BED, GFF, GTF, and GFF3 formats

GFF, GTF, GFF3 & BED files are all file formats that are used to store annotation (features) generally without containing any sequence. Although it is common that they will be accompanied by a fasta file containing the sequence only. They emerged as a way of exporting, or exchanging, information from a specified region of an entire genome without having to take the entire genome.

Most sequence formats were developed to be for a specific gene or protein. Although this is no longer true they are still orientated to be of a region of fixed length. These annotation files are not at all length specific and could potentially store just two features that were at either end of the same chromosome. They are a much more flexible way of dealing with annotation, especially a large amount, than a fixed length sequence format such as Genbank.

They also are not limited to a single sequence and can contain information from multiple sequences in the same file (Fasta files can also contain multiple sequences). For example you could store the entire human set of chromosomes in a pair of (quite large!) files. A multiple sequence Fasta file and a single GFF file.

The format of these annotation files does vary (who ever said Bioinformaticians had to be consistent!) but basically their format consists of a set of individual lines (one line per feature) along the following lines:

SEQUENCE ID, START, STOP, FEATURE TYPE, NOTE

Sequence ID is the sequence these annotations belong to.

START and STOP are the region of sequence they are annotated against

FEATURE TYPE is obvious! Note that this does not always correspond to a correct Genbank Feature Keyword

Genome Browsers

These tools (generally online web gateways) allow you to browse the entire chromosome or genome of a particular organism. Almost like a graphical model of a sequence database. All the information known about that particular organism’s sequence that has been submitted to one of the large sequence databases (e.g. Genbank at the NCBI) should be visualised within the genome browser. You can download all the annotation contained within a particular region fairly easily using one of these annotation formats. Then you can either annotate an existing file that you are working with (so preserving your own “private” annotation with all known public annotation).

To annotate a sequence with a BED/GFF/GFF3/GFT file in MacVector

From the UCSC’s Genome browser

The interface will change and show all annotation associated with that region. You can modify the amount or type of annotation being showed. This particular gene, C.elegans Sel-12 is located on Chromosome X

This will now allow you to export all the annotation associated with the previous displayed region (tracks).

If it is left at genome the entire genome will be downloaded

Now you need to open the sequence you want to annotate. For this example we could go to DATABASE > ENTREZ and search for and download Accession Number U35660. However, that only contains the mRNA and not the genomic sequence. So instead download the fasta sequence from the NCBI

Sequence : Chromosome: X; NC_003284.7 (915873..918235)

You will need to ensure that the start position of the downloaded file corresponds to the start of the region of the chromosome we have just downloaded the annotation for. This is easily done in MacVector.

Now we will import our downloaded annotation.

The Sequence ID (SeqID) contained within all features in the file will be shown in a dialog along with the number of features for each SeqID and the region of the sequence that these will be annotated against. A warning will be displayed if any of the features are outside of the region to be annotated.

A dialogue will be shown with the number of annotations that have been added. If you annotate a blank sequence (e.g. a fasta file) the resulting features may be initially hidden. However, you can easily show them from the Graphics Palette tree view.

You can choose to annotate your sequence with all the features contained within the imported file or to ignore duplicates.

