fasta3x.doc

序列对齐 Compare a protein sequence to a protein sequence database or a DNA sequence to a DNA sequenc

     environments without STDOUT (mostly for the Macintosh).

-o   Turn off default optimization of all scores greater than
     OPTCUT. Sort results by "initn" scores (reduces the accuracy
     of statistical estimates).

-p   Force query to be treated as protein sequence.

-Q,-q
     Quiet - does not prompt for any input.  Writes scores and
     alignments to the terminal or standard output file.

-r   Specify match/mismatch scores for DNA comparisons.  The
     default is "+5/-4". "+3/-2" can perform better in some
     cases.

-R file
     Save a results summary line for every sequence in the
     sequence library.  The summary line includes the sequence
     identifier, superfamily number (if available) position in
     the library, and the similarity scores calculated.  This
     option can be used to evaluate the sensitivity and
     selectivity of different search strategies (Pearson, 1995,
     Pearson, 1998).

-s file
     Specify the scoring matrix file.  fasta3 uses the same
     scoring matrices as Blast1.4/2.0.  Several scoring matrix
     files are included in the standard distribution.  For
     protein sequences: codaa.mat - based on minimum mutation
     matrix; idnaa.mat - identity matrix; pam250.mat - the PAM250
     matrix developed by Dayhoff et al. (Dayhoff et al., 1978);
     pam120.mat - a PAM120 matrix.  The default scoring matrix is
     BLOSUM50 ("-s BL50"). Other matrices available from within
     the program are: PAM250/"-s P250", PAM120/"-s P120",
     PAM40/"-s P40", PAM20/"-s P20", MDM10 - MDM40/"-s M10 - M40"
     (MDM are modern PAM matrices from Jones et al. (Jones et
     al., 1992),), BLOSUM50, 62, and 80/"-s BL50", "-s BL62", "-s
     BL80".

-S   Treat lower-case characters in the query or library
     sequences as "low-complexity" ("seg"-ed) residues.
     Traditionally, the "seg" program (Wootton and
     Federhen, 1993) is used to remove low complexity regions in
     DNA sequences by replacing the residues with an "X".  When
     the "-S" option is used, the FASTA33 programs provide a
     potentially more informative approach.  With "-S", lower
     case characters in the query or database sequences are
     treated as "X"'s during the initial scan, but are treated as
     normal residues during the final alignment display.  Since
     statistical significance is calculated from the similarity
     score calculated during the library search, when the lower
     case residues are "X"'s, low complexity regions will not
     produce statistically significant matches.  However, if a
     significant alignment contains low complexity regions, their
     alignmen is shown.  With "-S", lower case characters may be
     included in the alignment to indicate low complexity
     regions, and the final alignment score may be higher than
     the score obtained during the search.

     The pseg program can be used to produce databases (or query
     sequences) with lower case residues indicating low
     complexity regions using the command:

         pseg database.fasta -z 1 -q  > database.lc_seg

     (seg can also be used with some post processing, see
     readme.v33tx.)

-U   Treat the query sequence an RNA sequence.  In addition to
     selecting a DNA/RNA alphabet, this option causes changes to
     the scoring matrix so that 'G:A' , 'T:C' or 'U:C' are scored
     as 'G:G'.

-V str
     It is now possible to specify some annotation characters
     that can be included (and will be ignored), in the query
     sequence file.  Thus, One might have a file with:
     "ACVS*ITRLFT?", where "*" and "?"  are used to indicate
     phosphorylation.  By giving the option -V '*?', those
     characters in the query will be moved to an "annotation
     string", and alignments that include the annotated residues
     will be highlighted with the appropriate character above the
     sequence (on the number line).

-w # Line length (width) = number (<200)

-W #  context length (default is 1/2 of line width -w) for
     alignment, like fasta and ssearch, that provide additional
     sequence context.

-x # Specify the penalty for a match to an 'X', independently of
     the PAM matrix.  Particularly useful for fastx3/fasty3,
     where termination codons are encoded as 'X'.

-X   Specifies offsets for the beginning of the query and library
     sequence.  For example, if you are comparing upstream
     regions for two genes, and the first sequence contains 500
     nt of upstream sequence while the second contains 300 nt of
     upstream sequence, you might try:

         fasta -X "-500 -300" seq1.nt seq2.nt

     If the -X option is not used, FASTA assumes numbering starts
     with 1.  (You should double check to be certain the negative
     numbering works properly.)

