TFASTA*

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Table of Contents
FUNCTION
DESCRIPTION
EXAMPLE
OUTPUT
INPUT FILES
RELATED PROGRAMS
RESTRICTIONS
ALGORITHM
CONSIDERATIONS
SUGGESTIONS
ACKNOWLEDGEMENT
COMMAND-LINE SUMMARY
LOCAL DATA FILES
PARAMETER REFERENCE

FUNCTION

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TFastA does a Pearson and Lipman search for similarity between a protein query sequence and any group of nucleotide sequences. TFastA translates the nucleotide sequences in all six reading frames before performing the comparison. It is designed to answer the question, "What implied protein sequences in a nucleotide sequence database are similar to my protein sequence?"

DESCRIPTION

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TFastA uses the method of Pearson and Lipman (Proc. Natl. Acad. Sci. USA 85; 2444-2448 (1988)) to search for similarities between a query protein sequence and any group of nucleotide sequences. TFastA translates the nucleotide sequences in all six frames before performing the comparison. Each translated reading frame is treated as a separate sequence to be searched.

In the first step of this search, the comparison can be viewed as a set of dot plots, with the query as the vertical sequence and the group of sequences to which the query is being compared as the different horizontal sequences. This first step finds the registers of comparison (diagonals) having the largest number of short perfect matches (words) for each comparison. In the second step, these "best" regions are rescored using a scoring matrix that allows conservative replacements, ambiguity symbols, and runs of identities shorter than the size of a word. In the third step, the program checks to see if some of these initial highest-scoring diagonals can be joined together. Finally, the search set sequences with the highest scores are aligned to the query sequence for display.

What is a Word?

A word is any short sequence (n-mer or k-tuple) where you have set n to some small integer less than or equal to six. The word GGATGG is one of the 4,096 possible words of length six that can be created from an alphabet consisting of the four letters G, A, T, and C. The word QL is one of the 400 possible words of length two that you can make with the 20 letters of the amino acid alphabet.

EXAMPLE

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Here is a session using TFastA to identify sequences in the GenEMBL nucleotide sequence database that contain translated regions similar to a human globin protein:


% tfasta

 TFASTA with what query sequence ?  ggamma.pep

 Removing terminal * from query sequence...

                  Begin (* 1 *) ?
                End (*   147 *) ?

 Search for query in what sequence(s) (* GenEMBL:* *) ?

 What word size (* 2 *) ?

 Don't show scores whose E() value exceeds: (* 10.0 *):

 What should I call the output file (* ggamma.tfasta *) ?

          1 Sequences       1,497 aa searched    GB_BA:A16SRRNA
        501 Sequences     908,944 aa searched    GB_BA:AB004059

        ///////////////////////////////////////////////////////

 CPU time used:
       Database scan:  0:30:17.2
Post-scan processing:  0:00: 7.0
      Total CPU time:  0:30:24.2
 Output file: ggamma.tfasta

%

OUTPUT

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The output from TFastA is a list file, and is suitable for input to any GCG program that allows indirect file specifications. (For information about indirect file specification, see Chapter 2, Using Sequence Files and Databases of the User's Guide.)

Here is some of the output file:


!!SEQUENCE_LIST 1.0

(Peptide) TFASTA of: ggamma.pep  from: 1 to: 147  September 23, 1998 15:27

TRANSLATE of: gamma.seq check: 6474 from: 2179 to: 2270
      and of: gamma.seq check: 6474 from: 2393 to: 2615
      and of: gamma.seq check: 6474 from: 3502 to: 3630
generated symbols 1 to: 148.
Human fetal beta globins G and A gamma
from Shen, Slightom and Smithies,  Cell 26; 191-203. . . .

