

CLUSTAL-OMEGA is a general purpose multiple sequence alignment program
for protein and DNA/RNA.



INTRODUCTION

Clustal-Omega is a general purpose multiple sequence alignment (MSA)
program for protein and DNA/RNA. It produces high quality MSAs and is
capable of handling data-sets of hundreds of thousands of sequences in
reasonable time.

In default mode, users give a file of sequences to be aligned and
these are clustered to produce a guide tree and this is used to guide
a "progressive alignment" of the sequences.  There are also facilities
for aligning existing alignments to each other, aligning a sequence to
an alignment and for using a hidden Markov model (HMM) to help guide
an alignment of new sequences that are homologous to the sequences
used to make the HMM.  This latter procedure is referred to as
"external profile alignment" or EPA.

Clustal-Omega uses HMMs for the alignment engine, based on the HHalign
package from Johannes Soeding [1]. Guide trees are made using an
enhanced version of mBed [2] which can cluster very large numbers of
sequences in O(N*log(N)) time. Multiple alignment then proceeds by
aligning larger and larger alignments using HHalign, following the
clustering given by the guide tree.

In its current form Clustal-Omega has been extensivly tested for
protein sequences, DNA/RNA support has been added since version 1.1.0.



SEQUENCE INPUT:

-i, --in, --infile={<file>,-}
	Multiple sequence input file (- for stdin)

--hmm-in=<file>
	HMM input files

--dealign
	Dealign input sequences

--profile1, --p1=<file>
	Pre-aligned multiple sequence file (aligned columns will be kept fixed)

--profile2, --p2=<file>
	Pre-aligned multiple sequence file (aligned columns will be kept fixed)

--is-profile
	disable check if profile, force profile (default no)

-t, --seqtype={Protein, RNA, DNA} 
	Force a sequence type (default: auto)

--infmt={a2m=fa[sta],clu[stal],msf,phy[lip],selex,st[ockholm],vie[nna]} 
	Forced sequence input file format (default: auto)


For sequence and profile input Clustal-Omega uses the Squid library
from Sean Eddy [3].


Clustal-Omega accepts 3 types of sequence input: (i) a sequence file
with un-aligned or aligned sequences, (ii) profiles (a multiple
alignment in a file) of aligned sequences, (iii) a HMM. Valid
combinations of the above are:

(a) one file with un-aligned or aligned sequences (i); the sequences
    will be aligned, and the alignment will be written out. For this
    mode use the -i flag. If the sequences are aligned (all sequences
    have the same length and at least one sequence has at least one
    gap), then the alignment is turned into a HMM, the sequences are
    de-aligned and the now un-aligned sequences are aligned using the
    HMM as an External Profile for External Profile Alignment (EPA).
    If no EPA is desired use the --dealign flag.

    Use the above option to make a multiple alignment from a set of
    sequences. A sequence file must contain more than one sequence (at
    least two sequences).

(b) two profiles (ii)+(ii); the columns in each profile will be kept
    fixed and the alignment of the two profiles will be written
    out. Use the --p1 and --p2 flags for this mode.

    Use this option to align two alignments (profiles) together.


(c) one file with un/aligned sequences (i) and one profile (ii); the
    profile is converted into a HMM and the un-aligned sequences will
    be multiply aligned (using the HMM background information) to form
    a profile; this constructed profile is aligned with the input
    profile; the columns in each profile (the original one and the one
    created from the un-aligned sequences) will be kept fixed and the
    alignment of the two profiles will be written out. Use the -i flag
    in conjunction with the --p1 flag for this mode.
      The un/aligned sequences file (i) must contain at least two
    sequences. If a single sequence has to be aligned with a profile
    the profile-profile option (b) has to be used.

    Use the above option to add new sequences to an existing
    alignment.

(d) one file with un-aligned sequences (i) and one HMM (iii); the
    un-aligned sequences will be aligned to form a profile, using the
    HMM as an External Profile. So far only one HMM can be input and
    only HMMer2 and HMMer3 formats are allowed. The alignment will be
    written out; the HMM information is discarded. As, at the moment,
    only one HMM can be used, no HMM is produced if the sequences are
    already aligned. Use the -i flag in conjunction with the --hmm-in
    flag for this mode. Multiple HMMs can be inputted, however, in the
    current version all but the first HMM will be ignored.

    Use this option to make a new multiple alignment of sequences from
    the input file and use the HMM as a guide (EPA).

