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BLAST
BLAST searches for sequences similar to a query sequence. The query
and the database searched can be either peptide or nucleic acid in
any combination. BLAST can search databases on your own computer or
databases maintained at the National Center for Biotechnology
Information (NCBI) in Bethesda, Maryland, USA.
DESCRIPTION
BLAST, or Basic Local Alignment Search Tool, uses the method of
Altschul et al. (J. Mol. Biol. 215; 403-410 (1990)) to search for
similarities between a query sequence and all the sequences in a
database. The query sequence and the database you want to search can
be either protein or nucleic acid in any combination. The GCG BLAST
program supports five different programs in the BLAST family:
BLASTP, Protein Query Searching a Protein Database
Each database sequence is compared to the query in a separate
protein-protein pairwise comparison.
BLASTX, Nucleotide Query Searching a Protein Database
The query is translated, and each of the six products is
compared to each database sequence in a separate protein-protein
pairwise comparison.
BLASTN, Nucleotide Query Searching a Nucleotide Database
Each database sequence is compared to the query in a separate
nucleotide-nucleotide pairwise comparison.
TBLASTN, Protein Query Searching a Nucleotide Database
Each nucleotide database sequence is translated, and each of the
six products is compared to the query in a separate
protein-protein pairwise comparison.
TBLASTX, Nucleotide Query Searching a Nucleotide Database
The query and each database sequence are both translated in six
frames, and each of the 12 products is compared in 36 different
pairwise comparisons. This program is compute-intensive and is
therefore limited to searches of Alu, STS, and EST sequences.
Normally, BLAST decides which BLAST program you want to use simply by
looking at the type (protein or nucleic acid) of your query sequence
and the database you have selected. In the case of
nucleotide-nucleotide searches, there is more than one program that
can do the search. Adding -TBLASTX on the command line means you
want to use the TBLASTX instead of BLASTN.
BLAST either can search databases maintained at your institution (a
local search), or if you are attached to the Internet, it can search
databases maintained by NCBI (a remote search). Remote searches
require almost no resources from your own computer. More
importantly, the databases at NCBI are updated daily and may be more
current than those maintained locally.
BLAST is a statistically driven search method that finds regions of
similarity between your query and a database. Within these regions
of similarity, called segment pairs, the sum of the scoring matrix
values of their constituent symbol pairs is higher than some level
that you would expect to occur by chance alone.
You are prompted to set an expectation level for the entire search.
By default this level is 10.0, which means that hits are not reported
unless their scores are above scores that would be expected to occur
more than 10 times by chance alone in the whole search.
OUTPUT
Below is part of the output from the search in the example session:
///////////////////////////////////////////////////////////////////////
BLASTP 1.4.8MP [19-Dec-94] [Build 16:54:47 Mar 16 1995]
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= TITLE sw:zea2_maize
(235 letters)
Database: SWISS-PROT Release 31.0
43,470 sequences; 15,335,248 total letters.
Smallest
Sum
High Probability
Sequences producing High-scoring Segment Pairs: Score P(N) N
sp|P04704|ZEA2_MAIZE ZEIN-ALPHA PRECURSOR (19 KD) (CLONE 1164 2.7e-160 1
sp|P24449|ZEAC_MAIZE ZEIN-ALPHA PRECURSOR (19 KD) (PMS1) 1103 1.0e-151 1
sp|P02859|ZEA1_MAIZE ZEIN-ALPHA PRECURSOR (19 KD) (CLONE 644 2.3e-149 2
sp|P25146|INLA_LISMO INTERNALIN A. 56 0.99992 1
sp|P32917|STE5_YEAST STE5 PROTEIN. 48 0.99992 2
sp|P13483|DATI_YEAST OLIGO(DA)/OLIGO(DT)-BINDING PROTEIN . 51 0.99994 2
>sp|P04704|ZEA2_MAIZE ZEIN-ALPHA PRECURSOR (19 KD) (CLONE ZG99).
Length = 235
Score = 1164 (544.2 bits), Expect = 2.7e-160, P = 2.7e-160
Identities = 235/235 (100%), Positives = 235/235 (100%)
Query: 1 MAAKIFCLIMLLGLSASAATASIFPQCSQAPIASLLPPYLSPAMSSVCENPILLPYRIQQ 60
MAAKIFCLIMLLGLSASAATASIFPQCSQAPIASLLPPYLSPAMSSVCENPILLPYRIQQ
Sbjct: 1 MAAKIFCLIMLLGLSASAATASIFPQCSQAPIASLLPPYLSPAMSSVCENPILLPYRIQQ 60
Query: 61 AIAAGILPLSPLFLQQSSALLQQLPLVHLLAQNIRAQQLQQLVLANLAAYSQQQQFLPFN 120
AIAAGILPLSPLFLQQSSALLQQLPLVHLLAQNIRAQQLQQLVLANLAAYSQQQQFLPFN
Sbjct: 61 AIAAGILPLSPLFLQQSSALLQQLPLVHLLAQNIRAQQLQQLVLANLAAYSQQQQFLPFN 120
Query: 121 QLAALNSAAYLQQQQLLPFSQLAAAYPRQFLPFNQLAALNSHAYVQQQQLLPFSQLAAVS 180
QLAALNSAAYLQQQQLLPFSQLAAAYPRQFLPFNQLAALNSHAYVQQQQLLPFSQLAAVS
Sbjct: 121 QLAALNSAAYLQQQQLLPFSQLAAAYPRQFLPFNQLAALNSHAYVQQQQLLPFSQLAAVS 180
Query: 181 PAAFLTQQQLLPFYLHTAPNVGTLLQLQQLLPFDQLALTNPAAFYQQPIIGGALF 235
PAAFLTQQQLLPFYLHTAPNVGTLLQLQQLLPFDQLALTNPAAFYQQPIIGGALF
Sbjct: 181 PAAFLTQQQLLPFYLHTAPNVGTLLQLQQLLPFDQLALTNPAAFYQQPIIGGALF 235
////////////////////////////////////////////////////////////////////////
>sp|P13483|DATI_YEAST OLIGO(DA)/OLIGO(DT)-BINDING PROTEIN (DATIN).
