BLAST Reference Manual Pages

# BLAST HELP MANUAL

### DESCRIPTION

#

         This document describes the WWW BLAST interface.

         BLAST (Basic Local Alignment Search Tool) is  the  heuristic
         search  algorithm  employed  by the programs blastp, blastn,
         blastx, tblastn, and tblastx; these programs ascribe  signi-
         ficance  to  their findings using the statistical methods of
         Karlin and Altschul (1990, 1993) with  a  few  enhancements.
         The  BLAST  programs  were  tailored for sequence similarity
         searching -- for example to identify  homologs  to  a  query
         sequence.   The programs are not generally useful for motif-
         style searching.  For a discussion of basic issues in  simi-
         larity  searching of sequence databases, see Altschul et al.
         (1994).

         The five BLAST programs described here perform the following
         tasks:

         blastp    compares an amino acid query  sequence  against  a
                   protein sequence database;

         blastn    compares a nucleotide  query  sequence  against  a
                   nucleotide sequence database;

         blastx    compares  the  six-frame  conceptual   translation
                   products  of  a  nucleotide  query  sequence (both
                   strands) against a protein sequence database;

         tblastn   compares  a  protein  query  sequence  against   a
                   nucleotide     sequence    database    dynamically
                   translated  in  all  six  reading   frames   (both
                   strands).

         tblastx   compares the six-frame translations of  a  nucleo-
                   tide query sequence against the six-frame transla-
                   tions of a nucleotide sequence database.

    BLAST Search parameters

    HISTOGRAM
    Display a histogram of scores for each search; default
    is yes. (See parameter H in the BLAST Manual).
    DESCRIPTIONS
    Restricts the number of short descriptions of matching
    sequences reported to the number specified; default
    limit is 100 descriptions.  (See parameter V in the
    manual page).  See also EXPECT and CUTOFF.
    ALIGNMENTS
    Restricts database sequences to the number specified for
    which high-scoring segment pairs (HSPs) are reported;
    the default limit is 50.  If more database sequences
    than this happen to satisfy the statistical
    significance threshold for reporting (see EXPECT and
    CUTOFF below), only the matches ascribed the greatest
    statistical significance are reported.
    (See parameter B in the BLAST Manual).
    EXPECT

    The statistical significance threshold for reporting
    matches against database sequences; the default value
    is 10, such that 10 matches are expected to be found
    merely by chance, according to the stochastic model
    of Karlin and Altschul (1990).  If the statistical
    significance ascribed to a match is greater than the
    EXPECT threshold, the match will not be reported.
    Lower EXPECT thresholds are more stringent, leading
    to fewer chance matches being reported.  Fractional
    values are acceptable.  (See parameter E in the BLAST
    Manual).
    CUTOFF

    Cutoff score for reporting high-scoring segment pairs.
    The default value is calculated from the EXPECT value
    (see above).  HSPs are reported for a database sequence
    only if the statistical significance ascribed to them
    is at least as high as would be ascribed to a lone
    HSP having a score equal to the CUTOFF value.  Higher
    CUTOFF values are more stringent, leading to fewer
    chance matches being reported.  (See parameter S in
    the BLAST Manual).  Typically, significance thresholds
    can be more intuitively managed using EXPECT.
    MATRIX

    Specify an alternate scoring matrix for BLASTP, BLASTX,
    TBLASTN and TBLASTX.  The default matrix is BLOSUM62
    (Henikoff & Henikoff, 1992).  The valid alternative
    choices include:  PAM40, PAM120, PAM250 and IDENTITY.
    No alternate scoring matrices are available for BLASTN;
    specifying the MATRIX directive in BLASTN requests
    returns an error response.
    STRAND

    Restrict a TBLASTN search to just the top or bottom
    strand of the database sequences; or restrict a BLASTN,
    BLASTX or TBLASTX search to just reading frames on the
    top or bottom strand of the query sequence.
    FILTER

    Mask off segments of the query sequence that have
    low compositional complexity, as determined by the
    SEG program of Wootton & Federhen (Computers and
    Chemistry, 1993), or segments consisting of
    short-periodicity internal repeats, as determined
    by the XNU program of Claverie & States (Computers
    and Chemistry, 1993), or, for BLASTN, by the DUST
     program of Tatusov and Lipman (in preparation).
    Filtering can eliminate statistically significant but
    biologically uninteresting reports from the blast
    output (e.g., hits against common acidic-, basic- or
    proline-rich regions), leaving the more biologically
    interesting regions of the query sequence available
    for specific matching against database sequences.