Sequences that all have the same lengths but do not contain a single
gap are by default not recognised as a profile. If these sequences are
indeed a profile an not just a collection of unaligned sequences that
happen to have the same length then specify the --is-profile flag.


Invalid combinations of the above are:

(v) an un/aligned sequence file containing just one sequence (i)

(w) an un/aligned sequence file containing just one sequence and a profile
    (i)+(ii)

(x) an un/aligned sequence file containing just one sequence and a HMM
    (i)+(iii)

(y) two or more HMMs (iii)+(iii)+... cannot be aligned to one another.

(z) one profile (ii) cannot be aligned with a HMM (iii)


The following MSA file formats are allowed:

    a2m=fasta, (vienna)
    clustal,
    msf,
    phylip,
    selex,
    stockholm


Prior to MSA, Clustal-Omega de-aligns all sequence input (i). However,
alignment information is automatically converted into a HMM and used
during MSA, unless the --dealign flag is specifically set.  Profiles
(ii) are not de-aligned.

Since version 1.1.0 the Clustal-Omega alignment engine can process
DNA/RNA. Clustal-Omega tries to guess the sequence type (protein,
DNA/RNA), but this can be over-ruled with the --seqtype (-t) flag.


CLUSTERING:

  --distmat-in=<file>
	Pairwise distance matrix input file (skips distance computation)

  --distmat-out=<file>
	Pairwise distance matrix output file

  --guidetree-in=<file>
	Guide tree input file
	(skips distance computation and guide tree clustering step)

  --guidetree-out=<file>
	Guide tree output file

  --full
	Use full distance matrix for guide-tree calculation (slow; mBed is default)

  --full-iter
	Use full distance matrix for guide-tree calculation during iteration (mBed is default)

  --cluster-size=<n>        
	soft maximum of sequences in sub-clusters

  --clustering-out=<file>   
	Clustering output file

  --use-kimura
	use Kimura distance correction for aligned sequences (default no)

  --percent-id
	convert distances into percent identities (default no)

In order to produce a multiple alignment Clustal-Omega requires a
guide tree which defines the order in which sequences/profiles are
aligned. A guide tree in turn is constructed, based on a distance
matrix. Conventionally, this distance matrix is comprised of all the
pair-wise distances of the sequences. The distance measure
Clustal-Omega uses for pair-wise distances of un-aligned sequences is
the k-tuple measure [4], which was also implemented in Clustal 1.83
and ClustalW2 [5,6]. If the protein sequences inputted via -i are
aligned, then Clustal-Omega uses pairwise aligned identities, these
distances can be Kimura-corrected [7] by specifying --use-kimura. The
distances between aligned DNA/RNA sequences are determined from the
alignment, no Kimura correction can be used. The computational effort
(time/memory) to calculate and store a full distance matrix grows
quadratically with the number of sequences.  Clustal-Omega can improve
this scalability to N*log(N) by employing a fast clustering algorithm
called mBed [2]; this option is automatically invoked (default). If a
full distance matrix evaluation is desired, then the --full flag has
to be set. The mBed mode calculates a reduced set of pair-wise
distances. These distances are used in a k-means algorithm, that
clusters at most 100 sequences. For each cluster a full distance
matrix is calculated. No full distance matrix (of all input sequences)
is calculated in mBed mode. If there are less than 100 sequences in
the input, then in effect a full distance matrix is calculated in mBed
mode, however, no distance matrix can be outputted (see below).


Clustal-Omega uses Muscle's [8] fast UPGMA implementation to construct
its guide trees from the distance matrix. By default, the distance
matrix is used internally to construct the guide tree and is then
discarded. By specifying --distmat-out the internal distance matrix
can be written to file. This is only possible in --full or --full-iter
mode. The guide trees by default are used internally to guide the
multiple alignment and are then discarded. By specifying the
--guidetree-out option these internal guide trees can be written out
to file. Conversely, the distance calculation and/or guide tree
building stage can be skipped, by reading in a pre-calculated distance
matrix and/or pre-calculated guide tree. These options are invoked by
specifying the --distmat-in and/or --guidetree-in flags,
respectively. However, distance matrix reading is disabled in the
current version. By default, distance matrix and guide tree files are
not over-written, if a file with the specified name already exists. In
this case Clustal-Omega aborts during the command-line processing
stage. To force over-writing of already existing files use the --force
flag (see MISCELLANEOUS).  In mBed mode a full distance matrix cannot
be outputted, distance matrix output is only possible in --full mode.
mBed or --full distance mode do not affect the ability to write out
guide-trees. It is possible to perform an initial mBed (not-full)
distance calculation and a subsequent full distance calculation (see
section ITERATION). In this case a distance matrix can be outputted.

