A full list of Bowtie options can be found in the MANUAL file included with the Bowtie package; these examples focus on the shorter list of important options shown in Table Descriptions of important command line options for the bowtie alignment tool. Bowtie 0. A set of one or more files containing the reads. The reads used in this example are a set of simulated reads included in the Bowtie package. A set of index files containing the index of the reference genome. The index file format is unique to Bowtie, and FASTA formats are converted to this format using the bowtie-build tool discussed in Alternate Protocol 1.
The index files used for this example encode the whole genome of E. These files are included in the Bowtie package. Change to the directory containing Bowtie. The example index files are located in the indexes subdirectory, and reads are located in the reads subdirectory.
Run Bowtie on the example E. All other arguments to Bowtie are interpreted as options see Table Examine and interpret the output, also shown in Figure Sample Bowtie session.
In this and subsequent figures, the backslash character is used to indicate that some long lines are wrapped. Note that in practice, users rarely need to examine the alignment output manually. See Alternate Protocol 2 for an example.
The format for an alignment consists of 8 fields, separated by tabs. The fields, from left to right, are:. Offset of the leftmost position on the forward reference strand covered by the alignment. Read sequence aligned or its reverse complement, if the read aligned to the reverse strand. A string representing the differences between the read and the corresponding characters of the reference genome.
Each difference is described in a comma-separated field. This will produce output similar to the earlier example, except that the alignments printed to standard output will be in SAM format Fig. Sample Bowtie session when SAM output mode is enabled. The remaining lines are alignments. When aligning a large number of reads, the user will wish to capture the standard output to a file rather than to have it appear on the screen. The user can do this by redirecting standard output as shown here:.
This will align all reads contained in the E. This protocol uses the bowtie-build tool to take a collection of FASTA files for a reference genome and generate a collection of index files.
Index files can then be used by bowtie to align reads to the reference genome. The same set of index files can be used across multiple runs of bowtie. In the same directory, use gunzip to decompress the compressed FASTA files that were just downloaded:.
This builds an index consisting of the sequences in the chrX. If the bowtie-build executable is not in the search path, specify the full path to bowtie-build instead. This command typically takes about ten or fifteen minutes. The output should be similar to Figure Among other things, the output tells the user that the index is not for colorspace alignment first line , and shows the names and lengths of the two reference sequences included in the index last two lines.
Output of bowtie-inspect when inspecting the index consisting of the two human sex chromosomes. This protocol outlines how to accomplish this using the E. SAMtools 0. Align the reads to the E. If the samtools executable is not in the search path, specify the full path to samtools instead. Sorted BAM is a useful format because alignments are both compressed, which is convenient for long-term storage, and sorted, which is convenient for variant discovery and other downstream analyses. Output of the samtools consensus caller when calling SNPs from a simulated E.
Table This protocol steps through a series of examples that illustrate some of these options. The protocol uses the E. The purpose is to keep the figures as concise as possible. Align a test string to the E. The --suppress option has suppressed all output fields besides strand first column , offset second column and edit string third column. Here, bowtie finds 5 inexact hits in the E. Four are on the reverse reference strand and one is on the forward strand. Note that they are not listed in best-to-worst order.
In this case, a total of 5 valid alignments exist see Figure In this case, a total of 5 valid alignments exist see Figure 6 , so bowtie reports all 5. Leaving the reporting options at their defaults causes bowtie to report the first valid alignment it encounters. Because --best was not specified, we are not guaranteed that bowtie will report the best alignment, and in this case it does not the 1-mismatch alignment from Figure In this case, the 1-mismatch alignment is printed first, as expected.
There should be no alignment output. Because this read has 5 valid alignments see Figure Because this read has exactly 5 valid alignments, all alignments are reported. All Bowtie packages are compressed in the zip format. Optional : For convenience and compatibility with other tools that rely on Bowtie, add the extracted directory containing the bowtie and bowtie-build executables to the search path.
When the build process completes successfully, a set of binaries including bowtie, bowtie-build , and bowtie-inspect will be created in the extracted directory. Indexes are compressed in the zip format. This protocol steps through obtaining and using a pre-built index for the S. Download the pre-built index for the S. Place the downloaded file in a temporary directory. If the bowtie executable is not in the search path, specify the full path to bowtie instead.