    Low complexity sequence found by a filter program is
    substituted using the letter "N" in nucleotide sequence
    (e.g., "NNNNNNNNNNNNN") and the letter "X" in protein
    sequences (e.g., "XXXXXXXXX").  Users may turn off
    filtering by using the "Filter" option on the "Advanced
    options for the BLAST server" page.

    Filtering is only applied to the query sequence (or
    its translation products), not to database sequences.
    Default filtering is DUST for BLASTN, SEG for other
    programs.

    It is not unusual for nothing at all to be masked
    by SEG, XNU, or both, when applied to sequences
    in SWISS-PROT, so filtering should not be expected to
    always yield an effect.  Furthermore, in some cases,
    sequences are masked in their entirety, indicating that
    the statistical significance of any matches reported
    against the unfiltered query sequence should be suspect.

    NCBI-gi

    Causes NCBI gi identifiers to be shown in the output,
    in addition to the accession and/or locus name.

### SEARCH STRATEGY

         The fundamental unit of BLAST algorithm output is the  High-
         scoring Segment Pair (HSP).  An HSP consists of two sequence
         fragments of arbitrary but equal length whose  alignment  is
         locally  maximal  and for which the alignment score meets or
         exceeds a threshold or cutoff score.  A set of HSPs is  thus
         defined  by  two  sequences,  a scoring system, and a cutoff
         score; this set may be empty if the cutoff score  is  suffi-
         ciently  high.   In  the programmatic implementations of the
         BLAST algorithm described here, each HSP consists of a  seg-
         ment  from  the  query  sequence  and  one  from  a database
         sequence.  The sensitivity and speed of the programs can  be
         adjusted  via  the standard BLAST algorithm parameters W, T,
         and X (Altschul et al., 1990); selectivity of  the  programs
         can be adjusted via the cutoff score.

         A Maximal-scoring Segment  Pair  (MSP)  is  defined  by  two
         sequences and a scoring system and is the highest-scoring of
         all possible segment pairs that can be produced from the two
         sequences.   The  statistical methods of Karlin and Altschul
         (1990, 1993) are applicable to determining the  significance
         of MSP scores in the limit of long sequences, under a random
         sequence model that assumes independent and identically dis-
         tributed  choices  for  the residues at each position in the
         sequences.  In the programs described here,  Karlin-Altschul
         statistics  have  been extrapolated to the task of assessing
         the significance of HSP scores obtained from comparisons  of
         potentially short, biological sequences.

         The approach to similarity searching taken by the BLAST pro-
         grams  is  first to look for similar segments (HSPs) between
         the query sequence and a database sequence, then to evaluate
         the statistical significance of any matches that were found,
         and finally to report only  those  matches  that  satisfy  a
         user-selectable threshold of significance.  Findings of mul-
         tiple HSPs involving the query sequence and a  single  data-
         base  sequence  may be treated statistically in a variety of
         ways.  By default the programs use "Sum" statistics  (Karlin
         and  Altschul, 1993).  As such, the statistical significance
         ascribed to a set of HSPs may be higher than  that  ascribed
         to any individual member of the set.  Only when the ascribed
         significance  satisfies  the  user-selectable  threshold  (E
         parameter) will the match be reported to the user.

         The task of finding HSPs begins with identifying short words
         of  length  W  in  the  query  sequence that either match or
         satisfy some positive-valued threshold score T when  aligned
         with a word of the same length in a database sequence.  T is
         referred  to  as  the  neighborhood  word  score   threshold
         (Altschul  et  al.,  1990).  These initial neighborhood word
         hits act as seeds for initiating  searches  to  find  longer
         HSPs  containing  them.   The word hits are extended in both
         directions along each sequence for as far as the  cumulative
         alignment  score  can  be  increased.  Extension of the word
         hits in each direction  are  halted  when:   the  cumulative
         alignment score falls off by the quantity X from its maximum
         achieved value; the cumulative score goes to zero or  below,
         due  to  the  accumulation  of  one or more negative-scoring
         residue  alignments;  or  the  end  of  either  sequence  is
         reached.