 TO: GENEMBL:*  Sequences:    552,297  Symbols: 1,036,534,764  Word Size: 2

 Sequences too short to analyze: 26 (118 symbols)
 Databases searched:
   GenBank, Release 108.0, Released on 16Aug1998, Formatted on 16Aug1998
   EMBL, Release 55.0, Released on 16Jun1998, Formatted on 18Aug1998

 Searching all six frames.
 Scoring matrix: GenRunData:Blosum50.Cmp
 Variable pamfactor used
 Gap creation penalty: 16  Gap extension penalty: 2

Histogram Key:
 Each histogram symbol represents 1010 search set sequences
 Each inset symbol represents 15 search set sequences
 z-scores computed from opt scores

z-score obs    exp
        (=)    (*)

< 20   762     0:=
  22   287     0:=
  24   354     1:*
  26   580    12:*
  28   196   125:*
  30   307   760:*
  32   983  2937:= *
  34  2854  7965:===    *
  36  8036 16358:========        *
  38 16819 27033:=================         *
  40 30999 37709:===============================      *
  42 45853 46095:=============================================*
  44 55899 50847:==================================================*=====
  46 60552 51789:===================================================*========
  48 59619 49582:=================================================*==========
  50 52834 45243:============================================*========
  52 44792 39776:=======================================*=====
  54 37576 33976:=================================*====
  56 29109 28380:============================*
  58 22227 23300:=======================*
  60 16395 18874:================= *
  62 12883 15132:============= *
  64  9798 12034:========== *
  66  8069  9511:======== *
  68  7098  7481:=======*
  70  5521  5863:=====*
  72  4551  4581:====*
  74  3626  3572:===*
  76  2725  2780:==*
  78  2267  2161:==*
  80  1763  1678:=*
  82  1463  1284:=*
  84  1119  1017:=*
  86   801   787:*
  88   630   609:*
  90   487   471:*
  92   423   364:*         :========================*====
  94   330   282:*         :==================*===
  96   228   218:*         :==============*=
  98   141   169:*         :========== *
 100   154   131:*         :========*==
 102   122   101:*         :======*==
 104    80    78:*         :=====*
 106    64    61:*         :====*
 108    61    47:*         :===*=
 110    78    36:*         :==*===
 112    29    28:*         :=*
 114    20    22:*         :=*
 116    15    17:*         :=*
 118    11    13:*         :*
>120   707    10:*         :*=======================================

Joining threshold: 36, opt. threshold: 36, opt. width:  16, reg.-scaled

The best scores are:                  frame init1 initn   opt    z-sc E(...)..

GB_PR2:HUMHBGG
! M15386 Human hemoglobin gamma-G (HB...(3)   971   971   971  1547.8  1.2e-78
GB_PR1:HSGGGPHG
! X55656 H.sapiens mRNA for gamma-G g...(2)   843   843   843  1343.8  2.7e-67
GB_PAT:I42109
! I42109 Sequence 4 from patent US 56...(3)   765   765   776  1238.0  2.1e-61

//////////////////////////////////////////////////////////////////////////////

\\End of List

ggamma.pep
GB_PR2:HUMHBGG

LOCUS       HUMHBGG       545 bp    mRNA            PRI       24-MAR-1997
DEFINITION  Human hemoglobin gamma-G (HBG2) mRNA, partial cds.
ACCESSION   M15386
NID         g183884
KEYWORDS    .
SOURCE      human. . . .

SCORES  Frame: (3) Init1: 971 Initn: 971 Opt: 971 z-score: 1547.8 E(): 1.2e-78
 100.0% identity in 147 aa overlap

                          10        20        30        40        50
ggamma.pep        MGHFTEEDKATITSLWGKVNVEDAGGETLGRLLVVYPWTQRFFDSFGNLSSASAI
                  |||||||||||||||||||||||||||||||||||||||||||||||||||||||
HUMHBGG      PSPDAMGHFTEEDKATITSLWGKVNVEDAGGETLGRLLVVYPWTQRFFDSFGNLSSASAI
                     10        20        30        40        50        60

