Muscle download alignment windows 10

It is best to save files with the Unix format option to avoid hidden Windows characters. There is currently a file upload limit of sequences and 1MB of data. Format for generated multiple sequence alignment.

See example output formats. It's possible to identify the tool result by giving it a name. This name will be associated to the results and might appear in some of the graphical representations of the results. Running a tool is usually an interactive process, the results are delivered directly to the browser when they become available.

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The Phylip package supports two different multiple sequence alignment file formats, called sequential and interleaved respectively. In this section we give more details of the MUSCLE algorithm and the more important options offered by the muscle implementation. See citations on title page above. But hopefully a summary will help explain what some of the command-line options do and how they might be useful in your work.

The first step is to calculate a tree. Each pair of input sequences is aligned, and used to compute the pair-wise identity of the pair. Identities are converted to a measure of distance. MUSCLE uses a much faster, but somewhat more approximate, method to compute distances: it counts the number of short sub-sequences known as k - mers , k - tuples or words that two sequences have in common, without constructing an alignment.

We call this step " k - mer clustering". The second step is to use the tree to construct what is known as a progressive alignment. At each node of the binary tree, a pair-wise alignment is constructed, progressing from the leaves towards the root. The first alignment will be made from two sequences. Later alignments will be one of the three following types: sequence-sequence, profile-sequence or profile-profile, where "profile" means the multiple alignment of the sequences under a given internal node of the tree.

Now we have a multiple alignment, which has been built very quickly compared with conventional methods, mainly because of the distance calculation using k - mers rather than alignments. The quality of this alignment is typically pretty good—it will often tie or beat a T-Coffee alignment on our tests. However, on average, we find that it can be improved by proceeding through the following steps. From the multiple alignment , we can now compute the pair-wise identities of each pair of sequences.

This gives us a new distance matrix, from which we estimate a new tree. We compare the old and new trees, and re-align subgroups where needed to produce a progressive multiple alignment from the new tree. If the two trees are identical, there is nothing to do; if there are no subtrees that agree very unusual , then the whole progressive alignment procedure must be repeated from scratch.

Typically we find that the tree is pretty stable near the leaves, but some re-alignments are needed closer the root. This procedure compute pair-wise identities, estimate new tree, compare trees, re-align is iterated until the tree stabilizes or until a specified maximum number of iterations has been done.

We call this process "tree refinement", although it also tends to improve the alignment. We now keep the tree fixed and move to a new procedure which is designed to improve the multiple alignment. The set of sequences is divided into two subsets i. A profile is constructed for each of the two subsets based on the current multiple alignment. These two profiles are then re-aligned to each other using the same pair-wise alignment algorithm as used in the progressive stage. If this improves an "objective score" that measures the quality of the alignment, then the new multiple alignment is kept, otherwise it is discarded.

By default, the objective score is the classic sum-of-pairs score that takes the sequence weighted average of the pair-wise alignment score of every pair of sequences in the alignment. Bipartitions are chosen by deleting an edge in the guide tree, each of the two resulting subtrees defines a subset of sequences. This procedure is called "tree dependent refinement".

One iteration of tree dependent refinement tries bipartitions produced by deleting every edge of the tree in depth order moving from the leaves towards the center of the tree. Iterations continue until convergence or up to a specified maximum. For convenience, the major steps in MUSCLE are described as "iterations", though the first three iterations all do quite different things and may take very different lengths of time to complete. The tree-dependent refinement iterations 3, Distance matrix by k - mer clustering, estimate tree, progressive alignment according to this tree.

Distance matrix by pair-wise identities from current multiple alignment, estimate tree, progressive alignment according to new tree, repeat until convergence or specified maximum number of times. Tree-dependent refinement. One iteration visits every edge in the tree one time.

There are two types of command-line options: value options and flag options. All options are a dash not two dashes! Value options must be separated from their values by white space in the command line. Thus, muscle does not follow Unix , Linux or Posix standards, for which we apologize. The order in which options are given is irrelevant unless two options contradict, in which case the right-most option silently wins.

