Introduction

High-throughput sequencing of cDNA, also called RNA-Seq, can provide many levels of information about gene expression, such as information about previously unannotated genes, expression of pseudogenes,  differential expression both within and across samples, and alternative splicing.

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What follows is a description of RNA-Seq processing steps we do fairly routinely in the Loraine lab, along with links to software programs we've developed for in-house use. Please be aware that these programs are very much works in progress and so may not always work as advertised. If you find bugs or inconsistencies, please let us know - contact Ann (aloraine@uncc.edu) with feedback, suggestions, and bug reports.

RNA-Seq tutorial

The following protocol describes processing data from Illumina HiSeq pipeline. Whether you should perform exactly these steps with your data sets will depend on your data - its age, whether you've done single-end or paired-end sequencing, the read lengths, your reference genome, and so on. For example, when you run TopHat, you may need to adjust parameters to accommodate smaller read lengths if you are running data from pre-HiSeq instruments.

Check sequence quality.

Your first step upon downloading your reads will be to check the quality of your sequence. One of the easiest to use tools we (in the Loraine lab) have found for quality checking is a terrific program called FastQC, from the Babraham Institute. You can run it interactively or as a command line tool, and it's written in Java, which means you can run it on a Mac, a Linux machine, or a Windows computer. Like IGB, any computer that supports Java can run FastQC.

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In our experience, low yields and low quality generally happen because something went wrong with sequence. Over-represented sequences and so-called PCR duplicates (many copies of the same sequence) are usually due to problems in library construction.

Align your sequence.

Let's assume that (happily) you have good-quality sequence. Your next step should be to align your sequences onto a reference genome or refernece transcriptome. Indeed, even if you haven't got high-quality sequence, you should still try to align it, because the alignments can tell you a lot about what went wrong.

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IGB has a synonyms system that allows it to "understand" that chr1 and Chr1 are really the same thing, but other programs you will run into won't be smart enough to match up names in this way. For this reason, it's a good idea to use the same names for chromosome throughout all the different steps of processing data.

Process output files

Index (and rename) your alignment file

When TopHat finishes, it will have created a file called "accepted_hits.bam" that contains your alignments. Your next step in the process will be to index this file, creating what's called a "bai" index file. For this, you'll use samtools, a freely available command-line tool with many useful functions. You can learn about samtools at this Web site and download the code from this page hosted at sourceforge.net.

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which will create an index file named Sample.bam.bai.

Make a bedgraph (wiggle) file

Older versions of TopHat used to create a depth (also called coverage) file as part of the TopHat processing step. However, more recent versions of Tophat no longer do this. The program TopHat used to make the coverage graph file was called wiggles; we obtained a copy and made a few modifications to it. You can obtain compiled versions from this wiki page (click "Attachments") or from our subversion repository.

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For details on how to do this, see the section titled Not done yet -- using tabix to create a sorted, random-access bedgraph file in Creating a new genome release for IGB QuickLoad - an example from Zea mays.

View data in IGB

Once you've created the files, you should then be able to open them in IGB. However, be sure to keep the index files (.bai from samtools and .tbi from tabix) in the same folder with their corresponding alignments or bedgraph files.

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