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.

Both T-Coffee and Muscle are progressive alignment algorithms as is ClustalW. Progressive alignments generally build a guide tree that represents the pairwise relationships between each possible pair of sequences in the alignment. A multiple sequence alignment is then built sequentially using the tree as a construction guide. T-Coffee builds a library of all pairwise alignments but also aligns each sequence in the pair with a third sequence in the sequence set before building the MSA. Muscle does not do a pairwise alignment but instead uses an approximate method of comparing the number of short subsequences (k-mers, k-tuples or words) that each pair of sequences share. You can immediately see how this is much faster for alignments containing many sequences where the number of pairwise alignments needed to construct the tree is high.

T-Coffee is regarded as being slightly slower than ClustalW but will produce more accurate alignments for distantly related amino acid sequences. Here’s the original publication. Incidentally T-Coffee stands for Tree based Consistency Objective Function For AlignmEnt Evaluation.

Muscle is generally regarded as faster than Clustalw and T-Coffee at the penalty of being slightly less accurate.

All three algorithms are integrated into the MSA editor. This means you can try all three algorithms on the same alignment to see the results.

Here’s a few benchmark for protein sequence alignments (the files are in the Sample Files folder in the MacVector application folder:

- Clustalw: 1min26secs

- Muscle: 12 seconds

- T-Coffee: 17mins22secs

Because of the initial step of constructing the pairwise alignments tree, progressive alignment algorithms have difficulties with alignments where all sequences do not share a common region. For example take an alignment of three sequences where you have 5kb sequence and two regions of this sequence of around 1KB long. One subsequence aligns from 1,000 to 2,000 of the 10kb sequence and the other aligns at 4,000 to 5,000. Most progressive alignments will try to create initial pairwise alignments of all combinations of sequences and that skews the alignment so that it prefers to align the sequences so that there is at least one segment of overlap between all of the input sequence. Due to not creating pairwise alignments the “Muscle” algorithm is unique amongst these three algorithms as it will align this type of data as long as the “Diagonals” optimization parameter is set to “On”.

Technorati Tags: Sequence Alignments, MacVector

Assembler has always made it easy to assemble your sequencing projects. It hides the complicated algorithms and provides a point and click interface to show you the results. With the release of MacVector 12.5 the range of tasks that Assembler will perform has been greatly extended. Now with the addition of high throughput reference assembly using the popular Bowtie algorithm, Assembler can now support the alignment of many millions of NGS reads against genome sized references. Assembler will also generate output in the popular BAM format. Collections of contigs can be exported in FastQ format for additional analysis. Additionally the interface has been enhanced to increase the number of reads that can be submitted for de novo assembly. ..and to analyse your assemblies SNP detection and reporting has been enhanced with VCF output from Bowtie alignments and listing of all the codon and amino acid changes between the consensus and reference sequence alignments.

Here’s a selection of tasks that Assembler will make easy for you

De novo assembly of Sanger trace files

Add in your Sanger sequencing trace files, basecall the reads to improve accuracy then assemble using quality scores.

de novo short read Assembly

Add short reads from a variety of sources in FASTQ format as well as Sanger sequencing. Great for Hybrid assemblies

Reference assembly to identify SNPs in a bacterial/viral isolate

Add a Reference sequence and an NGS file(s) representing the sequence of an individual isolate and assemble using Bowtie. View a report listing all the potential SNPs based on the differences between the consensus and the reference. Be able to quickly identify the genes that the SNPs lie in and drill down to view the nucleotide and amino acid changes.

De novo bacterial assembly assisted by a Reference scaffold

Create a reference assembly using Bowtie, then take the individual contig consensus sequences along with all the input NGS Reads that did not assemble and assemble with a de novo assembler (Phrap) directly from within a new Assembly Project.

Assembly to multiple similar references

Take reference sequences from a series of closely related strains of virus or bacteria. The reads come from a single isolate. Use Bowtie to assemble these against the collection of References to determine which is most closely related (or identical) to the isolate.

Assembly to multiple dissimilar references to identify SNPs

Essentially similar to the bacterial SNP assembly, but using yeast or some other organism with multiple genomes (or even some bacteria that have multiple chromosomes or large plasmids).

Exome (transcriptome) sequencing)

Use the genomic sequence of an organism as a reference and align reads from sequenced mRNA, cDNA or total RNA of that same organism. Uses are splice site junction identification, novel gene identification amongst many others.

