So transcriptome sequencing AKA RNA-seq can evaluate absolute transcript levels of sequenced and unsequenced organisms. The sequence are generated as short and will be mapped to the reference genome if there are any or or sample to the transcriptome if the reference genome is not available. The RNA is extracted from the cell reverse transcribed to cDNA and topped to short sequence, followed by a massive parallel sequencing. Why does it get popular among the research communities since the past few years? What's advantage of RNA-seq over microarray? What limits of the RNA-seq? And the topic of this lecture what's the standard pipeline to posses RNA seq data? So RNA-seq is a high through put or next generation sequencing method to measure the genome libo transcriptome or RNA content of the human sample. Before we start the analysis, let's first review some simple facts about RNA-seq. Let's first start with the RNA-seq analysis. I will also introduce the basic usage of mimic slash Unix commands, and software are as bioinformatics tools are built to these open source platforms. In this and the following lectures, I will guide the basic analysis of next generation sequencing step by step. I'm a PhD student at Doctor Abby Ma'ayan's lab. For those participants that do not work in the field, the course introduces the current research challenges faced in the field of computational systems biology. The ultimate aim of the course is to enable participants to utilize the methods presented in this course for analyzing their own data for their own projects. The course presents software tools developed by the Ma’ayan Laboratory () from the Icahn School of Medicine at Mount Sinai, but also other freely available data analysis and visualization tools. The course should be useful for researchers who encounter large datasets in their own research. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research.
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