+++ /dev/null
-\r
-\r
-CLUSTAL-OMEGA is a general purpose multiple sequence alignment program\r
-for proteins.\r
-\r
-\r
-\r
-INTRODUCTION\r
-\r
-Clustal-Omega is a general purpose multiple sequence alignment (MSA)\r
-program for proteins. It produces high quality MSAs and is capable of\r
-handling data-sets of hundreds of thousands of sequences in reasonable\r
-time.\r
-\r
-In default mode, users give a file of sequences to be aligned and\r
-these are clustered to produce a guide tree and this is used to guide\r
-a "progressive alignment" of the sequences. There are also facilities\r
-for aligning existing alignments to each other, aligning a sequence to\r
-an alignment and for using a hidden Markov model (HMM) to help guide\r
-an alignment of new sequences that are homologous to the sequences\r
-used to make the HMM. This latter procedure is referred to as\r
-"external profile alignment" or EPA.\r
-\r
-Clustal-Omega uses HMMs for the alignment engine, based on the HHalign\r
-package from Johannes Soeding [1]. Guide trees are made using an\r
-enhanced version of mBed [2] which can cluster very large numbers of\r
-sequences in O(N*log(N)) time. Multiple alignment then proceeds by\r
-aligning larger and larger alignments using HHalign, following the\r
-clustering given by the guide tree.\r
-\r
-In its current form Clustal-Omega can only align protein sequences but\r
-not DNA/RNA sequences. It is envisioned that DNA/RNA will become\r
-available in a future version.\r
-\r
-\r
-\r
-SEQUENCE INPUT:\r
-\r
--i, --in, --infile={<file>,-}\r
- Multiple sequence input file (- for stdin)\r
-\r
---hmm-in=<file>\r
- HMM input files\r
-\r
---dealign\r
- Dealign input sequences\r
-\r
---profile1, --p1=<file>\r
- Pre-aligned multiple sequence file (aligned columns will be kept fixed)\r
-\r
---profile2, --p2=<file>\r
- Pre-aligned multiple sequence file (aligned columns will be kept fixed)\r
-\r
-\r
-For sequence and profile input Clustal-Omega uses the Squid library\r
-from Sean Eddy [3].\r
-\r
-\r
-Clustal-Omega accepts 3 types of sequence input: (i) a sequence file\r
-with un-aligned or aligned sequences, (ii) profiles (a multiple\r
-alignment in a file) of aligned sequences, (iii) a HMM. Valid\r
-combinations of the above are:\r
-\r
-(a) one file with un-aligned or aligned sequences (i); the sequences\r
- will be aligned, and the alignment will be written out. For this\r
- mode use the -i flag. If the sequences are aligned (all sequences\r
- have the same length and at least one sequence has at least one\r
- gap), then the alignment is turned into a HMM, the sequences are\r
- de-aligned and the now un-aligned sequences are aligned using the\r
- HMM as an External Profile for External Profile Alignment (EPA).\r
- If no EPA is desired use the --dealign flag.\r
-\r
- Use the above option to make a multiple alignment from a set of\r
- sequences. A sequence file must contain more than one sequence (at\r
- least two sequences).\r
-\r
-(b) two profiles (ii)+(ii); the columns in each profile will be kept\r
- fixed and the alignment of the two profiles will be written\r
- out. Use the --p1 and --p2 flags for this mode.\r
-\r
- Use this option to align two alignments (profiles) together.\r
-\r
-(c) one file with un/aligned sequences (i) and one profile (ii); the\r
- profile is converted into a HMM and the un-aligned sequences will\r
- be multiply aligned (using the HMM background information) to form\r
- a profile; this constructed profile is aligned with the input\r
- profile; the columns in each profile (the original one and the one\r
- created from the un-aligned sequences) will be kept fixed and the\r
- alignment of the two profiles will be written out. Use the -i flag\r
- in conjunction with the --p1 flag for this mode.\r
- The un/aligned sequences file (i) must contain at least two\r
- sequences. If a single sequence has to be aligned with a profile\r
- the profile-profile option (b) has to be used.\r
-\r
- Use the above option to add new sequences to an existing\r
- alignment.\r
-\r
-(d) one file with un-aligned sequences (i) and one HMM (iii); the\r