ToxoDB Genome browser

Here’s a similar workflow from the ToxoDB Genome browser

Toxoplasma gondii ME49

"CCTTCCCTGCGTCAGAGGAGAAGAGAACGGCTTGACCGATGGAGGACCCCGCAAACATGAGGGCGAAGGTAGTCTGCATGATCTCTGAACAAGGAACACGGCGCGGAAAGGGAAGCACAGAAGGAAGTCGATCAAGACACCCTGCGTTGTTTTTCGGGGAGCCCCAGAGAGGGAGCTCGCGGCTCTGGACTTCAAGGTCCGTCGAGCAGCAGAACGCTTCACTCGGCAAGGAAGGAGCAGTTTCTTCTCTCGCGTCTTGTTTCGCTTTCACGGCTTCGTTTTCTCGCCGCGACCTGCGAAAAGAAAACAGCTCCCCTATAAGAACTCGACTCTCGAGCCTGCGGTTTGGTATCGGCTTTTTCTTCAGAGTTTTTTCTGTCGCGCGTTCGGACAACCAGTTCCGTGCTTGCGCGCCTCCTCTGAAGGCCGCGCCCGCCTCTCGACTCCCGTCGCTTCTCTCTTCGGCTGGATAAGAGAAAACGCTGAACGAAGAGGAGAGTACGCACTGGCATCGTTTGTCGACTTTCGTCTCCAGGTGGGGGAGTGTCGGTCGACTCACCCAAGGGATTCTTCCCTTCGCTGCTCACGATCTGGCCGCCATACCAAAAAATCAGCGCCTGCAGAGCGTACTGAGCTCCCTGACAGACACGCAGACGCAGCGGCAAGGAGACGCTGAAAAGAAGAAAGACAACCGGAGAGCGCGGAGAACAAAGAACTGTGAGCGTGCAACGACGGGATCAAGGACGACAGCGAATCTCCCGTCTTCAGGACCTCGACGGGCATTCCGCATGGCAGGTCCTTTGACTCCGAAAAACTCTGCGGCAGCCTCGATGACCCTTACCCCCCCGGAACATCCCCGAGAGCTCGGTGGAAAAAACCTCTCATTCGAGAGCGACAGATCAGGCTTTGCTAGTCGAGCCAGAAGGCAGGAGAAGGAACGGAGCGAACCGCGGATGCGTCTCTCTGCGCGGACGAGTCTTCATGAGCAGGCACCGCGACGTTCCAAGAAGCAGAAAGAGAGAGAGGAGAGAGGAGAGAAGCGAGAAGCTCGGGAACTCACGAGGAAGCAACAAAGATCTCTCCTCGTCACTCACGTTCCGTCGACCTGCATGGCAGGCGTGACGCGGCATGCACAGCAGAAGACCTTTCGAGGTCACCACACACACCGCCTCGGACGTCGAGAAGTCTCGATCTTTGTGAACCACAGGGCTCTGTTTTGTGTGGCGGAACGAAGAAACCAAGCGCTTAGGATGGAGCTCACTGGAGAGCAGGAAACGGATCTTCAAACGAGTGTCGACGTCCTCCCGCGCATCCGAACCGAAACTCAAACGCGCTCCAGAGAGACGACATAGAAGACAGAGACGTACAATGAGAGAAGAAGAGACAACGCGGCAGGGGGAGTCTGACGTCCGACCTCGACTCGAGAAGTCGCTCGCCAAAACGTGTGTGCAGTGTCTTCTGTTTCTTTCCAAGTTCTCCAGTCCGAAGAAACCGGACACTCTGACATGACTCGATACAGGGACCTGCCCGCCGACTCTTTCCTACTTTCAGCGGTCCTCCCTGTTCATCTTTCCTGTGACATTTCGGCATCTCTTTTTCTTGGTTTCCTCGCCTTCTCACCTGACTGAAGCCCCAGAAAAAGCCGAGGAGAGCCGCAGCGCGCTCTTCTTCCTTCAGCGTCCTCAGAAGAACGCTCTGGTACCGTTCCGTGAAGTGAGGCTCTAAACCGAACGCTGAAACAATGCGAATACCGTTCAGAGCCTCGCTCATCACGAAGGCAGCGGTGTCGCGGTCCTCCACCTTCTCCGCCTTCTTGTTCGCCCCCTCACCTGCAGAAAAAACTCCAAGGTTTCCAAAGCCTCGTGAGGCTCCCCATGAAGCTCTCCGCCTACGCTTGCGCAGATTGAGGCAGAGCAAACTACGCAAATGTGAGCCTACATGTACACACAGTTTCGTCGAGATTTGTACCTATATCTAAGAAGATTTGTACGGAAATGCGGGTGTGAAGCGGCAGTTTTCGAGGTGGCGTGCATACATCGACGCGACTCGGAGACCCAGCTTTGAGGAGACAGGAGAGAGAAAAGGAAACGGAGATAGAGCAGGTGGGGAGATCAGGTTTGCTCTGGGAGACGTGGACGGTCGCAGACGAAGAAGCAGACGCACGGAGCGAGCAGTGCAAAAAAGCGCGAGACAGAGCCGGCGCTGGGGAACTTCTGAGGAAGAATTCGAGAGAGAGAGGACCGTGGTGAAAAGCCAAGCTAAACGCGTGGACTTCCAGTTCTGCGGACTTTTCGGAGCCGAAATGTGAGACTGAGCGGCAGTGGCGGGGAGCAGAAGAACAAAACATGCGAGGACCACGGCGGCCAAGCGCGCGTCTCCAAAGAACGCGATGATGACACCTGAAATAAAGATAATGGAAAAAACAGAAGAAAAGCAACGGTCTCTCGCGCAGTCTTCAATCACGAGTTGACGCACACACGCATTAGGGGAGAACAACCTCTGCGATGTTGGATGCTTCTTTAGTGAGTGGACGTTTCCAGAAATCAAATCAAAGTAGCACGACACCGACAAGCAAAGAGATGTATACTTTCGTTCACGAGCACTGAAAGACGCGTCTAGACGCCTACGGATGCAGAGGGATCTGAAGCGACGAGTGAACAGTCAAAAAGCTTTCCGGCCTACCAGTGACAACAGCAGCCAATCCCTGGGTCATCGCGAGTGCGTTTCCAGCGCTTCCTGTCTTGACGAGAAGGACGTCGCTGGAGAGAACTCCCGTGAGATATCCTGAGGTCATTTTTTAGAAGAAGCGAACACTCTGGCGCGGCGTCTTCACTCTCGTGCTCACAGAAAGAATGAACTCACGAGACCCATGGCAGGACAAGTCTCAGACAGACACACAC"