Guide trees can be iterated to refine the alignment (see section
ITERATION). Clustal-Omega takes the alignment, that was produced
initially and constructs a new distance matrix from this alignment.
The distance measure used at this stage is a full alignment distance
(as opposed the initial pairwise k-tuple distance); distances of
protein sequences can be Kimura corrected [7], DNA/RNA distances are
not. By default, Clustal-Omega constructs a reduced distance matrix at
this stage using the mBed algorithm, which will then be used to create
an improved (iterated) new guide tree. To turn off mBed-like
clustering at this stage the --full-iter flag has to be set. While
full alignment distances in general are much faster to calculate than
k-tuple distances, time and memory requirements still scale
quadratically with the number of sequences and --full-iter clustering
should only be considered for smaller cases (<< 10,000 sequences) or
if response time and resources are not an issue.

The default cluster size in mBed mode is 100. This means that
sequences are grouped into clusters with a soft maximum of 100
sequences, full distance matrices are calculated for these clusters,
guide-trees are calculated for the clusters and the clusters are then
strung together with an over-arching guide-tree. It is possible to
change the cluster-size with the --cluster-size flag. The clustering
can be outputted to file. The output is comprised of the cluster
index, a running index for the sequences within each cluster, the
running index for the sequence within the input file, the name of the
sequence and the bi-section sequence (see EXAMPLES). 

Clustal-Omega uses pair-distances. Between unaligned sequences these
are so called k-tuple distance, between aligned sequences they are
full alignment distances, as employed by Squid. These values range
between 0.0 (identical) and 1.0 (completely different). The distances
are used to construct the guide-tree and are by default outputted if
--distmat-out is specified (and --full and/or --full-iter are
set). For full alignment distances there is a so called Kimura
correction [7] which more closely reflects evolutionary
distance. Kimura-corrected distances range from 0.0 (identical) to
theoretically infinity (completely different). In practice there
appears to be a maximum value. In Clustal-Omega these Kimura-corrected
distance can be outputted for protein if the --use-kimura flag is
specified. Kimura correction is not available for DNA/RNA.  Up to and
including version 1.1.1 Kimura-corrected distances were outputted by
default (where possible). Since version 1.2.0 the default is to output
uncorrected distances.


Pair-distances closely correspond to percentage pair-wise identities
through i=100*(1-d), where i is the percentage pair-wise identity and
d is the pair-wise distance. Percentage pair-wise identities can be
outputted in Clustal-Omega instead of the distance matrix by
specifying the --percent-id flag as well as --distmat-out, --full
and/or --full-iter. Percentage pair-wise identities cannot be
outputted if --use-kimura is specified.


ALIGNMENT OUTPUT:

  -o, --out, --outfile={file,-} 
	Multiple sequence alignment output file (default: stdout)

  --outfmt={a2m=fa[sta],clu[stal],msf,phy[lip],selex,st[ockholm],vie[nna]} 
	MSA output file format (default: fasta)

  --residuenumber, --resno  
	in Clustal format print residue numbers (default no)

  --wrap=<n>  
	number of residues before line-wrap in output

  --output-order={input-order,tree-order} 
	MSA output order like in input/guide-tree


By default Clustal-Omega writes its results (alignments) to stdout. An
output file can be specified with the -o flag. Output to stdout is not
possible in verbose mode (-v, see MISCELLANEOUS) as verbose/debugging
messages would interfere with the alignment output.  By default,
alignment files are not over-written, if a file with the specified
name already exists. In this case Clustal-Omega aborts during the
command-line processing stage. To force over-writing of already
existing files use the --force flag (see MISCELLANEOUS).

Clustal-Omega can output alignments in various formats by setting the
--outfmt flag:

  * for Fasta format set: --outfmt=a2m  or  --outfmt=fa  or  --outfmt=fasta

  * for Clustal format set: --outfmt=clu  or  --outfmt=clustal

  * for Msf format: set --outfmt= msf

  * for Phylip format set: --outfmt=phy  or  --outfmt=phylip

  * for Selex format set: --outfmt=selex

  * for Stockholm format set: --outfmt=st  or  --outfmt=stockholm

  * for Vienna format set: --outfmt=vie  or  --outfmt=vienna

In ClustalW one could print the residue number of the last residue in
each line in Clustal-Format. This feature can be turned on by setting
the --resno or --residuenumber flag.

