first page PLNT4610/PLNT7690Bioinformatics
Lecture 5, part 3 of 3
nextpage

B. Database Similarity Searches

1. Defining the problem

Database searches are essentially the same problem as pairwise searches.You can either think of the database search as being a large succession of pairwise searches, or as a pairwise search between a query sequence and a very large sequence. As such, the time required to search a database is O(mn), where m is the length of the query sequence, and n is the size of the database. The current release of GenBank 221.0 (August 2017) contains 
240,343,378,258 bases, from 203,180,606 reported sequences

Fortunately, the same strategies that speed up pairwise searches can be applied to database searches. The two most common database search packages, FASTA and BLAST, both use lookup tables of k-tuple words to make searches quick. Additionally, sites such as NCBI's BLAST server employ high performance computers that have large numbers of CPUs, allowing many pairwise comparisons to be performed in parallel.


NCBI
FASTA
DNA vs. DNA database
blastn
fasta3
ssearch3* (slow, full Smith-Waterman alignment)
protein vs. protein database
blastp
fasta3
ssearch3 (slow, full Smith-Waterman alignment)
protein vs. translated DNA database
tblastn
tfasta3
translated DNA vs. translated DNA database
tblastx
tfastx3, tfasty3
translated DNA vs. protein database
blastx
fastx3, fasty3 (especially well-suited for cDNAs, which often contain frameshift errors)
protein vs. protein database (iterative refinement
  of hits)
psi-blast

protein pattern vs. protein database
phi-blast

*It is worth pointing out that BLAST comparisons are always an approximation. BLAST does not do a complete Smith-Waterman alignment. When you want optimal alignment, use SSEARCH.

There are a number of more specialized BLAST programs than are listed here. A more complete listing of BLAST programs can be found at
https://www.ncbi.nlm.nih.gov/blast/producttable.shtml#pstab

The BLAST Scheduling Problem

A Fault-Tolerant Parallel Scheduler for BLAST Kevin Bealer, George Coulouris, Ilya Dondoshansky, Thomas L. Madden, Yuri Merezhuk, Yan Raytselis National Center for Biotechnology Information Bethesda, MD 20894 {bealer, coulouri, dondosha, madden, merezhuk, raytseli}@ncbi.nlm.nih.gov The National Center for Biotechnology Information (NCBI) Alignment Search Tool (BLAST).
ftp://ftp.ncbi.nlm.nih.gov/blast/documents/blast-sc2004.pdf




Aside from the scale of the search itself, server-implemented database searches have additional layers of complexity. The servers must be able to schedule and process requests from a number of different types of web clients. In the case of NCBI, over 100,000 requests per day. (1 day = 86,400 seconds, so that is more than one request processed per second.)

At NCBI, requests from web browsers or PC-based client programs are read by an interface layer, which sends the specifics of the request to a database server.

The Queuing Components server schedules jobs and monitors the progress of jobs. It launches searches, sending each query sequence to one of 280 Worker Nodes, which are Linux servers, each of which has multiple CPUs and large amounts of memory.

Each worker node stores part of the database in memory, such that the entire binary database is stored permanently in memory. This is a critical speedup step, because simply reading the data from disk can be a major bottleneck in database searches. The time required for a program to read data from a disk drive is much slower than reading data from RAM (random access memory). Collectively, the worker nodes store two complete copies of the data to increase the throughput.

When a query has been compared to all parts of the database as requested, the results are returned to the client.
 

2. Statistical significance of hits

In comparison to pairwise searches, database searches carry some additional problems. The most critical is evaluating the statistical significance of a match. Statistical significance depends on the size of the query sequence, the size of the database, and the sample of sequences represented by the database.

The larger the database, the higher the probability of finding a match at a particular score by random chance. Therefore, as the size of the database increases, the score required to infer statistical significance is greater. However, it has been observed that in database searches, sequence scores are not distributed according to a Gaussian (normal) distribution.

The Extreme Value Distribution (EVD) does show a good fit to observed score distributions.

where

E (the "E value") is the expected number of hits with score >= S
m is the size of the query sequence
n is the size of the database
and K are empirically derived constants for the scoring system and the search space size, respectively
Bill Pearson shows an example of how observed FASTA scores match up with the Extreme Value Distribution.  [ http://www.people.virginia.edu/~wrp/talk95/prot_talk12-95_5.html ]

The critical observation to make about the EVD is that the right tail, representing the highest scores, is strongly skewed, compared to a normal distribution. This means that a normal distribution would predict a much lower number of expected scores above some high score S. Consequently, the significance of some high scores might be tremendously overestimated. Put simply, the normal distribution would judge many similarities to be significant which were not significant. The right tail of the curve is shown in the inset on an expanded scale. Remember, scores  in this region are simply part of the random distribution of scores. By definition, they are not deemed to be significant.