Length = 248
Score = 51 (23.8 bits), Expect = 9.8, Sum P(2) = 1.0
Identities = 12/26 (46%), Positives = 16/26 (61%)
Query: 166 QQQQLLPFSQLAAVSPAAFLTQQQLL 191
QQQQLL ++ V P F+T + LL
Sbjct: 119 QQQQLLQQQRVDVVPPKPFITHKILL 144
///////////////////////////////////////////
Parameters:
V=250
B=100
-ctxfactor=1.00
E=10
Query
Frame MatID Length Eff.Length E S W T X E2 S2
+0 0 235 235 10. 57 3 10 22 0.24 32
Statistics:
Query Expected Observed HSPs HSPs
Frame MatID High Score High Score Reportable Reported
+0 0 60 (28.0 bits) 1164 (544.2 bits) 683 683
Database: SWISS-PROT Release 31.0
Release date: March 1995
Posted date: 1:06 PM EST Mar 21, 1995
# of letters in database: 15,335,248
# of sequences in database: 43,470
# of database sequences satisfying E: 80
No. of states in DFA: 530 (52 KB)
Total size of DFA: 120 KB (128 KB)
Time to generate neighborhood: 0.04u 0.01s 0.05t Real: 00:00:00
No. of processors used: 8
Time to search database: 55.05u 1.40s 56.45t Real: 00:00:08
Total cpu time: 55.33u 1.50s 56.83t Real: 00:00:09
The output has four parts: 1) an introduction that tells where the
search occurred and what database and query were compared; 2) a list
of the sequences in the database containing segment pairs whose
scores were least likely to have occurred by chance; 3) alignments of
the high-scoring segment pairs showing identical and similar
residues; and 4) a complete list of the parameter settings used for
the search.
Since BLAST only finds segment pairs that do not contain gaps, there
will often be more than one segment pair associated with each
database sequence.
INTERPRETING_OUTPUT
Scores
For the list of sequences, the High Score is the score of the
highest-scoring segment pair for the pairwise comparison of that
database sequence and your query. For the segment pair display,
the Score is the sum of the scoring matrix values in the segment
pair being displayed.
Probabilities
There is a probability, for instance 2.7e-160 in the example,
associated with each pairwise comparison in the list and with
each segment pair alignment. For the list, this number is the
probability that by chance you would observe a score or group of
scores as high as the observed high score (or scores) when you
do a search of the same size. For the segment displays, the
probability P is the probability that a segment pair score as
high as that particular segment pair's score would be observed
in a search of the same size. This probability may not be as
low as the probability for the whole pairwise comparison if more
than one segment pair contributed to the probability for the
whole comparison.
An ideal search would find hits that go from extremely unlikely
to ones whose best scores should have occurred by chance alone
(that is, with probabilities approaching 1.0).
N Score
If the Smallest Sum Probability shown for any member of the list
arose from combining more than one HSP score from the same
pairwise comparison, this number tells how many HSPs were
involved in the combination. There is more about the concept of
combining the scores of more than one HSP under the THEORY
topic.
Bit Score
Each aligned segment pair has a normalized score expressed in
bits that lets you estimate the magnitude of the search space
you would have to look through before you would expect to find
an HSP score as good as or better than this one by chance. If
the bit score is 30, you would have to score, on average, about
1 billion independent segment pairs to find a score this good by
chance. Each additional bit doubles the size of the search
space. This bit score represents a probability; one over two
raised to this power is the probability of finding such a
segment by chance. Bit scores represent a probability level for
sequence comparisons that is independent of the size of the
search.
The size of the search space is proportional to the product of
the query sequence length times the sum of the lengths of the
sequences in the database. This product, referred to as N in
Altschul's publications, is multiplied by a coefficient K to get
the size of the search space. When searching protein databases
with protein queries, K is about 0.13. This product for the
search in the example session is 235 x 12,496,420 x 0.13 or
about 0.38 billion, so a bit score of 30 corresponding to a
space 1 billion could easily have occurred by chance alone.
BLAST Parameters
At the end of the output is a complete listing of parameter
settings along with some trace information about the search.