The line lengths in Clustal Format is usually 60 residues, in Fasta
format it is usually 60 or 80 residues. This value can be set using
the --wrap flag.

By default the order of sequences in the output is the same as in the
input (--output-order=input-order). This can be changed to the order
in which the sequences appear in the guide-tree by setting
--output-order=tree-order.


ITERATION:

  --iterations, --iter=<n>  Number of (combined guide tree/HMM) iterations

  --max-guidetree-iterations=<n> Maximum guide tree iterations

  --max-hmm-iterations=<n>  Maximum number of HMM iterations


By default, Clustal-Omega calculates (or reads in) a guide tree and
performs a multiple alignment in the order specified by this guide
tree. This alignment is then outputted. Clustal-Omega can 'iterate'
its guide tree. The hope is that the full alignment distances, that
can be derived from the initial alignment, will give rise to a better
guide tree, and by extension, to a better alignment.

A similar rationale applies to HMM-iteration. MSAs in general are very
'vulnerable' at their early stages. Sequences that are aligned at an
early stage remain fixed for the rest of the MSA. Another way of
putting this is: 'once a gap, always a gap'. This behaviour can be
mitigated by HMM iteration. An initial alignment is created and turned
into a HMM. This HMM can help in a new round of MSA to 'anticipate'
where residues should align. This is using the HMM as an External
Profile and carrying out iterative EPA.  In practice, individual
sequences and profiles are aligned to the External HMM, derived after
the initial alignment. Pseudo-count information is then transferred to
the (internal) HMM, corresponding to the individual
sequence/profile. The now somewhat 'softened' sequences/profiles are
then in turn aligned in the order specified by the guide
tree. Pseudo-count transfer is reduced with the size of the
profile. Individual sequences attain the greatest pseudo-count
transfer, larger profiles less so. Pseudo-count transfer to profiles
larger than, say, 10 is negligible. The effect of HMM iteration is
more pronounced in larger test sets (that is, with more sequences).

Both, HMM- and guide tree-iteration come at a cost of increasing the
run-time. One round of guide tree iteration adds on (roughly) the time
it took to construct the initial alignment. If, for example, the
initial alignment took 1min, then it will take (roughly) 2min to
iterate the guide tree once, 3min to iterate the guide tree twice, and
so on. HMM-iteration is more costly, as each round of iteration adds
three times the time required for the alignment stage. For example, if
the initial alignment took 1min, then each additional round of HMM
iteration will add on 3min; so 4 iterations will take 13min
(=1min+4*3min). The factor of 3 stems from the fact that at every
stage both intermediate profiles have to be aligned with the
background HMM, and finally the (softened) HMMs have to be aligned as
well. All times are quoted for single processors.

By default, guide tree iteration and HMM-iteration are coupled. This
means, at each iteration step both, guide tree and HMM, are
re-calculated. This is invoked by setting the --iter flag. For
example, if --iter=1, then first an initial alignment is produced
(without external HMM background information and using k-tuple
distances to calculate the guide tree). This initial alignment is then
used to re-calculate a new guide tree (using full alignment distances)
and to create a HMM. The new guide tree and the HMM are then used to
produce a new MSA.

Iteration of guide tree and HMM can be de-coupled. This means that the
number of guide tree iterations and HMM iterations can be
different. This can be done by combining the --iter flag with the
--max-guidetree-iterations and/or the --max-hmm-iterations flag.  The
number of guide tree iterations is the minimum of --iter and
--max-guidetree-iterations, while the number of HMM iterations is the
minimum of --iter and --max-hmm-iterations.  If, for example, HMM
iteration should be performed 5 times but guide tree iteration should
be performed only 3 times, then one should set --iter=5 and
--max-guidetree-iterations=3. All three flags can be specified at the
same time (however, this makes no sense). It is not sufficient just to
specify --max-guidetree-iterations and --max-hmm-iterations but not
--iter. If any iteration is desired, then --iter has to be
set. Conversely, if no alignment is desired but only distance
calculation and tree construction, then --max-hmm-iterations=-1 will
terminate the calculation before the alignment stage; --iter does not
have to be specified in this case.