                60        70        80        90       100       110
ggamma.pep   MGNPKVKAHGKKVLTSLGDAIKHLDDLKGTFAQLSELHCDKLHVDPENFKLLGNVLVTVL
             ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
HUMHBGG      MGNPKVKAHGKKVLTSLGDAIKHLDDLKGTFAQLSELHCDKLHVDPENFKLLGNVLVTVL
                     70        80        90       100       110       120

               120       130       140
ggamma.pep   AIHFGKEFTPEVQASWQKMVTGVASALSSRYH
             ||||||||||||||||||||||||||||||||
HUMHBGG      AIHFGKEFTPEVQASWQKMVTGVASALSSRYHXARCPXCRASRIGFILQAIQIINLFCXE
                    130       140       150       160       170       180

HUMHBGG      I

/////////////////////////////////////////////////////////////////////////

! Distributed over 1 thread.
!      Start time: Wed Sep 23 14:15:13 1998
! Completion time: Wed Sep 23 15:29:29 1998

! CPU time used:
!        Database scan:  0:30:17.2
! Post-scan processing:  0:00:07.0
!       Total CPU time:  0:30:24.2
! Output File: ggamma.tfasta

What is the Output?

The first part of the output file contains a histogram showing the distribution of the z-scores between the query and search set sequences. (See the ALGORITHM topic for an explanation of z-score.) The histogram is composed of bins of size 2 that are labeled according to the higher score for that bin (the leftmost column of the histogram). For example, the bin labeled 24 stores the number of sequence pairs that had scores of 23 or 24.

The next two columns of the histogram list the number of z-scores that fell within each bin. The second column lists the number of z-scores observed in the search and the third column lists the number of z-scores that were expected.

The body of the histogram displays a graphical representation of the score distributions. Equal signs (=) indicate the number of scores of that magnitude that were observed during the search, while asterisks (*) plot the number of scores of that magnitude that were expected.

At the bottom of the histogram is a list of some of the parameters pertaining to the search.

Below the histogram, TFastA displays a listing of the best scores. This listing includes the reading frame in the original nucleotide sequence from which the reported translated sequence is derived.

Following the list of best scores, TFastA displays the alignments of the regions of best overlap between the query and search sequences. In these alignments, stop codons are represented by the letter X.

This program displays only the region of overlap between the two aligned sequences (plus some residues on either side of the region to provide context for the alignment) unless you use -SHOWall. The display of identities and conservative replacements between the aligned sequences depends on the value of -MARKx. By default ( -MARKx=3), the pipe character (|) is used to denote identities and the colon (:) to denote conservative replacements.

INPUT FILES

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TFastA accepts a single protein sequence as the query sequence. The search set is either a single nucleic acid sequence or multiple nucleic acid sequences. You can specify multiple sequences in a number of ways: by using a list file, for example @project.list; by using an MSF or RSF file, for example project.msf{*}; or by using a sequence specification with an asterisk (*) wildcard, for example GenEMBL:*. If TFastA rejects your protein sequence, see Appendix VI for information on how to change or set the type of a sequence.

RELATED PROGRAMS

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FastA does a Pearson and Lipman search for similarity between a query sequence and a group of sequences of the same type (nucleic acid or protein). For nucleotide searches, FastA may be more sensitive than BLAST.

BLAST searches one or more nucleic acid or protein databases for sequences similar to one or more query sequences of any type. BLAST can produce gapped alignments for the matches it finds. NetBLAST searches for sequences similar to a query sequence. The query and the database searched can be either peptide or nucleic acid in any combination. NetBLAST can search only databases maintained at the National Center for Biotechnology Information (NCBI) in Bethesda, Maryland, USA.

SSearch does a rigorous Smith-Waterman search for similarity between a query sequence and a group of sequences of the same type (nucleic acid or protein). This may be the most sensitive method available for similarity searches. Compared to BLAST and FastA, it can be very slow.

TFastX does a Pearson and Lipman search for similarity between a protein query sequence and any group of nucleotide sequences, taking frameshifts into account. It is designed to be a replacement for TFastA, and like TFastA, it is designed to answer the question, "What implied protein sequences in a nucleotide sequence database are similar to my protein sequence?"