This will find a single hit and display a link

You can use FILE > NEW FROM CLIPBOARD to bring the sequence into MacVector quickly

Again if you do not change the start coordinate of the sequence the IMPORT FEATURES dialogue will show an error

The dialogue will show that it has found 6 features. Note that the GFF3 file contains annotation from a much longer sequence than our initial query sequence. MacVector will ignore any annotations that lies outside the query sequence. It will show an warning message to indicate this.

Keeping a sequence updated

You will be able to “update” and add new annotation to your existing sequence. For example after a few months I could revisit these two genome browsers website and download an updated GFF3 file. Upon importing these features it will optionally replace any duplicate features and add new ones. So you can work with a sequence and also keep it updated as other researchers find more about this particular sequence.

Duplicate Features

Due to the lack of strict standards across the many different file formats it may be that a potential duplicate is not recognised as such because the wording or keyword is different. In the majority of cases some degree of manual curation of the annotated sequence will be required. In all cases MacVector will err on the side of caution and will never throw away any potentially interesting or important information contained within a feature. Only entries that are 100% the same (after being parsed during the import) will be considered as duplicates. MacVector will never class a feature as a duplicate if the START, STOP or FEATURE TYPE are different in any way. Even if they differ by just a single base.

When: Thursday February 9th 9:30 to 11:00
Where: Blockley Hall, Room 1311

“Dr Kevin Kendall will present an informal 90 minute workshop for both beginners and long-time users of MacVector.The workshop will review how to use the basic functions and features in MacVector, as well as covering more advanced workflows and new functionality in the latest releases, MacVector 12.0 and 12.5. He will be happy to answer any questions about MacVector that you may have, as well as previewing the functionality planned for MacVector 12.6.”

Light refreshments will be provided.

Please register for the workshop on the University Bioinformatics Group website

Technorati Tags: MacVector

When: Wednesday February 8 from 11:00 to 12:30
Where: Room RRL B20

“Dr Kevin Kendall will present an informal 90 minute workshop for both beginners and long-time users of MacVector.The workshop will review how to use the basic functions and features in MacVector, as well as covering more advanced workflows and new functionality in the latest releases, MacVector 12.0 and 12.5. He will be happy to answer any questions about MacVector that you may have, as well as previewing the functionality planned for MacVector 12.6.”

Technorati Tags: MacVector

When: Tuesday. Feb. 7, 2012 at 10:00 - 11:30 and 1:00 - 2:30
Where: Rockefeller, Weiss 302

“Dr Kevin Kendall will present two informal 90 minute workshop for both beginners and long-time users of MacVector.The workshop will review how to use the basic functions and features in MacVector, as well as covering more advanced workflows and new functionality in the latest releases, MacVector 12.0 and 12.5. He will be happy to answer any questions about MacVector that you may have, as well as previewing the functionality planned for MacVector 12.6.”

Light refreshments will be provided.

Please register for the workshop.

Technorati Tags: MacVector

The first one, which we recommend all new users to read, is the short “Getting Started with MacVector 11″ guide. This document is geared for those who already use MacVector and introduces you to the new functions and features from MacVector 9 onwards. It is still useful if you are not very familiar with any version of MacVector.

Down toward the bottom of the page you will also find some tutorials on specific tasks. For example here’s a great one on Auto Annotation.