LIMITS (will exit early, if exceeded):

  --maxnumseq=<n>           Maximum allowed number of sequences

  --maxseqlen=<l>           Maximum allowed sequence length

Limits can be imposed on the number of sequences in the input file
and/or the lengths of the sequences. This cap can be set with the
--maxnumseq and --maxseqlen flags, respectively. Clustal-Omega will
exit early, if these limits are exceeded.


MISCELLANEOUS:

  --auto                    Set options automatically (might overwrite some of your options)

  --threads=<n>             Number of processors to use

  -l, --log=<file>          Log all non-essential output to this file

  -h, --help                Print help and exit

  -v, --verbose             Verbose output (increases if given multiple times)

  --version                 Print version information and exit

  --long-version            Print long version information and exit

  --force                   Force file overwriting


Users may feel unsure which options are appropriate in certain
situations even though using ClustalO without any special options
should give you the desired results. The --auto flag tries to
alleviate this problem and selects accuracy/speed flags according to
the number of sequences. For all cases will use mBed and thereby
possibly overwrite the --full option. For more than 1,000 sequences
the iteration is turned off as the effect of iteration is more
noticeable for 'larger' problems. Otherwise iterations are set to 1 if
not already set to a higher value by the user. Expert users may want
to avoid this flag and exercise more fine tuned control by selecting
the appropriate options manually.

Certain parts of the MSA calculation have been parallelised. Most
noticeably, the distance matrix calculation, and certain aspects of
the HMM building stage. Clustal-Omega uses OpenMP. By default,
Clustal-Omega will attempt to use as many threads as possible. For
example, on a 4-core machine Clustal-Omega will attempt to use 4
threads. The number of threads can be limited by setting the --threads
flag. This may be desirable, for example, in the case of
benchmarking/timing.

Usually, non-essential (verbose) output is written to screen. This
output can be written to file by specifying the --log flag.

Help is available by specifying the -h flag.

By default Clustal-Omega does not print any information to stdout
(other than the final alignment, if no output file is
specified). Information concerning the progress of the alignment can
be obtained by specifying one verbosity flag (-v). This may be
desirable, to verify what Clustal-Omega is actually doing at the
moment. If two verbosity flags (-v -v) are specified, command-line
flags (explicitly and implicitly set) are printed in addition to the
progress report.  Triple verbose level (-v -v -v) is the most verbose
level. In addition to single- and double-verbose information much more
information is displayed: input sequences and names, details of the
tree construction and intermediate alignments. Tree construction
information includes pairwise distances. The number of pairwise
distances scales with the square of the number of sequences, and
double verbose mode is probably only useful for a small number of
sequences.

The current version number of Clustal-Omega can be displayed by
setting the --version flag.

The current version number of Clustal-Omega as well as the code-name
and the build date can be displayed by setting the --long-version
flag.

By default, Clustal-Omega does not over-write files. These can be (i)
alignment output, (ii) distance matrix and (iii) guide
tree. Overwriting can be forced by setting the --force flag.


EXAMPLES:

./clustalo -i globin.fa

Clustal-Omega reads the sequence file globin.fa, aligns the sequences
and prints the result to screen in fasta/a2m format.


./clustalo -i globin.fa -o globin.sto --outfmt=st

If the file globin.sto does not exist, then Clustal-Omega reads the
sequence file globin.fa, aligns the sequences and prints the result to
globin.sto in Stockholm format. If the file globin.sto does exist
already, then Clustal-Omega terminates the alignment process before
reading globin.fa.


./clustalo -i globin.fa	-o globin.aln --outfmt=clu --force

Clustal-Omega reads the sequence file globin.fa, aligns the sequences
and prints the result to globin.aln in Clustal format, overwriting the
file globin.aln, if it already exists.


./clustalo -i globin.fa --distmat-out=globin.mat --guidetree-out=globin.dnd --force

Clustal-Omega reads the sequence file globin.fa, aligns the sequences,
prints the result to screen in fasta/a2m format (default), the guide
tree to globin.dnd and the distance matrix to globin.mat, overwriting
those files if they already exist.


./clustalo -i globin.fa	--guidetree-in=globin.dnd

Clustal-Omega reads the files globin.fa and globin.dnd, skipping
distance calculation and guide tree creation, using instead the guide
tree specified in globin.dnd.


