Before you start the alignment and analysis processes, it us useful to perform some initial quality checks on your raw data. Here we will assume you have data from GSAF's Illumina HiSeq sequencer.
When following along here, please start an idev session for running any example commands:
Illumina sequence data format (FASTQ)
GSAF gives you paired end sequencing data in two matching fastq format files, contining reads for each end sequenced -- for example Sample_ABC_L005_R1.cat.fastq and Sample_ABC_L005_R2.cat.fastq. Each read end sequenced is representd by a 4-line entry in the fastq file.
A 4-line fastq file entry looks like this:
- Line 1 is the read identifier, which describes the machine, flowcell, cluster, grid coordinate, end and barcode for the read. Except for the barcode information, read identifiers will be identical for corresponding entries in the R1 and R2 fastq files.
- Line 2 is the sequence reported by the machine.
- Line 3 is always '+' from GSAF (it can optionally include a sequence description)
- Line 4 is a string of Ascii-encoded base quality scores, one character per base in the sequence. For each base, an integer quality score = -10 log(probabilty base is wrong) is calculated, then added to 33 to make a number in the Ascii printable character range.
See the Wikipedia FASTQ format page for more information.
Exercise: Examine the 2nd sequence in a FASTQ file
What is the 2nd sequence in the file /corral-repl/utexas/BioITeam/ngs_course/intro_to_mapping/data/SRR030257_1.fastq?
Use the head command.
Executing the command above reports that the 2nd sequence has ID = @SRR030257.2 HWI-EAS_4_PE-FC20GCB:6:1:407:767/1, and the sequence TAAGCCAGTCGCCATGGAATATCTGCTTTATTTAGC
One of the first thing to check is that your fastq files are the same length, and that length is evenly divisible by four. The wc command (word count) using the -l switch to tell it to count lines, not words, is perfect for this:
Exercise: Counting FASTQ file lines
How many sequences are in the FASTQ file above?
The wc -l command says there are 15200720 lines. FASTQ files have 4 lines per sequence, so the file has 15,200,720/4 or 3,800,180 sequences.
What if your fastq file has been compressed, for example by gzip? You can still count the lines, and you don't have to uncompress the file to do it:
Here you use gunzip -c to write decompressed data to standard output (-c means "to console", and leaves the original .gz file untouched). You then pipe that output to wc -l to get the line count.
Exercise: Counting compressed FASTQ lines
How many sequences are in the compressed FASTQ file above?
The wc -l command says there are 2368720 lines so the file has 2,368,720/4 or 592,180 sequences.
The bash shell has a really strange syntax for arithmetic: it uses a double-parenthesis operator. Go figure.
FASTQ Quality Assurance tools
The first order of business after receiving sequencing data should be to check your data quality. This often-overlooked step helps guide the manner in which you process the data, and can prevent many headaches.
FastQC is a tool that produces a quality analysis report on FASTQ files.
- FastQC report for a good Illumina dataset
- FastQC report for a bad Illumina dataset
- Online documentation for each FastQC report
First and foremost, the FastQC "Summary" should generally be ignored. Its "grading scale" (green - good, yellow - warning, red - failed) incorporates assumptions for a particular kind of experiment, and is not applicable to most real-world data. Instead, look through the individual reports and evaluate them according to your experiment type.
The FastQC reports I find most useful are:
- The Per base sequence quality report, which can help you decide if sequence trimming is needed before alignment.
- The Sequence Duplication Levels report, which helps you evaluate library enrichment / complexity. But note that different experiment types are expected to have vastly different duplication profiles.
- The Overrepresented Sequences report, which helps evaluate adapter contamination.
- For many of its reports, FastQC analyzes only the first 200,000 sequences in order to keep processing and memory requirements down.
- Some of FastQC's graphs have a 1-100 vertical scale that is tricky to interpret. The 100 is a relative marker for the rest of the graph. For example, sequence duplication levels are relative to the number of unique sequences,
FastQC is not currently available from the TACC module system, but the command-line version has been installed in the $BI/bin/FastQC directory (downloaded from the Babraham Bioinformatics web site; interactive GUI versions are also available for Windows and Macintosh).
FastQC creates a sub-directory for each analyzed FASTQ file, so we should copy the file we want to look at locally first. Here's how to run FastQC using the version we installed:
Exercise: FastQC results
What did FastQC create?
The Sample_Yeast_L005_R1.cat.fastq.gz file is what we analyzed, so FastQC created the other two items. Sample_Yeast_L005_R1.cat_fastqc is a directory (the "d" in "drwxrwxr-x"), so use ls Sample_Yeast_L005_R1.cat_fastqc to see what's in it. Sample_Yeast_L005_R1.cat_fastqc.zip is just a Zipped (compressed) version of the whole directory.
Looking at FastQC output
You can't run a web browser directly from your "dumb terminal" command line environment. The FastQC results have to be placed where a web browser can access them. We put a copy at this URL:
Exercise: Should we trim this data?
Based on this FastQC output, should we trim this data?
The Per base sequence quality report does not look good. The data should probably be trimmed (to 40 or 50 bp) before alignment.