We also have some short screencasts available on this page of our website:

You can also visit the MacVector forums where we welcome discussion. There’s some useful tips on there, and news of any updates we release:

You can also subscribe to our RSS feed to be kept updated on all MacVector news or follow us on Twitter.

Finally there’s a full PDF copy of our manual

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MacVector

Using the Find dialog

For quickly finding a single primer in a sequence the Find dialog is the first point of call. This allows you to find any sequence, whether it binds to the complementary strand, it is reversed or both. It also allows you to state which end of the sequence to start from and in which direction to scan the sequence (not necessarily obvious!). The Find dialog (see below) is a little daunting to look at first (in fact due to recent user feedback we will hiding most of the functionality in the next release). However, it is very powerful and in most cases you do not need to change anything as the default settings will work for the majority of cases.

To use find:

(i) open up your sequence and use the menu option EDIT > FIND or use the key combination CMD – F

(ii) Enter your primer sequence in the FIND box and click FIND.

When to use: When you need to find a single primer very quickly and do not need to store the results

Benefits: quick and easy to use

Limitations: You can only find a single primer. Only perfect matches are allowed. No primer statistics.

Align to Reference

If you want to quickly align a large set of primers against a template sequence, then as long as each primer is in a separate MacVector file, or a multi sequence fasta file then you can use Sequence Confirmation in the Align to Reference function:

(i) Open the template file and go to ANALYZE > ALIGN TO REFERENCE

(ii) Import your primer sequences.

(iii) Click on ALIGN

(iv) Change the drop down menu to Sequence Confirmation then change these parameters:

- MATCH to 5.

- SENSITIVITY to 10.

- If you suspect your primers may not be a perfect match then reduce SCORE THRESHOLD until your primer aligns.

The resulting alignment will show your primers aligned against the template. You can switch to the Map view to show a graphical overview of all your primers and where they are located on the sequence. You can use the Editor view for a sequence level representation of the primer aligned against the template.

When to use: When you need to find many primers over a large sequence

Benefits: Easy to visualize a great quantity of primers against a template

Limitations: Your primer sequences need to be in a file already. No primer statistics.

Analyze Primer->Test PCR Primer Pair..

This function is fairly easy to use and gives a large amount of detail about your primers. For example secondary structure, what size the product will be and even the most ideal Tm for your PCR run.

(i) Open your sequence and go to PRIMERS > Test PCR Primer Pairs

(ii) Copy and paste your two sequences in the two boxes. Note you will need to have pasted them into an external application.

(iii) Click APPLY and see your primers detailed statistics.

(iv) Click OK to see the full statistics on the primers and product.

Primer pair details:
	Major product size: 538 bp

Product details:
[  1] primer 1: score 20, mismatches 0, upper strand 1055 to 1074
				Tm: 51.4 deg C (from target sequence)
				The 3' end of the primer binds within the product
	  primer 2: score 20, mismatches 0, lower strand 1592 to 1573
				Tm: 51.7 deg C (from target sequence)
				The 3' end of the primer binds within the product
	  Tm difference of pair: 0.3 deg C
	  Product:  538 bp (1055 to 1592)
				Optimal annealing temp:   55.0,
				pct G+C:   46.7          	Tm:   77.8 deg C

       5'  -GGTCCACTTCGTATGCTGGT- 3' (primer 1)
            ||||||||||||||||||||
  1055 5'-..GGTCCACTTCGTATGCTGGT..<-   498bp ->..
       3'-..CCAGGTGAAGCATACGACCA..             ..

                         ..CATCACCTTTGGGCTTGTTT..   1592
                         ..GTAGTGGAAACCCGAACAAA..
                           ||||||||||||||||||||
                       3' -GTAGTGGAAACCCGAACAAA- 5' (primer 2)

When to use: When you need detailed output about a pair of primers and their product. Including recommended Tm for the annealing stage of the PCR.

Benefits: highly detailed output about primers and product

Limitations: You can only analyze a pair of primers.

Nucleic Acid Subsequence search

Subsequence searching allows you to find any significant region with a consensus sequence, in your sequence. This function allows you to keep a library of sequence patterns of either nucleic acid or proteins. You can use subsequences with complex patterns for the search as this function uses a powerful nomenclature (similar to Prosite’s) for creating patterns. Furthermore each pattern can have up to three distinct segments, separated by variable inter-segment regions, and you can control the overall similarity required for a match as well as defining residues which must be 100% conserved.

