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Viral bioinformatics: Sequence searcher

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virology toolboxThis week’s addition to the virology toolbox was written by Chris Upton

Sequence Searcher is a Java program that allows users to search for specific sequence motifs in protein or DNA sequences. For example, it can be used to identify restriction enzyme cleavage sites or find similar sequence patterns among multiple sequences. Most searches run in a few seconds.

Sequence Searcher is part of the Virology.ca suite of programs available at the University of Victoria.

Help files:

Some of the key features of Sequence Searcher include:

  • Searching through multiple sequences
  • Use of regular expressions or fuzzy search patterns.
  • Searching for patterns on both strands of a DNA sequence
  • Graphical representation of results and ability to save search results
  • It can run on multiple computer platforms (Java)

For DNA, the searches are conducted by finding the motif within a sequence from the 5’ to 3’ end on the top strand. The searches are also processed from the 5’ to 3’ end of the bottom strand. As a result, bases are numbered from 1 starting at the 5’ at either the top or bottom strand.

Regular expression and fuzzy pattern searches are available:

Fuzzy searches provide the option for the program to allow a certain number of mismatches from a sequence input at any position.  Note that the maximum number of mismatches that the program allows is 40% of the length of the sequence motif.

Regular expression allows for inputs of precise motifs along with considerable user-specified flexibility at specific positions.

figure 1

Figure 1. The input tab is where you can import DNA or protein sequences (must be in FASTA format) and type in the specific pattern to search within in the sequence(s). The search type can be selected as “Regular expression” or “Fuzzy” by using the drop down menu.

figure 2

Figure 2. When a search has been completed, the results tab is presented in a table format. The results in the table can be sorted depending on the column header (sequence, match, start, stop, confidence, and strand). The results can also be filtered by sequence and strand by selecting the drop down menus at the top.

Marass, F., & Upton, C. (2009). Sequence Searcher: A Java tool to perform regular expression and fuzzy searches of multiple DNA and protein sequences BMC Research Notes, 2 (1) DOI: 10.1186/1756-0500-2-14

Multiplicity of infection

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Multiplicity of infection (MOI) is a frequently used term in virology which refers to the number of virions that are added per cell during infection. If one million virions are added to one million cells, the MOI is one. If ten million virions are added, the MOI is ten. Add 100,000 virions, and the MOI is 0.1. The concept is straightforward.

But here is the tricky part. If you infect cells at a MOI of one, does that mean that each cell in the cutlure receives one virion?

The answer is no.

Here is another way to look at this problem: imagine a room containing 100 buckets. If you threw 100 tennis balls into that room – all at the same time – would each bucket get one ball? Most likely not.

How many tennis balls end up in each bucket, or the number of virions that each cell receives at different MOI, is described by the Poisson distribution:

P(k) = e-mmk/k!

In this equation, P(k) is the fraction of cells infected by k virus particles, and m is the MOI. The equation can be simplified to calculate the fraction of uninfected cells (k=0), cells with a single infection (k=1), and cells with multiple infection (k>1):

P(0) = e-m

P(1) = me-m

P(>1) = 1-e-m(m+1)*

*this value is obtained by subtracting from unity (the sum of all probabilities for any value of k) the probabilities P(0) and P(1)

Here are some examples of how these equations can be used. If we have a million cells in a culture dish and infect them at a MOI of 10, how many cells receive 0, 1, and more than one virion? The fraction of uninfected cells – those which receive 0 particles – is

P(0) = e-10

= 4.5 x 10-5

In a culture of one million cells this is 45 uninfected cells. That’s why an MOI of 10 is used in many virology experiments – it assures that essentially every cell is infected.

At the same MOI of 10, the number of cells that receive 1 particle is calculated by

P(1) = 10e-10

= 10 x 4.5 x 10-5

= 4.5 x 10-4

In a culture of one million cells, 450 cells receive 1 particle.

How many cells receive more than one particle is calculated by

P(>1) = 1-e-10(10+1)

=0.995

In a culture of one million cells, 999,500 cells receive more than one particle.

Using the same formulas, we can determine the fraction of cells receiving 0, 1, and more than one virus particle if we infect one million cells at a MOI of 1:

P(0) = e-1 = 0.37 = 37% of cells are uninfected

P(1) = 1 x e-1 = 37% of cells receive one virion

P(>1) = 1 – e-1(1+1) = 26% of cells are multiply infected

An assumption inherent in these calculations is that all cells in a culture are identical in their ability to be infected. In a clonal cell culture (such as HeLa cells) the deviations in size and surface properties are small enough to be negligible. However, in a multicellular animal there are substantial differences in cell types that affect susceptibility to infection. Under these conditions, it is experimentally difficult to determine how many virions infect different cells.

