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- #E TRANSCRIPT BUNDLE VIEWER FOR MAC FULL#
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In particular, TopHat and Cufflinks require a sequenced genome (see below for references to tools that can be used without a reference genome). TopHat and Cufflinks do not address all applications of RNA-seq, nor are they the only tools for RNA-seq analysis. All tools used in the protocol are fully documented on the web, actively maintained by a team of developers and adopt well-accepted data storage and transfer standards.
#E TRANSCRIPT BUNDLE VIEWER FOR MAC SOFTWARE#
Figure 1 shows the software used in this protocol and highlights the main functions of each tool.
CummeRbund renders Cuffdiff output in publication-ready figures and plots. These tools are gaining wide acceptance and have been used in a number of recent high-resolution transcriptome studies 14– 17. Cuffdiff, a part of the Cufflinks package, takes the aligned reads from two or more conditions and reports genes and transcripts that are differentially expressed using a rigorous statistical analysis. Cufflinks 8 ( ) uses this map against the genome to assemble the reads into transcripts. These alignments are used during downstream analysis in several ways.
TopHat 13 ( ) aligns reads to the genome and discovers transcript splice sites. We have developed two popular tools that together serve all three roles, as well as a newer tool for visualizing analysis results.
RNA-seq analysis tools generally fall into three categories: (i) those for read alignment (ii) those for transcript assembly or genome annotation and (iii) those for transcript and gene quantification. Fortunately, the bioinformatics community has been hard at work developing mathematics, statistics and computer science for RNA-seq and building these ideas into software tools (for a recent review of analysis concepts and software packages see Garber et al. RNA-seq experiments must be analyzed with robust, efficient and statistically principled algorithms. Furthermore, because the number of reads produced from an RNA transcript is a function of that transcript's abundance, read density can be used to measure transcript 7, 8 and gene 2, 3, 9, 10 expression with comparable or superior accuracy to expression microarrays 1, 11.
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Just as cDNA sequencing with Sanger sequencers drastically expanded our catalog of known human genes 5, RNA-seq reveals the full repertoire of alternative splice isoforms in our transcriptome and sheds light on the rarest and most cell- and context-specific transcripts 6. Although the volume of data from RNA-seq experiments is often burdensome, it can provide enormous insight. Moreover, sequencing costs are reducing exponentially, opening the door to affordable personalized sequencing and inviting comparisons with commodity computing and its impact on society 4. However, even small RNA-seq experiments involving only a single sample produce enormous volumes of raw sequencing reads-current instruments generate more than 500 gigabases in a single run. High-throughput mRNA sequencing (RNA-seq) offers the ability to discover new genes and transcripts and measure transcript expression in a single assay 1– 3. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ~1 h of hands-on time. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results.
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This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay.
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