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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.
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 NetBLAST program is an interface to the BLAST service provided by NCBI's web server at www.ncbi.nlm.nih.gov, or by their email server blast@ncbi.nlm.nih.gov.
NetBLAST is very similar to BLAST, but it differs in two respects. First, whereas BLAST can either run locally and search a local database, or request remote service from NCBI, NetBLAST can only perform remote searches. Second, BLAST obtains its remote service from NCBI's "experimental" BLAST server, which is destined to be taken out of service in the near future. NetBLAST uses a new "official" BLAST server, which we expect to be stable. Currently, the two programs are algorithmically identical, but the official service used by NetBLAST may diverge from the experimental service in the future.
NetBLAST supports five different programs in the BLAST family:
Each database sequence is compared to the query in a separate protein-protein pairwise comparison.
The query is translated, and each of the six products is compared to each database sequence in a separate protein-protein pairwise comparison.
Each database sequence is compared to the query in a separate nucleotide-nucleotide pairwise comparison.
Each nucleotide database sequence is translated, and each of the six products is compared to the query in a separate protein-protein pairwise comparison.
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. Because this program involves more computation than the others, it is limited to searches of Alu, STS, EST, and GSS databases when doing remote searches of the NCBI databases.
Normally, NetBLAST 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 are two programs that can do the search. By default, BLASTN is used. To search using TBLASTX instead, add -TBLASTX to the command line.
NetBLAST can only search remote databases maintained by NCBI. Remote searches require almost no resources from your own computer. More importantly, the databases at NCBI are updated daily.
BLAST is a statistically driven search method that finds regions of similarity between your query and database sequences. These are called segment pairs, and consist of gapless alignments of any part of two sequences. Within these aligned regions, 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 reported only if they have a score that would be expected to occur purely by chance fewer than 10 times in this particular search.
Here is a session using NetBLAST to find the sequences in SWISS-PROT with similarities to a zein gene:
% netblast NetBLAST search with what query sequence ? SW:zea2_maize Search for query in what sequence database: 1) nr p Non-redundant GenBank CDS translations+PDB+SwissProt+PIR 2) pdb p PDB protein sequences 3) swissprot p SwissProt sequences 4) yeast p Saccharomyces cerevisiae protein sequences 5) kabat p Kabat Sequences of Proteins of Immunological Interest 6) alu p Translations of Select Alu Repeats from REPBASE 7) month p All new or revised GenBank CDS translation+PDB+SwissProt+PI 8) nr n Non-redundant GenBank+EMBL+DDBJ+PDB sequences (but no EST's 9) pdb n PDB nucleotide sequences 10) vector n Vector subset of GenBank 11) yeast n Saccharomyces cerevisiae genomic nucleotide sequences 12) est n Non-redundant Database of GenBank+EMBL+DDBJ EST Division 13) sts n Non-redundant Database of GenBank+EMBL+DDBJ STS Division 14) gss n Genome Survey Sequences 15) mito n Database of mitochondrial sequences, Rel. 1.0, July 1995 16) kabat n Kabat Sequences of Nucleic Acid of Immunological Interest 17) epd n Eukaryotic Promotor Database 18) alu n Select Alu Repeats from REPBASE 19) month n All new or revised GenBank+EMBL+DDBJ+PDB sequences released Please choose one (* 1 *): 3 Ignore hits expected to occur by chance more than (* 10.0 *) times? Limit the number of sequences in my output to (* 250 *) ? What should I call the output file (* zea2_maize.blastp *) ? Sending query... Awaiting results... Done. Wrote search results to zea2maize.blastp %
Below is part of the output from the search in the example session:
///////////////////////////////////////////////////////////////////////////////
BLASTP 1.4.9MP [26-March-1996] [Build 14:27:01 Apr 1 1996]
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= sw:zea2_maize P04704 zea mays (maize). zein-alpha precursor (19 kd)
(clone zg99). 6/94
(235 letters)
Database: Non-redundant SwissProt sequences
59,576 sequences; 21,219,450 total letters.