./clustalo -i globin.fa --hmm-in=PF00042.hmm

Clustal-Omega reads the sequence file globin.fa and the HMM file
PF00042.hmm (in HMMer2 or HMMer3 format).  It then performs the
alignment, transferring pseudo-count information contained in
PF00042.hmm to the sequences/profiles during the MSA.


./clustalo -i globin.sto

Clustal-Omega reads the file globin.sto (of aligned sequences in
Stockholm format). It converts the alignment into a HMM, de-aligns the
sequences and re-aligns them, transferring pseudo-count information to
the sequences/profiles during the MSA. The guide tree is constructed
using a full distance matrix.


./clustalo -i globin.sto  --dealign

Clustal-Omega reads the file globin.sto (of aligned sequences in
Stockholm format). It de-aligns the sequences and then re-aligns
them. No HMM is produced in the process, no pseudo-count information
is transferred. Consequently, the output must be the same as for
unaligned output (like in the first example ./clustalo -i globin.fa)


./clustalo -i globin.fa --iter=2

Clustal-Omega reads the file globin.fa, creates a UPGMA guide tree
built from k-tuple distances, and performs an initial alignment. This
initial alignment is converted into a HMM and a new guide tree is
built from the (preliminary) full alignment distances of the initial
alignment. The un-aligned sequences are then aligned (for the second
time but this time) using pseudo-count information from the HMM
created after the initial alignment (and using the new guide
tree). This second alignment is then again converted into a HMM and a
new guide tree is constructed. The un-aligned sequences are then
aligned (for a third time), again using pseudo-count information of
the HMM from the previous step and the most recent guide tree. The
final alignment is written to screen.


./clustalo -i globin.fa --iter=5 --max-guidetree-iterations=1

Clustal-Omega reads the file globin.fa, creates a UPGMA guide tree
built from k-tuple distances, and performs an initial alignment. This
initial alignment is converted into a HMM and a new guide tree is
built from the (preliminary) full alignment distances of the initial
alignment. The un-aligned sequences are then aligned (for the second
time but this time) using pseudo-count information from the HMM
created after the initial alignment (and using the new guide
tree). For the last 4 iterations the guide tree is left unchanged and
only HMM iteration is performed. This means that intermediate
alignments are converted to HMMs, and these intermediate HMMs are used
to guide the MSA during subsequent iteration stages.


./clustalo -i globin.fa -o globin.a2m -v

In case the file globin.a2m does not exist, Clustal-Omega reads the
file globin.fa, prints a progress report to screen and writes the
alignment in (default) Fasta format to globin.a2m. The progress report
consists of the number of threads used, the number of sequences read,
the current progress in the k-tuple distance calculation, completion
of the guide tree computation and current progress of the MSA stage.
If the file globin.a2m already exists Clustal-Omega aborts before
reading the file globin.fa. Note that in verbose mode an output file
has to be specified, because progress/debugging information, which is
printed to screen, would interfere with the alignment being printed to
screen.


./clustalo -i PF00042_full.fa --dealign --full --outfmt=vie -o PF00042_full.vie --force

Clustal-Omega reads the file PF00042_full.fa. This file contains
several thousand aligned sequences. --dealign tells Clustal-Omega to
erase all alignment information and re-align the sequences from
scratch. As there are several thousand sequences calculating a full
distance matrix may be slow. Setting the --full flag specifically
selects the full distance mode over the default mBed mode. The
alignment is then written out in Vienna format (fasta format all on
one line, no line breaks per sequence) to file PF00042_full.vie.


./clustalo -i PF00042_full.fa --dealign --outfmt=vie -o PF00042_full.vie --force

Clustal-Omega reads the file PF00042_full.fa. This file contains
several thousand aligned sequences. --dealign tells Clustal-Omega to
erase all alignment information and re-align the sequences from
scratch. Calculating the distance matrix will be done by mBed
(default). Clustal-Omega will calculate pairwise distances to a
small number of reference sequences only. This will give a significant
speed-up. The speed-up is greater for larger families (more
sequences). The alignment is then written out in Vienna format (fasta
format all on one line, no line breaks per sequence) to file
PF00042_full.vie.


./clustalo --p1=globin.sto --p2=PF00042_full.vie -o globin+pf00042.fa

Clustal-Omega reads files globin.sto and PF00042_full.vie of aligned
sequences (profiles). Both profiles are then aligned. The relative
positions of residues in both profiles are not changed during this
alignment, however, columns of gaps may be inserted into the profiles,
respectively. The final alignment is written to file globin+pf00042.fa
in fasta format.