Some of these parameters are described in this document, but to
get complete documentation on these parameters, look at the
BLAST document on the World Wide Web at
http://www.ncbi.nlm.nih.gov/Recipon/bm_blast.html.
RELATED_PROGRAMS
ToBLAST combines any set of GCG sequences into a database that you
can search with BLAST.
FastA does a Pearson and Lipman search for similarity between a query
sequence and any group of sequences. For nucleotide database
searches, FastA is more sensitive than BLAST.
ProfileSearch uses a profile (representing a group of aligned
sequences) as a query to search the database for new sequences with
similarity to the group. The profile is created with the program
ProfileMake.
FindPatterns, StringSearch, and Names are other sequence
identification programs.
CHOOSING_SEARCH_SETS
BLAST can search only a specially compressed form of the data.
Therefore, you can search only those databases that are available in
this form, and you must search them in their entirety. If you want
to restrict the search to a specific set of sequences, use the
program ToBLAST to create a specially compressed database consisting
of just those sequences.
To name a searchable database interactively, choose the number of the
database of interest from the menu. On the command line, use an
expression like -INfile2=genbank to choose the name of the database
you want to search.
Unfortunately, because there are both nucleotide and protein
databases called nr (and pdb) in the menu, BLAST cannot be sure which
nr you mean if you use an expression like -INfile2=nr on the command
line. Therefore, if the database you want to search cannot be named
unambiguously with the -INfile2 qualifier, add either
-DBNucleotideonly or -DBProteinonly to the command line.
THEORY
This section is for advanced users.
Definition of a Maximal-scoring Segment Pair (MSP)
In this discussion a segment pair refers to a gapless alignment
of any part of two sequences. The score for any particular
segment pair is the sum of the scoring matrix values for the
symbol pairs in the segment. A maximal-scoring segment pair or
MSP is the segment pair with the highest score in a pairwise
comparison. MSPs scores cannot be improved by extending or
shortening the segment pair at either end. There will be at
least one MSP for every pairwise comparison.
Highest MSP Score Expected by Chance Can Be Estimated
Karlin and Altschul have shown that for a set of sequence
comparisons of known size and composition, the highest segment
pair score S expected to occur by chance can be estimated if you
can assume that 1) the frequencies of substitution of each
residue to every other residue at some level of divergence are
known and 2) the probability of finding each amino acid at each
position in a protein is simply proportional to its frequency in
the database (rather than being heavily constrained by
neighboring residues). (Proc. Natl. Acad. Sci. USA 87; 2264-68
(1990)). Using the highest expected S-score as a cutoff, all
segment pairs scoring above it would not be expected to occur by
chance. The ability to discriminate between expected and
unexpected segment pair scores is the core concept in BLAST.
In later work, Karlin and Altschul showed that you can find
statistically significant groups of high-scoring segment pairs
(HSPs) at locations consistent with their being joined together
in an alignment even when each HSP score taken by itself would
not have been significant. (Proc. Natl. Acad. Sci. USA 90;
5873-5877 (1993)).
PAM Matrices Defined
Dayhoff measured the rate at which any residue is substituted
for any other residue in a set of alignments where there were 10
or fewer substitutions per 100 residues. (With few
substitutions she could be sure of the alignments and reasonably
sure that each observed residue difference arose from a single
substitution event.) Given a particular starting residue, the
expected frequency of each kind of residue observed at its
position depends on how long the sequences being compared have
been diverging from each other. (Different protein families
diverge at different rates.) Dayhoff invented a scale based on
the number of point mutations incorporated into proteins. The
scale is referred to as PAM, a rearranged acronym for Accepted
Point Mutation. The distance that two protein sequences have
diverged from one another is often expressed in PAM units and
referred to as a PAM distance Two homologous proteins at a PAM
distance of 10 should have about 10 substitutions per 100
residues. (Atlas of Protein Sequences and Structure,
M.O. Dayhoff, ed., Volume 5, Supplement 3, pp; 353-358,
National Biomedical Research Foundation, Washington, D.C., USA,
(1979)).
Dayhoff scored substitutions in sequences whose PAM distances
were small, 10 or less. This measurement let her infer the rate
of substitution per PAM distance unit of each amino acid into
every other. Knowing the frequency of each amino acid, the
frequency of each amino acid pair at any PAM distance could then
be calculated. These frequencies are referred to as target
frequencies in the literature. They are the expected frequency
that you would observe each amino-acid pair in correctly aligned
homologs that have diverged by a known amount.
The ratio of each of these target frequencies to the frequency
for that same amino acid pair in the background is the
information that makes homologous sequences detectable. PAM
matrices contain, for a given PAM distance, the negative
logarithms of this ratio for each amino acid pair. A segment
pair score, in the context of BLAST, is simply the sum of the
corresponding PAM matrix values for each amino acid pair in the
segment pair.
Interesting Alignments Probably Contain HSPs
One assumption of BLAST is that an optimal alignment between any
two diverged, but still observably-similar sequences will
contain one or more segment pairs with scores high enough not to
be expected by chance. If this is true, then you have a
statistic that can distinguish pairs of sequences that are
related from pairs that are not. If you could discover a fast
way to find all the segment pairs in a pairwise comparison that
score above some reasonable cutoff score, you could build a tool
to search a sequence database and report only sequences whose
similarity to your query is significant. BLAST uses a heuristic
algorithm that tries to do this.