- un-aligned sequences will be aligned to form a profile, using the\r
- HMM as an External Profile. So far only one HMM can be input and\r
- only HMMer2 and HMMer3 formats are allowed. The alignment will be\r
- written out; the HMM information is discarded. As, at the moment,\r
- only one HMM can be used, no HMM is produced if the sequences are\r
- already aligned. Use the -i flag in conjunction with the --hmm-in\r
- flag for this mode. Multiple HMMs can be inputted, however, in the\r
- current version all but the first HMM will be ignored.\r
-\r
- Use this option to make a new multiple alignment of sequences from\r
- the input file and use the HMM as a guide (EPA).\r
-\r
-\r
-Invalid combinations of the above are:\r
-\r
-(v) an un/aligned sequence file containing just one sequence (i)\r
-\r
-(w) an un/aligned sequence file containing just one sequence and a profile\r
- (i)+(ii)\r
-\r
-(x) an un/aligned sequence file containing just one sequence and a HMM\r
- (i)+(iii)\r
-\r
-(y) two or more HMMs (iii)+(iii)+... cannot be aligned to one another.\r
-\r
-(z) one profile (ii) cannot be aligned with a HMM (iii)\r
-\r
-\r
-The following MSA file formats are allowed:\r
-\r
- a2m=fasta, (vienna)\r
- clustal,\r
- msf,\r
- phylip,\r
- selex,\r
- stockholm\r
-\r
-\r
-Prior to MSA, Clustal-Omega de-aligns all sequence input (i). However,\r
-alignment information is automatically converted into a HMM and used\r
-during MSA, unless the --dealign flag is specifically set. Profiles\r
-(ii) are not de-aligned.\r
-\r
-The Clustal-Omega alignment engine can at the moment not process\r
-DNA/RNA. If a sequence input file (i) or a profile (ii) is interpreted\r
-as DNA/RNA the program will terminate during the file input stage.\r
-\r
-\r
-\r
-CLUSTERING:\r
-\r
- --distmat-in=<file>\r
- Pairwise distance matrix input file (skips distance computation)\r
-\r
- --distmat-out=<file>\r
- Pairwise distance matrix output file\r
-\r
- --guidetree-in=<file>\r
- Guide tree input file\r
- (skips distance computation and guide tree clustering step)\r
-\r
- --guidetree-out=<file>\r
- Guide tree output file\r
-\r
- --full\r
- Use full distance matrix for guide-tree calculation (slow; mBed is default)\r
-\r
- --full-iter\r
- Use full distance matrix for guide-tree calculation during iteration (mBed is default)\r
-\r
-\r
-In order to produce a multiple alignment Clustal-Omega requires a\r
-guide tree which defines the order in which sequences/profiles are\r
-aligned. A guide tree in turn is constructed, based on a distance\r
-matrix. Conventionally, this distance matrix is comprised of all the\r
-pair-wise distances of the sequences. The distance measure\r
-Clustal-Omega uses for pair-wise distances of un-aligned sequences is\r
-the k-tuple measure [4], which was also implemented in Clustal 1.83\r
-and ClustalW2 [5,6]. If the sequences inputted via -i are aligned\r
-Clustal-Omega uses the Kimura-corrected pairwise aligned identities\r
-[7]. The computational effort (time/memory) to calculate and store a\r
-full distance matrix grows quadratically with the number of sequences.\r
-Clustal-Omega can improve this scalability to N*log(N) by employing a\r
-fast clustering algorithm called mBed [2]; this option is\r
-automatically invoked (default). If a full distance matrix evaluation\r
-is desired, then the --full flag has to be set. The mBed mode\r
-calculates a reduced set of pair-wise distances. These distances are\r
-used in a k-means algorithm, that clusters at most 100 sequences. For\r
-each cluster a full distance matrix is calculated. No full distance\r
-matrix (of all input sequences) is calculated in mBed mode. If there\r
-are less than 100 sequences in the input, then in effect a full\r
-distance matrix is calculated in mBed mode, however, no distance\r
-matrix can be outputted (see below).\r
-\r
-\r
-Clustal-Omega uses Muscle's [8] fast UPGMA implementation to construct\r
-its guide trees from the distance matrix. By default, the distance\r
-matrix is used internally to construct the guide tree and is then\r
-discarded. By specifying --distmat-out the internal distance matrix\r