./clustalo -i globin.fa --p1=PF00042_full.vie -o pf00042+globin.fa

Clustal-Omega reads file globin.fa of un-aligned sequences and the
profile (of aligned sequences) in file PF00042_full.vie. A HMM is
created from the profile. This HMM is used to guide the alignment of
the un-aligned sequences in globin.fa. The profile that was generated
during this alignment of un-aligned globin.fa sequences is then
aligned to the input profile PF00042_full.vie. The relative positions
of residues in profile PF00042_full.vie is not changed during this
alignment, however, columns of gaps may be inserted into the
profile. The final alignment is output to file pf00042+globin.fa in
fasta format. The alignment in this example may be slightly different
from the alignment in the previous example, because no HMM guidance
was used generate the profile globin.sto. In this example HMM guidance
was used to align the sequences in globin.fa; the hope being that this
intermediate alignment will have profited from the bigger profile.


./clustalo -i globin.fa --clustering-out=globin.aux  --cluster-size=3

globin.fa contains 7 sequences. Usually a full distance matrix is
created for less than 100 sequences. This is over-written by
specifying --cluster-size=3. Clustal-Omega attempts to create clusters
of no more than 3 sequences. This clustering is recorded in the file
globin.aux which looks like

	Cluster 0: object 0 has index 0 (=seq P1|HBB_HUMAN )     00
	Cluster 0: object 1 has index 1 (=seq P1|HBB_HORSE )     00
	Cluster 1: object 0 has index 4 (=seq P1|MYG_PHYCA )     1
	Cluster 1: object 1 has index 5 (=seq P1|GLB5_PETMA )    1
	Cluster 1: object 2 has index 6 (=seq P1|LGB2_LUPLU )    1
	Cluster 2: object 0 has index 2 (=seq P1|HBA_HUMAN )     01
	Cluster 2: object 1 has index 3 (=seq P1|HBA_HORSE )     01

There are 3 clusters, named Cluster~0, Cluster~1 and
Cluster~2. Cluster~0 has 2 sequences, which are sequence 0 and 1 from
the input file, named P1|HBB_HUMAN and P1|HBB_HORSE. Cluster~1 has 3
sequences, sequences 4,5,6 from the input file and Cluster~2 has 2
sequences, sequences 2 and 3 from the input file. The binary string at
the end of each line encode the bi-section that led to this
clustering. The first digit indicated the initial split. The '0'
indicates that in the first split sequences 0,1,2,3 were grouped
together and the '1' that sequences 4,5,6 were grouped together. The
size of Cluster~1 does not exceed --cluster-size, so it does need to
be broken up. The Cluster (with the initial '0') containing sequences
0,1,2,3 is comprised of 4 sequences; this number exceed
--cluster-size, so that it will have to be broken up. This second
split is indicated by the second digit of the binary string. The
second '0' indicates that sequences 0,1 fall into one Cluster (which
will ultimately be Cluster~0), and the second '1' indicates that
sequences 2,3 fall into another cluster (ultimately Cluster~2).


LITERATURE:

[1] Johannes Soding (2005) Protein homology detection by HMM-HMM
    comparison. Bioinformatics 21 (7): 951–960.

[2] Blackshields G, Sievers F, Shi W, Wilm A, Higgins DG.  Sequence
    embedding for fast construction of guide trees for multiple
    sequence alignment.  Algorithms Mol Biol. 2010 May 14;5:21.

[3] http://www.genetics.wustl.edu/eddy/software/#squid

[4] Wilbur and Lipman, 1983; PMID 6572363

[5] Thompson JD, Higgins DG, Gibson TJ.  (1994). CLUSTAL W: improving
    the sensitivity of progressive multiple sequence alignment through
    sequence weighting, position-specific gap penalties and weight
    matrix choice. Nucleic Acids Res., 22, 4673-4680.

[6] Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA,
    McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD,
    Gibson TJ, Higgins DG.  (2007). Clustal W and Clustal X version
    2.0. Bioinformatics, 23, 2947-2948.

[7] Kimura M (1980). "A simple method for estimating evolutionary
    rates of base substitutions through comparative studies of
    nucleotide sequences". Journal of Molecular Evolution 16: 111–120.

[8] Edgar, R.C. (2004) MUSCLE: multiple sequence alignment with high
    accuracy and high throughput.Nucleic Acids Res. 32(5):1792-1797.