FastX does a Pearson and Lipman search for similarity between a nucleotide query sequence and a group of protein sequences, taking frameshifts into account. FastX translates both strands of the nucleic sequence before performing the comparison. It is designed to answer the question, "What implied protein sequences in my nucleic acid sequence are similar to sequences in a protein database?"

FrameSearch searches a group of protein sequences for similarity to one or more nucleotide query sequences, or searches a group of nucleotide sequences for similarity to one or more protein query sequences. For each sequence comparison, the program finds an optimal alignment between the protein sequence and all possible codons on each strand of the nucleotide sequence. Optimal alignments may include reading frame shifts.

WordSearch identifies sequences in the database that share large numbers of common words in the same register of comparison with your query sequence. The output of WordSearch can be displayed with Segments.

ProfileSearch and MotifSearch use a profile (derived from a set of aligned sequences) instead of a query sequence to search a collection of sequences. FindPatterns uses a pattern described by a regular expression to search a collection of sequences.

StringSearch, LookUp, and Names identify sequences by searching the annotation (non-sequence) portions of seqence files or sequence databases.

RESTRICTIONS

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The query sequence may not be longer than 32,000 symbols. You cannot select a list size of more than 1,000 best scores nor view more than 1,000 alignments. The word size must be either 1 or 2.

For the estimates of statistical significance to be valid, the search set must contain a large sample of unrelated sequences. The statistical estimates will not be calculated at all if there are fewer than 60 frames searched (equivalent to 10 sequences when all six frames are searched, or 20 sequences when only the top three frames are searched).

With -NOOPTall, the estimates of statistical significance will not be accurate.

ALGORITHM

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For a description of the algorithm, see the FastA program documentation.

CONSIDERATIONS

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TFastA treats each reading frame as a different sequence. If a nucleotide sequence contains a gene coding for a protein similar to your query, but with an intervening sequence that changes the reading frame, the program will find and display two matches, one for each reading frame. If the individual matches each have fairly low scores, they may not make the list of best scores. If you suspect that the gene for your query sequence contains intervening sequences, or if you are searching a nucleotide database known to contain sequencing errors that may cause a frameshift (such as the EST division of GenBank), use TFastX instead of TFastA.

TFastA translates stop codons in search set sequences to the sequence symbol X.

The E() scores are affected by similarities in sequence composition between the query sequence and the search set sequence. Unrelated sequences may have "significant" scores because of composition bias.

If there is a database entry that overlaps your query in several places, but there are large gaps between the matching regions, only the best overlap appears in the alignment display.

There are two ways to control the size of the list of best scores. By default, scores are listed until a specific E() value is reached. You may set the value in response to the program prompt or by using -EXPect; otherwise the program uses 10.0 for protein searches, 2.0 for nucleic acid searches. (If you are running the program interactively, it will show no more than 40 scores initially, and ask if you want to see more scores if there are any more that are less than the E() value.)

If you use -LIStsize, the E() value is ignored, and the program will list the number of scores you requested.

You can control the number of alignments using -NOALIgn and -ALIgn. The program behaves differently depending on whether it is being run noninteractively (in batch or with -Default on the command line) or interactively. In the noninteractive case, the program displays the number of alignments set by -ALIgn. (If this is not present, it shows 40 alignments or the number of scores that were listed, whichever is smaller.) If you run the program interactively, it displays the list of best scores, then asks you how many alignments you want to see. (This prompt does not appear if you use -NOALIgn or -ALIgn.)

Increasing Sensitivity By Adjusting Word Size

By default, TFastA uses a word size of 2. If it finds few or no matches, especially if your query sequence is short, rerun the search using -WORdsize=1 to increase the sensitivity. Note that this will dramatically increase the amount of CPU time required to run the program.