..and remember if you purchase an upgrade or a new license before the release of MacVector 12.5 you will get a 10% discount and a free upgrade to MacVector 12.5 when it is released. Contact Sales@macvector.com and quote “MV12510″ for the discount.

Technorati Tags: bowtie, MacVector

<Description> (<Start>) 

The full list of metatags is:

• <Description> or <Desc> the text of the description associated with a feature replaces this tag. In the case of a Restriction Enzyme, this is the name of the Enzyme.
• <Start> displays the start location of the feature (for an RE that is the cut site).
• <Stop> displays the stop location of the feature (for an RE that is the cut site and is identical to <Start>).
• <Size> displays the size of a feature.
• <Length> (functionally equivalent with the above metatag).
• <Type> – substitutes the type of feature e.g. “CDS”, or “mRNA” or “source”.

The following metatag applies only to feature graphic types and is disabled for others:

• <Segment> shows the position of this feature in multi-segmented features.

The following three metatags apply only to restriction enzyme cut sites and are disabled for other features:

• <Total> shows the count of this particular feature type.
• <Count> shows the count of this particular feature type (functionally equivalent with <Total>).
• <Index> shows the order of an individual feature in the above number.

Incidentally the last three metatags do provide very useful information on restriction enzyme cut sites, especially for cloning.

e.g. <Desc> (<Index>/<Total>) would generate labels like “BamHI (1/3)”, which means it’s the first cut site our of a total of three, “BamHI (2/3)” second of three etc.

You can also use metatags to display subsets of information in the feature description using qualifier information. Qualifiers are GenBank tags that supply extra information about a Feature keyword other than the type and location that are already supplied. Some Feature Keywords have mandatory qualifers, whereas some are entirely optional and can have none. For example the Feature Keyword CDS might contain the Gene name (/gene), the translated product (/product), the actual translation (/translation) and perhaps even the protein’s function (/function). As an example of a mandatory qualifier the Source Feature Type must contain the /organism qualifier. You can read the full set of Feature Keywords and Qualifiers on the NCBI’s Feature Table page.

The <Description> metatag displays the entire set of Qualifiers. Using the actual qualifier as a metatag will just use that specfic piece of information instead. For example <gene>(<number>)” will display My Gene (1) if the qualifiers /gene=”My Gene” and /number=”1” are present in the feature description. Using a specific qualifier usually provides a far more succinct label than the full <Description>, particularly for feature-rich sequences imported from GenBank or Entrez. Please note that Qualifier based meta-tags are skipped if the qualifier doesn’t exist in the feature description.

Metatags are case insensitive and if a metatag is not found it will be removed from the label string.

Finally to make all this easier there is a popup menu in the Feature Symbol editor that lets you view all of the available meta-tags and insert them into the label. Click on the small triangle to the left of the label edit box.

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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.

Technorati Tags:
MacVector, pcr, primer3

If you want to quickly cluster a sequence alignment in MacVector then you can use a phylogenetic tree to do this.

- Take your sequence alignment and run a phylogenetic reconstruction. In the MSA editor click the TREE button or run the menu command:

ANALYZE > CONSTRUCT TREE

- From the resulting phylogenetic window, you can rearrange sequences if necessary by selecting nodes and “rotating” them with the toolbar buttons.

- Then, with the focus on the Phylogeny window, sort the alignment to match the tree using the Sort MSA toolbar button or using the menu command:

ANALYZE > PHYLOGENETIC ANALYSES > SORT MSA TO MATCH TREE

This will rearrange the sequences in the editor to match the phylogeny branches.

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MacVector

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

In our recently completed survey one common request was promoter analysis. MacVector has subsequence files of transcription factors for analysis of promoter regions. MacVector includes some transcription factor subsequence files and recently we have also added the Jaspar transcription factor database.  This open database is available as a set of profiles, however, we have converted this to a subsequence library. By its nature searching using a regular expression match is less sensitive than a profile based search. However, MacVector does possess a profiles search so if you are looking for a particular transcription factor site, then you can search with a profile from the set of the original Jaspar profiles (in TRANSFAC profile format) that are also supplied. This function that will take one or two profiles and search a sequence for likely binding sites of those profiles.

