When you generate a reference contig with Bowtie, the Map view of a reference or child contig will show a plot of the depth of reads along the entire reference. This coverage map shows four statistics. A single plot line (default color is black) shows a running average of the number of reads at that point, calculated using a moving window of varying length depending on the zoom level. Such a plot is not sensitive when the window shows a large region of sequence at a high level, for example when viewing megabases of sequence). So two shaded areas indicate the highest value (default color is dark blue) and the lowest value (default color is light blue) of the reads averaged for that window. As the coverage map is viewed at higher magnifications then the window from which the running average is calculated becomes shorter and so these three values will become closer to the extent that when viewed at, or close to, sequence level these three plots will become identical.

Regions of zero coverage

Areas of zero coverage are shown in light grey. Note that these areas are always displayed even when they are disproportionate to the level of magnification. For example a region of zero coverage will always be displayed even when you are viewing a 20 megabase contig in its entirety. Also note that there are no areas of zero coverage in child contigs as by definition they are bounded by either end of the reference contig and/or an area of zero coverage. If you hover the mouse over the coverage map it will give the exact number of reads at that position (for example X reads over base XX).

Regions with low coverage

There are many reasons why regions will have lower than average coverage. These generally are caused by the base composition over that region. For example regulatory elements in a sequence, where proteins such as transcription factor bind, do have lower than average coverage perhaps due to their GC content being low.

Regions with high coverage

Short regions with excessively high coverage can be indicative of a repeated region that may or may not be present in the reference sequence. Reads will be piled up on one of the repeated sections rather than being spread out over each repeated region. Paired end reads can go some way to help detect these and allow correct alignment of reads. Also do remember that you may have the same read mapped to multiple locations on the reference unless you select the “USE BEST ALIGNMENT ONLY option

Further Analysis

The coverage map makes it very easy to design primers for further sequence, for example Sanger sequencing for hybrid assembly. Remember that you can run general MacVector analysis tools directly on a contig and it will act as if you are running that analysis on a single sequence.

Here’s how easy it is to design primers:

Technorati Tags: MacVector, bowtie

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

(a) using some out of date values for pI,

Mixing your reads

Currently there are three major types of short read data about, due to the fact that there are mainly three next generation sequencers producing considerably different read lengths.

- Illumina reads which are generally 66bp in length (the first generation of Solexa sequencers produced reads of 33bp long).

- 454 reads which can be 400 to 500 bp long

- SOLiD reads which are around 50bp long.

It is important to know which sequencer your reads have come from as length is not the only specific characteristic they possess. So with MacVector 11.1 we have introduced new feature types to indicate which types of read it is. So whether you mix Sanger, with 454, and a dash of Illumina reads, your assembly project will always keep the source of the reads stored in the metadata of the project. Furthermore the default symbol for each read type is different, and can be easily visualised.

Yet another sequence file format inconsistency!

An unfortunate fact about file formats in the bioinformatics world is that there are just so many of them! Most of them giving a slightly different approach to the same question. So it is nice that the Fastq format seems to be already fairly ubiquitous amongst raw data storage for short read data. Which is the main reason that we chose it to support short read data in MacVector. However, on the downside already there are three variations in the way that quality scores are stored in the format (actually there are variations in the sequence labels as well, but let’s just consider the quality scores). As well as the basecalled sequence, the Fastq format stores quality data encoded as a single character using the ASCII code for that character representing the value. All three variations of the format use that strategy. However, the actual number stored and the way it is encoded into the ASCII character does vary. The first generation of Illumina machines (when the company was still called Solexa) used a proprietary quality code. The current generation of their sequencers use the more recognised Phrap quality scores. However, they do store this number differently to the more recognised “Sanger” format which stores a Phrap quality score with a range of 0 – 93 using ASCII codes of 33 to 126. It is very important to distinguish when data is in the old Solexa scores. It is less important to distinguish between the later Illumina format, as reads will only be relevant when scores are very high and such high Phrap scores are unlikely.

So as well as distinguishing which sequencer produced the data, Assembler now also support the import of Fastq reads in all three types of quality data, and will take this into account when being assembled.

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.

One of the major changes was in MacVector 10. Prior to this release single sequences were displayed in the sequence editor with static views of the graphical map and feature table available from this single window. The actual sequence was only represented in the editor. In MacVector 10 this changed to a tabbed window with multiple dynamic views representing the same sequence model underneath. With this more flexible approach regardless of which view you are looking at, or modifying, you are acting on the sequence directly. Furthermore because of this single sequence model all windows are linked, and you can view multiple windows (we call them replicas) of that same sequence. For example with three replicas open, one showing the sequence, another the feature table and the third the map view, then if you select a gene feature in the Feature table, then the sequence representing that gene will be highlighted, and the feature in the Map window will also be highlighted. This is great for visualising your sequences. Especially with large ones.

However, this major change has left quite a few legacy functions that do not fit the new model so well. One such function is the linear/circular button in the floating graphics palette. This button has always been a visual function, and it does not affect the real topology of the sequence underneath. There are times when it is nice to display a circular sequence in linear mode – when you want to zoom in on a crowded region for example, or looking at the MCS of a plasmid whilst cloning fragments.

This made complete sense with the old sequence model of MacVector. However with the new unified tabbed windows viewing a circular view of a linear sequence gives the incorrect impression that you are viewing a circular sequence. Thus meaning that some algorithms would provide unexpected results. A classic example is the cloning vector pBR322, which has an EcoRI site that crosses the origin. As a plasmid the sequence’s topology should be circular. However, if you have toggled the topology button in the sequence window, the sequence is now linear and the EcoR I site disappears. Prior to MacVector 11 if you now viewed this as a circular plasmid the restriction site is still absent, yet the molecule appears to be circular. That’s going to produce a confusing set of fragments on a gel!

We thought that it was safer to change this behaviour in MacVector 11, and so now although you can view a circular molecule as linear, you cannot view a linear molecule as a circular one. We could not think of any user need for viewing a linear sequence as circular, although there are many advantages for viewing a circular sequence as linear. e.g. the aforementioned example of viewing the MCS of a plasmid. So now whenever you view a linear sequence the floating graphics palette linear/circular button is disabled to prevent you from inadvertently changing the Map and *thinking* you are dealing with a circular molecule. It’s a fairly small change, but unfortunately it has confused quite a few users! We do think that it is safer to have made this change. Displaying misleading results is something that we definitely do not want to do!

Currently, only the restriction enzyme, subsequence searching and (most) nucleic acid toolbox plots consider linearity/circularity in their algorithms. However, we plan on adding much better support for circular sequences in MacVector 11.5, so we thought it important to clean up this ambiguous linear/circular issue in this release. To this end, you will also see that the Topology button has now been added to the default toolbar button set of all the sequence window tabs to help indicate it is an important property of the sequence.

• 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.