Searching..................................................done
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 3.7e-160 1
sp|P24449|ZEAC_MAIZE ZEIN-ALPHA PRECURSOR (19 KD) (PMS1) 1103 1.4e-151 1
sp|P02859|ZEA1_MAIZE ZEIN-ALPHA PRECURSOR (19 KD) (CLONE ... 644 6.4e-149 2
///////////////////////////////////////////////////////////////////////////////
sp|P03748|VTFP_BPT7 TAIL FIBER PROTEIN 57 0.9999 1
sp|P42286|THR_DROME THREE ROWS PROTEIN 60 0.99992 2
sp|P04724|GDA4_WHEAT ALPHA/BETA-GLIADIN PRECURSOR (PROLAM... 46 0.99992 2
>sp|P04704|ZEA2_MAIZE ZEIN-ALPHA PRECURSOR (19 KD) (CLONE ZG99)
Length = 235
Score = 1164 (544.2 bits), Expect = 3.7e-160, P = 3.7e-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|P01125|REL_MELGA C-REL PROTO-ONCOGENE PROTEIN (C-REL PROTEIN) (P68)
Length = 538
Score = 57 (26.6 bits), Expect = 9.1, P = 1.0
Identities = 10/23 (43%), Positives = 16/23 (69%)
Query: 9 IMLLGLSASAATASIFPQCSQAP 31
+ML GL A+++ ++P CSQ P
Sbjct: 338 LMLPGLGTLASSSQMYPACSQMP 360
///////////////////////////////////////////////////////////////////////////////
Parameters:
V=250
B=100
H=0
E=10.000
P=4
-ctxfactor=1.00
Query ----- As Used ----- ----- Computed ----
Frame MatID Matrix name Lambda K H Lambda K H
+0 0 BLOSUM62 0.324 0.134 0.375 same same same
Query
Frame MatID Length Eff.Length E S W T X E2 S2
+0 0 235 235 10. 58 3 10 22 0.24 32
Statistics:
Query Expected Observed HSPs HSPs
Frame MatID High Score High Score Reportable Reported
+0 0 61 (28.5 bits) 1164 (544.2 bits) 655 655
Query Neighborhd Word Excluded Failed Successful Overlaps
Frame MatID Words Hits Hits Extensions Extensions Excluded
+0 0 6728 14776662 3614336 11139868 22458 803
Database: Non-redundant SwissProt sequences
Release date: July 18, 1997
Posted date: 7:56 AM EDT Jul 18, 1997
# of letters in database: 21,219,450
# of sequences in database: 59,576
# of database sequences satisfying E: 62
No. of states in DFA: 530 (52 KB)
Total size of DFA: 120 KB (128 KB)
Time to generate neighborhood: 0.01u 0.00s 0.01t Real: 00:00:00
No. of processors used: 4
Time to search database: 25.35u 0.17s 25.52t Real: 00:00:07
Total cpu time: 25.37u 0.19s 25.56t Real: 00:00:07
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) a display of the 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 looks for alignments that do not contain gaps, there will often be more than one segment pair associated with each database sequence.
In the list of sequences, the column labeled High Score contains the scores of the highest-scoring segment pair for the pairwise comparison of that database sequence and your query. In the segment pair alignment display, the Score is the sum of the scoring matrix values in the segment pair being displayed.
There is a probability, for instance 3.7e-160 in the example, associated with each pairwise comparison in the list and with each segment pair alignment. In the list, this number (which means 3.7 x 10(-160)) is the probability that you would observe a score or group of scores as high as the observed high score (or scores) purely by chance when you do a search of the this size. More than one HSP may contribute to the probability in the list, which is why the P(N) column has the superheading "Smallest Sum Probability." In the alignment 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 this size. This probability may be higher than 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).
This column beneath the Smallest Sum Probability heading in the list of sequences indicates how many HSPs were involved in computing P(N). If P(N) arose from combining more than one HSP score from the same pairwise comparison, this number will be greater than 1. There is more about the concept of combining the scores of more than one HSP under the THEORY topic.
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 (2(30))
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.
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/BLAST/blast_help.html.
NetBLAST accepts a single protein sequence or a single nucleic acid sequence as the query sequence. The function of NetBLAST 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.