-can be written to file. This is only possible in --full mode. The\r
-guide trees by default are used internally to guide the multiple\r
-alignment and are then discarded. By specifying the --guidetree-out\r
-option these internal guide trees can be written out to\r
-file. Conversely, the distance calculation and/or guide tree building\r
-stage can be skipped, by reading in a pre-calculated distance matrix\r
-and/or pre-calculated guide tree. These options are invoked by\r
-specifying the --distmat-in and/or --guidetree-in flags,\r
-respectively. However, distance matrix reading is disabled in the\r
-current version. By default, distance matrix and guide tree files are\r
-not over-written, if a file with the specified name already exists. In\r
-this case Clustal-Omega aborts during the command-line processing\r
-stage. To force over-writing of already existing files use the --force\r
-flag (see MISCELLANEOUS). In mBed mode a full distance matrix cannot\r
-be outputted, distance matrix output is only possible in --full mode.\r
-mBed or --full distance mode do not affect the ability to write out\r
-guide-trees.\r
-\r
-Guide trees can be iterated to refine the alignment (see section\r
-ITERATION). Clustal-Omega takes the alignment, that was produced\r
-initially and constructs a new distance matrix from this alignment.\r
-The distance measure used at this stage is the Kimura distance [7]. By\r
-default, Clustal-Omega constructs a reduced distance matrix at this\r
-stage using the mBed algorithm, which will then be used to create an\r
-improved (iterated) new guide tree. To turn off mBed-like clustering\r
-at this stage the --full-iter flag has to be set. While Kimura\r
-distances in general are much faster to calculate than k-tuple\r
-distances, time and memory requirements still scale quadratically with\r
-the number of sequences and --full-iter clustering should only be\r
-considered for smaller cases (<< 10,000 sequences).\r
-\r
-\r
-\r
-ALIGNMENT OUTPUT:\r
-\r
- -o, --out, --outfile={file,-} Multiple sequence alignment output file (default: stdout)\r
-\r
- --outfmt={a2m=fa[sta],clu[stal],msf,phy[lip],selex,st[ockholm],vie[nna]} MSA output file format (default: fasta)\r
-\r
-\r
-By default Clustal-Omega writes its results (alignments) to stdout. An\r
-output file can be specified with the -o flag. Output to stdout is not\r
-possible in verbose mode (-v, see MISCELLANEOUS) as verbose/debugging\r
-messages would interfere with the alignment output. By default,\r
-alignment files are not over-written, if a file with the specified\r
-name already exists. In this case Clustal-Omega aborts during the\r
-command-line processing stage. To force over-writing of already\r
-existing files use the --force flag (see MISCELLANEOUS).\r
-\r
-Clustal-Omega can output alignments in various formats by setting the\r
---outfmt flag:\r
-\r
- * for Fasta format set: --outfmt=a2m or --outfmt=fa or --outfmt=fasta\r
-\r
- * for Clustal format set: --outfmt=clu or --outfmt=clustal\r
-\r
- * for Msf format: set --outfmt= msf\r
-\r
- * for Phylip format set: --outfmt=phy or --outfmt=phylip\r
-\r
- * for Selex format set: --outfmt=selex\r
-\r
- * for Stockholm format set: --outfmt=st or --outfmt=stockholm\r
-\r
- * for Vienna format set: --outfmt=vie or --outfmt=vienna\r
-\r
-\r
-ITERATION:\r
-\r
- --iterations, --iter=<n> Number of (combined guide tree/HMM) iterations\r
-\r
- --max-guidetree-iterations=<n> Maximum guide tree iterations\r
-\r
- --max-hmm-iterations=<n> Maximum number of HMM iterations\r
-\r
-\r
-By default, Clustal-Omega calculates (or reads in) a guide tree and\r
-performs a multiple alignment in the order specified by this guide\r
-tree. This alignment is then outputted. Clustal-Omega can 'iterate'\r
-its guide tree. The hope is that the (Kimura) distances, that can be\r
-derived from the initial alignment, will give rise to a better guide\r
-tree, and by extension, to a better alignment.\r
-\r
-A similar rationale applies to HMM-iteration. MSAs in general are very\r
-'vulnerable' at their early stages. Sequences that are aligned at an\r