If you specify 1, 2 or 3, then this is exactly the number of iterations that will be performed. If the value is greater than 3, then muscle will continue up to the maximum you specify or until convergence is reached, which ever happens sooner. The default is If you have a large number of sequences, refinement may be rather slow. This option controls the maximum number of new trees to create in iteration 2.

Our experience suggests that a point of diminishing returns is typically reached after the first tree, so the default value is 1. If a larger value is given, the process will repeat until convergence or until this number of trees has been created, which ever comes first. If you have a large alignment, muscle may take a long time to complete. It is sometimes convenient to say "I want the best alignment I can get in 24 hours" rather than specifying a set of options that will take an unknown length of time.

This is done by using — maxhours , which specifies a floating-point number of hours. If this time is exceeded, muscle will write out current alignment and stop. Note that the actual time may exceed the specified limit by a few minutes while muscle finishes up on a step.

It is also possible for no alignment to be produced if the time limit is too small. This is especially problematic when MUSCLE is used for batch processing, where one or two very large alignments can cause a batch to effectively hang. Starting in version 3. Under Linux and Windows, this works well. To override this default, you can specify the maximum number of megabytes to allocate by using the — maxmb option, for example to set a limit of 1.

This feature has been hacked on top of code that wasn't really designed for it. So it doesn't always work perfectly, but is better than nothing.

The ideal solution would be to implement linear space dynamic programming code e. One day I might do this if there is sufficient interest. If you are interested in contributing the code, e. Three different protein profile scoring functions are supported, the log-expectation score — le option and a sum of pairs score using either the PAM matrix — sp or the VTML matrix — sv.

The log-expectation score is the default as it gives better results on our tests, but is typically somewhere between two or three times slower than the sum-of-pairs score. For nucleotides, — spn is currently the only option which is of course the default for nucleotide data, so you don't need to specify this option.

A trick used in algorithms such as BLAST is to reduce the size of this matrix by using fast methods to find "diagonals", i. This speeds up the algorithm at the expense of some reduction in accuracy. It is disabled by default because of the slight reduction in average accuracy and can be turned on by specifying the — diags option. To enable diagonal optimization in the first iteration, use — diags1 , to enable diagonal optimization in the second iteration, use — diags2.

These are provided separately because it would be a reasonable strategy to enable diagonals in the first iteration but not the second because the main goal of the first iteration is to construct a multiple alignment quickly in order to improve the distance matrix, which is not very sensitive to alignment quality; whereas the goal of the second iteration is to make the best possible progressive alignment.

Tree-dependent refinement iterations 3, Block boundaries are found by identifying high-scoring columns e. Each vertical block is then refined independently before reassembling the complete alignment, which is faster because of the L 2 factor in dynamic programming e. The — noanchors option is used to disable this feature. This option has no effect if —maxiters 1 or — maxiters 2 is specified.

On benchmark tests, enabling anchors has little or no effect on accuracy, but if you want to be very conservative and are striving for the best possible accuracy then — noanchors is a reasonable choice.

Using — log causes any existing file to be deleted, — loga appends to any existing file. A message will be written to the log file when muscle starts and stops.

Error and warning messages will also be written to the log. If — verbose is specified, then more information will be written, including the command line used to invoke muscle , the resulting internal parameter settings, and also progress messages.

The content and format of verbose log file output is subject to change in future versions. The use of a log file may seem contrary to Unix conventions for using standard output and standard error. I like these conventions, but never found a fully satisfactory way to use them. I like progress messages see below , but they mess up a file if you re-direct standard error and there are errors or warning messages too. I could try to detect whether a standard file handle is a tty device or a disk file and change behavior accordingly, but I regard this as too complicated and too hard for the user to understand.

On Windows it can be hard to re-direct standard file handles, especially when working in a GUI debugger. Maybe one day I will figure out a better solution suggestions welcomed. This enables you to verify whether a particular alignment was completed and to review any errors or warnings that occurred. By default, muscle writes progress messages to standard error periodically so that you know it's doing something and get some feedback about the time and memory requirements for the alignment.

