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Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the detection of differentially expressed genes. Since most bulk RNA-seq methods assume equal group variances, we introduce two new approaches that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design ( voomQWB ). Compared to current gold-standard methods that do not account for group heteroscedasticity, we show results from simulations and various experiments that demonstrate the superior performance of voomByGroup and voomQWB in terms of error control and power when group variances in pseudo-bulk single-cell RNA-seq data are unequal.

Single‐cell RNA sequencing and spatial transcriptomics enable unprecedented insight into cellular and molecular pathways implicated in human skin aging and regeneration. Senescent cells are individual cells that are irreversibly cell cycle arrested and can accumulate across the human lifespan due to cell‐intrinsic and ‐extrinsic stressors. With an atlas of single‐cell RNA‐sequencing and spatial transcriptomics, epidermal and dermal senescence and its effects were investigated, with a focus on melanocytes and fibroblasts. Photoaging due to ultraviolet light exposure was associated with higher burdens of senescent cells, a sign of biological aging, compared to chronological aging. A skin‐specific cellular senescence gene set, termed SenSkin™, was curated and confirmed to be elevated in the context of photoaging, chronological aging, and non‐replicating CDKN1A+ (p21) cells. In the epidermis, senescent melanocytes were associated with elevated melanin synthesis, suggesting haphazard pigmentation, while in the dermis, senescent reticular dermal fibroblasts were associated with decreased collagen and elastic fiber synthesis. Spatial analysis revealed the tendency for senescent cells to cluster, particularly in photoaged skin. This work proposes a strategy for characterizing age‐related skin dysfunction through the lens of cellular senescence and suggests a role for senescent epidermal cells (i.e., melanocytes) and senescent dermal cells (i.e., reticular dermal fibroblasts) in age‐related skin sequelae. Bioinformatic analysis of scRNA‐seq and spatial transcriptomics of human skin aging revealed increased senescent cells, identified as CDKN1A+ non‐replicating cells, with sun exposure and chronological age. Senescent melanocytes in the epidermis expressed increased melanin biosynthesis, while senescent fibroblasts in the reticular dermis expressed decreased collagen and elastic fiber genes. Senescent cells showed a tendency to cluster, and their phenotypes were inferred to change with time. The graphical figure was created with BioRender.com.

Background Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches. Methods In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure. Results Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89. Conclusions Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.

Summary Cell wall recalcitrance is the major challenge to improving saccharification efficiency in converting lignocellulose into biofuels. However, information regarding the transcriptional regulation of secondary cell wall biogenesis remains poor in switchgrass (Panicum virgatum), which has been selected as a biofuel crop in the United States. In this study, we present a combination of computational and experimental approaches to develop gene regulatory networks for lignin formation in switchgrass. To screen transcription factors (TFs) involved in lignin biosynthesis, we developed a modified method to perform co‐expression network analysis using 14 lignin biosynthesis genes as bait (target) genes. The switchgrass lignin co‐expression network was further extended by adding 14 TFs identified in this study, and seven TFs identified in previous studies, as bait genes. Six TFs (PvMYB58/63, PvMYB42/85, PvMYB4, PvWRKY12, PvSND2 and PvSWN2) were targeted to generate overexpressing and/or down‐regulated transgenic switchgrass lines. The alteration of lignin content, cell wall composition and/or plant growth in the transgenic plants supported the role of the TFs in controlling secondary wall formation. RNA‐seq analysis of four of the transgenic switchgrass lines revealed downstream target genes of the secondary wall‐related TFs and crosstalk with other biological pathways. In vitro transactivation assays further confirmed the regulation of specific lignin pathway genes by four of the TFs. Our meta‐analysis provides a hierarchical network of TFs and their potential target genes for future manipulation of secondary cell wall formation for lignin modification in switchgrass.

Background Recent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline. Results We describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods. Conclusion Our methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data.