-early stage remain fixed for the rest of the MSA. Another way of\r
-putting this is: 'once a gap, always a gap'. This behaviour can be\r
-mitigated by HMM iteration. An initial alignment is created and turned\r
-into a HMM. This HMM can help in a new round of MSA to 'anticipate'\r
-where residues should align. This is using the HMM as an External\r
-Profile and carrying out iterative EPA. In practice, individual\r
-sequences and profiles are aligned to the External HMM, derived after\r
-the initial alignment. Pseudo-count information is then transferred to\r
-the (internal) HMM, corresponding to the individual\r
-sequence/profile. The now somewhat 'softened' sequences/profiles are\r
-then in turn aligned in the order specified by the guide\r
-tree. Pseudo-count transfer is reduced with the size of the\r
-profile. Individual sequences attain the greatest pseudo-count\r
-transfer, larger profiles less so. Pseudo-count transfer to profiles\r
-larger than, say, 10 is negligible. The effect of HMM iteration is\r
-more pronounced in larger test sets (that is, with more sequences).\r
-\r
-Both, HMM- and guide tree-iteration come at a cost of increasing the\r
-run-time. One round of guide tree iteration adds on (roughly) the time\r
-it took to construct the initial alignment. If, for example, the\r
-initial alignment took 1min, then it will take (roughly) 2min to\r
-iterate the guide tree once, 3min to iterate the guide tree twice, and\r
-so on. HMM-iteration is more costly, as each round of iteration adds\r
-three times the time required for the alignment stage. For example, if\r
-the initial alignment took 1min, then each additional round of HMM\r
-iteration will add on 3min; so 4 iterations will take 13min\r
-(=1min+4*3min). The factor of 3 stems from the fact that at every\r
-stage both intermediate profiles have to be aligned with the\r
-background HMM, and finally the (softened) HMMs have to be aligned as\r
-well. All times are quoted for single processors.\r
-\r
-By default, guide tree iteration and HMM-iteration are coupled. This\r
-means, at each iteration step both, guide tree and HMM, are\r
-re-calculated. This is invoked by setting the --iter flag. For\r
-example, if --iter=1, then first an initial alignment is produced\r
-(without external HMM background information and using k-tuple\r
-distances to calculate the guide tree). This initial alignment is then\r
-used to re-calculate a new guide tree (using Kimura distances) and to\r
-create a HMM. The new guide tree and the HMM are then used to produce\r
-a new MSA.\r
-\r
-Iteration of guide tree and HMM can be de-coupled. This means that the\r
-number of guide tree iterations and HMM iterations can be\r
-different. This can be done by combining the --iter flag with the\r
---max-guidetree-iterations and/or the --max-hmm-iterations flag. The\r
-number of guide tree iterations is the minimum of --iter and\r
---max-guidetree-iterations, while the number of HMM iterations is the\r
-minimum of --iter and --max-hmm-iterations. If, for example, HMM\r
-iteration should be performed 5 times but guide tree iteration should\r
-be performed only 3 times, then one should set --iter=5 and\r
---max-guidetree-iterations=3. All three flags can be specified at the\r
-same time (however, this makes no sense). It is not sufficient just to\r
-specify --max-guidetree-iterations and --max-hmm-iterations but not\r
---iter. If any iteration is desired --iter has to be set.\r
-\r
-\r
-LIMITS (will exit early, if exceeded):\r
-\r
- --maxnumseq=<n> Maximum allowed number of sequences\r
-\r
- --maxseqlen=<l> Maximum allowed sequence length\r
-\r
-Limits can be imposed on the number of sequences in the input file\r
-and/or the lengths of the sequences. This cap can be set with the\r
---maxnumseq and --maxseqlen flags, respectively. Clustal-Omega will\r
-exit early, if these limits are exceeded.\r
-\r
-\r
-MISCELLANEOUS:\r
-\r
- --auto Set options automatically (might overwrite some of your options)\r
-\r
- --threads=<n> Number of processors to use\r
-\r
- -l, --log=<file> Log all non-essential output to this file\r
-\r