### KARLIN-ALTSCHUL STATISTICS

         From Karlin and  Altschul  (1990),  the  principal  equation
         relating  the  score  of an HSP to its expected frequency of
         chance occurrence is:

                            E = K N exp(-Lambda S)

         where E is the expected frequency of chance occurrence of an
         HSP having score S (or one scoring higher); K and Lambda are
         Karlin-Altschul parameters; N is the product  of  the  query
         and  database  sequence  lengths,  or the size of the search
         space; and exp is the exponentiation function.

         Lambda may be thought of as the expected increase in  relia-
         bility  of  an  alignment associated with a unit increase in
         alignment score.  Reliability in this case is  expressed  in
         units  of  information,  such  as bits or nats, with one nat
         being equivalent to 1/log(2) (roughly 1.44) bits.

         The expectation E (range 0 to infinity)  calculated  for  an
         alignment between the query sequence and a database sequence
         can be  extrapolated  to  an  expectation  over  the  entire
         database search, by converting the pairwise expectation to a
         probability (range 0-1) and multiplying the  result  by  the
         ratio of the entire database size (expressed in residues) to
         the length of the matching database sequence.  In detail:

                       E_database = (1 - exp(-E)) D / d

         where D is the size of the database; d is the length of  the
         matching  database  sequence; and the quantity (1 - exp(-E))
         is the probability, P, corresponding to  the  expectation  E
         for  the  pairwise  sequence  comparison.   Note that in the
         limit of infinite E, P approaches 1; and in the limit  as  E
         approaches  0, E and P approach equality.  Due to inaccuracy
         in the statistical methods as they are applied in the  BLAST
         programs, whenever E and P are less than about 0.05, the two
         values can be practically treated as being equal.

         In contrast to the random sequence  model  used  by  Karlin-
         Altschul statistics, biological sequences are often short in
         length -- an HSP may involve a relatively large fraction  of
         the  query or database sequence, which reduces the effective
         size of the 2-dimensional search space defined  by  the  two
         sequences.   To obtain more accurate significance estimates,
         the BLAST programs compute effective lengths for  the  query
         and database sequences that are their real lengths minus the
         expected length of the HSP, where the expected length for an
         HSP is computed from its score.  In no event is an effective
         length for the query or database sequence  permitted  to  go
         below  1.  Thus, the effective length of either the query or
         the database sequence is computed according to  the  follow-
         ing:

              Length_eff = MAX( Length_real - Lambda S / H , 1)

         where H is the relative entropy of the target and background
         residue  frequencies (Karlin and Altschul, 1990), one of the
         statistics reported by the BLAST programs.  H may be thought
         of as the information expected to be obtained from each pair
         of aligned residues in a real alignment  that  distinguishes
         the alignment from a random one.

### SCORING SCHEMES

         The default scoring matrix used by blastp, blastx,  tblastn,
         and  tblastx  is the BLOSUM62 matrix (Henikoff and Henikoff,
         1992).

         Several PAM (point  accepted  mutations  per  100  residues)
         amino  acid  scoring  matrices  are  provided  in  the BLAST
         software distribution,  including  the  PAM40,  PAM120,  and
         PAM250.  While the BLOSUM62 matrix is a good general purpose
         scoring matrix and is the default matrix used by  the  BLAST
         programs,  if  one  is  restricted to using only PAM scoring
         matrices, then the PAM120 is recommended for general protein
         similarity  searches  (Altschul,  1991).  The pam(1) program
         can be used to produce PAM matrices of any desired iteration
         from  2  to  511.   Each matrix is most sensitive at finding
         similarities at  its  particular  PAM  distance.   For  more
         thorough searches, particularly when the mutational distance
         between potential homologs is unknown and  the  significance
         of  their  similarity  may be only marginal, Altschul (1991,
         1992) recommends performing at  least  three  searches,  one
         each with the PAM40, PAM120 and PAM250 matrices.