In this case, bowtie should print one alignment. Once points of origin are identified, downstream tools use that information, for example, to characterize differences between the subject and reference genome e. Alignment programs, together with appropriate reference sequences, serve this purpose because genomes of individuals of the same species tend to be highly similar. For example, two humans typically have on the order of 3—4 million single-nucleotide differences between them out of a total of 3 billion bases.
However comparative strategies also have inherent drawbacks that should be kept in mind when interpreting Bowtie results. Some genomes, including the human genome, have substantial repetitive content, i. Repeats come in several forms e.
Repeats also affect alignments because reads originating from repetitive portions of the genome are difficult or impossible to unambiguously assign to a point of origin. Paired-end reads mitigate but do not necessarily eliminate this problem.
Repetitive alignments in turn affect downstream analyses. For instance, if ambiguous alignments are included in the output from Bowtie, a SNP could yield false positives and false negatives purely owing to the repeat structure.
Take a look at your output directory using ls bowtie2 to see what new files have appeared. These files are binary files, so looking at them with head or tail isn't instructive and can cause issues with your terminal.
If you insist on looking at them and your terminal begins behaving oddly, simply close it and log back into lonestar with a new terminal. Why do so many different mapping programs create an index as a first step you may be wondering? Like an index for a book in the olden days before Kindles and Nooks , creating an index for a computer database allows quick access to any "record" given a short "key".
In the case of mapping programs, creating an index for a reference sequence allows it to more rapidly place a read on that sequence at a location where it knows at least a piece of the read matches perfectly or with only a few mismatches. By jumping right to these spots in the genome, rather than trying to fully align the read to every place in the genome, it saves a ton of time.
Indexing is a separate step in running most mapping programs because it can take a LONG time if you are indexing a very large genome like our own overly complicated human genome. Furthermore, you only need to index a genome sequence once, no matter how many samples you want to map. Keeping it as a separate step means that you can skip it later when you want to align a new sample to the same reference sequence.
Try reading the help to figure out how to run the command yourself. This is longer than we want to run a job on the head node especially when all of us are doing it at once. In fact, TACC noticed the spike in usage last time we taught the class and we got in trouble. But first, try to figure out the command and start it in interactive mode. Remember these are paired-end reads. Use control-c to stop the job once you are sure it is running without an immediate error! Then, submit your command that is working to the TACC queue.
Your final output file is in SAM format. It's just a text file, so you can peek at it and see what it's like inside. Two warnings though:. Still, you should recognize some of the information on a line in a SAM file from the input FASTQ, and some of the other information is relatively straightforward to understand, like the position where the read mapped.
Give this a try:. We have actually massively under-utilized Lonestar in this example. We submitted a job that reserved a single node on the cluster, but that node has 12 processors. Bowtie was only using one of those processors a single "thread"! For programs that support multithreaded execution and most mappers do because they are obsessed with speed we could have sped things up by using all 12 processors for the bowtie process. You need to use the -p , for "processors" option.
Since we had 12 processors available to our job. One consequence of using multithreading that might be confusing is that the aligned reads might appear in your output SAM file in a different order than they were in the input FASTQ. This happens because small sets of reads get continuously packaged, "sent" to the different processors, and whichever set "returns" fastest is written first.
You can force them to appear in the same order at a slight cost in speed by adding the --reorder flag to your command, but is typically only necessary if the reads are already ordered or you intend to do some comparison between the input and output.
In the bowtie2 example, we mapped in --local mode. Try mapping in --end-to-end mode aka global mode. The next steps are often to view the output using a specific viewer on your local machine, or to begin identifying variant locations where the reads differ from the reference sequence.
These will be the next things we cover in the course. Here is a link to help you return to the GVA course schedule. Pages Blog. Space shortcuts File lists How-to articles. Page tree. Browse pages. A t tachments 0 Page History. Hide Inline Comments. Jira links. Created by Daniel Edward Deatherage , last modified on May 27, Other read mappers Previous versions of this class and tutorial have covered using bowtie and bwa.
If you need a little help but don't want the answer yet, click the triangle Remember that to copy an entire folder requires the use of the recursive -r option. Still stuck? Beware the cat command when working with NGS data NGS data can be quite large, a single lane of an Illumina Hi-Seq run generates 2 files each with s of millions of lines.
How to count the total number of lines in a file Expand source. How to determine the total number of sequences in a fastq file Expand source. How to determine how long the reads are in a fastq file Expand source. Click here for a hint.
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