Simple Bayesian Gene Network Learning in Populus Drought Transcriptome Data

Constructing a sensibly functional gene interaction network is highly appealing for better understanding system-level biological processes governing various Populus traits. Bayesian Network (BN) learning provides an elegant and compact statistical approach for modeling causal gene-gene relationships in microarray data. Therefore, it could come with the illumination of functional molecular playing in Biology Systems. In the present study, different forms of gene Bayesian networks were detected on Populus cellular transcriptome data. Markov blankets would likely be emerging at every possible gene regulatory Bayesian network level. Results showed that PtpAffx.1257.4.S1_a_at,1.0 hypothetical protein is the most important in its possible regulatory program. This paper illustrates that the gene network regulatory inference is possible to encapsulate within a single BN model. Therefore, such a BN model can serve as a promising training tool for Populus gene expression data for better future experimental scenarios. Bangladesh J. Bot. 50(4): 1077-1086, 2021 (December)

Transglutaminase Type 2 regulates the Wnt/β-catenin pathway in vertebrates

TG2 is a multifunctional enzyme involved in several cellular processes and has emerging as a potential regulator of gene expression. In this regard, we have recently shown that TG2 is able to activate HSF1, the master transcriptional regulator of the stress‐responsive genes; however, its effect on the overall gene expression remains unclear. To address this point, we analyzed, by RNA-seq, the effect of TG2 on the overall transcriptome as well as we characterized the TG2 interactome in the nucleus. The data obtained from these omics approaches reveal that TG2 markedly influences the overall cellular transcriptome profile and specifically the Wnt and HSF1 pathways. In particular, its ablation leads to a drastic downregulation of many key members of these pathways. Interestingly, we found that key components of the Wnt/β-catenin pathway are also downregulated in cells lacking HSF1, thus confirming that TG2 regulates the HSF1 and this axis controls the Wnt signaling. Mechanistic studies revealed that TG2 can regulate the Wnt pathway by physically interacts with β-catenin and its nuclear interactome includes several proteins known to be involved in the regulation of the Wnt signaling. In order to verify whether this effect is playing a role in vivo, we ablated TG2 in Danio rerio. Our data show that the zebrafish lacking TG2 cannot complete the development and their death is associated with an evident downregulation of the Wnt pathway and a defective heat-shock response. Our findings show for the first time that TG2 is essential for the correct embryonal development of lower vertebrates, and its action is mediated by the Wnt/HSF1 axis.

A Transcriptomically Guided Computational Quantification of the SRR8671434 RNA Sequence

Ribonucleic acid (RNA) is a single strand nucleic acid responsible for genetic coding, decoding, regulation, and expression that consists of phosphate and ribose groups with several purposeful variants, notably messenger, transfer, and ribosomal RNA. RNA sequencing is a next-generation sequencing (NGS) technique capable of continuously analyzing cellular transcriptome and revealing the presence and quantity of RNA within a biological sample. The process entails reverse transcribing the extracted RNA into cDNA; subsequently, the cDNA is fragmented, enabling its input into an NGS workflow, after which adapters are added to both ends of the fragments. RNA sequencing is an expensive and time-consuming process due to its necessitation of an entire genomic library that is often difficult to analyze through traditional methods; however, applying computational methods can overcome these challenges through cutting edge data mining and informatics technologies. In this study, the SRR8671434 RNA sequence is quantified and analyzed for its ability to serve as an indicator for congenital afflictions. The findings in this paper can be applied towards preventing and curing associated diseases, as well as discerning other potential biomarkers within genetic materials.

Single-cell entropy to quantify the cellular transcriptome from single-cell RNA-seq data

We present the use of single-cell entropy (scEntropy) to measure the order of the cellular transcriptome profile from single-cell RNA-seq data, which leads to a method of unsupervised cell type classification through scEntropy followed by the Gaussian mixture model (scEGMM). scEntropy is straightforward in defining an intrinsic transcriptional state of a cell. scEGMM is a coherent method of cell type classification that includes no parameters and no clustering; however, it is comparable to existing machine learning-based methods in benchmarking studies and facilitates biological interpretation.

Cell “hashing” with barcoded antibodies enables multiplexing and doublet detection for single cell genomics

ABSTRACTDespite rapid developments in single cell sequencing technology, sample-specific batch effects, detection of cell doublets, and the cost of generating massive datasets remain outstanding challenges. Here, we introduce cell “hashing”, where oligo-tagged antibodies against ubiquitously expressed surface proteins are used to uniquely label cells from distinct samples, which can be subsequently pooled. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its sample of origin, and robustly identify doublets originating from multiple samples. We demonstrate our approach by pooling eight human PBMC samples on a single run of the 10x Chromium system, substantially reducing our per-cell costs for library generation. Cell “hashing” is inspired by, and complementary to, elegant multiplexing strategies based on genetic variation, which we also leverage to validate our results. We therefore envision that our approach will help to generalize the benefits of single cell multiplexing to diverse samples and experimental designs.

Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge

ABSTRACTMotivationSingle cell RNA-seq (scRNA-seq) data contains a wealth of information which has to be inferred computationally from the observed sequencing reads. As the ability to sequence more cells improves rapidly, existing computational tools suffer from three problems. (1) The decreased reads-per-cell implies a highly sparse sample of the true cellular transcriptome. (2) Many tools simply cannot handle the size of the resulting datasets. (3) Prior biological knowledge such as bulk RNA-seq information of certain cell types or qualitative marker information is not taken into account. Here we present UNCURL, a preprocessing framework based on non-negative matrix factorization for scRNA-seq data, that is able to handle varying sampling distributions, scales to very large cell numbers and can incorporate prior knowledge.ResultsWe find that preprocessing using UNCURL consistently improves performance of commonly used scRNA-seq tools for clustering, visualization, and lineage estimation, both in the absence and presence of prior knowledge. Finally we demonstrate that UNCURL is extremely scalable and parallelizable, and runs faster than other methods on a scRNA-seq dataset containing 1.3 million cells.AvailabilitySource code is available at https://github.com/yjzhang/[email protected], [email protected]