ALGORITHM
If you have understood the THEORY topic you are ready to learn more
about how BLAST is implemented.
Most HSPs Will Have Regions of Near Identity
BLAST assumes that any high-scoring segment pair will contain
two similar (but not necessarily identical) words within it.
Similar Words Can Be Treated As Synonyms
Consider every possible word pair in the language of words of
fixed length W as tiny segment pairs, each with some score t.
Like a segment pair score, this t-score is the sum of the
scoring matrix values for the symbol pairs in the word pair.
There is a second user-setable parameter T, at or above which
the t-score from any word pair indicates that the words in that
pair should be considered synonymous. Altschul and company
refer to any group of synonyms of length W and score T or
greater as a neighborhood. For a given scoring matrix and value
of W, increasing T reduces the size of the neighborhood, while
decreasing T increases the size of the neighborhood.
Finding Word Matches
BLAST belongs to a class of comparisons that use k-tuple
preprocessing. More specifically, BLAST uses what is known in
our trade as a discrete finite automaton (DFA) that, for any
word in a database sequence, returns the positions where that
word or its synonyms occur in the query sequence. (The details
of the DFA's implementation have not, to our knowledge, been
published.) The search consists of taking each word in each of
the database sequences and finding out if it or any word
synonymous with it occurs in the query sequence.
Scoring the Segment Pair Associated With Each Word Match
If a word or synonym from a searched sequence occurs in the
query, BLAST extends the alignment where the word pair occurs in
both directions to find the highest score for the segment pair
that contains that word pair.
To minimize the time spent extending the alignment, BLAST needs
to decide if the best score has been found without scoring all
the symbol pairs on the diagonal. In practice, when the segment
pair score falls to zero or decreases by an amount equal to or
greater than a parameter X, extension is terminated. If the
score ever reaches S, the whole diagonal is scored. The segment
extension drop-off score X is normally calculated for the user
to be an amount equal to 10 bits of information representing a
fall-off in the significance of the score by a factor of about
1,000. For nucleotide database searches with nucleotide
queries, X is calculated to be equivalent to a drop-off of 20
bits of information.
HSPs and MSPs Revisited
If the score for the segment pair is higher than some threshold,
the score and end points of the segment pair are stored. These
high-scoring segment pairs are referred to as HSPs. The highest
scoring segment pair for the whole pairwise comparison is
referred to as the maximal-scoring segment pair or MSP.
Determining What to Report
When each pairwise comparison is complete, all the HSPs are
analyzed to see if some could be combined into an alignment
(that is, they do not overlap in either dimension by more than a
certain percentage of their lengths). If the MSP score or the
best combined HSP score is above a cutoff score, then the
sequence is listed and the high-scoring segment pairs in it are
displayed in the output. The MSP score is the High Score shown
next to each sequence in the output list.
Displaying the Best Comparisons and Their Segment Pairs
When the search is complete, BLAST sorts the pairwise
comparisons with MSPs above the cutoff by probability and makes
a list with one sequence per line showing the most significant
hits. You must set a list size to limit the number of sequences
displayed in this list. Following the list are alignments of
the high-scoring segment pairs from the 100 highest-scoring
sequences displayed in the list. You can display more segment
pairs with the optional parameter -SEGments if you can tolerate
the large amount of output involved.
CONSIDERATIONS
Bit Scores and the Size of the Search
Altschul has shown that for sequences that have diverged by a
certain amount, there is an informativeness (or ability to
discriminate between chance scores and significant scores)
associated with each residue pair in the segment pair. This
informativeness is the amount of information obtainable from
each residue pair in a real alignment that can be used to
distinguish the real alignment from a random one. This
informativeness can be expressed in bits. The sum of the
information available from each residue pair in a segment is the
segment pair's score in bits. Such scores are intuitively
understandable as the significance of a segment pair score. To
express such scores as a fraction you would divide 1 by 2 to the
number of bits in the score. For example, if a segment pair has
a bit-score of 16, then the appropriate fraction would suggest
that you should see a score this high by chance about once for
every 65,000 independent segment pairs you examine.
For nucleotide sequences that have not diverged, there should be
an informativeness of about 2 bits per nucleotide pair. For
protein sequences that have not diverged, the informativeness
should be slightly over 4 bits per amino acid pair. (The
informativeness per pair goes down as the sequences diverge and
a segment pair score is maximally informative only when a
scoring matrix appropriate to the extent of divergence between
the sequences is used to calculate the score.)
The bit scores are absolute, but the expectation of finding any
particular score depends on the size of the search space. The
number of places where a segment pair might originate is
proportional to the product of the length of the query times the
sum of the lengths of all the sequences searched. This product
is multiplied by a coefficient K to get the size of the search
space. When searching protein databases with protein queries, K
is approximately 0.13.
For a query sequence of length 300 aa searching a database of 12
million residues, the size of the search space would be 300 x
12,000,000 x 0.13 or 468,000,000. For a search this size, a
score that only occurs once in every 65,000 potential segment
pairs (that is, with a bit score of 16) would be expected to
occur about 7,200 times by chance alone.
