Module 2 Sequence DBs and Similarity Searches Learning objectives Understand how information is stored in GenBank. Learn how to read a Genbank flat file. Learn how to search Genbank for information. Understand difference between header, features and sequence. Learn the difference between a primary database and secondary database. Principle of similarity searches using the BLAST program
Module 2 Sequence DBs and Similarity Searches. Learning objectives Understand how information is stored in GenBank. Learn how to read a Genbank flat file. Learn how to search Genbank for information. Understand difference between header, features and sequence. - PowerPoint PPT Presentation
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Module 2Sequence DBs and Similarity Searches
Learning objectives Understand how information is stored in GenBank. Learn how to read a Genbank flat file. Learn how to search Genbank for information. Understand difference between header, features and
sequence. Learn the difference between a primary database and
secondary database. Principle of similarity searches using the BLAST
program
What is GenBank?
Gene sequence database
Annotated records that represent single contiguous stretches of DNA or RNA-may have more than one coding region (limit 350 kb)
Generated from direct submissions to the DNA sequence databases from the authors.
Part of the International Nucleotide Sequence Database Collaboration.
Exchange of information on a daily basis
GenBank(NCBI)
EMBL (EBI)United Kingdom
DDBJJapan
International Nucleotide Sequence Database Collaboration
History of GenBank
Began with Atlas of Protein Sequences and Structures (Dayhoff et al., 1965)In 1986 it collaborated with EMBL and in 1987 it collaborated with DDBJ.It is a primary database-(i.e., experimental data is placed into it)Examples of secondary databases derived from GenBank/EMBL/DDBJ: Swiss-Prot, PRI.GenBank Flat File is a human readable form of the records.
General Comments on GBFF
Three sections: 1) Header-information about the whole record 2) Features-description of annotations-each
represented by a key. 3) Nucleotide sequence-each ends with // on
last line of record.
DNA-centered
Translated sequence is only a feature
Feature Keys
Purpose: 1) Indicates biological nature of sequence 2) Supplies information about changes to
sequences
Feature Key Description conflict Separate deter’s of the same seq. differ
rep_origin Origin of replication
protein_bind Protein binding site on DNA
CDS Protein coding sequence
Feature Keys-Terminology
Feature Key Location/Qualifiers
CDS 23..400
/product=“alcohol dehydro.”
/gene=“adhI”
Interpretation-The feature CDS is a coding sequence beginning at base 23 and ending at base 400, has a product called “alcohol dehydrogenase” and corresponds to the gene called “adhI”.
Feature Keys-Terminology (Cont.)
Feat. Key Location/Qualifiers
CDS join (544..589,688..1032)
/product=“T-cell recep. B-ch.”
/partial
Interpretation-The feature CDS is a partial coding sequence formed by joining the indicated elements to form one contiguous sequence encoding a product called T-cell receptor beta-chain.
Record from GenBank
LOCUS SCU49845 5028 bp DNA PLN 21-JUN-1999
DEFINITION Saccharomyces cerevisiae TCP1-beta gene, partial cds, and
Axl2p (AXL2) and Rev7p (REV7) genes, complete cds.
Partial sequence on the 5’ end. The 3’ end is complete.
There are three parts to the feature key: a keyword (indicates functional group), a location (instruction for finding the feature), and a qualifier (auxiliary information about a feature)
Keys
Location
Qualifiers
Descriptive free text must be quotations
Start of open reading frame
Database cross-refsProtein sequence ID #
Note: only a partial sequence
Values
Record from GenBank (cont.3) gene 687..3158 /gene="AXL2" CDS 687..3158 /gene="AXL2" /note="plasma membrane glycoprotein" /codon_start=1 /function="required for axial budding pattern of S. cerevisiae" /product="Axl2p" /protein_id="AAA98666.1" /db_xref="GI:1293615"
Primary databases contain experimental biological information
GenBank/EMBL/DDBJAlu-alu repeats in human DNAdbEST-expressed sequence tags-single pass cDNA sequences (high error freq.)
It is non-redundantHTGS-high-throughput genomic sequence database (errors!)PDB-Three-dimensional structure coordinates of biological moleculesPROSITE-database of protein domain/function relationships.
Types of secondary databases that contain biological information
dbSTS-Non-redundant db of sequence-tagged sites (useful for physical mapping)
Genome databases-(there are over 20 genome databases that can be searched
EPD:eukaryotic promoter database
NR-non-redundant GenBank+EMBL+DDBJ+PDB. Entries with 100% sequence identity are merged as one.
Vector: A subset of GenBank containing vector DNA
ProDom
PRINTS
BLOCKS
Workshop 2 A-Look up a Genbank record. Usethe annotations to determine the the first openreading frame.
