Download muscle as fasta file

Makefiles with sse2 e. These Smith-Waterman versions are typically more than X faster than unaccelerated versions, and can provide very fast sequence and profile Smith-Waterman searches.

Once the programs are compiled, you can test whether fasta works by typing Once the programs are compiled, you may want to copy them to a more visible location, 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. Default is to group sequences by similarity — group. Use 4-way test for treatment of terminal gaps. Cannot be disabled in this version. To determine the default, use — verbose —log and check the log file. Edgar Version 3. Citation: Edgar, Robert C. Table of Contents 1 Introduction.

Now type: muscle -in seqs. For example: muscle -in seqs. For example, muscle -in seqs. Typical usage is: muscle -profile -in1 one. Typical usage is: muscle -cluster -in seqs. You can use — neighborjoining if you prefer, but note that this is substantially slower than UPGMA for large numbers of sequences, and is also slightly less accurate.

Usage is: muscle -in input. Output is written to the following destination s : If no other output option is given, then standard output. Iteration Actions 1 Distance matrix by k - mer clustering, estimate tree, progressive alignment according to this tree. For example, muscle -in huge. Iter 2 Iteration currently in progress.

A brief description of the current step. Should be negative. Must be negative. Entrez Direct by default will download uncompressed data so you will end up spending more time downloading a larger file instead of downloading a smaller, compressed file from FTP more quickly. If you were to use Entrez Direct for this purpose, I'd not bother with a bash script and use epost to first post the entire list of accessions and then pipe it to efetch as shown below:.

You can also get this link directly, by using Chrome's developer tools F12 , viewing the Network tab, then loading the page in 1. Sorry, I can't post a comment with my reputation score. However, your command is downloading all sequences from the input file into a single fasta file.

I believe that if you have a big bunch of sequences, it could be a little bit tricky after to manipulate that kind of file. Just by curiosity, do you have a trick to create one sequence per file using efetch? Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group.

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