If the database being searched is highly redundant (as it might
be if it contained several hundred homologous cytochromes), then
size of the search space calculated by these methods will
overestimate the size of the real search space.
Using BLAST for Nucleotide Searches
By default BLAST ignores HSPs that do not contain a perfect
match of at least 11 nucleotides (22 bits). This is stringent
enough that many obviously-significant relationships are not
found.
The detection of distant relationships between proteins is
easier than between nucleotide sequences, even if the nucleotide
sequences have to be translated in all six frames to make the
amino acid comparison. To give a rough magnitude to this
generalization, it is possible to detect similarities in
proteins that have diverged by 250 substitutions per 100
residues (250 PAM units) while nucleotide similarities become
obscure at distances much greater than 50 substitutions per 100
nucleotides (50 DNA PAM units). Nonetheless, when the
nucleotide sequences being compared do not code for proteins,
you have no alternative but to search at the nucleotide level.
We suggest you consider either reducing the word size for BLAST
from its default of 11 to perhaps six or seven or use the FastA
program when looking for nucleotide homologs.
Sensitivity
Quoting from the man pages for BLAST, "At some point accumulated
mutations and errors completely obscure the presence of a
relationship between two sequences; the BLAST programs' focus on
ungapped alignments will sometimes cause this point to be
reached sooner than for other alignment methods."
SUGGESTIONS
List Size Limit
A list size that is too small to display all the significant
hits is a common problem. Both the screen and the output file
will print a warning showing the number of significant hits that
are not shown in the list. To see the unlisted hits you must
run the search again with the list size limit set high enough to
include everything significant. The output can get very very
large if you set the list size to 1,000. It cannot be set to
more than 1,000. If you cannot display everything of interest
with a list size limit of 1,000, see the topic FILTERING OUT LOW
COMPLEXITY SEQUENCES below.
Segment Pair Display Limit
BLAST displays segment pairs from the top 100 sequences in the
list. You can adjust this limit with the -SEGments option.
Sensitivity
For nucleotide sequence comparisons, the word size defaults to
11--no segment pair can be scored unless it contains a perfect
match of this length. If sensitivity is much more important
than selectivity, and your search cannot be done at the amino
acid level, you might want to reduce the word size to seven or
even six, but no lower.
BLAST uses a word size of 3 for proteins which is appropriate
for a wide range of searches, but you can adjust the synonym
threshold T downwards to increase sensitivity at the price of
speed. Read the OPTIONAL PARAMETERS topic for the parameters
-SYNonym, and -EXPect.
Batch Queue
Using BLAST to search a large local database can take a long
time. You may want to run comparisons either remotely or in the
batch queue. You can run this program in the batch queue on
many computers by using the command-line option -BATch. Run
this way, the program prompts you for all the required
parameters and then automatically submits itself to the batch or
at queue. Batch jobs free your terminal for other work and may
allow the system manager to distribute the load on your computer
more evenly. For more information, see "Using the Batch Queue"
in Chapter 3, Basic Concepts: Using Programs in the User's
Guide.
Relationship to FastA
For protein database searches, BLAST and FastA have similar
sensitivity, although the different algorithms employed make it
possible, at least in principle, for FastA to find things that
BLAST misses and vice versa. For nucleotide database searches
with nucleotide query sequences, FastA may be more sensitive,
since by default BLAST ignores segment pairs that do not contain
a perfect match of at least 11 adjacent nucleotides (22 bits).
This default misses many obviously-significant relationships.
If you are looking for nucleotide sequence homologs that do not
code for proteins (that is, if your search cannot be done at the
amino acid level), we suggest you either reduce the word size to
seven or use the FastA program instead of BLAST.
Search Orientation
BLAST searches both strands of nucleic acid query sequences and
database entries. Read the OPTIONAL PARAMETERS topic for the
parameters -TOPstrand, -BOTtomstrand, -DBTOPstrand, and
-DBBOTtomstrand for information about how to limit the search.
FILTERING_OUT_LOW_COMPLEXITY_SEQUENCES
Short repeats and low complexity sequences, such as glutamine-rich
regions, confound most database searching methods. For BLAST the
random model against which the significance of segment pair scores is
evaluated assumes that at each position, each residue has a
probability of occurring which is proportional to its composition in
the database as a whole. Low complexity or highly repetitive
sequences are inconsistent with this assumption. Suspect this
problem when the number of significant segment pair scores is much
higher than you would expect. The output is either enormous or the
output size limits cut off your output long before all the segments
are displayed.
You can filter out repeats and low complexity regions from protein
query sequences by adding -FILter=xs to the command line. The x
filter (Claverie and States, Computers Chem. 17 191-201, (1993))
masks short repeats, the s filter masks low complexity sequences
(Wootton and Federhen, Computers Chem. 17 149-163 (1993)).
When you run BLAST with filtering on, any masked regions are simply
excluded from the search. The output will show the masked regions
with X's to let you see which regions were excluded from the search.