- -h, --help Print help and exit\r
-\r
- -v, --verbose Verbose output (increases if given multiple times)\r
-\r
- --version Print version information and exit\r
-\r
- --long-version Print long version information and exit\r
-\r
- --force Force file overwriting\r
-\r
-\r
-Users may feel unsure which options are appropriate in certain\r
-situations even though using ClustalO without any special options\r
-should give you the desired results. The --auto flag tries to\r
-alleviate this problem and selects accuracy/speed flags according to\r
-the number of sequences. For all cases will use mBed and thereby\r
-possibly overwrite the --full option. For more than 1,000 sequences\r
-the iteration is turned off as the effect of iteration is more\r
-noticeable for 'larger' problems. Otherwise iterations are set to 1 if\r
-not already set to a higher value by the user. Expert users may want\r
-to avoid this flag and exercise more fine tuned control by selecting\r
-the appropriate options manually.\r
-\r
-Certain parts of the MSA calculation have been parallelised. Most\r
-noticeably, the distance matrix calculation, and certain aspects of\r
-the HMM building stage. Clustal-Omega uses OpenMP. By default,\r
-Clustal-Omega will attempt to use as many threads as possible. For\r
-example, on a 4-core machine Clustal-Omega will attempt to use 4\r
-threads. The number of threads can be limited by setting the --threads\r
-flag. This may be desirable, for example, in the case of\r
-benchmarking/timing.\r
-\r
-Usually, non-essential (verbose) output is written to screen. This\r
-output can be written to file by specifying the --log flag.\r
-\r
-Help is available by specifying the -h flag.\r
-\r
-By default Clustal-Omega does not print any information to stdout\r
-(other than the final alignment, if no output file is\r
-specified). Information concerning the progress of the alignment can\r
-be obtained by specifying one verbosity flag (-v). This may be\r
-desirable, to verify what Clustal-Omega is actually doing at the\r
-moment. If two verbosity flags (-v -v) are specified, command-line\r
-flags (explicitly and implicitly set) are printed in addition to the\r
-progress report. Triple verbose level (-v -v -v) is the most verbose\r
-level. In addition to single- and double-verbose information much more\r
-information is displayed: input sequences and names, details of the\r
-tree construction and intermediate alignments. Tree construction\r
-information includes pairwise distances. The number of pairwise\r
-distances scales with the square of the number of sequences, and\r
-double verbose mode is probably only useful for a small number of\r
-sequences.\r
-\r
-The current version number of Clustal-Omega can be displayed by\r
-setting the --version flag.\r
-\r
-The current version number of Clustal-Omega as well as the code-name\r
-and the build date can be displayed by setting the --long-version\r
-flag.\r
-\r
-By default, Clustal-Omega does not over-write files. These can be (i)\r
-alignment output, (ii) distance matrix and (iii) guide\r
-tree. Overwriting can be forced by setting the --force flag.\r
-\r
-\r
-EXAMPLES:\r
-\r
-./clustalo -i globin.fa\r
-\r
-Clustal-Omega reads the sequence file globin.fa, aligns the sequences\r
-and prints the result to screen in fasta/a2m format.\r
-\r
-\r
-./clustalo -i globin.fa -o globin.sto --outfmt=st\r
-\r
-If the file globin.sto does not exist, then Clustal-Omega reads the\r
-sequence file globin.fa, aligns the sequences and prints the result to\r
-globin.sto in Stockholm format. If the file globin.sto does exist\r
-already, then Clustal-Omega terminates the alignment process before\r
-reading globin.fa.\r
-\r
-\r
-./clustalo -i globin.fa -o globin.aln --outfmt=clu --force\r
-\r
-Clustal-Omega reads the sequence file globin.fa, aligns the sequences\r
-and prints the result to globin.aln in Clustal format, overwriting the\r
-file globin.aln, if it already exists.\r
-\r
-\r
-./clustalo -i globin.fa --distmat-out=globin.mat --guidetree-out=globin.dnd --force\r
-\r
-Clustal-Omega reads the sequence file globin.fa, aligns the sequences,\r