Here is a typical progress message. Elapsed time since muscle started. Peak memory use in megabytes i. The number in parentheses is the fraction of physical memory see — maxmb option for more discussion.

How much of the current step has been completed percentage. The — quiet command-line option disables writing progress messages to standard error. If the — verbose command-line option is specified, a progress message will be written to the log file when each iteration completes. So — quiet and — verbose are not contradictory.

The muscle code tries to deal gracefully with low-memory conditions by using the following technique. A block of "emergency reserve" memory is allocated when muscle starts. If a later request to allocate memory fails, this reserve block is made available, and muscle attempts to save the current alignment. With luck, the reserved memory will be enough to allow muscle to save the alignment and exit gracefully with an informative error message. See also the — maxmb option. Here is some general advice on what to do if muscle fails and you don't understand what happened.

The code is designed to fail gracefully with an informative error message when something goes wrong, but there will no doubt be situations I haven't anticipated not to mention bugs. Try dividing the file into two halves and using each half individually as input.

If one half fails and the other does not, repeat until the problem is localized as far as possible. Look at the peak memory requirements reported in progress messages to see if you may be exceeding the physical or virtual memory capacity of your computer.

If muscle crashes without giving an error message, or hangs, then you may need to refer to the source code or use a debugger. A "debug" version, muscled , may be provided. This is built from the same source code but with the DEBUG macro defined and without compiler optimizations.

This version runs much more slowly perhaps by a factor of three or more , but does a lot more internal checking and may be able to catch something that is going wrong in the code. When — core is specified, an exception may result in a debugger trap or a core dump, depending on the execution environment. The — nocore option has the opposite effect.

I am happy to provide support. But I am busy, and am offering this program at no charge, so I ask you to make a reasonable effort to figure things out for yourself before contacting me. Value option. Legal values. Clustering method. File name. Maximum distance between two diagonals that allows them to merge into one diagonal. Discard this many positions at ends of diagonal. If you specify your own matrix, you should also specify:.

Maximum time to run in hours. The actual time may exceed the requested limit by a few minutes. Decimals are allowed, so 1. Minimum score a column must have to be an anchor. Minimum smoothed score a column must have to be an anchor. Write output in Phylip interleaved format to given file name. Write output in Phylip sequential format to given file name. Method used to root tree; root1 is used in iteration 1 and 2, root2 in later iterations.

File name where to write a score file. This contains one line for each column in the alignment. The line contains the letters in the column followed by the average BLOSUM62 score over pairs of letters in the column. Maximum value of column score for smoothing purposes. Save tree produced in first or second iteration to given file in Newick Phylip -compatible format. Use given tree as guide tree. Must by in Newick Phyip -compatible format. Flag option. Set by default? Use anchor optimization in tree dependent refinement iterations.

Use Steven Brenner's method for computing the root alignment. Perform fast clustering of input sequences. Use the — tree1 option to save the tree. Use dimer approximation for the SP score faster, slightly less accurate. This is useful when a post-processing step is picky about the file header. Use diagonal optimizations. Faster, especially for closely related sequences, but may be less accurate.

Use diagonal optimizations in second iteration. Group similar sequences together in the output. This is the default. See also — stable. Use log-expectation profile score VTML Alternatives are to use — sp or — sv.

This is the default for amino acid sequences. Designed to be compatible with the GCG package. Compute profile-profile alignment. Input alignments must be given using — in1 and — in2 options. Input file is already aligned, skip first two iterations and begin tree dependent refinement. Refine an alignment by dividing it into non-overlapping windows and re-aligning each window.

Typically used for whole-genome nucleotide alignments. Use sum-of-pairs protein profile score PAM Default is — le. Compute alignment score of profile-profile alignment. These must be pre-aligned with gapped columns as needed, i. Use sum-of-pairs nucleotide profile score.

This is the only option for nucleotides, and is therefore the default. Preserve input order of sequences in output file.