The most recent Jaspar subsequences and profiles were generated from the JASPAR_CORE 2010, All_Species redundant set of profiles. This is described on the Jaspar website:

“The JASPAR CORE database contains a curated, non-redundant set of profiles, derived from published collections of experimentally defined transcription factor binding sites for eukaryotes. The prime difference to similar resources (TRANSFAC, etc) consist of the open data access, non-redundancy and quality.”

The Jaspar subsequence files will be freely downloadable from our downloads page.

Technorati Tags:
MacVector, transfac, jaspar

(1) You can show either three or six frame translations directly in the editor. To do so simply press and hold down the STRANDS toolbar button. You’ll see the following menu and be able to select what you want to show:

(2) If you have CDS or exons annotated to your sequence you can display a text view that translates these features and displays them with the sequence. This is the TEXT VIEW button in the toolbar. The TEXT VIEW is highly configurable. For example you can also have each codon spaced out and aligned with its amino acid if you select “BLOCK TO PHASE”. As in the following screenshot. You may find this easier to see the frame from which the amino acid sequence is translated from.

If the sequence that you want to display does not have your desired translation annotated, then it is straightforward to do so.

1 – Highlight the sequence in the EDITOR

2 – Click CREATE in the toolbar to open the FEATURE EDITOR

3 – Choose CDS in the top drop down menu and choose appropriate qualifiers in the bottom section. Click OK

You can also use the ANALYZE > OPEN READING FRAMES… tool to find regions in your sequence. Once run, select the appropriate ORF in the ORF results to highlight the sequence in the editor. You can then go to step 2 above to annotate the sequence.

When MacVector 12 comes out you will also be able to see translations directly in the graphical Map view:

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MacVector

It may not be at the cutting edge of sequence analysis but sooner or later most users of such apps are going to have to produce a graphic of their work, whether that is a plasmid map, a tree, an alignment etc..  There’s a world of difference between producing a rough and ready map of a construct for your labbook and producing a publication quality map for a publication. MacVector tries to make producing both as painless as possible. Most of the graphics within MacVector (including the Map view) use the Apple graphics library called Quartz. Quartz uses PDF technology to display graphics (reminiscent of NextStep and DisplayPostscript which was the predecessor to OS X!). It easily allows graphics to be exported directly in PDF format.  More importantly since most modern OS X applications also support PDF format, data can be exchanged via the clipboard using simple copy and paste procedures.  So exporting a plasmid map from MacVector can be as easy as clicking EDIT > COPY in the Map View, moving to the desired application (e.g. Microsoft Powerpoint) and clicking EDIT > PASTE. However, although easy it is important to understand that using copy/paste does not limit the quality in anyway. When you copy and paste from MacVector you are copying as a PDF, rather than the information that is on the screen. PDF is a vector format, as opposed to being a raster format, and can be enlarged to almost any degree without any loss of quality.  Furthermore when you paste into an application that supports this format, such as Adobe Illustrator, then you can directly edit the plasmid map in that applications.

In real terms this means that plasmid maps copied in this way can be resized without any loss of quality and without those jaggy lines.  Fonts will remain crisp as well regardless of what size they are displayed at. Whilst at the booth at the ASM2010 recently we thought it was a great example of how good the graphics could be by showing the metre high plasmid map and other graphics on the booth behind us. These were taken directly from MacVector.  As a smaller example the map below was also copy and pasted directly from MacVector.

Using PDF in this way is the Apple recommended way of dealing with graphics, and it is supported by all the professional applications, such as Adobe Illustrator, Photoshop etc.

In addition the FILE > EXPORT menu will allow you to directly save the MAP view to a PDF file, and if you require a format other than PDF, then you can save a plasmid map in any OS X supported graphics format by utilising PREVIEW. This is an often underlooked utility found in the Applications folder on all Macs. It’s an incredibly useful tool for graphical data as it supports so many formats.

- Open MacVector on the MAP view, and select EDIT > COPY
- Open PREVIEW and select FILE > NEW FROM CLIPBOARD.

Once you’ve done this you can save the file as a PNG, JPG or any supported file.

If you need to obtain a exported image with a specific DPI (e.g. for a paper) then the easiest way is:

- Open MacVector at the MAP view, and select EDIT > COPY
- Open PREVIEW and select FILE > NEW FROM CLIPBOARD.
- Resize the map to the size you require.
- Open FILE – SAVE AS.
- Change the file type to TIFF, and change the DPI to the required value.
- Save the file.

In the next post we’ll discuss how to customise the actual graphics.

Technorati Tags: MacVector, Quartz