         In blastn, the M parameter sets the reward score for a  pair
         of matching residues; the N parameter sets the penalty score
         for mismatching residues.  M and  N  must  be  positive  and
         negative integers, respectively.  The relative magnitudes of
         M and N determines the number of nucleic  acid  PAMs  (point
         accepted mutations per 100 residues) for which they are most
         sensitive  at  finding  homologs.   Higher  ratios  of   M:N
         correspond to increasing nucleic acid PAMs (increased diver-
         gence).  The default values for M and N, respectively 5  and
         -4,  having  a ratio of 1.25, correspond to about 47 nucleic
         acid PAMs, or about 58 amino acid PAMs; an M:N  ratio  of  1
         corresponds  to  30 nucleic acid PAMs or 38 amino acid PAMs.
         At higher than about 40 nucleic acid PAMs, or 50 amino  acid
         PAMs,  better  sensitivity at detecting similarities between
         coding regions is expected by performing comparisons at  the
         amino  acid  level (States et al., 1991), using conceptually
         translated nucleotide sequences (re:  blastx,  tblastn,  and
         tblastx).

         Independent of the values chosen for M and  N,  the  default
         wordlength  W=11  used  by  blastn  restricts the program to
         finding sequences that share at least an 11-mer  stretch  of
         100%  identity  with  the  query.  Under the random sequence
         model, stretches of 11  consecutive  matching  residues  are
         unlikely  to  occur  merely  by  chance  even  between  only
         moderately diverged homologs.  Thus, blastn with its default
         parameter  settings is poorly suited to finding anything but
         very similar sequences.  If better  sensitivity  is  needed,
         one should use a smaller value for W.

         For the blastn program, it may be easy to see how  multiply-
         ing both M and N by some large number will yield proportion-
         ally larger alignment scores with their statistical signifi-
         cance  remaining  unchanged.  This scale-independence of the
         statistical significance estimates from blastn has its  ana-
         log  in  the  scoring  matrices used by the other BLAST pro-
         grams:  multiplying all elements in a scoring matrix  by  an
         arbitrary  factor  will  proportionally  alter the alignment
         scores but will not  alter  their  statistical  significance
         (assuming  numerical precision is maintained).  From this it
         should be clear that raw alignment  scores  are  meaningless
         without  specific  knowledge  of the scoring matrix that was
         used.

### SCORING REQUIREMENTS

         Regardless of the scoring  scheme  employed,  two  stringent
         criteria  must  be  met in order to be able to calculate the
         Karlin-Altschul parameters Lambda and K.  First,  given  the
         residue  composition  for the query sequence and the residue
         composition assumed for the database,  the  alignment  score
         expected  for  any  randomly  selected pair of residues (one
         from the query sequence and one from the database)  must  be
         negative.   Second,  given the sequence residue compositions
         and the scoring scheme, a positive score must be possible to
         achieve.   For  instance,  the  match reward score of blastn
         must have a positive value; and given the assumption made by
         blastn  that the 4 nucleotides A, C, G and T are represented
         at equal 25% frequencies in the database, a  wide  range  of
         value  combinations  for  M  and N are precluded from use --
         namely those combinations where the magnitude of  the  ratio
         M:N is greater than or equal to 3.

### GENETIC CODES

         The parameter C can be set to a positive integer  to  select
         the  genetic code that will be used by blastx and tblastx to
         translate the query sequence.  The -dbgcode parameter can be
         used  to select an alternate genetic code for translation of
         the database by the programs tblastn and tblastx.   In  each
         case,  the  default genetic code is the so-called "Standard"
         or "Universal" genetic code.  To obtain  a  listing  of  the
         genetic codes available and their associated numerical iden-
         tifiers, invoke blastx or  tblastx  with  the  command  line
         parameter C=list. Note:  the numerical identifiers used here
         for  genetic  codes  parallel  those  defined  in  the  NCBI
         software  Toolbox;  hence  some  numerical  values  will  be
         skipped as genetic codes are updated.

         The list of genetic codes  available  and  their  associated
         values for the parameters C and -dbgcode are:

         1 Standard or Universal

         2 Vertebrate Mitochondrial

         3 Yeast Mitochondrial

         4   Mold,   Protozoan,   Coelenterate   Mitochondrial    and
         Mycoplasma/Spiroplasma

         5 Invertebrate Mitochondrial

         6 Ciliate Macronuclear

         9 Echinodermate Mitochondrial

         10 Alternative Ciliate Macronuclear

         11 Eubacterial

         12 Alternative Yeast

         13 Ascidian Mitochondrial

         14 Flatworm Mitochondrial

### P-VALUES, ALIGNMENT SCORES, AND INFORMATION

         The Expect and P-values reported for HSPs are  dependent  on
         several factors including:  the scoring system employed, the
         residue composition of the query sequence, an assumed  resi-
         due  composition for a typical database sequence, the length
         of the query sequence, and the total length of the database.
         HSP  scores from different program invocations are appropri-
         ate for comparison even if the  databases  searched  are  of
         different  lengths,  as  long as the other factors mentioned
         here do  not  vary.   For  example,  alignment  scores  from
         searches  with  the  default  BLOSUM62  matrix should not be
         directly compared  with  scores  obtained  with  the  PAM120
         matrix;  and  scores produced using two versions of the same
         PAM matrix, each created to different  scales  (see  above),
         can  not  be meaningfully compared without conversion to the
         same scale.