-y   Set the width of the band used for calculating "optimized"
     scores.  For proteins and ktup=2, the width is 16.  For
     proteins with ktup=1, the width is 32 by default.  For DNA
     the width is 16.

-z -1,0,1,2,3,4,5
     -z -1 turns off statistical calculations. z 0 estimates the
     significance of the match from the mean and standard
     deviation of the library scores, without correcting for
     library sequence length.  -z 1 (the default) uses a weighted
     regression of average score vs library sequence length; -z 2
     uses maximum likelihood estimates of Lambda and K; -z 3 uses
     Altschul-Gish parameters (Altschul and Gish, 1996); -z 4 - 5
     uses two variations on the -z 1 strategy. -z 1 and -z 2 are
     the best methods, in general.

-z 11,12,14,15
     estimate the statistical parameters from shuffled copies of
     each library sequence.  This doubles the time required for a
     search, but allows accurate statistics to be estimated for
     libraries comprised of a single protein family.

-Z db_size
     set the apparent size of the database to be used when
     calculating expectation E() values.  If you searched a
     database with 1,000 sequences, but would like to have the
     E()-values calculated in the context of a 100,000 sequence
     database, use '-Z 100000'.

-1   sort output by init1 score (for compatibility with FASTP -
     do not use).

-3   translate only three forward frames

For example:

    fasta -w 80 -a seq1.aa seq.aa

would compare the sequence in seq1.aa to that in seq2.aa and
display the results with 80 residues on an output line, showing
all of the residues in both sequences.  Be sure to enter the
options before entering the file names, or just enter the options
on the command line, and the program will prompt for the file
names.

     (November, 1997) In addition, it is now possible to provide
the fasta programs with the query sequence (fasta, fasty,
ssearch, tfastx), or two sequences (prss, lalign, plalign) from
the unix "stdin" stream.  This makes it much easier to set up
FASTA or PRSS WWW pages.  To specify that stdin be used, rather
than a file, the file name should be specified as '-' or '@' (the
latter file name makes it possible to specify a subset of the
sequence).  Thus:

    cat query.aa | fasta -q @:25-75 s

would take residues 25-75 from query.aa and search the 's'
library (see the discussion of FASTLIBS).

5.2.  Environment variables

     Because the current version of the program allows the user
to set virtually every option on the command line (except the
ktup, which must be set as the third command line argument), only
the FASTLIBS environment variable is routinely used.

FASTLIBS
     specifies the location of the file which contains the list
     of library descriptions, locations, and library types (see
     section on finding library files).

6.  Frequently Asked Questions

 (1)   Which program should I use? See Table I.

 (2)   How do I search with both DNA strands with fasta3 and
       fastx3? With version 32 of the FASTA program package, all
       searches that use DNA queries (e.g. fasta3, fastx3/y3)
       examine both strands. To revert to earlier FASTA behavior
       - only looking at the forward or reverse strand - use -3
       to search only the forward strand and -i -3 to search only
       the reverse strand.

 (3)   When I search Genbank - the program reports: 0 residues in
       0 sequences.  This typically happens because the program
       does not know that you are searching a Genbank flatfile
       database and is looking for a FASTA format database.  Be
       certain to specify the library type ("1" for Genbank
       flatfile) with the database name.

 (4)   What is the difference between fastx3 and fasty3 (or
       tfastx3 and tfasty3).  [t]fastx3 uses a simpler codon
       based model for alignments that does not allow frameshifts
       in some codon positions (see ref. (Zhang et al., 1997)).
       tfastx3 is about 30% faster, but tfasty3 can produce
       higher quality alignments in some cases.

 (5)   When I run fasta3 -q, I don't see any (or very little)
       output, but I get lots of scores when I run interactively.
       With the -Q option, the number of high scores displayed is
       limited by the -E # cutoff, which is 10.0 for protein
       comparisons, 2.0 for DNA comparisons, and 5.0 for
       translated DNA:protein comparisons.  In interactive mode
       (without -Q), by default you see 20 high scores,
       regardless of E() value.