Adjusting Gap Creation and Extension Penalties

Unlike other GCG programs, TFastA does not read the default gap creation and gap extension penalties from the scoring matrix file. It uses default gap creation and extension penalties that were empirically determined to be appropriate for the BLOSUM50 scoring matrix. If you select a different scoring matrix with -MATRix, you may need to change the gap penalties. The histogram display gives a qualitative view of the quality of fit between the actual distribution of scores and the expected distribution of scores. This information may indicate whether or not suitable gap creation and extension penalties were used for the search. When the histogram shows poor agreement between the actual distribution and the theoretical distribution, you might consider using -GAPweight and/or -LENgthweight to specify higher gap creation and extension penalties, respectively. For example, you might increase the gap creation penalty from 16 to 20 and the gap extension penalty from 4 to 6.

Differences in Applying Gap Extension Penalties

There are two different philosophies on how to penalize gaps in an alignment. One way is to penalize a gap by the gap creation penalty plus the extension penalty times the length of the gap (gapweight + (lengthweight x gap length)). The other way is to use the gap creation penalty plus the extension penalty times the gap length excluding the first residue in the gap (gapweight + (lengthweight x (gap length - 1)).

"Native" GCG programs, such as Framesearch and Bestfit, handle gap extension penalties the first way, while the FastA-family programs use the second way. Therefore a value for -LENgthweight that gives good results with one of the FastA-family programs may not give equivalent results with a native GCG program, and vice versa.

Increasing Program Speed Using Multithreading

This program is multithreaded. It has the potential to run faster on a machine equipped with multiple processors because different parts of the analysis can be run in parallel on different processors. By default, the program assumes you have one processor, so the analysis is performed using one thread. You can use -PROCessors to increase the number of threads up to the number of physical processors on the computer.

Under ideal conditions, the increase in speed is roughly linear with the number of processors used. But conditions are rarely ideal. If your computer is heavily used, competition for the processors can reduce the program's performance. In such an environment, try to run multithreaded programs during times when the load on the system is light.

As the number of threads increases, the amount of memory required increases substantially. You may need to ask your system administrator to increase the memory quota for your account if you want to use more than two threads.

Never use -PROCessors to set the number of threads higher than the number of physical processors that the machine has -- it does not increase program performance, but instead uses up a lot of memory needlessly and makes it harder for other users on the system to get processor time. Ask your system administrator how many processors your computer has if you aren't sure.

SUGGESTIONS

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Identifying the Search Set

If you want to search a single database division instead of an entire database, see the "Using Database Sequences" topic of Chapter 2, Using Sequence Files and Databases of the User's Guide for a list of the logical names used for the databases and the divisions of each database. The search set can also consist of a group of sequence files that are not in a database. Use a multiple sequence specification to name these. For information about naming groups of sequences for the search set, see the topics "Specifying Files" and "Using Wildcards" in Chapter 1, Getting Started, and "Using Database Sequences," "Using Multiple Sequence Format (MSF) Files", "Using Rich Sequence Format (RSF) Files", and "Using List Files" in Chapter 2, Using Sequence Files and Databases of the User's Guide.

Batch Queue

TFastA is one of the few programs in the Wisconsin Package(TM) that can take more than a few minutes to run. Most comparisons should probably be run in the batch queue. You can specify that this program run at a later time in the batch queue by using -BATch. Run this way, the program prompts you for all the required parameters and then automatically submits itself to the batch or at queue. For more information, see "Using the Batch Queue" in Chapter 3, Using Programs in the User's Guide. Very large comparisons may exceed the CPU limit set by some systems.

Interrupting a Search: <Ctrl>C

You can type <Ctrl>C to interrupt a search and see the results from the part of the search that has already been completed. Because the program is multithreaded, the search may not be interrupted immediately, but will continue until one of the threads finishes processing its data and returns for more data.

ACKNOWLEDGEMENT

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The FASTA program family (FastA, TFastA, FastX, TFastX, and SSearch) was written by Professor William Pearson of the University of Virginia Department of Biochemistry (Pearson and Lipman, Proc. Natl. Acad. Sci., USA 85; 2444-2448 (1988)). In collaboration with Dr. Pearson, the programs were modified and documented for distribution with GCG Version 6.1 by Mary Schultz and Irv Edelman, and for Versions 8 through 10 by Sue Olson.