Here is the query sequence from the example session aligned to itself
after filtering to show the parts of the sequence that were filtered
out:
1 MAAKIFCLIMXXXXXXXXXXXXIFPQCSQAPIASLLPPYLSPAMSSVCENPILLPYRIQQ 60
1 MAAKIFCLIMLLGLSASAATASIFPQCSQAPIASLLPPYLSPAMSSVCENPILLPYRIQQ 60
61 AIAAGIXXXXXXXXXXXXXXXXXXXXXXXXXXNIRXXXXXXXXXXXXXXYSQQQQFLPFN 120
61 AIAAGILPLSPLFLQQSSALLQQLPLVHLLAQNIRAQQLQQLVLANLAAYSQQQQFLPFN 120
121 QXXXXXXXXXXXXXXXXPFSQLAAAYPRQFLPFNQLAALNSHAYVXXXXXXPFSQLAAVS 180
121 QLAALNSAAYLQQQQLLPFSQLAAAYPRQFLPFNQLAALNSHAYVQQQQLLPFSQLAAVS 180
181 PAAFLTQQQLLPFYLHTAPNVGTXXXXXXXXXXXXXXXTNPAAFYQQPIIGGALF 235
181 PAAFLTQQQLLPFYLHTAPNVGTLLQLQQLLPFDQLALTNPAAFYQQPIIGGALF 235
AMINO_ACID_SCORING
BLAST normally uses the BLOSUM62 scoring matrix from Henikoff and
Henikoff (Proc. Natl. Acad. Sci. USA 89; 10915-10919 (1992))
whenever the sequences being compared are proteins (including cases
where nucleotide databases or query sequences are translated into
protein sequences before comparison). You can use the more
traditional PAM40, PAM120 and PAM250 scoring matrices with
command-line options like -MATrix=PAM40 etc. Each matrix is most
sensitive for finding homologs at the corresponding PAM distance.
The seminal paper on this subject is Stephen Altschul's "Amino acid
substitution matrices from an information theoretic perspective" (J.
Mol. Biol. 219; 555-565 (1991)). If you are new to this
literature, an easier place to start reading might be Altschul et
al., "Issues in searching molecular sequence databases" (Nature
Genetics, 6; 119-129 (1994)).
Here are the values in the default scoring matrix, BLOSUM62. The
values for perfect amino acid matches are shown in bold.
BLOSUM Clustered Scoring Matrix in 1/2 Bit Units
Blocks Database = /data/blocks5.0/blocks.dat
Cluster Percentage: >= 62
Entropy = 0.6979, Expected = -0.5209
A R N D C Q E G H I L K M F P S T W Y V B Z X *
A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 -4
R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4
N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 -1 -4
D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 -1 -4
C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 -4
Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -4
E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4
G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4
H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 -1 -4
I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4
L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 -1 -4
K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 -4
M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 -1 -1 -4
F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 -4
P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 -2 -4
S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 -4
T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 -4
W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 -2 -4
Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 -1 -4
V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -4
B -2 -1 3 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4 1 -1 -4
Z -1 0 0 1 -3 3 4 -2 0 -3 -3 1 -1 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4
X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 -1 -1 -4
* -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 1
NUCLEOTIDE_SCORING
For nucleotide-nucleotide searches (that is, searches where both the
query and the database are nucleotide sequences and where -TRANSlate
is not on the command line) there is no scoring matrix. You can
specify a nucleotide-nucleotide scoring matrix for any PAM distance
by adjusting the numerator of the match/mismatch ratio. You can
adjust this ratio, which defaults to +5/-4, by adding an expression
like -MATCH=4 to the command line.
The explanation of nucleotide substitutions scoring below is taken
from Dr. Altschul's description at
http://www.ncbi.nlm.nih.gov/Recipon/bm_bnscoring.html.
If all nucleotides were equally frequent and all substitutions were
equally likely, it would take only two scores, match and mismatch, to
represent a complete scoring matrix at any PAM distance. Fixing the
score for a mismatch at -4 lets you to select PAM matrices simply by
setting the match score as summarized in the following table.
Nucleotide Scoring Matrix Properties for Mismatch of -4
Match PAM Percent Bits/Unit Average information
Setting distance conserved score per position (bits)
1 0.3 99.7 1.992 1.97
2 5.3 94.9 0.968 1.63
3 16.0 85.6 0.595 1.18
4 30.2 75.0 0.396 0.79
5 47.0 65.1 0.275 0.51
6 65.0 56.5 0.196 0.32
7 86.0 48.8 0.138 0.19
8 109.0 42.5 0.096 0.11
The default match score of +5 corresponds to a PAM distance of 47.
Such a scoring matrix would be maximally informative when used to
compare sequences that, when back mutations are considered, are about
65% conserved. At this distance, there is about half a bit of
information for each position in an alignment of an homologous
segment pair. Searching a database of 64,000,000 bp with a query of
length 1000 bp would require about 36 bits of information to achieve
significance. A segment of this significance (36 bits) at this level
of divergence would have to be about 72 nucleotides long. By varying
match value as shown above, you can, in effect, select other PAM
matrices, which would be more efficient for other levels of sequence
conservation. Running BLASTN three different times with match
settings of 3, 5, and 7 (PAM matrices 16, 47, and 86), would give at
least 90% efficiency over the range of PAM distances 1 to 108.