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. GCGToBLAST 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 a group of sequences of the same type (nucleic acid or protein). For nucleotide searches, FastA may be more sensitive than BLAST. TFastA does a Pearson and Lipman search for similarity between a query peptide 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 peptide sequences in a nucleotide sequence database are similar to my peptide sequence?" 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.
Searching remote databases opens up
the possibility of unauthorized access
to your query sequence.
You should not use confidential
query sequences for remote searches.
You cannot select a list size or expectation threshold of more than 1,000. The output file can be quite large. If you run out of disk space you may have to delete one or more files before you can continue working.
To name a searchable database interactively, choose the number of the database of interest from the menu. On the command line, use a parameter 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, NetBLAST cannot be sure which nr you mean if you use parameter like -INfile2=nr on the command line. Therefore, if the database you want to search cannot be named unambiguously with the -INfile2 parameter, add either -DBNucleotideonly or -DBProteinonly to the command line.
This section is for advanced users.
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. MSP scores cannot be improved by extending or shortening the alignment at either end. There will be at least one MSP for every pairwise comparison.
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)).
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 alignment.
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 of 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.
If you have understood the THEORY topic you are ready to learn more about how BLAST is implemented.
BLAST assumes that any high-scoring segment pair will contain two similar (but not necessarily identical) words within it.
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-settable 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.
BLAST belongs to a class of comparisons that use k-tuple preprocessing. To implement this, 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.
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.
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.
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 as alignments in the output. The MSP score is reported next to each sequence in the output list in the column labeled High Score.
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 alignments of segment pairs with the optional parameter -SEGments if you can tolerate the large amount of output involved.
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 (1/2(16)=1/65,536) 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.
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 using the FastA program when looking for nucleotide homologs.
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."
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.
NetBLAST displays alignments of segment pairs from the top 100 sequences in the list. You can adjust this limit with the -SEGments parameter.
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 use the FastA program instead of BLAST.
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.
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)).
You can perform similar filtering of short repeats from nucleic acid query sequences by adding -FILter=dust to the command line, thereby using the dust filter (Hancock and Armstrong, Comput. Appl. Biosci. 10; 67-70 (1994)).
When you run BLAST with filtering on, masked regions are excluded from the search. These regions are replaced with X's in the output to let you identify the regions that were excluded. Here is the query sequence from the example session aligned to a filtered copy of itself to show which parts of the original sequence 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
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, or the IDENTITY matrix, with command-line parameters 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
There is no external scoring matrix 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).
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
You can specify the genetic code for the query and the database independently. Use the command-line parameter -TRANSlate=2 to tell BLAST to use the vertebrate mitochondrial code to translate the query. The parameter -DBTRANSlate=3 tells BLAST to use the yeast mitochondrial code to translate the database. (Note that most of the genes in GenBank will be translated inappropriately if you select a nonstandard genetic code for database translation.)
The numbering for each of
these codes has changed from
version 1.3 of BLAST (used
in release 8.0 of the
Wisconsin Package) and you can
no longer use the numbers
zero, seven, and eight!
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.
All parameters for this program may be put on the command line. Use the parameter -CHEck to see the summary below and to have a chance to add things to the command line 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: % netblast [-INfile1=]SW:Zea2_Maize -Default
Prompted Parameters:
[-INfile2=]swissprot database to search
-EXPect=10.0 ignores scores that would occur by chance
more than 10 times
-LIStsize=250 maximum number of sequences listed in the output
[-OUTfile=]zea2.blastp output file name
Local Data Files:
[-DATa1=BLAST.rDBs] list of available remote databases
Optional Parameters:
-TOPstrand searches only the top strand of nuc. query
-BOTtomstrand searches only the bottom strand of nuc. query
-DBTOPstrand translates only top strand of nuc. database
-DBBOTtomstrand translates only bottom strand of nuc. database
-FILter[=xs|dust] filters repeats and low complexity segments
out of protein query sequences
-MATrix=blosum62 specifies a protein scoring matrix
-TBLASTX if query and database are both nucleotide,
translates both and does protein comparisons
-TRANSlate=1 genetic code for translating query
-DBTRANSlate=1 genetic code for translating database
-DBNucleotideonly searches only nucleic databases ( " " )
-DBProteinonly searches only protein databases ( " " )
-URL=www.blast.ncbi.nlm.nih.gov-cgi-bin-BLAST-nph-blast_report
sends HTTP query to NCBI's net server (default)
-MAIL[=blast@ncbi.nlm.nih.gov]
sends email to NCBI's email server
-SEGments=100 displays segment pairs from top 100 sequences
-HIStogram shows histogram of hits versus expectation level
-APPend="string;string..." appends each string, on a separate line,
to the query (NCBI's email format)
If you use NetBLAST to search the NCBI databases, NCBI requests that you 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.