High MOI is used when the experiment requires that every cell in the culture is infected. By contrast, low MOI is used when multiple cycles of infection are required. However, it is not possible to calculate the MOI unless the virus titer can be determined – for example by plaque assay or any other method of quantifying infectivity.

Viral bioinformatics: Multiple sequence alignment – Base-By-Base (BBB) editor

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This week’s addition to the virology toolbox was written by Chris Upton (Disclaimer: BBB was developed in the Upton lab, so this is a biased review.)

This will be a multi-part posting describing the key features of BBB.

Base-By-Base (BBB) is a Java (platform independent) multiple sequence alignment (MSA) editor. Development was begun many years ago to provide a virologist-friendly tool to work with MSAs of proteins, gene sequences and also genome sequences (up to about 500 kb). Over the years we have added many unique features, as they have been needed by our own research with a variety of virus genomes; development has been driven by the users – who spend a lot of their day looking at a variety of MSAs!

BBB is available here.

The program is Open Source and freely available to all academic labs.

Help files:

Key features:

  1. The program edits MSAs and uses a unique system to display differences between sequences in a MSA. This makes it easier for the user to spot mis-aligned regions that need correcting (Figure 1A-D). The differences can be from adjacent pairs of sequences, sequences compared to a consensus, to the top sequence (Figures 1A and B).
  2. Sequences can be temporarily hidden/revealed. See “eyes” at left of window.
  3. 3-frame translations can be displayed for DNA sequences (Figure 2A).
  4. Top or Bottom strand can be shown for DNA sequences; compare Figures 2A and B.
  5. Sequence annotations can be read from a GenBank file, or added by the user (Figure 3). Users can also use a text window (Edit menu: Edit MSA notes) to jot down notes about an alignment; these are saved with the alignment in the .bbb file.
  6. You can also edit sequences! i.e. change the nucleotides or amino acids; see Figure 4. This can be very useful when you need to edit an assembled/annotated sequence for an occasional sequencing error.Figure 1A.  Differences between sequences are high-lighted. Blue=nt substitution; Green=nt deletion; Red=nt insertion.

Figure 1A. Differences between sequences are high-lighted. Blue=nt substitution; Green=nt deletion; Red=nt insertion.

Figure 1B. Edited version of Figure 1A (my opinion).

Figure 1C. Same alignment as 1B, but differences are set for compare against top sequence.

Figure 1D. Same alignment and differences setting, but Sequence-2 has been moved to the bottom of the alignment (use arrows on left edge of window).

Figure 2A. Two sequences have been hidden. 3-frame translation is shown. Top strand is shown by default.

Figure 2B. Switched to bottom strand display (use top right button; 5’Top3’ in Fig. 2A). Notice direction of arrows in aa translation.

Figure 3. Gene annotations from GenBank file (pink); mouse-over gives gene annotation (#083) and nucleotide position. User-added comments: Blue=comment on Top stand; comments associated with strand not currently displayed are shown as outlines.

Figure 4A. Editing a sequence. First, select a region.

Figure 4B. Editing a sequence. Second, delete the selection.

Figure 4C. Editing a sequence. Third, add new nucleotides.

Tips:

  1. Save alignments as .bbb files on your local computer. These documents can be reloaded back into BBB.
  2. You’ll find the Preferences menu, under the File menu.
  3. You can edit the names of the sequences (Edit menu)
  4. Paper+Pencil icon is for editing; Paper+arrow icon is for selecting.

Viral Bioinformatics: Multiple sequence alignment – Jalview

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This week’s addition to the virology toolbox was written by Chris Upton

The Jalview package: a multiple alignment editor.

This software is primarily aimed at the alignment of protein sequences. Some of the key features are:

  • It allows you to edit the alignment
  • It has functions to display associated protein structures
  • It can connect to software to predict protein secondary structure
  • It’s under active development
  • Jalview has great documentation and tutorials
  • More: Overview, Documentation

Tips:

  1. Although you can install Jalview on your computer very easily, using the Start with Java Web Start button is even easier and ensures you always have the latest version of the software.
  2. There is also an Applet version of Jalview that is intended to be an alignment viewer – it doesn’t have all the functionality.