         Some isolation from the many factors involved  in  assessing
         the  statistical  significance  of  HSPs  can be attained by
         observing the information content reported (in bits) for the
         alignments.   While  the  information  content of an HSP may
         change when different scoring systems are used  (e.g.,  with
         different  PAM matrices), the number of bits reported for an
         HSP will at least be independent of the scale to  which  the
         scoring  matrix was generated.  (In practice, this statement
         is not quite true, because the alignment scores used by  the
         BLAST  programs  are integers that lack much precision).  In
         other words, when conveying the statistical significance  of
         an  alignment,  the  alignment  score  itself  is not useful
         unless the specific scoring matrix that was employed is also
         provided, but the informativeness of an alignment is a mean-
         ingful statistic that can be  used  to  ascribe  statistical
         significance  (a  P-value)  to  the  match  independently of
         specific knowledge about the scoring matrix.

### SAMPLE OUTPUT

         The BLAST programs all provide information  in  roughly  the
         same  format.   First  comes (A) an introduction to the pro-
         gram; (B) a histogram of expectations (see above) if one was
         requested; (C) a series of one-line descriptions of matching
         database sequences; (D) the actual sequence alignments;  and
         finally  the parameters and other statistics gathered during
         the search.

         Sample blastp output from comparing pir|A01243|DXCH  against
         the SWISS-PROT database is presented below.

      A. Program Introduction
         The introductory output provides the program name (BLASTP in
         this  case),  the version number (1.4.6MP in this case), the
         date the program  source  code  last  changed  substantially
         (June  13,  1994), the date the program was built (Sept. 22,
         1994), and a description of the query sequence and  database
         to be searched.  These may all be important pieces of infor-
         mation if a  bug  is  suspected  or  if  reproducibility  of
         results is important.

         The "Searching..." indicator  indicates  progress  that  the
         program made in searching the database.  A complete database
         search will yield 50 periods (.), or one period per database
         sequence,  whichever  number  is  smaller.  When searching a
         database consisting of 50 sequences or more, if  fewer  than
         50  periods  are  displayed and the program aborted for some
         reason, dividing the number of periods by 0.5 will yield the
         approximate  percentage  (0-100%)  of  the database that was
         searched before the program died.  If the program had diffi-
         culty  making  progress  through  the  database, one or more
         asterisks (*) may be interspersed  between  the  periods  at
         one-minute intervals.

      B. Histogram of Expectations
         Shown in the output below is a histogram of the lowest (most
         significant)  Expect  values  obtained  with  each  database
         sequence.  This information is  useful  in  determining  the
         numbers  of  database  sequences  that achieved a particular
         level of statistical significance.  It indicates the  number
         of database matches that would be reportable at various set-
         tings for the expectation threshold (E parameter).

      C. One-line Summaries
         The one-line sequence descriptions and summaries of  results
         are useful for identifying biologically interesting database
         matches and correlating this interest with  the  statistical
         significance  estimates.   Unless  otherwise  requested, the
         database sequences are sorted by increasing P-value  (proba-
         bility).   Identifiers  for the database sequences appear in
         the first column;  then  come  brief  descriptions  of  each
         sequence,  which may need to be truncated in order to fit in
         the available space.  The "High Score" column  contains  the
         score  of  the  highest-scoring HSP found with each database
         sequence.  The "P(N)" column  contains  the  lowest  P-value
         ascribed  to any set of HSPs for each database sequence; and
         the "N" column displays the number of HSPs in the set  which
         was  ascribed  the lowest P-value.  The P-values are a func-
         tion of N, as used in Karlin-Altschul  "Sum"  statistics  or
         Poisson  statistics, to treat situations where multiple HSPs
         are found.  It should be noted that the highest-scoring  HSP
         whose  score  is  reported in the "High Score" column is not
         necessarily a member of the set of  HSPs  which  yields  the
         lowest P-value; the highest-scoring HSP may be excluded from
         this set on the basis of  consistency  rules  governing  the
         grouping  of HSPs (see the -consistency option).  Numbers of
         the form "7.7e-160" are in  scientific  notation.   In  this
         particular  example,  the  number  being  represented is 7.7
         times 10 to the minus 160th power.  which is  astronomically
         close to zero.