E values  in BLAST and FASTA
The most important difference between BLAST and FASTA is the way in which the E values are calculated. Both FASTA and BLAST start with the calculation of probabilities for a similarity of a given score between two randomized sequences of the same size, with equivalent amino acid compositions.

BLAST -  K and are pre-calculated from a sample of 10,000 randomly-shuffled sequences BLAST therefore calculates E values based on the total length of the database.

FASTA - K and are calculated from the actual distribution of similarity scores obtained in each search. This makes FASTA slower than BLAST, since FASTA must calcluate an approximate similarity score for every sequence in the database. FASTA thus calculates E values based on the number of sequences in the database.

Pearson, W. R. and Wood, T. C. (2001) Statistical significance in biological sequence comparison. Handbook of Statistical Genetics, Balding, D. J., Bishop, M. and Cannings, C. eds. London:Wiley pp. 39-65 PDF version


A good summary of the statistical issues related to database similarity searches can be found in "The Statistics of Sequence Similarity Scores" by Stephen Altschul at NCBI [ http://www.ncbi.nlm.nih.gov/BLAST/tutorial/Altschul-1.html ].
 

3. Protein searches are faster and more sensitive than DNA searches

Speed

4. Low complexity sequences yeild false positives

The complexity of a sequence is defined as the longest non-repetitive sequence that can be derived from a sequence.
 
sequence
complexity
AAAAAAAAAA
TTTTTTTTTT
1
ATATATATAT
TATATATATA
2
ATGATGATG
TACTACTAC
3
ATGCATGC
TACGTACG
4
ATGCCATGCC
TACGGTACGG
5

Low complexity sequences are sequences which contain many repeats of short sequence elements. Sequences with skewed amino acid or nucleotide compositions will have a low complexity. When two sequences with similarly skewed compositions are compared, the probability of local similarities is much greater than for pairs of sequences with fairly even compositions. For example, two A/T-rich DNA sequences are far more likely to have matches by random chance than sequences in which all four nucleotides are present at a frequency of  approximately 0.25.

Example: Comparison of maize and rice hydroxyproline-rich glycoproteins

SSEARCH searches a sequence data bank
version 35.04 Oct. 7, 2008
Please cite:
T. F. Smith and M. S. Waterman, (1981) J. Mol. Biol. 147:195-197;
W.R. Pearson (1991) Genomics 11:635-650

Query: bio537987931014488970.tmp.seq1
1>>>S20500 369 bp - 369 aa
Library: bio537987931014488970.tmp.seq2 350 residues in 1 sequences

350 residues in 1 sequences
Statistics: (shuffled [500]) MLE statistics: Lambda= 0.0325; K=6.695e+07
Algorithm: Smith-Waterman (SSE2, Michael Farrar 2006) (6.0 Mar 2007)
Parameters: BL50 matrix (15:-5), open/ext: -10/-2
Scan time: 0.090

The best scores are: s-w bits E(1)
S22456 350 bp ( 350) 1539 46.2 1.6e-09

>>S22456 350 bp (350 aa)
s-w opt: 1539 Z-score: 203.2 bits: 46.2 E(): 1.6e-09
Smith-Waterman score: 1539; 62.8% identity (73.0% similar) in 392 aa overlap (1-369:1-349)

10 20 30 40
S20500 MGGK--AALLMVLVAMSVVLEARADAGGYGGGYTPTPTPVKPAPKPEKPP----------
:::. ::::..:::.:...: .:::: :: :::::::. :.:::::::
S22456 MGGSGTAALLLALVAVSLAVEIQADAG-YG--YTPTPTPATPTPKPEKPPTKGPKPEKPP
10 20 30 40 50

50 60 70 80 90 100
S20500 KEHKPPHHHEPKPEKPPKEHKP--PAYTPPKPTPTPPTYTPTPKPTPPPYTPKPTPPAHT
::::::..: :::::::::::: :.::: .: :::::::::: ::: :::::::..:
S22456 KEHKPPKEHGPKPEKPPKEHKPTPPTYTP-SPKPTPPTYTPTP--TPP--TPKPTPPTYT
60 70 80 90 100 110