A common usage of this function is storing a library of primers.

You can easily create such a library by creating a CSV file of your primers. Then you can use the Primer Convertor tool to convert this CSV file into a subsequence file. Primer Convertor is supplied with MacVector, however, you can download an updated version of this utility from here.

Once you have created this file then use these steps to find primers in any sequence:

(i) Open up your sequence

(ii) Select ANALYZE.. > SUBSEQUENCE.

(iii) Choose your subsequence file and click OK.

When to use: When you have a need to repeat the same set of primers multiple times against different sequences

Benefits: Easy way to perform regular analyses

Limitations: You need to prepare a subsequence file containing all your primers.

Design Primers (Primer3)

Currently Design Primers (Primer3) is not very flexible when it comes to testing primers as opposed to designing them. There are no preset defaults for testing primers. However, it is a powerful tool and will uniquely allow you to design a new primer to match an existing primer. To use this tool you need to change the default settings which are meant to design a pair of primers to amplify the selected sequence.

If you just want to test a pair of primers and you have no other criteria:

(i) Select ANALYZE > PRIMERS > DESIGN PRIMERS (PRIMER3)

(ii) Change the drop down menus of each primer field to USE THIS PRIMER and paste in your primers

(iii) Select REGION TO SCAN from the design method drop-down list.

(iv) Change area to scan to be the full length of your sequence.

(v) Ensure that the REGION TO SCAN values entirely encompasses the area that contains the expected product (easiest way is to select the entire sequence).

(vi) Ensure that the PRODUCT SIZE limits are above and below the expected product size (TIP: make them generously above and below).

If you do have a specific product in mind, then:

(i) Select the feature that you want to amplify.

(ii) Select ANALYZE > PRIMERS > DESIGN PRIMERS (PRIMER3).

(iii) Change the drop down menus of each primer field to USE THIS PRIMER and paste in your primers.

or

(iii) If you want to design a primer to match an existing primer change the drop down menu of your existing primer field to USE THIS PRIMER and paste in your primer. Leave the other drop down menu to FIND PRIMER.

(iv) If you know your primers are either 200bp upstream or downstream from the 5′/3′ end of your feature then keep AMPLIFY FEATURE otherwise select FLANKING REGIONS from the design method drop-down list and enter a large value for each region.

The above steps are more complex than they should be. However, most settings are preserved between runs, and the next time you run it the settings will already be correct.

When to use: When you need to easily visualize a pair of matching primers and their product

Benefits: nice visualisation of primers and product

Limitations: You can only analyze a pair of primers. Some of the output is limited for example you will not find the recommended Tm.

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Many labs have collections of commonly used primers, and one popular use of subsequence searching is to store these primers in a subsequence library. This makes it a simple procedure to scan a sequence with the entire lab’s primer library.

It is fairly easy to create a single subsequence. However, if you have many it is time consuming to do this manually. So we have an application called PrimerConverter (that is included with MacVector) that is designed specifically for batch conversion of many primer sequences. All you need is a comma delimited text file of your primer sequences. The CSV file needs to be in the following format:

<name>, <sequence> (, <optional comment>)

So a sample file might look like:

Primer 1, AGCTGGATCGATCGATCGTAGCT, My primer 1 comment
Primer 2, TTCGGGCTAGGCTAGCTAGGGC
Another primer, AAAGCTAGCTAGCTAG, this is the last one

Open this file with PrimerConverter and then save it as a MacVector Subsequence file. The application is included with MacVector, however, you can download an updated version of this utility from here.

As well as indicating the number of mismatches allowed Subsequence searching also allows you to choose which residue of a match needs to match perfectly. For the CSV file you can set residues to be lower case to indicate they don’t have to be perfect matches.

For example the following CSV file input:

Primer_example, AGCTGGAtCGAtcgaTCGTAGCT, primer with five mismatches allowed.

Will produce the following subsequence

Make sure that the Allowed Mismatch field is set appropriately (the default will be to allow only the characters that do not need to match perfectly). You must be using the above release of PrimerConvertor to do this.

Here’s an example done with eight primers against the template

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MacVector

First, we added pseudo-Restriction Enzyme sites representing the TOPO and att recognition sequences to the Common Enzymes file. This is the file containing the default set of enzymes that are automatically displayed in the Map tab of all open sequences.