 (6)   What is ktup - All of the programs with fast in their name
       use a computer science method called a lookup table to
       speed the search.  For proteins with ktup=2, this means
       that the program does not look at any sequence alignment
       that does not involve matching two identical residues in
       both sequences.  Likewise with DNA and ktup = 6, the
       initial alignment of the sequences looks for 6 identical
       adjacent nucleotides in both sequences.  Because it is
       less likely that two identical amino-acids will line up by
       chance in two unrelated proteins, this speeds up the
       comparison.  But very distantly related sequences may
       never have two identical residues in a row but will have
       single aligned identities.  In this case, ktup = 1 may
       find alignments that ktup=2 misses.

 (7)   Sometimes, in the list of best scores, the same sequence
       is shown twice with exactly the same score.  Sometimes,
       the sequence is there twice, but the scores are slightly
       different. When any of the fasta3 programs searches a long
       sequence, it breaks the sequence up into overlapping
       pieces.  The length of the piece depends on the length of
       the query and the particular program being used (it can
       also be controlled with the -N #### option).  Since the
       pieces overlap by the length of the query sequence (or
       3*query_length for fastx/y3 and tfasta/x/y3), if the
       highest scoring alignment is at the end of one piece, it
       will be scored again at the beginning of the next piece.
       If the alignment is not be completely included in the
       overlap region, one of the pieces will give a higher score
       than the other.  These duplications can be detected by
       looking at the coordinates of the alignment.  If either
       the beginning or end coordinate is identical in two
       alignments, the alignments are at least partially
       duplicates.

As always, please inform me of bugs as soon as possible.

William R. Pearson
Department of Biochemistry
Jordan Hall Box 800733
U. of Virginia
Charlottesville, VA

[e-mail]

7.  References

Altschul, S. F., Boguski, M. S., Gish, W., and Wootton, J. C.
(1994). Issues in searching molecular sequence databases. Nature
Genet. 6,119-129.

Altschul, S. F. and Gish, W. (1996). Local alignment statistics.
Methods Enzymol. 266,460-480.

Bairoch, A. and Apweiler, R. (1996). The Swiss-Prot protein
sequence data bank and its new supplement TrEMBL. Nucleic Acids.
Res. 24,21-25.

Barker, W. C., Garavelli, J. S., Haft, D. H., Hunt, L. T.,
Marzec, C. R., Orcutt, B. C., Srinivasarao, G. Y., Yeh, L. S. L.,
Ledley, R. S., Mewes, H. W., Pfeiffer, F., and Tsugita, A.
(1998). The PIR-International Protein Sequence Database. Nucleic
Acids Res 26,27-32.

Dayhoff, M., Schwartz, R. M., and Orcutt, B. C. (1978). A model
of evolutionary change in proteins. In Atlas of Protein Sequence
and Structure, vol. 5, supplement 3. M. Dayhoff, ed. (Silver
Spring, MD: National Biomedical Research Foundation), pp.
345-352.

Jones, D. T., Taylor, W. R., and Thornton, J. M. (1992). The
rapid generation of mutation data matrices from protein
sequences. Comp. Appl. Biosci. 8,275-282.

Pearson, W. R. (2000). Flexible similarity searching with the
FASTA3 program package. In Bioinformatics Methods and Protocols,
S. Misener and S. A. Krawetz, ed. (Totowa, NJ: Humana Press), pp.
185-219.

Pearson, W. R. and Lipman, D. J. (1988). Improved tools for
biological sequence comparison. Proc. Natl. Acad. Sci. USA
85,2444-2448.

Pearson, W. R. (1995). Comparison of methods for searching
protein sequence databases. Prot. Sci. 4,1145-1160.

Pearson, W. R. (1996). Effective protein sequence comparison.
Methods Enzymol. 266,227-258.

Pearson, W. R. (1998). Empirical statistical estimates for
sequence similarity searches. J. Mol. Biol. 276,71-84.

Smith, T. F. and Waterman, M. S. (1981). Identification of common
molecular subsequences. J. Mol. Biol. 147,195-197.

Wootton, J. C. and Federhen, S. (1993). Statistics of local
complexity in amino acid sequences and sequence databases.
Comput. Chem. 17,149-163.

Zhang, Z., Pearson, W. R., and Miller, W. (1997). Aligning a DNA
sequence with a protein sequence. J. Computational Biology
4,339-349.