      D. Alignments
         Alignments found with  the  BLAST  algorithm  are  ungapped.
         Several  statistics  are used to describe each HSP:  the raw
         alignment Score; the raw score converted to bits of informa-
         tion  by  multiplying by Lambda (see the Statistics output);
         the number of times one might Expect to see such a match (or
         a  better one) merely by chance; the P-value (probability in
         the range 0-1) of observing such a  match;  the  number  and
         fraction  of  total residues in the HSP which are identical;
         the number and fraction of residues for which the  alignment
         scores  have positive values.  When Sum statistics have been
         used to calculate the Expect and P-values,  the  P-value  is
         qualified  with  the  word "Sum" and the N parameter used in
         the Sum statistics is provided in  parentheses  to  indicate
         the  number of HSPs in the set; when Poisson statistics have
         been used to calculate the Expect and P-values, the  P-value
         is qualified with the word "Poisson".  Between the two lines
         of Query and Subject (database) sequence is a line  indicat-
         ing  the  specific  residues which are identical, as well as
         those which are non-identical but nevertheless have positive
         alignment scores defined in the scoring matrix that was used
         (the BLOSUM62 matrix in this case).   Identical  letters  or
         residues,  when  paired with each other, are not highlighted
         if their alignment score is negative or zero.   Examples  of
         this  would  be  an X juxtaposed with an X in two amino acid
         sequences, or an N juxtaposed with another N in two  nucleo-
         tide sequences.  Such ambiguous residue-residue pairings may
         be uninformative and thus lend no  support  to  the  overall
         alignment being either real or random; however, the informa-
         tiveness of these pairings is left up to  the  user  of  the
         BLAST  programs to decide, because any values desired can be
         specified in a scoring matrix of the user's own making.

         BLASTP 1.4.6MP [13-Jun-94] [Build 13:58:36 Sep 22 1994]

         Reference:  Altschul, Stephen F., Warren Gish, Webb Miller, Eugene W. Myers,
         and David J. Lipman (1990).  Basic local alignment search tool.  J. Mol. Biol.
         215:403-10.

         Query =  pir|A01243|DXCH  232 Gene X protein - Chicken (fragment)
                 (232 letters)

         Database:  SWISS-PROT Release 29.0
                    38,303 sequences; 13,464,008 total letters.
         Searching..................................................done

              Observed Numbers of Database Sequences Satisfying
              Various EXPECTation Thresholds (E parameter values)

                 Histogram units:      = 31 Sequences     : less than 31 sequences

          EXPECTation Threshold
          (E parameter)
             |
             V   Observed Counts-->
           10000 4863 1861 |============================================================
            6310 3002  782 |=========================
            3980 2220  812 |==========================
            2510 1408  303 |=========
            1580 1105  393 |============
            1000  712  179 |=====
             631  533  161 |=====
             398  372   80 |==
             251  292   73 |==
             158  219   50 |=
             100  169   32 |=
            63.1  137   18 |:
            39.8  119    9 |:
            25.1  110    6 |:
            15.8  104    9 |:
          >>>>>>>>>>>>>>>>>>>>>  Expect = 10.0, Observed = 95  <<<<<<<<<<<<<<<<<
            10.0   95    4 |:
            6.31   91    3 |:
            3.98   88    1 |:
            2.51   87    3 |:
            1.58   84    0 |
            1.00   84    2 |:

                                                                              Smallest
                                                                                Sum
                                                                       High  Probability
         Sequences producing High-scoring Segment Pairs:              Score  P(N)      N

         sp|P01013|OVAX_CHICK GENE X PROTEIN (OVALBUMIN-RELATED) (...  1191  7.7e-160  1
         sp|P01014|OVAY_CHICK GENE Y PROTEIN (OVALBUMIN-RELATED).       949  7.0e-127  1
         sp|P01012|OVAL_CHICK OVALBUMIN (PLAKALBUMIN).                  645  3.4e-100  2
         sp|P19104|OVAL_COTJA OVALBUMIN.                                626  1.2e-96   2
         sp|P05619|ILEU_HORSE LEUKOCYTE ELASTASE INHIBITOR (LEI).       216  3.7e-71   3
         sp|P80229|ILEU_PIG   LEUKOCYTE ELASTASE INHIBITOR (LEI) (...   325  4.0e-71   2
         sp|P29508|SCCA_HUMAN SQUAMOUS CELL CARCINOMA ANTIGEN (SCC...   439  3.5e-70   2
         sp|P30740|ILEU_HUMAN LEUKOCYTE ELASTASE INHIBITOR (LEI) (...   211  1.3e-66   3
         sp|P05120|PAI2_HUMAN PLASMINOGEN ACTIVATOR INHIBITOR-2, P...   176  1.8e-65   4
         sp|P35237|PTI_HUMAN  PLACENTAL THROMBIN INHIBITOR.             473  1.3e-61   1
         sp|P29524|PAI2_RAT   PLASMINOGEN ACTIVATOR INHIBITOR-2, T...   183  9.4e-61   4
         sp|P12388|PAI2_MOUSE PLASMINOGEN ACTIVATOR INHIBITOR-2, M...   179  1.8e-60   4
         sp|P36952|MASP_HUMAN MASPIN PRECURSOR.                         198  2.6e-58   4
         sp|P32261|ANT3_MOUSE ANTITHROMBIN-III PRECURSOR (ATIII).       142  4.0e-48   5
         sp|P01008|ANT3_HUMAN ANTITHROMBIN-III PRECURSOR (ATIII).       122  7.5e-48   5

         WARNING:  Descriptions of 80 database sequences were not reported due to the
                   limiting value of parameter V = 15.

           ... alignments with the top 8 database sequences deleted ...

         >sp|P05120|PAI2_HUMAN PLASMINOGEN ACTIVATOR INHIBITOR-2, PLACENTAL (PAI-2)
                     (MONOCYTE ARG- SERPIN).
                     Length = 415

          Score = 176 (80.2 bits), Expect = 1.8e-65, Sum P(4) = 1.8e-65
          Identities = 38/89 (42%), Positives = 50/89 (56%)

         Query:     1 QIKDLLVSSSTDLDTTLVLVNAIYFKGMWKTAFNAEDTREMPFHVTKQESKPVQMMCMNN 60
                      +I +LL   S D DT +VLVNA+YFKG WKT F  +     PF V   +  PVQMM +
         Sbjct:   180 KIPNLLPEGSVDGDTRMVLVNAVYFKGKWKTPFEKKLNGLYPFRVNSAQRTPVQMMYLRE 239

         Query:    61 SFNVATLPAEKMKILELPFASGDLSMLVL 89
                        N+  +   K +ILELP+A      L+L
         Sbjct:   240 KLNIGYIEDLKAQILELPYAGDVSMFLLL 268

          Score = 165 (75.2 bits), Expect = 1.8e-65, Sum P(4) = 1.8e-65
          Identities = 33/78 (42%), Positives = 47/78 (60%)

         Query:   155 ANLTGISSAESLKISQAVHGAFMELSEDGIEMAGSTGVIEDIKHSPESEQFRADHPFLFL 214
                      AN +G+S    L +S+  H A ++++E+G E A  TG +   +      QF ADHPFLFL
         Sbjct:   338 ANFSGMSERNDLFLSEVFHQAMVDVNEEGTEAAAGTGGVMTGRTGHGGPQFVADHPFLFL 397

         Query:   215 IKHNPTNTIVYFGRYWSP 232
                      I H  T  I++FGR+ SP
         Sbjct:   398 IMHKITKCILFFGRFCSP 415

          Score = 144 (65.6 bits), Expect = 1.8e-65, Sum P(4) = 1.8e-65
          Identities = 26/62 (41%), Positives = 41/62 (66%)

         Query:    90 LPDEVSDLERIEKTINFEKLTEWTNPNTMEKRRVKVYLPQMKIEEKYNLTSVLMALGMTD 149
                      + D  + LE +E  I ++KL +WT+ + M +  V+VY+PQ K+EE Y L S+L ++GM D
         Sbjct:   272 IADVSTGLELLESEITYDKLNKWTSKDKMAEDEVEVYIPQFKLEEHYELRSILRSMGMED 331