110 120 130 140 150 160
S20500 PTPPTYTPTPTP-PKPTPPTYKPQPKPTPAPYTPTPTPPMYKPQPKPTPA--PYTPTPTP
:.: . ::::: : :::::: :.::: : :: :::: : :.::: :: : :: :::
S22456 PAPTPHKPTPTPKPTPTPPTYTPSPKP---P-TPKPTPPTYAPSPKP-PATKPPTPKPTP
120 130 140 150 160

170 180 190 200 210 220
S20500 PTYKPQPKPTPPPYTPTPAPPTYKPQPKPNPPPTYKPAPKPTPTPYQPAPPTYKPQPKPN
::: :.::: : :: :.:::: :.:::.:: :: :.::: ::: .:.:::: :.:::
S22456 PTYTPSPKP--P--TPKPTPPTYTPSPKPTPP-TYTPSPKP-PTP-KPTPPTYTPSPKP-
170 180 190 200 210

230 240 250 260 270
S20500 PPPTYKPQPKPNPPPTYKPAPKPTPTPYKPAPPTYKPQPKP---NP-PPTYKPQPKP-TP
: : : :::.:: :: :.::: ::: ::.:::: :.::: .: :::: :.::: ::
S22456 -PATKPPTPKPTPP-TYTPSPKP-PTP-KPTPPTYTPSPKPPATKPTPPTYTPSPKPPTP
220 230 240 250 260 270

280 290 300 310 320 330
S20500 TPYTPPTYKPQPKP-TPTPTPYTPTPKPNPPPTYKPQPKPTPTPTPYKPQPKPTPSPYTP
: ::::: :.::: :: ::: : ::.:.:: : : ::::: :: : : :::
S22456 KP-TPPTYTPSPKPPTPKPTPPTYTPSPKPPATKPPTPKPTP-PT-YTPTPKP-------
280 290 300 310 320

340 350 360
S20500 KPTPTPPTYTPTPTPPYHKPPPSYTPGPPPPY
:. :::::::: : :.::.:::::
S22456 -PATKPPTYTPTP-------PVSHTPSPPPPYY
330 340 350
The alignment is deceptive, because the high percentages of Hydroxyproline, Lysine and Tyrosine ensure than a very large number of alignments could have very high scores. This is best illustrated in the dot-matrix plot of the same comparison:
P2HOM         Version  5/13/91 
X-axis: S20500
Y-axis: S22456
SIMILARITY RANGE:  10      MIN.PERCENT SIMILARITY:  40
SCALE FACTOR:    0.90      COMPRESSION:             10

              100       200       300

       Y        .         .         .         .
        W       .         .         .         .
         WV    de Z c b d .         d   cc    .
          N   WSXZTTXYVVXW.bd  bd bcLLWWWWY   .
          XQZX Uadbaea XcXYcYYYcaYeZcMaYa b   .
           IIK cd         .         .d    c   .
           PIJcd.         .         .     c   .
           NSKTSURXRZYZUW .c   cY bYX aaZWe   .
           d  QHIQPLLONJITVQTVVQRVQSRUURQKcc  .
   100 ...V...UMIMJSLNJKJWbTYbbTWbSVSUWTQGLZ...
          dZ  NMMLLLSRROQQYUWZYUTbWXUQaPPNOe  .
          Va  UPMSLXTVTVSVaZVd ZV aZVTaUPOUb  .
          dd  LJPOTTTUTQQScVZccVZcUTOSRVLHW   .
          dd  ZISOMTUXSPPSYaWYXaWWWXPTaZVIUZ  .
          bb  QPQQQNVRTNSRXWWYXWTXYUXUYTVQZ   .
          ce aSYeYUYbadYbYbZYbaZaYXVWXXd cW   .
          e   MLLPNOSSQONRYTVXYTSYSUTZXUTNZZ  .
          db  ZNPSPVSVQSNTYXWYXXWWWWUTaRUOUa  .
          ed  OHOPPLUPUKRPUSTVUSRUQRRUVRONYb  .
   200 ...dd..QIJQPQNWPRKUXQWXXQUXQVTWURSLYZ...
          eb dOMNOQNTRTNPRYWWYXWTWWUUTYRSNUe  .
          a  aaMMPPOSaSPOSXZWYXZUXXUVUXdVNUZ  .
          ed dMLMRTPSSUPPabTabZTYbSWUZXUURa   .
          dd  ZLLPNOSSQONRYaVXXaSWWUTWaZTNUZ  .
          eb dOMMOPNSRSNORXWWYXWTXWUUTYRSNZZ  .
          ab ccQNORVSXSVPWbXYbaXWYTXWUZUXOUa  .
          dd dRNSLLPTWRSOVYXVXXXSWWRVWaVVPbZ  .
          eb dOMNOQNTRTNPRYWWYYWTYWUUTYRSNUe  .
          dd  ZLOSNPSXQONRYaVXXaSWWVTTaZTNUZ  .
   300 ...db.dOMMOPNSRSNORXWWYXWTXWUUUYRSNZZ...
          ad aSScWSYbadYbYbZYbaZaYXVWVXdccUe  .
          Z   KJLMMOOSOPLSXTWYXTUXSUSUXUUKZZ  .
          Ye ZVOaVMWOXOXMVbZYbbZYaYVWTXYYJRe  .
              XUbWXXaaaaZbaeZbaeZaeabZdedWca  .
It's easy to see that the HPRG proteins would show high scores with any protein containing a high proline content.

