Transcriptome Sequencing for Precise and Accurate Measurement of Transcripts and Accessibility of TCGA for Cancer Datasets and Analysis

Next-generation sequencing (NGS) technologies are now well established and have become a routine analysis tool for its depth, coverage, and cost. RNA sequencing (RNA-Seq) has readily replaced the conventional array-based approaches and has become method of choice for qualitative and quantitative analysis of transcriptome, quantification of alternative spliced isoforms, identification of sequence variants, novel transcripts, and gene fusions, among many others. The current chapter discusses the multi-step transcriptome data analysis processes in detail, in the context of re-sequencing (where a reference genome is available). We have discussed the processes including quality control, read alignment, quantification of gene from read level, visualization of data at different levels, and the identification of differentially expressed genes and alternatively spliced transcripts. Considering the data that are freely available to the public, we also discuss The Cancer Genome Atlas (TCGA), as a resource of RNA-Seq data on cancer for selection and analysis in specific contexts of experimentation. This chapter provides insights into the applicability, data availability, tools, and statistics for a beginner to get familiar with RNA-Seq data analysis and TCGA

MTar: a computational microRNA target prediction architecture for human transcriptome

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.</p> <p>Results</p> <p>We present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.</p> <p>Conclusion</p> <p>MTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.</p

Cardamonin Attenuates Experimental Colitis and Associated Colorectal Cancer

Cardamonin is a naturally occurring chalcone, majorly from the Zingiberaceae family, which includes a wide range of spices from India. Herein, we investigated the anti-inflammatory property of cardamonin using different in vitro and in vivo systems. In RAW 264.7 cells, treatment with cardamonin showed a reduced nitrous oxide production without affecting the cell viability and decreased the expression of iNOS, TNF-α, and IL-6, and inhibited NF-kB signaling which emphasizes the role of cardamonin as an anti-inflammatory molecule. In a mouse model of dextran sodium sulfate (DSS)-induced colitis, cardamonin treatment protected the mice from colitis. Subsequently, we evaluated the therapeutic potential of this chalcone in a colitis-associated colon cancer model. We performed microRNA profiling in the different groups and observed that cardamonin modulates miRNA expression, thereby inhibiting tumor formation. Together, our findings indicate that cardamonin has the potential to be considered for future therapy against colorectal cancer