Background Phytophthora palmivora is a devastating oomycete pathogen in durian, one of the most economically important crops in Southeast Asia. The use of fungicides in Phytophthora management may not be a long-term solution because of emerging chemical resistance issues. It is crucial to develop Phytophthora -resistant durian cultivars, and information regarding the underlying resistance mechanisms is valuable for smart breeding programs. Results In this study, we conducted RNA sequencing (RNA-seq) to investigate early gene expression responses (at 8, 24, and 48 h) after the P. palmivora infection in three durian cultivars, which included one resistant cultivar (Puangmanee; PM) and two susceptible cultivars (Monthong; MT and Kradumthong; KD). We performed co-expression and differential gene expression analyses to capture gene expression patterns and identify the differentially expressed genes. The results showed that genes encoding heat shock proteins (HSPs) were upregulated in all infected durians. The expression levels of genes encoding HSPs, such as ERdj3B, were high only in infected PM. A higher level of P. palmivora resistance in PM appeared to be associated with higher expression levels of various genes encoding defense and chitin response proteins, such as lysM domain receptor-like kinases. MT had a lower resistance level than PM, although it possessed more upregulated genes during P. palmivora infection. Many photosynthetic and defense genes were upregulated in the infected MT, although their expression levels were lower than those in the infected PM. KD, the least resistant cultivar, showed downregulation of genes involved in cell wall organization or biogenesis during P. palmivora infection. Conclusions Our results showed that the three durian cultivars exhibited significantly different gene expression patterns in response to P. palmivora infection. The upregulation of genes encoding HSPs was common in all studied durians. The high expression of genes encoding chitin response proteins likely contributed to P. palmivora resistance in durians. Durian susceptibility was associated with low basal expression of defense genes and downregulation of several cell wall-related genes. These findings enhance our understanding of durian resistance to Phytophthora infection and could be useful for the development of elite durian cultivars.

• Lignin is a key target for modifying lignocellulosic biomass for efficient biofuel production. Brown midrib 12 (bmr12) encodes the sorghum caffeic acid O-methyltransferase (COMT) and is one of the key enzymes in monolignol biosynthesis. Loss of function mutations in COMT reduces syringyl (S) lignin subunits and improves biofuel conversion rate. Although lignin plays an important role in maintaining cell wall integrity of xylem vessels, physiological and molecular consequences due to loss of COMT on root growth and adaptation to water deficit remain unexplored. • We addressed this gap by evaluating the root morphology, anatomy and transcriptome of bmr12 mutant. The mutant had reduced lateral root density (LRD) and altered root anatomy and response to water limitation. The wild-type exhibits similar phenotypes under water stress, suggesting that bmr12 may be in a water deficit responsive state even in well-watered conditions. • bmr12 had increased transcript abundance of genes involved in (a)biotic stress response, gibberellic acid (GA) biosynthesis and signaling. We show that bmr12 is more sensitive to exogenous GA application and present evidence for the role of GA in regulating reduced LRD in bmr12. • These findings elucidate the phenotypic and molecular consequences of COMT deficiency under optimal and water stress environments in grasses.

Sucrose nonfermenting 2 (Snf2) family proteins, as the catalytic core of ATP-dependent chromatin remodeling complexes, play important roles in nuclear processes as diverse as DNA replication, transcriptional regulation, and DNA repair and recombination. The Snf2 gene family has been characterized in several plant species; some of its members regulate flower development in Arabidopsis. However, little is known about the members of the family in barley (Hordeum vulgare). Here, 38 Snf2 genes unevenly distributed among seven chromosomes were identified from the barley (cv. Morex) genome. Phylogenetic analysis categorized them into 18 subfamilies. They contained combinations of 21 domains and consisted of 3 to 34 exons. Evolution analysis revealed that segmental duplication contributed predominantly to the expansion of the family in barley, and the duplicated gene pairs have undergone purifying selection. About eight hundred Snf2 family genes were identified from 20 barley accessions, ranging from 38 to 41 genes in each. Most of these genes were subjected to purification selection during barley domestication. Most were expressed abundantly during spike development. This study provides a comprehensive characterization of barley Snf2 family members, which should help to improve our understanding of their potential regulatory roles in barley spike development.

Single-cell RNA sequencing (scRNA-Seq) technology provides the scope to gain insight into the interplay between intrinsic cellular processes as well as transcriptional and behavioral changes in gene–gene interactions across varying conditions. The high level of scarcity of scRNA-seq data, however, poses a significant challenge for analysis. We propose a complete differential co-expression (DCE) analysis framework for scRNA-Seq data to extract network modules and identify hub-genes. The performance of our method has been shown to be satisfactory after validation using an scRNA-Seq esophageal squamous cell carcinoma (ESCC) dataset. From comparison with four other existing hub-gene finding methods, it has been observed that our method performs better in the majority of cases and has the ability to identify unique potential biomarkers that were not detected by the other methods. The potential biomarker genes identified by our framework, differential co-expression analysis method for single-cell RNA sequencing data (scDiffCoAM), have been validated both statistically and biologically.

Background Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human. Results In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery. Conclusions We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.