BLAST from NCBI preproccesses all sequences to mask low complexity regions [http://www.ncbi.nlm.nih.gov/blast/blastcgihelp.shtml#filter].

5. Repetitive genomic elements can "outcompete" unique parts of a query sequence because there are so many copies of them in genomes, and therefore, in databases.

Eukaryotic genomes are full of middle-repetitive sequences that are interspersed throughout the genome. For example, in the human genome, 300 bp sequences referred to as the AluI family are present in almost 106 copies per haploid genome, making up about 10% of the total human genome.  Probably MOST genomic clones from the human genome will contain one or more AluI or other repetitive sequences. In particular, BAC clones are large enough that they would almost certainly contain AluI sequences. Below we see the map of a 3107 bp clone containing the human ubiquitin gene (X04803), with two copies of the AluI family repeat in the upstream region.



If you used this clone as a hybridization probe against a Southern blot, the entire blot would show a smear of hybridization at all molecular weight classes. To probe for the gene itself, you would need to extract a fragment from the ubiquitin coding sequence (eg. by PCR) and use that as a probe.

Similarly, if a query sequence containing the gene you want to search for also contains part of an AluI sequence, most or all of the hits listed on the output of a database search would be BACs or orther large sequence entries containg AluI sequences.You would never see the true positive matches with your gene, because the output would be overwhelmed by BACs that match the AluI sequence.

Particularly when using genomic DNA sequences in database searches, a smaller unique sequence is the best query sequence, rather than a long sequence.

BLAST from NCBI automatically checks query DNA sequences for known repetitive elements such as AluI, and flags the presence of these sequences in the output.


What if I use BLAST to search the Human Genome with an AluI family sequence? (eg. HUMRSA27)

Do NOT try this at home! You'll just get the same results, and add load to the NCBI servers.
type of search
result
blastn with defaults
"No significant similarity found" This is because BLAST automatically filters out known repeats such as AluI.
blastn; no filtering for species-specific repeats
268967 hits. For a 262 base search sequence, this corresponds to 7 x 107 bases. Since the human genome is about  3 x 109 bp, a BLAST search would estimate that about 2.3% of the human genome is AluI family sequences.
blastn; no filtering for species-specific repeats; discontiguous megablast which breaks the query words into shorter words, which in effect allows gaps within the larger query words
430533 hits. For a 262 base search sequence, this corresponds to 11.3 x 107 bases. Since the human genome is about  3 x 109 bp, a BLAST search would estimate that > 3.8% of the human genome is AluI family sequences.

6. Web-based database searches

Blast searches can be run at the NCBI web site at http://blast.ncbi.nlm.nih.gov.  Blast can also be run through BioLegato.

The major database centers such as GenBank, EMBL, DDBJ, SwissProt, PIR and others all have web-based database services. Many other sites have comparable services.

Advantages

Disadvantages
Unless otherwise cited or referenced, all content on this page is licensed under the Creative Commons License Attribution Share-Alike 2.5 Canada
first page PLNT4610/PLNT7690Bioinformatics
Lecture 5, part 3 of 3
nextpage