Second, we added a large number of Invitrogen vectors that are installed in the /Applications/MacVector 11/Common Vectors/Invitrogen/ folder. These include a selection of “Entry” and “Destination” vectors. Others can be downloaded from the Invitrogen web site – you can use MacVector’s Auto-Annotation function to quickly annotate additional vectors with common TOPO and Gateway features.

A typical Gateway cloning vector with att and TOPO sites highlighted.

Its then very easy to simulate a TOPO or Gateway cloning experiment to create a constructed molecule with the identical sequence at the junctions to the one you would get in the lab. To clone a PCR fragment, simply select the region in a source molecule (or from an external text editor) and copy it to the clipboard, then select the TopoTA/BLNT site in the vector and click on the Ligate button. A Ligation dialog opens showing you the compatible blunt ends.

This gives you the option of flipping the source fragment if you wish. Note that this also works for TA cloning as well as ZERO-BLUNT cloning manipulations, even though the dialog does not show the overhanging T-A ends.

When Ligate is clicked, the fragment gets inserted into the vector. To perform a Gateway cloning, you can select the two att sites (attL1 and attL2) and copy the fragment to the clipboard.

Then open a suitable target/destination vector – these typically have attR1 and attR2 sites.

Now when you click on the Ligate button you’ll see the core recombination sequences of the att sites shown as overhanging ends.

Note that there is a single residue difference between the core sequences of the attL1/attR1 and attL2/attR2 sites;

The single T-C mismatch between the two core sequences ensures that the att recombination can only occur in one orientation. MacVector understands this and will automatically flip the source fragment if needed so that it becomes inserted in the biologically correct orientation. Finally, clicking Ligate inserts the fragment into the target vector.

For a more in-depth discussion of the new Gateway and TOPO cloning functionality in MacVector, download the Gateway and TOPO Cloning Tutorial from our web site.

Here’s the idea – over time you build up a collection of plasmids and sequence fragments of the genes and vectors you work with the most. Perhaps you always like to make your favorite gene appears as a striped red box. Now, when you get a new sequence, just run the auto annotation algorithm (Database | Auto Annotate Sequence) and point it at a folder containing your annotated sequences. The algorithm not only finds the matching features and copies them onto your bare sequence, but it also copies the graphic appearance symbol information. Lets look at an example.

MacVector 11 comes with a large set of pre-annotated vectors. You can find them in the /Applications/MacVector 11/Common Vectors/ folder. We’ve also included an /Annotated Fragments/ folder here with a started set of genes and replication origins you’ll find on many cloning vectors. Here’s a composite graphic image of a selection of those fragments.

There is a plain text copy of pBR322 in /MacVector 11/Tutorial Files/AutoAnnotation/pBR322Ascii.txt. If you open this file in MacVector and toggle its topology to linear, you’ll see there are no features assigned to the plasmid.

The next step is to invoke Database | Auto Annotate Sequence, then click on the Choose… button to select the /MacVector 11/Common Vectors/Annotated Fragments/ folder. Finally, click on the OK button and the algorithm will search through all of the files in the folder looking for matching features. When complete, a report is displayed – when you close that, you’ll see the newly annotated sequence.

In this case, pBR322 has picked up the tetracycline and ampiciliin resistance CDS features, along with the rop gene and replication origin.

Prefer a different way of graphically displaying the features? Try repeating the analysis, but selecting the /MacVector 11/Common Vectors/NEB/ folder – this contains a selection of vectors available from New England Biolabs, formatted to match the appearance in their catalog.

This time, when the algorithm completes, the features take on the typical appearance seen in the catalog. Note that the CDS features have not been duplicated – MacVector realizes the features already exist and just replaces the graphic symbols. You can also optionally set the algorithm to ignore duplicate features completely, in which case the sequence appearance would have been left unchanged.

You can use the Auto Annotation function to scan any folder containing DNA sequences. They don’t have to be in MacVector format, although features from GenBank or EMBL files will be given the default appearance for the feature type. There is a certain amount of fuzziness built into the algorithm – it can handle mismatches and even a few gaps and still identify matching features. We’ll be posting a more detailed tutorial in the next week with more information about the different parameters and limitations of the algorithm. In the meantime, take it for a spin and build up a collection of curated sequences containing all your favorite genes formatted for that great visual impact in your presentations.

