhuman primates (NHPs), representing the largest comparative study of microbial diversity across primate host species and body sites. Analysis of 898 samples predominantly acquired in the wild demonstrated that oral microbiomes were unique in their clustering, with distinctive divergence from all other body site microbiomes. In contrast, all other body site microbiomes clustered principally by host species and differentiated by body site within host species. These results highlight two key findings: (i) the oral microbiome is unique compared to all other body site microbiomes and conserved among diverse nonhuman primates, despite their considerable dietary and phylogenetic differences, and (ii) assessments of the determinants of host-microbial diversity are relative to the level of the comparison (i.e., intra-/inter-body site, -host species, and -individual), emphasizing the need for broader comparative microbial analyses across diverse hosts to further elucidate host-microbial dynamics, evolutionary and biological patterns of variation, and implications for human-microbial coevolution. IMPORTANCE The microbiome is critical to host health and disease, but much remains unknown about the determinants, levels, and evolution of host-microbial diversity. The relationship between hosts and their associated microbes is complex. Most studies to date have focused on the gut microbiome; however, large gaps remain in our understanding of host-microbial diversity, coevolution, and levels of variation in microbiomes across multiple body sites and host species. To better understand the patterns of variation and evolutionary context of host-microbial communities, we conducted one of the largest comparative studies to date, which indicated that the oral microbiome was distinct from the microbiomes of all other body sites and convergent across host species, suggesting conserved niche specialization within the Primates order. We also show the importance of host species differences in shaping the microbiome within specific body sites. This large, comparative study contributes valuable information on key patterns of variation among hosts and body sites, with implications for understanding host-microbial dynamics and human-microbial coevolution. 

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4. danRerLib: a Python package for zebrafish transcriptomics

   https://doi.org/10.1093/bioadv/vbae065  

   Schwartz, Ashley V; Sant, Karilyn E; George, Uduak Z (May 2024, Bioinformatics Advances)

   Fiston-Lavier, Anna-Sophie (Ed.)

   Abstract SummaryUnderstanding the pathways and biological processes underlying differential gene expression is fundamental for characterizing gene expression changes in response to an experimental condition. Zebrafish, with a transcriptome closely mirroring that of humans, are frequently utilized as a model for human development and disease. However, a challenge arises due to the incomplete annotations of zebrafish pathways and biological processes, with more comprehensive annotations existing in humans. This incompleteness may result in biased functional enrichment findings and loss of knowledge. danRerLib, a versatile Python package for zebrafish transcriptomics researchers, overcomes this challenge and provides a suite of tools to be executed in Python including gene ID mapping, orthology mapping for the zebrafish and human taxonomy, and functional enrichment analysis utilizing the latest updated Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. danRerLib enables functional enrichment analysis for GO and KEGG pathways, even when they lack direct zebrafish annotations through the orthology of human-annotated functional annotations. This approach enables researchers to extend their analysis to a wider range of pathways, elucidating additional mechanisms of interest and greater insight into experimental results. Availability and implementationdanRerLib, along with comprehensive documentation and tutorials, is freely available. The source code is available at https://github.com/sdsucomptox/danrerlib/ with associated documentation and tutorials at https://sdsucomptox.github.io/danrerlib/. The package has been developed with Python 3.9 and is available for installation on the package management systems PIP (https://pypi.org/project/danrerlib/) and Conda (https://anaconda.org/sdsu_comptox/danrerlib) with additional installation instructions on the documentation website. 

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5. Overcoming biases in causal inference of molecular interactions

   https://doi.org/10.1093/bioinformatics/btac206  

   Kumar, Sajal; Song, Mingzhou; Robinson, ed., Peter (April 2022, Bioinformatics)

   Abstract MotivationComputer inference of biological mechanisms is increasingly approachable due to dynamically rich data sources such as single-cell genomics. Inferred molecular interactions can prioritize hypotheses for wet-lab experiments to expedite biological discovery. However, complex data often come with unwanted biological or technical variations, exposing biases over marginal distribution and sample size in current methods to favor spurious causal relationships. ResultsConsidering function direction and strength as evidence for causality, we present an adapted functional chi-squared test (AdpFunChisq) that rewards functional patterns over non-functional or independent patterns. On synthetic and three biology datasets, we demonstrate the advantages of AdpFunChisq over 10 methods on overcoming biases that give rise to wide fluctuations in the performance of alternative approaches. On single-cell multiomics data of multiple phenotype acute leukemia, we found that the T-cell surface glycoprotein CD3 delta chain may causally mediate specific genes in the viral carcinogenesis pathway. Using the causality-by-functionality principle, AdpFunChisq offers a viable option for robust causal inference in dynamical systems. Availability and implementationThe AdpFunChisq test is implemented in the R package ‘FunChisq’ (2.5.2 or above) at https://cran.r-project.org/package=FunChisq. All other source code along with pre-processed data is available at Code Ocean https://doi.org/10.24433/CO.2907738.v1 Supplementary informationSupplementary materials are available at Bioinformatics online. 

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