ALTERNATIVE_GENETIC_CODES
BLAST normally uses the standard genetic code if either the query or
the database sequences requires translation. If your query comes
from a system where this genetic code is inappropriate, you can
select any of these alternative codes by number:
1 Standard or Universal
2 Vertebrate Mitochondrial
3 Yeast Mitochondrial
4 Mold, Protozoan, Coelenterate Mitochondrial and Mycoplasma/Spiroplasma
5 Invertebrate Mitochondrial
6 Ciliate Macronuclear
7 Do not use this index
8 Do not use this index
9 Echinodermate Mitochondrial
10 Alternative Ciliate Macronuclear
11 Eubacterial
12 Alternative Yeast
13 Ascidian Mitochondrial
14 Flatworm Mitochondrial
Use the command-line expression -TRANSlate=2 to tells BLAST to use
the vertebrate mitochondrial code to translate the query. The
expression -DBTRANSlate=3 tells BLAST to use the yeast mitochondrial
code to translate the database. (Notice that most of the genes in
GenBank will be translated inappropriately if you select a
nonstandard genetic code for database translation.)
Notice that the numbering for each of these codes has changed from
version 1.3 of BLAST and that you can no longer use the numbers zero,
seven, and eight! Also new in this release is the feature that BLAST
now lets you specify the genetic code for the database and the query
independently.
NETWORK_CONSIDERATIONS
There are a number of possible problems with client/server
applications running over the Internet. You should try to find out
if you are being charged for network communications and you should
certainly worry about the security and integrity of your sequences.
There is always a possibility that a server will become overloaded
and that your search will take much longer than normal or that your
output will be lost altogether because of a network or server
computer glitch. If you are working in Europe there may be services
available through EMBnet that are more appropriate or robust than the
NCBI BLAST server. Nonetheless, we continue to be impressed with the
speed and reliability of the NCBI BLAST server.
SEQUENCE_TYPE
The function of BLAST depends on whether your input sequence(s) are
protein or nucleotide. Programs determine the type of a sequence by
the presence of either Type: N or Type: P on the last line of the
text heading just above the sequence. If your sequence(s) are not
the correct type, turn to Appendix VI for information on how to
change or set the type of a sequence.
CITING_BLAST
There may be information near the top of your BLAST output that looks
something like: If results of this search are reported or published,
please mention that the computation was performed at NCBI using the
BLAST network service. The original paper describing BLAST is
Altschul, Stephen F., Gish, Warren, Miller, Webb, Myers, Eugene W.,
and Lipman, David J. (1990). Basic local alignment search tool. J.
Mol. Biol. 215; 403-410. This paper remains a good way to learn
more about BLAST.
OPTIONAL_PARAMETERS
The parameters and switches listed below can be set from the command
line. For more information, see "Using Program Parameters" in
Chapter 3, Basic Concepts: Using Programs in the User's Guide.
Following some of the optional parameters described below is a letter
or short expression in parentheses. These are the names of the
corresponding parameters at the bottom of your BLAST output.
-TOPstrand
searches only with the top strand of your nucleotide query
sequence. The top strand is the sequence represented in the
file.
-BOTtomstrand
searches only with the bottom strand of your nucleotide query
sequence. The bottom strand is the reverse complement of the
sequence represented in the file.
-DBTOPstrand
In searches that translate nucleotide database sequences
(TBLASTX, TBLASTN), this qualifier restricts the translation to
the top strand of each database sequence. (The top strand is
the sequence represented in the database.)
-DBBOTtomstrand
Like -DBTOPstrand, this qualifier restricts translated
nucleotide database searches to the bottom strand of each
database sequence. (The bottom strand is the reverse-complement
of the sequence represented in the database.)
-FILter=xs
filters out repeats and low complexity regions from protein
query sequences. The x filter removes short repeats; the s
filter removes low complexity sequences (Wootton and Federhen,
Computers Chem. 17(2); 149-163 (1993)). You can use either
filter alone or use both filters in any order.
When you run BLAST with filtering on, any masked regions are
simply excluded from the search. The output will show the
masked regions with X's to let you see which regions were
excluded from the search.
-MATrix=PAM120,PAM250...
BLAST normally uses the BLOSUM62 amino acid substitution matrix
from Henikoff and Henikoff for protein sequence comparisons
(including all cases where nucleotide database or query
sequences are translated before comparison). You can select the
more traditional PAM40, PAM120, and PAM250 scoring matrices with
this command-line option.
With version 1.4 of BLAST, you can use more than one protein
scoring matrix when searching a protein sequence database with a
protein query. If you use this option each segment is scored
with every matrix, and the score from the matrix which scores
highest is retained. For more information, see the topic
SCORING MATRICES.
-MATCH=4 (M)
You can set the match/mismatch range for nucleotide pair scoring
(which defaults to +5/-4) by adjusting the numerator of this
ratio to a value other than 5. See the topic NUCLEOTIDE
SCORING.
-TRANSlate=2
When your search requires BLAST to translates a nucleotide query
sequence, the genetic code used is normally the standard code.
If your query comes from a system where this is inappropriate
you can select any of the alternative codes listed under the
topic ALTERNATIVE GENETIC CODES.