-prints the result to screen in fasta/a2m format (default), the guide\r
-tree to globin.dnd and the distance matrix to globin.mat, overwriting\r
-those files if they already exist.\r
-\r
-\r
-./clustalo -i globin.fa --guidetree-in=globin.dnd\r
-\r
-Clustal-Omega reads the files globin.fa and globin.dnd, skipping\r
-distance calculation and guide tree creation, using instead the guide\r
-tree specified in globin.dnd.\r
-\r
-\r
-./clustalo -i globin.fa --hmm-in=PF00042.hmm\r
-\r
-Clustal-Omega reads the sequence file globin.fa and the HMM file\r
-PF00042.hmm (in HMMer2 or HMMer3 format). It then performs the\r
-alignment, transferring pseudo-count information contained in\r
-PF00042.hmm to the sequences/profiles during the MSA.\r
-\r
-\r
-./clustalo -i globin.sto\r
-\r
-Clustal-Omega reads the file globin.sto (of aligned sequences in\r
-Stockholm format). It converts the alignment into a HMM, de-aligns the\r
-sequences and re-aligns them, transferring pseudo-count information to\r
-the sequences/profiles during the MSA. The guide tree is constructed\r
-using a full distance matrix of Kimura distances.\r
-\r
-\r
-./clustalo -i globin.sto --dealign\r
-\r
-Clustal-Omega reads the file globin.sto (of aligned sequences in\r
-Stockholm format). It de-aligns the sequences and then re-aligns\r
-them. No HMM is produced in the process, no pseudo-count information\r
-is transferred. Consequently, the output must be the same as for\r
-unaligned output (like in the first example ./clustalo -i globin.fa)\r
-\r
-\r
-./clustalo -i globin.fa --iter=2\r
-\r
-Clustal-Omega reads the file globin.fa, creates a UPGMA guide tree\r
-built from k-tuple distances, and performs an initial alignment. This\r
-initial alignment is converted into a HMM and a new guide tree is\r
-built from the Kimura distances of the initial alignment. The\r
-un-aligned sequences are then aligned (for the second time but this\r
-time) using pseudo-count information from the HMM created after the\r
-initial alignment (and using the new guide tree). This second\r
-alignment is then again converted into a HMM and a new guide tree is\r
-constructed. The un-aligned sequences are then aligned (for a third\r
-time), again using pseudo-count information of the HMM from the\r
-previous step and the most recent guide tree. The final alignment is\r
-written to screen.\r
-\r
-\r
-./clustalo -i globin.fa --iter=5 --max-guidetree-iterations=1\r
-\r
-Clustal-Omega reads the file globin.fa, creates a UPGMA guide tree\r
-built from k-tuple distances, and performs an initial alignment. This\r
-initial alignment is converted into a HMM and a new guide tree is\r
-built from the Kimura distances of the initial alignment. The\r
-un-aligned sequences are then aligned (for the second time but this\r
-time) using pseudo-count information from the HMM created after the\r
-initial alignment (and using the new guide tree). For the last 4\r
-iterations the guide tree is left unchanged and only HMM iteration is\r
-performed. This means that intermediate alignments are converted to\r
-HMMs, and these intermediate HMMs are used to guide the MSA during\r
-subsequent iteration stages.\r
-\r
-\r
-./clustalo -i globin.fa -o globin.a2m -v\r
-\r
-In case the file globin.a2m does not exist, Clustal-Omega reads the\r
-file globin.fa, prints a progress report to screen and writes the\r
-alignment in (default) Fasta format to globin.a2m. The progress report\r
-consists of the number of threads used, the number of sequences read,\r
-the current progress in the k-tuple distance calculation, completion\r
-of the guide tree computation and current progress of the MSA stage.\r
-If the file globin.a2m already exists Clustal-Omega aborts before\r
-reading the file globin.fa. Note that in verbose mode an output file\r
-has to be specified, because progress/debugging information, which is\r
-printed to screen, would interfere with the alignment being printed to\r
-screen.\r
-\r
-\r
-./clustalo -i PF00042_full.fa --dealign --full --outfmt=vie -o PF00042_full.vie --force\r
-\r
-Clustal-Omega reads the file PF00042_full.fa. This file contains\r