         Query:   150 LF 151
                       F
         Sbjct:   332 AF 333

          Score = 61 (27.8 bits), Expect = 1.8e-65, Sum P(4) = 1.8e-65
          Identities = 10/17 (58%), Positives = 16/17 (94%)

         Query:    81 SGDLSMLVLLPDEVSDL 97
                      +GD+SM +LLPDE++D+
         Sbjct:   259 AGDVSMFLLLPDEIADV 275

         WARNING:  HSPs involving 86 database sequences were not reported due to the
                   limiting value of parameter B = 9.

         Parameters:
           V=15
           B=9
           H=1

           -ctxfactor=1.00
           E=10

           Query                        -----  As Used  -----    -----  Computed  ----
           Frame  MatID Matrix name     Lambda    K       H      Lambda    K       H
            +0      0   BLOSUM62        0.316   0.132   0.370    same    same    same

           Query
           Frame  MatID  Length  Eff.Length   E    S W   T  X     E2  S2
            +0      0      232       232      10. 57 3  11 22    0.22 33

         Statistics:
           Query          Expected         Observed           HSPs       HSPs
           Frame  MatID  High Score       High Score       Reportable  Reported
            +0      0    62 (28.2 bits)  1191 (542.5 bits)     330         24

           Query         Neighborhd  Word      Excluded    Failed   Successful  Overlaps
           Frame  MatID   Words      Hits        Hits    Extensions Extensions  Excluded
            +0      0      4988     5661199     1146395     4504598    10187        13

           Database:  SWISS-PROT Release 29.0
             Release date:  June 1994
             Posted date:  1:29 PM EDT Jul 28, 1994
           # of letters in database:  13,464,008
           # of sequences in database:  38,303
           # of database sequences satisfying E:  95
           No. of states in DFA:  561 (55 KB)
           Total size of DFA:  110 KB (128 KB)
           Time to generate neighborhood:  0.03u 0.01s 0.04t  Real: 00:00:00
           No. of processors used:  8
           Time to search database:  32.27u 0.78s 33.05t  Real: 00:00:04
           Total cpu time:  32.33u 0.91s 33.24t  Real: 00:00:05

         WARNINGS ISSUED:  2

### COPYRIGHT

         This work is in the public domain.

### REFERENCES

         Altschul,  Stephen  F.  (1991).   Amino  acid   substitution
         matrices  from an information theoretic perspective. J. Mol.
         Biol.  219:555-65.

         Altschul, S. F. (1993).  A protein alignment scoring  system
         sensitive  at  all  evolutionary  distances.  J.  Mol. Evol.
         36:290-300.

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

         Altschul, Stephen F., Warren Gish, Webb  Miller,  Eugene  W.
         Myers,  and  David  J. Lipman (1990).  Basic local alignment
         search tool. J. Mol. Biol.  215:403-10.

         Claverie,  J.-M.  and  D.  J.  States  (1993).   Information
         enhancement  methods for large scale sequence analysis. Com-
         puters in Chemistry 17:191-201.

         Gish, W. and D. J. States (1993).  Identification of protein
         coding  regions by database similarity search. Nature Genet-
         ics 3:266-72.

         Henikoff, Steven and Jorga G. Henikoff (1992).   Amino  acid
         substitution matrices from protein blocks. Proc. Natl. Acad.
         Sci. USA 89:10915-19.

         Karlin, Samuel and Stephen F. Altschul (1990).  Methods  for
         assessing the statistical significance of molecular sequence
         features by using general scoring schemes. Proc. Natl. Acad.
         Sci. USA 87:2264-68.

         Karlin, Samuel and Stephen F. Altschul (1993).  Applications
         and statistics for multiple high-scoring segments in molecu-
         lar sequences. Proc. Natl. Acad. Sci. USA 90:5873-7.

         States, D. J. and W. Gish (1994).  Combined use of  sequence
         similarity  and codon bias for coding region identification.
         J. Comput. Biol.  1:39-50.

         States, D. J., W. Gish and S. F. Altschul (1991).   Improved
         sensitivity  of  nucleic  acid  database similarity searches
         using application specific scoring matrices. Methods: A com-
         panion to Methods in Enzymology 3:66-70.

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