-DBTRANSlate=2
If BLAST has to translate each nucleotide sequence in the
database, it will use the standard genetic code to do the
translation. If you are searching for proteins from a system
where this code is inappropriate you might select a code listed
under the topic ALTERNATIVE GENETIC CODES to search for homologs
from that system. Most of the genes in GenBank will be
translated inappropriately if you select a nonstandard genetic
code. See -TRANSlate.
-TBLASTX
With version 1.4 of BLAST you can use this qualifier on the
command line when using a nucleotide query sequence to search a
nucleotide sequence database. For this case, BLAST translates
both the query and every sequence in the database and then looks
for similarities among the products of translation.
Because such doubly-translated searches require a lot of
computing, NCBI currently restricts such searches to the STS,
EST and Alu databases.
The search set menu can scroll off your screen if it contains all of
the searchable databases at NCBI as well as those supported locally
(on your computer). The next four parameters simplify that menu.
-REMoteonly
confines the menu to those search sets that are available from
remote BLAST servers.
-LOCalonly
confines the menu to those search sets that are available
locally.
-DBNucleotideonly
confines the menu to search sets containing nucleotide
sequences.
-DBProteinonly
confines the menu to search sets containing protein sequences.
-SERver=cruncher.nlm.nih.gov
If BLAST is able to search remotely, it finds a list of the
servers available for that purpose in a data file called
GenRunData:blast.servers. This file lists the servers in the
order in which they will be tried. If a server fails to
respond, BLAST will try the next one on the list. The names of
the servers look like addresses without the user name and the @
symbol. This option lets you name a particular server on the
command line thus forcing BLAST to ignore the servers named in
the file.
The next four parameters allow advanced users of BLAST to adjust the
sensitivity or speed of the search. You should be familiar with the
algorithm before trying to use these parameters.
-EXPect=10 (E)
This parameter, for which there is a prompt if you don't set it
on the command line, lets you increase the number of hits in
your output with scores that would be expected to have occurred
by chance alone. Your setting for the number of random hits you
will tolerate is used to calculate a cutoff score (S) below
which MSP's or multiple HSP's (S2) are ignored. BLAST
calculates this cutoff score for you. There is nothing to
prevent many biologically-significant, but
statistically-insignificant segment pairs from being screened
out so you may sometimes want to increase this parameter in
order to have an opportunity see them.
-WORdsize=7 (W)
To score a nucleotide-nucleotide segment pair, BLAST must first
see a word match of size 11 bp. If you set the word size lower,
BLAST will find segment pairs that were skipped when the
database was searched with the default value. NCBI asks that
you not go below six or seven.
The default word size of three for proteins seems to be
appropriate for a wide range of protein search requirements and
NCBI suggests that you not change it. Use the -SYNonym
parameter if you are interested more in sensitivity than
selectivity.
-SYNonym=10 (T)
For protein comparisons, BLAST only scores HSPs when two
identical or very similar words of length three are seen. The
score at or above which two words are considered equivalent is
set by this parameter. The value in question is the sum of the
scoring matrix values when any two words of the same length are
aligned. Satisfactory values for -SYNonym are calculated
automatically by the program. The value is calculated by BLAST
for each run and is shown under the parameters section at the
end of your output as T, which for the example OUTPUT above is
10.
SUGGESTION: The value of T is of course a function of the
scoring matrix used so if you use a matrix other than BLOSUM62,
you should run BLAST once with the default settings to see what
value it calculates for T and then adjust it downwards slightly
from that value.
-LIStsize=250 (V=250)
-SEGments=100 (B=100)
By default, BLAST lists no more than 250 pairwise comparisons in
your output file even if more than 250 sequences had scores
above the cutoff score. The list is sorted in order of
increasing probability, that is, with the most significant
sequences first. Then BLAST displays the high-scoring segment
pairs from the best 100 sequences in the list. These list-size
and segment-display limits are often too small to display
everything significant. A warning appears in your output below
the list and the segments if these parameters were too low to
include everything found. This truncation of your output is by
intention since the output from BLAST is usually very large and
the first level of inference from most searches can be made from
the most significant hits. Nonetheless, you can adjust either
of these limits either up or down from 0 to 1,000.
-HIStogram (H=1)
This qualifier will make BLAST print a histogram near the
beginning of your output that shows the number of hits found at
each level of expectation.
-APPend="string"
The GCG implementation of BLAST is what is known as a shell
program. After collecting your input parameters, the shell
calls either the original BLAST programs or the NCBI BLAST
server. If you are familiar with the interface to the BLAST
programs as they were originally written, you can pass
command-line parameters to them directly using this option.
Please call us if there are additional parameters you want to
use with BLAST that you would like to look more like native GCG
parameters.
You can read the current version of the BLAST documentation on
the World Wide Web at
http://www.ncbi.nlm.nih.gov/Recipon/bm_blast.html.
-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.
|
Arne Elofsson Department of Biochemistry, Arrheniuslaboratoriet Stockholms Universitet 10691 Stockholm, Sweden |
Tel: +46-(0)8/161553 Fax: +46-(0)8/153679 Hem: +46-(0)8/6413158 Email: arne@rune.biokemi.su.se WWW: /~arne/ |
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