The samstat program can also produce a quality report for FASTQ files, and it can also report on aligned sequences in a BAM file.
Again, this program is not available through the TACC module system but is available in our $BI/bin directory (which is on your $PATH because of our common profile). You should be able just to type samstat and see some documentation.
Running samstat on FASTQ files
This produces a file named SRR030257_1.fastq.html which you need to view in a web browser. We put a copy at this URL:
Running samstat on BAM files
This produces a file named yeast_chip_sort.bam.html which you need to view in a web browser. We put a copy at this URL:
Note that by default, samstat only considers mapped reads for BAM files, although this behavior can be changed by piping the subset of reads you want analyzed to samstat from a samtools view command.
FASTQ Manipulation Tools
Trimming low quality bases
Low quality base reads from the sequencer can cause an otherwise mappable sequence not to align. There are a number of open source tools that can trim off 3' bases and produce a FASTQ file of the trimmed reads to use as input to the alignment program.
The FASTX-Toolkit provides a set of command line tools for manipulating fasta and fastq files. The available modules are described on their website. They include a fast fastx_trimmer utility for trimming fastq sequences (and quality score strings) before alignment.
FASTX-Toolkit is available via the TACC module system.
Here's an example of how to run fastx_trimmer to trim all input sequences down to 50 bases. By default the program reads its input data from standard input and writes trimmed sequences to standard output:
- The -l 50 option says that base 50 should be the last base (i.e., trim down to 50 bases)
- the -Q 33 option specifies how base qualities on the 4th line of each fastq entry are encoded. The FASTX toolkit is an older program, written in the time when Illumina base qualities were encoded differently. These days Illumina base qualities follow the Sanger FASTQ standard (Phred score + 33 to make an ASCII character).
Exercise: compressing the fastx_trimmer output
How would you tell fastx_trimmer to compress (gzip) its output file?
Type fastx_trimmer -h to see program documentation
You could supply the -z option like this:
Or you could gzip the output yourself:
Exercise: fastx toolkit programs
What other fastx manipulation programs are part of the fastx toolkit?
Type fastx_ then tab to see their names
See all the programs like this:
Data from RNA-seq or other library prep methods that resulted in very short fragments can cause problems with moderately long (50-100bp) reads since the 3' end of sequence can be read through to the 3' adapter at a variable position. This 3' adapter contamination can cause the "reql" insert sequence not to align because the adapter sequence does not correspond to the bases at the 3' end of the reference genome sequence.
Unlike general fixed-length trimming (e.g. trimming 100 bp sequences to 40 or 50 bp), adapter trimming removes differing numbers of 3' bases depending on where the adapter sequence is found.
The GSAF website describes the flavaors of Illumina adapter and barcode sequence in more detail https://wikis.utexas.edu/display/GSAF/Illumina+-+all+flavors
The cutadapt program is an excellent tool for removing adapter contamination. The program is not available through TACC's module system but we've installed a copy in our $BI/bin directory.
The most common application of cutadapt is to remove adapter contamination from small RNA library sequence data, so that's what we'll show here.
Running cutadapt on small RNA library data
When you run cutadapt you give it the adapter sequence to trim, and this is different for R1 and R2 reads.
- The -m 22 option says to discard any sequence that is smaller than 22 bases after trimming. This avoids problems trying to map very short, highly ambiguous sequences.
- the -O 10 option says not to trim 3' adapter sequences unless at least the first 10 bases of the adapter are ssen at the 3' end of the read. This prevents trimming short 3' sequences that just happen by chance to match the first few adapter sequence bases.
Please refer to https://wikis.utexas.edu/display/GSAF/Illumina+-+all+flavors for Illumina library adapter layout.
The top strand, 5' to 3', of a read sequence looks like this.
The -a argument to cutadapt is documented as the "sequence of adapter that was ligated to the 3' end". So we care about the <Read 2 primer> for R1 reads, and the <Read 1 primer> for R2 reads.
The "contaminent" for adapter trimming will be the <Read 2 primer> for R1 reads. There is only one Read 2 primer:
The "contaminent" for adapter trimming will be the <Read 1 primer> for R2 reads. However, there are three different Read 1 primers, depending on library construction:
Since R2 reads are the reverse complement of R1 reads, the R2 adapter contaminent will be the RC of the Read 1 primer used.
For ChIP-seq libraries where reads come from both DNA strands, the TruSeq Read 1 primer is always used.
Since it is the RC of the Read 2 primer, its RC is just the Read 1 primer back
Therefore, for ChIP-seq libraries only one cutadapt command is needed:
For RNAseq libraries, we use the small RNA sequencing primer as the Read 1 primer.
The contaminent is then the RC of this, minus the 1st and last bases:
Flexbar provides a flexible suite of commands for demultiplexing barcoded reads and removing adapter sequences from the ends of reads.
Note that flexbar only searches for the exact sequences given (with options to allow for a given number of mismatches) not the reverse complement of those sequences therefore you must provide them yourself.
Trimmomatic offers similar options with the potential benefit that many illumina adaptor sequences are already "built-in". It is available here.