If you use Jalview in your work, you should cite the Jalview 2 publication:

• Waterhouse, A.M., Procter, J.B., Martin, D.M.A, Clamp, M., Barton, G.J (2009), Jalview version 2: A Multiple Sequence Alignment and Analysis Workbench. Bioinformatics 25:1189-91.

• Clamp, M., Cuff, J., Searle, S. M. and Barton, G. J. (2004), The Jalview Java Alignment Editor. Bioinformatics 20: 426-7.

Viral bioinformatics: Introduction to multiple sequence alignment

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This week’s addition to the virology toolbox was written by Chris Upton

Generating multiple sequence alignments (MSA) is one of the most commonly used bioinformatics techniques. The “sequences” to be compared can be DNA (promoters, genes, genomes) or proteins. Note that the length and number of sequences to be aligned has an impact on the methods (algorithms) that can be used; what is suitable for aligning 20 proteins probably won’t work for alignment of 5 poxvirus genomes (200 kb each).

Some useful links:

So you see, there lots of options (did you say: “too many!”?). Further confusion may arise because 1) the same algorithm may be used in many different software programs, and 2) referencing a software package may give no clue to the algorithm used. For many molecular biologists, Clustal is synonymous with sequence alignment. However, newer algorithms such as T-Coffee and MUSCLE are often offered in current software packages, and may be faster and more accurate.

Specialized alignment tools are almost always needed for long, genome sized DNA sequences.

In this set of posts, I’ll provide some information on favorite general MSA tools (that are free) that should be useful to the average molecular virologist. The lists noted above provide a multitude of tools, but many are for specific analyses.

Detecting viral proteins in infected cells or tissues by immunostaining

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Many virological techniques are based on the specificity of the antibody-antigen reaction. Examples in our virology toolbox include western blot analysis and ELISA. While very useful, these methods cannot be used to visualize viral proteins in infected cells or tissues. To do that we must turn to immunostaining.

In direct immunostaining (illustrated), an antibody that recognizes a viral antigen is coupled directly to an indicator (a fluorescent dye or an enzyme). Indirect immunostaining is a more sensitive method because a second antibody is coupled to the indicator. The second antibody recognizes a common epitope on the virus-specific antibody. Multiple second antibodies can bind to the first antibody, leading to an increased signal from the indicator compared to direct immunostaining.

To carry out immunostaining, virus-infected cells are fixed to preserve cell morphology or tissue architecture. This step is usually accomplished with acetone, methanol, or paraformadehyde. After incubation of fixed cells with the appropriate antibody, excess antibody is removed by washing, followed by microscopy. Common indicators that are coupled to antibody molecules include fluorescein and rhodamine, which fluoresce on exposure of the cells to ultraviolet light. Filters are placed between the specimen and the eyepiece to remove blue and ultraviolet light; this ensures that the field is dark, except for cells that have bound antibody. These emit green (fluorescein) or red (rhodamine) light.

Antibodies can be coupled to indicators other than fluorescent molecules. Examples are enzymes such as alkaline phosphatase, horseradish peroxidase, and β-galactosidase. These enzymes can convert an added substrate to a colored dye. For example, the bacterial enzyme β-galactosidase converts the chromogenic substrate X-gal to a blue product, which can be visualized by microscopy.

Immunostaining is widely used in the research laboratory to determine subcellular location of proteins in cells. An example is the location of the herpes simplex viral protein VP22 in the nucleus of infected cells. To produce this image, virus-infected cells were stained with an antibody against VP22 and a mouse monoclonal antibody against α-tubulin, a cellular protein. Second antibodies bound to indicator molecules were then added: fluorsecein-conjugated anti-rabbit antibody, and Texas red-conjugated anti-mouse antibody (Texas red is another red fluorescent dye). The stained cells were then photographed with a microscope using ultraviolet light. The results show that VP22 (green) is located in the cell nucleus. Cellular α-tubulin is stained red. Photo courtesy of John Blaho.

Other uses of immunostaining include monitoring the synthesis of viral proteins, determining the effects of mutation on protein production, and investigating the sites of virus replication in animal hosts. Immunostaining of viral antigens in clinical specimens is also used to diagnose viral infections. Direct and indirect immunofluorescence assays with nasal swabs or washes are routine for diagnosis of infections with respiratory syncytial virus, influenza virus, parainfluenza virus, measles virus, and adenovirus.

When cultured cells are examined by this technique it is called immunocytochemistry; when tissues are studied, the procedure is immunohistochemistry. Flow cytometry is yet another way to use immunostaining to study the synthesis of one or more proteins in cells.