FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight.

The quantification of developmental potential is critical for determining developmental stages and identifying essential molecular signatures in single-cell studies. Here, we present FitDevo, a novel method for inferring developmental potential using scRNA-seq data. The main idea of FitDevo is first to generate sample-specific gene weight (SSGW) and then infer developmental potential by calculating the correlation between SSGW and gene expression. SSGW is generated using a generalized linear model that combines sample-specific information and gene weight learned from a training dataset covering scRNA-seq data of 17 previously published datasets. We have rigorously validated FitDevo's effectiveness using a testing dataset with scRNA-seq data from 28 existing datasets and have also demonstrated its superiority over current methods. Furthermore, FitDevo's broad application scope has been illustrated using three practical scenarios: deconvolution analysis of epidermis, spatial transcriptomic data analysis of hearts and intestines, and developmental potential analysis of breast cancer. The source code and related data are available at https://github.com/jumphone/fitdevo.

Revealing the History and Mystery of RNA-Seq.

Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data.

Comparative transcriptomic and evolutionary analysis of FAD-like genes of Brassica species revealed their role in fatty acid biosynthesis and stress tolerance.

BACKGROUND: Fatty acid desaturases (FADs) are involved in regulating plant fatty acid composition by adding double bonds to growing hydrocarbon chain. Apart from regulating fatty acid composition FADs are of great importance, and are involved in stress responsiveness, plant development, and defense mechanisms. FADs have been extensively studied in crop plants, and are broadly classed into soluble and non-soluble fatty acids. However, FADs have not yet been characterized in Brassica carinata and its progenitors. RESULTS: Here we have performed comparative genome-wide identification of FADs and have identified 131 soluble and 28 non-soluble FADs in allotetraploid B. carinata and its diploid parents. Most soluble FAD proteins are predicted to be resided in endomembrane system, whereas FAB proteins were found to be localized in chloroplast. Phylogenetic analysis classed the soluble and non-soluble FAD proteins into seven and four clusters, respectively. Positive type of selection seemed to be dominant in both FADs suggesting the impact of evolution on these gene families. Upstream regions of both FADs were enriched in stress related cis-regulatory elements and among them ABRE type of elements were in abundance. Comparative transcriptomic data analysis output highlighted that FADs expression reduced gradually in mature seed and embryonic tissues. Moreover, under heat stress during seed and embryo development seven genes remained up-regulated regardless of external stress. Three FADs were only induced under elevated temperature whereas five genes were upregulated under Xanthomonas campestris stress suggesting their involvement in abiotic and biotic stress response. CONCLUSIONS: The current study provides insights into the evolution of FADs and their role in B. carinata under stress conditions. Moreover, the functional characterization of stress-related genes would exploit their utilization in future breeding programs of B. carinata and its progenitors.

Elucidating mechanistic insights into drug action for atopic dermatitis: a systems biology approach.

BACKGROUND: Topical Betamethasone (BM) and Pimecrolimus (PC) are widely used drugs in the treatment of atopic dermatitis (AD). Though the biomolecules and biological pathways affected by the drugs are known, the causal inter-relationships among these pathways in the context of skin is not available. We aim to derive this insight by using transcriptomic data of AD skin samples treated with BM and PC using systems biology approach. METHODS: Transcriptomic datasets of 10 AD patients treated with Betamethasone and Pimecrolimus were obtained from GEO datasets. We used a novel computational platform, eSkIN ( www.persistent.com/eskin ), to perform pathway enrichment analysis for the given datasets. eSkIN consists of 35 skin specific pathways, thus allowing skin-centric analysis of transcriptomic data. Fisher's exact test was used to compute the significance of the pathway enrichment. The enriched pathways were further analyzed to gain mechanistic insights into the action of these drugs. RESULTS: Our analysis highlighted the molecular details of the mechanism of action of the drugs and corroborated the known facts about these drugs i.e. BM is more effective in triggering anti-inflammatory response but also causes more adverse effect on skin barrier than PC. In particular, eSkIN helped enunciate the biological pathways activated by these drugs to trigger anti-inflammatory response and its effect on skin barrier. BM suppresses pathways like TNF and TLRs, thus inhibiting NF-κB while PC targets inflammatory genes like IL13 and IL6 via known calcineurin-NFAT pathway. Furthermore, we show that the reduced skin barrier function by BM is due to the suppression of activators like AP1 transcription factors, CEBPs. CONCLUSION: We thus demonstrate the detailed mechanistic insight into drug action of AD using a novel computational approach.

Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology.

Research in synthetic biology and metabolic engineering require a deep understanding on the function and regulation of complex pathway genes. This can be achieved through gene expression profiling which quantifies the transcriptome-wide expression under any condition, such as a cell development stage, mutant, disease, or treatment with a drug. The expression profiling is usually done using high-throughput techniques such as RNA sequencing (RNA-Seq) or microarray. Although both methods are based on different technical approaches, they provide quantitative measures of the expression levels of thousands of genes. The expression levels of the genes are compared under different conditions to identify the differentially expressed genes (DEGs), the genes with different expression levels under different conditions. DEGs, usually involving thousands in number, are then investigated using bioinformatics and data analytic tools to infer and compare their functional roles between conditions. Dealing with such large datasets, therefore, requires intensive data processing and analyses to ensure its quality and produce results that are statistically sound. Thus, there is a need for deep statistical and bioinformatics knowledge to deal with high-throughput gene expression data. This represents a barrier for wet biologists with limited computational, programming, and data analytic skills that prevent them from getting the full potential of the data. In this chapter, we present a step-by-step protocol to perform transcriptome analysis using GeneCloudOmics, a cloud-based web server that provides an end-to-end platform for high-throughput gene expression analysis.

TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining.

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

Predicting Gene and Genomic Regulation in Saccharomyces cerevisiae, using the YEASTRACT Database: A Step-by-Step Guided Analysis.

Transcriptional regulation is one of the key steps in the control of gene expression, with huge impact on the survival, adaptation, and fitness of all organisms. However, it is becoming increasingly clear that transcriptional regulation is far more complex than initially foreseen. In model organisms such as the yeast Saccharomyces cerevisiae evidence has been piling up showing that the expression of each gene can be controlled by several transcription factors, in the close dependency of the environmental conditions. Furthermore, transcription factors work in intricate networks, being themselves regulated at the transcriptional, post-transcriptional, and post-translational levels, working in cooperation or antagonism in the promoters of their target genes.In this chapter, a step-by-step guide using the YEASTRACT database is provided, for the prediction and ranking of the transcription factors required for the regulation of the expression a single gene and of a genome-wide response. These analyses are illustrated with the regulation of the PDR18 gene and of the transcriptome-wide changes induced upon exposure to the herbicide 2,4-Dichlorophenoxyacetic acid (2,4-D), respectively. The newest potentialities of this information system are explored, and the various results obtained in the dependency of the querying criteria are discussed in terms of the knowledge gathered on the biological responses considered as case studies.