BLAST was written by Warren Gish of the National Center for Biotechnology Information (NCBI) in collaboration with Stephen Altschul, Webb Miller, Eugene Myers, David Lipman, and David States. The NetBLAST interface was written by Philip Delaquess, who reused large pieces of service and database selecting code written by Scott Rose. The document you are now reading was written for GCG's BLAST by John Devereux, and slightly modified for NetBLAST by Philip Delaquess. We are extremely grateful to Stephen Altschul and Warren Gish for their careful and original work on BLAST and for their critical comments on GCG's BLAST documentation and we are very grateful to NCBI for making these programs and services available to the molecular biology community.
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.
NetBLAST reads the file netblast.rdbs, which lists the available remote databases in the menu. We update netblast.rdbs when we send database updates to your institution.
The parameters listed below can be set from the command line. For more information, see "Using Program Parameters" in Chapter 3, 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.
searches only with the top strand of your nucleotide query sequence. The top strand is the sequence represented in the file.
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.
In searches that translate nucleotide database sequences (TBLASTX, TBLASTN), this parameter restricts the translation to the top strand of each database sequence. (The top strand is the sequence represented in the database.)
Like -DBTOPstrand, this parameter 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.)
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, masked regions are excluded from the search. These regions are replaced with X's in the output to let you identify the regions that were excluded.
filters out repeats from nucleic acid query sequences. (See Hancock and Armstrong, Comput Appl Biosci 10; 67-70 (1994)).
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, or the IDENTITY matrix, with this command-line parameter. For more information, see the topic AMINO ACID SCORING.
When BLAST must translate a nucleotide query sequence, it uses the standard ("universal") genetic 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.
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. Note that most of the genes in the nucleotide databases will be translated inappropriately if you select a nonstandard genetic code. See -TRANSlate.
You can use this parameter on the command line when searching a nucleotide sequence database with a nucleotide query sequence. BLAST will then translate the query and every sequence in the database and examine all pairwise combinations to find similarities at the amino acid level.
Because such doubly translated searches require a lot of computing, NCBI currently restricts such searches to the STS, EST, Alu, and GSS databases.
confines the menu to search sets containing nucleotide sequences.
confines the menu to search sets containing protein sequences.
By default NetBLAST makes use of the HTTP server maintained by NCBI at the URL given above. Should there ever be a reason to change this URL, you may do so by specifying -URL on the command line.
You can send your query to NCBI's email server instead by including this qualifier. The HTTP and email servers produce the same result; they differ only in the network mechanism with which they communicate.
The next parameter allows advanced users of BLAST to adjust the sensitivity of the search. You should be familiar with the algorithm before trying to use this parameter.
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 MSPs or multiple HSPs (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.
By default, NetBLAST 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 NetBLAST displays the alignments of high-scoring segment pairs from the best 100 sequences in the list. These list-size and segment-display (alignment) limits are often too small to display everything significant. A warning appears in your output below the list and the alignments of 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 from 0 to 1,000.
This parameter will make BLAST print a histogram near the beginning of your output that shows the number of hits found at each level of expectation.
The GCG implementation of NetBLAST is what is known as a shell program. After collecting your input parameters, the shell calls the NCBI BLAST server and sends it a query in NCBI's email query format. If you are familiar with that format, you can pass additional parameters to NCBI by using this parameter. Use semicolons to separate multiple command lines. 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/BLAST/blast_help.html.
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