COMMAND-LINE SUMMARY

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All parameters for this program may be added to the command line. Use -CHEck to view the summary below and to specify parameters before the program executes. In the summary below, the capitalized letters in the parameter names are the letters that you must type in order to use the parameter. Square brackets ([ and ]) enclose parameter values that are optional. For more information, see "Using Program Parameters" in Chapter 3, Using Programs in the User's Guide.


Minimal Syntax: % tfasta [-INfile1=]ggamma.pep -Default

Prompted Parameters:

[-INfile2=]GenEMBL:*           specifies search set
[-OUTfile=]ggamma.tfasta       names the output file
-BEGin=1 -END=148              sets the range of interest
-WORdsize=2                    sets the word size
-EXPect=2.0                    lists scores until E() value reaches 2.0

Local Data Files:

-MATRix=blosum50.cmp           assigns the scoring matrix for proteins

Optional Parameters:

-PROCessors=2      sets the number of threads devoted to the analysis
                     on a multiprocessor computer
-MINLength=1000    searches only sequences of 1000 or more residues
-MAXLength=5000    searches only sequences of 5000 or fewer residues
-SINce=6.90        limits search to sequences dated on or after June 1990
-DBTOPstrand       translates and searches only the three forward reading
                     frames of the search set sequences
-DBBOTtomstrand    translates and searches only the three reverse reading
                     frames of the search set sequences
-NOPAMfactor       uses a constant factor to calculate initial diagonal scores
-GAPweight=16      sets the gap creation penalty
-LENgthweight=2    sets the gap extension penalty
-OPTall=20         computes opt score when the initn score is 20
                     or higher; sorts on opt score
-NOOPTall          doesn't compute opt score during search; sorts on initn
-SWalign           creates final alignment as unlimited Smith-Waterman
-LIStsize=40       shows the best 40 scores (overrides EXPect)
-ALIgn=20          shows the best 20 alignments
-NOALIgn           suppresses sequence alignments
-SHOWall           shows complete sequences in alignment, not just overlaps
-MARKx=3           sets the alignment display mode
-NOHIStogram       suppresses printing the histogram
-LINEsize=60       sets number of sequence symbols per line of the alignment
-NODOCLines        suppresses sequence documentation in the alignment
-BATch             submits the program to run in the batch queue
-NOMONitor         suppresses the screen trace for each search set sequence

LOCAL DATA FILES

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The files described below supply auxiliary data to this program. The program automatically reads them from a public data directory unless you either 1) have a data file with exactly the same name in your current working directory; or 2) name a file on the command line with an expression like -DATa1=myfile.dat. For more information see Chapter 4, Using Data Files in the User's Guide.

Local Scoring Matrices

This program reads one or more scoring matrices for the comparison of sequence characters. The program automatically reads the program's default scoring matrix in a public data directory unless you either 1) have a data file with exactly the same name as the program default scoring matrix in your current working directory; or 2) have a data file with exactly the same name as the program default scoring matrix in the directory with the logical name MyData; or 3) name a file on the command line with an expression like -MATRix=mymatrix.cmp. If you don't include a directory specification when you name a file with -MATRix, the program searches for the file first in your local directory, then in the directory with the logical name MyData, then in the public data directory with the logical name GenMoreData, and finally in the public data directory with the logical name GenRunData. For more information see "Using a Special Kind of Data File: A Scoring Matrix" in Chapter 4, Using Data Files in the User's Guide.

TFastA reads a scoring matrix containing the values for every possible match from your working directory or the public database. The default matrix is blosum50.cmp, which is a BLOSUM50 matrix. You can use the Fetch program to obtain a copy of this file if you need to modify it for your own needs.

PARAMETER REFERENCE

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You can set the parameters listed below from the command line. For more information, see "Using Program Parameters" in Chapter 3, Using Programs in the User's Guide.