-several thousand aligned sequences. --dealign tells Clustal-Omega to\r
-erase all alignment information and re-align the sequences from\r
-scratch. As there are several thousand sequences calculating a full\r
-distance matrix may be slow. Setting the --full flag specifically\r
-selects the full distance mode over the default mBed mode. The\r
-alignment is then written out in Vienna format (fasta format all on\r
-one line, no line breaks per sequence) to file PF00042_full.vie.\r
-\r
-\r
-./clustalo -i PF00042_full.fa --dealign --outfmt=vie -o PF00042_full.vie --force\r
-\r
-Clustal-Omega reads the file PF00042_full.fa. This file contains\r
-several thousand aligned sequences. --dealign tells Clustal-Omega to\r
-erase all alignment information and re-align the sequences from\r
-scratch. Calculating the distance matrix will be done by mBed\r
-(default). Clustal-Omega will calculate pairwise distances to a\r
-small number of reference sequences only. This will give a significant\r
-speed-up. The speed-up is greater for larger families (more\r
-sequences). The alignment is then written out in Vienna format (fasta\r
-format all on one line, no line breaks per sequence) to file\r
-PF00042_full.vie.\r
-\r
-\r
-./clustalo --p1=globin.sto --p2=PF00042_full.vie -o globin+pf00042.fa\r
-\r
-Clustal-Omega reads files globin.sto and PF00042_full.vie of aligned\r
-sequences (profiles). Both profiles are then aligned. The relative\r
-positions of residues in both profiles are not changed during this\r
-alignment, however, columns of gaps may be inserted into the profiles,\r
-respectively. The final alignment is written to file globin+pf00042.fa\r
-in fasta format.\r
-\r
-\r
-./clustalo -i globin.fa --p1=PF00042_full.vie -o pf00042+globin.fa\r
-\r
-Clustal-Omega reads file globin.fa of un-aligned sequences and the\r
-profile (of aligned sequences) in file PF00042_full.vie. A HMM is\r
-created from the profile. This HMM is used to guide the alignment of\r
-the un-aligned sequences in globin.fa. The profile that was generated\r
-during this alignment of un-aligned globin.fa sequences is then\r
-aligned to the input profile PF00042_full.vie. The relative positions\r
-of residues in profile PF00042_full.vie is not changed during this\r
-alignment, however, columns of gaps may be inserted into the\r
-profile. The final alignment is output to file pf00042+globin.fa in\r
-fasta format. The alignment in this example may be slightly different\r
-from the alignment in the previous example, because no HMM guidance\r
-was used generate the profile globin.sto. In this example HMM guidance\r
-was used to align the sequences in globin.fa; the hope being that this\r
-intermediate alignment will have profited from the bigger profile.\r
-\r
-\r
-\r
-LITERATURE:\r
-\r
-[1] Johannes Soding (2005) Protein homology detection by HMM-HMM\r
- comparison. Bioinformatics 21 (7): 951–960.\r
-\r
-[2] Blackshields G, Sievers F, Shi W, Wilm A, Higgins DG. Sequence\r
- embedding for fast construction of guide trees for multiple\r
- sequence alignment. Algorithms Mol Biol. 2010 May 14;5:21.\r
-\r
-[3] http://www.genetics.wustl.edu/eddy/software/#squid\r
-\r
-[4] Wilbur and Lipman, 1983; PMID 6572363\r
-\r
-[5] Thompson JD, Higgins DG, Gibson TJ. (1994). CLUSTAL W: improving\r
- the sensitivity of progressive multiple sequence alignment through\r
- sequence weighting, position-specific gap penalties and weight\r
- matrix choice. Nucleic Acids Res., 22, 4673-4680.\r
-\r
-[6] Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA,\r
- McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD,\r
- Gibson TJ, Higgins DG. (2007). Clustal W and Clustal X version\r
- 2.0. Bioinformatics, 23, 2947-2948.\r
-\r
-[7] Kimura M (1980). "A simple method for estimating evolutionary\r
- rates of base substitutions through comparative studies of\r
- nucleotide sequences". Journal of Molecular Evolution 16: 111–120.\r
-\r
-[8] Edgar, R.C. (2004) MUSCLE: multiple sequence alignment with high\r
- accuracy and high throughput.Nucleic Acids Res. 32(5):1792-1797.\r
-\r
git://source.jalview.org / jabaws.git / blobdiff