• ClustalW – we also call this the “standard” Multiple Sequence Alignment (MSA)
• Align to Reference
• Pustell Matrix (also known as a Dot Plot)
• Internet BLAST
• Align to Folder
• Contig Assembly

ClustalW/Multiple Sequence Alignment (MSA)

If you have two or more related sequences (DNA or Protein) and you want to examine the relationship between them, use this function. Choose File->New->Protein Alignment (or File->New->Nucleic Acid Alignment) to create an empty MSA window. Add sequences to the alignment by using the Edit->Add Sequences from File menu item then click on the Align toolbar button to automatically align the sequences using ClustalW. Click on the Prefs toolbar button to control the appearance and behavior of the data in each of tabs that represent different views or analyses of the alignment. This functionality is most suited for protein alignments, or for nucleic acid sequences where you are interested in examining phylogenetic relationships. If you wish to compare two or more DNA sequences, you should definitely consider if one of the other alignment functions may be more suitable.

Align to Reference

The Align to Reference Editor window

Use this if you have a reference sequence and you want to align one or more DNA sequences against it. A typical use would be in resequencing e.g. sequencing a cloned PCR fragment to check no errors were introduced, sequencing across end junctions, scanning for successful mutagenesis clones etc. In each case, open the file that represents the parent or “reference” sequence, then choose Analyze->Align to Reference. In the window that opens, click on the “+” button to add sequences from disk – these can be in any format that MacVector can read – typically ABI or SCF chromatogram files, but you can add plain sequences as well. When you click on the Align button, choose the Sequence Confirmation algorithm – this is tuned to expect the small insertions/deletions you would expect in raw chromatogram files. Compared to ClustalW, Align to Reference has the advantage that it will automatically “flip” sequences to guarantee optimal alignment.

Align to Reference can also be used to align cDNA clones against a genome sequence. The steps are similar – use the genomic sequence as the reference, then add one or more cDNA clones to the alignment. Again, these can be chromatogram files. Now choose the cDNA Alignment algorithm when you Align – this is tuned to expect large insertions representing the intron regions.

Pustell Matrix

Repetitive sequence elements identified using a dot plot

This “Dot Plot” function is great for identifying weak regions of similarity between two sequences. It is not designed to show full-length alignments between two sequences, but instead shows shorter segments of direct or inverted similarity. You can use this to identify shorter regions of similarity, then copy those sections to new sequence windows for more in depth analysis using ClustalW or Align to Reference. Dot Plots are also the best way of identifying sequence rearrangements – the display clearly shows insertions and deletions (the main diagonal will be broken and have an offset) and even inversions (the inverted diagonal will run bottom left to top right and be colored blue). Finally you can use it to identify repetitive regions which appear as parallel diagonals offset from the main diagonal. Pustell Matrix can be used not only to compare DNA:DNA and Protein:Protein, but you can also use it to compare DNA:Protein where the algorithm will translate the DNA in all 6 frames before aligning to the protein.

Internet BLAST

Use this to identify and align a test sequence to the databases at the NCBI using the popular BLAST algorithm. One slightly hidden function in MacVector is that you can select sequences in the “hitlist” and then choose Database->Retrieve to Disk or Database->Retrieve to Desktop to download the matching sequences from the NCBI. You don’t even need to select the entire line – just select part of a line and use the Retrieve menu item.

Align to Folder

This allows you to scan a local folder full of sequences (in any format MacVector can recognize) and align them using the FastA alignment algorithm. Its kind of like a local BLAST, but more sensitive. Like the Pustell Matrix, you can choose to search DNA with Protein and vice versa. Many users like this function because the text alignment output also shows the features in the test sequence. This can be very useful for demonstrating the differences between your sequence and other sequences for patent purposes.

Contig Assembly

This requires our optional Assembler add-on. Use this if you want to align two or more DNA sequences with the idea of assembling them into a longer sequence with a consensus. Its is primarily designed for de novo sequencing, where you have no reference or scaffold sequence to align the individual sequences to. The MacVector implementation uses the popular phred, phrap and cross_match algorithms from the University of Washington that use quality values for improved accuracy of assembly. While you can use this for resequencing, you should consider whether the Align to Reference function might be a better choice.

Tutorials

There are tutorials for Sequence Confirmation and Contig Assembly in the Documentation folder of your MacVector installation. You can also download copies from our website.

So there we have at least five ways to align sequences using MacVector. Now if I can just find another 4 ways of skinning a catfish (or even just ONE thats easier than my current method) then I’ll be all set.