-WORdsize=2

sets the size of the word (k-tuple) to use for the hashing step.

-MATRix=mymatrix.cmp

allows you to specify a scoring matrix file name other than the program default. If you don't include a directory specification when you name a file with -MATRix, the program searches for the file first in your local directory, then in the directory with the logical name MyData, then in the public data directory with the logical name GenMoreData, and finally in the public data directory with the logical name GenRunData.

For more information see the Local Scoring Matrices section.

-EXPect=2.0

shows all scores whose E() value is less than 2.0. Ignored if -LIStsize is used.

-PROCessors=2

tells the program to use 2 threads for the database search on a multiprocessor computer.

-MINLength=1000

restricts the search to search set sequences that are equal to or longer than 1000 residues.

-MAXLength=5000

restricts the search to search set sequences that are equal to or shorter than 5000 residues.

-SINce=6.1990

limits the search to sequences that have been entered into the database or modified since June 1990. As this is being written, only the EMBL, GenBank, and SWISS-PROT databases support this parameter.

-DBTOPstrand

translates and searches only the three forward reading frames.

-DBBOTtomstrand

translates and searches only the three reverse complement reading frames.

-NOPAMfactor

uses a constant factor for the calculation of initial diagonal scores, instead of using the identical match scores from the scoring matrix.

-GAPweight=12

specifies the gap creation penalty that is subtracted from the alignment score whenever a gap is created.

-LENgthweight=2

specifies the gap extension penalty that is subtracted from the alignment score for each residue added to an existing gap.

-OPTall=20

immediately performs an alignment and calculates the opt score when the initn score is greater than or equal to 20. This parameter allows you to override the default threshold calculated by the program. Scores are sorted and saved by opt score during the search. -NOOPTall doesn't compute the opt score until the search is complete. In this case scores are sorted and saved by initn score instead of by opt score.

-SWalign

does an unlimited Smith-Waterman alignment as the final alignment for TFastA searches, instead of the "alignment in a band" version of Smith-Waterman. (Note: this can be very slow.)

-LIStsize=40

shows the best 40 scores. Overrides -EXPect.

-ALIgn=10

limits the number of alignments to display in the output file to the 10 best matches in the list. Use the -NOALIgn to suppress the sequence alignments in the output file.

-SHOWall

shows entire sequences in the alignment display, instead of just the best region of overlap and its surroundings.

-MARKx=3

determines the alignment display mode -- especially the symbols that identify matches and mismatches. The default value, 3, uses a pipe character (|) to show identities and a colon (:) to show conservative replacements. -MARKx=0 uses a colon to show identities and a period (.) to show conservative replacements. -MARKx=1 will not mark identities; instead, conservative replacements are connected with a lowercase x, and non-conservative substitutions are connected with an uppercase X. If -MARKx=2, the residues in the second sequence are shown only if they differ from the first sequence.

Use -MARKx=10 to get aligned sequences in the FastA parsable output format.

-NOHIStogram

suppresses printing the histogram.

-LINesize=60

lets you set the number of sequence symbols in each line of the alignment to any number between 60 and 200.

-NODOCLines

suppresses the documentation from the search set sequence accompanying the alignment in the output file. Use -DOCLines=5 to copy only five non-blank lines of documentation.

-BATch

submits the program to the batch queue for processing after prompting you for all required user inputs. Any information that would normally appear on the screen while the program is running is written into a log file. Whether that log file is deleted, printed, or saved to your current directory depends on how your system manager has set up the command that submits this program to the batch queue. All output files are written to your current directory, unless you direct the output to another directory when you specify the output file.

-MONitor=500

monitors this program's progress on your screen. Use this parameter to see this same monitor in the log file for a batch process. If the monitor is slowing down the program because your terminal is connected to a slow modem, suppress it with -NOMONitor.

The monitor is updated every time the program processes 500 sequences or files. You can use a value after the parameter to set this monitoring interval to some other number.

Printed: December 9, 1998 16:24 (1162)

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