Dot Plots
A T G C C T A G
A T G C C T A G
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Window = 1
Note that 25% ofthe table will befilled due to randomchance. 1 in 4 chanceat each position
Dot Plots with window = 2
A T G C C T A GA T G C C T A G
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Window = 2The larger the windowthe more noise canbe filtered
What is thepercent chance thatyou will receive a match randomly?1/16 * 100 = 6.25%
{{{{{{{
Identity Matrix
Simplest type of scoring matrix
LICA
1000L
100I
10C
1A
H2N CH C
CH2
OH
O
CH CH3
CH3
H2N CH C
CH
OH
O
CH3
CH2
CH3
Similarity Searching
It is easy to score if an amino acid is identical to another (thescore is 1 if identical and 0 if not). However, it is not easy togive a score for amino acids that are somewhat similar.
Leucine Isoleucine
Should they get a 0 (non-identical) or a 1 (identical) or something in between?
Purpose of finding differences and similarities of amino acids.
Infer structural information
Infer functional information
Infer evolutionary relationships
Evolutionary Basis of Sequence Alignment
1. Similarity: Quantity that relates to how alike two sequences are.2. Identity: Quantity that describes how aliketwo sequences are in the strictest terms.3. Homology: a conclusion drawn from datasuggesting that two genes share a commonevolutionary history.
Evolutionary Basis of Sequence Alignment (Cont. 1)
1. Example: Shown on the next page is a pairwise alignment of two proteins. One is mouse trypsin and the other is crayfish trypsin. They are homologous proteins. The sequences share 41% identity.
2. Underlined residues are identical. Asterisks and diamond represent those residues that participate in catalysis. Five gaps are placed to optimize the alignment.
Evolutionary Basis of Sequence Alignment (Cont. 2)
Why are there regions of identity?
1) Conserved function-residues participate in reaction.
2) Structural-residues participate in maintaining structure of protein. (For example, conserved cysteine residues that
form a disulfide linkage) 3) Historical-Residues that are conserved solely due to a
common ancestor gene.
Modular nature of proteins
The previous alignment was global. However, many proteins do not display global patterns of similarity. Instead, they possess local regions of similarity.
Proteins can be thought of as assemblies of modular domains. Think Mr. Potatohead
Scoring Matrices
Scoring matrices tell how similar amino acids are.
There are two main sets of scoring matrices: PAM and BLOSUM.
PAM is based on evolutionary distances
BLOSUM is based on structure/function similarities
The bottom line on PAM
Frequencies of alignmentFrequencies of occurrence
The probability that two amino acids, i and j arealigned by evolutionary descent divided by the
probability that they are aligned by chance
BLOSUM Matrices
BLOSUM is built from distantly related sequences whereas PAM is built from closely related sequences
BLOSUM is built from conserved blocks of aligned protein segment found in the BLOCKS database (remember the BLOCKS database is a secondary database that depends on the PROSITE Family)
Global Alignment vs. Local Alignment
Global alignment is used when the overall gene sequence is similar to another sequence-often used in multiple sequence alignment. Clustal W algorithm
Local alignment is used when only a small portion of one gene is similar to a small portion of another gene.
Speed is achieved by: Pre-indexing the database before the search Parallel processing
Uses a hash table that contains neighborhood words rather than just identical words.
Neighborhood words
The program declares a hit if the word taken from the query sequence has a score >= T when a substitution matrix is used.
This allows the word size (W (this is similar to ktup value)) to be kept high (for speed) without sacrificing sensitivity.
If T is increased by the user the number of background hits is reduced and the program will run faster
The expectation (E) value
The Expect value (E) is a parameter that describes the number of hits one can "expect" to see just by chance when searching a database of a particular size. It decreases exponentially with the Similarity Score (S) that is assigned to a match between two sequences. The higher the score, the lower the E value. Essentially, the E value describes the random background noise that exists for matches between sequences. The Expect value is used as a convenient way to create a significance threshold for reporting results. When the Expect value is increased from the default value of 10, a larger list with more low-scoring hits can be reported. An E value of 1 assigned to a hit can be interpreted as meaning that in a database of the current size you might expect to see 1 match with a similar score simply by chance.
What influences the E Value?
Length of sequence The longer the query the lower the probability that
it will find a sequence in the database by chance.
Size of database The larger the database the higher the probability
that the query will find a match by chance.
Increase the word size (W) The larger the word size the lower the probability
that the query will find a sequence in the database by chance.
The scoring matrix The less stringent the scoring matrix the higher the
probability that the query will find a sequence in the database by chance.
E value
E value
E value
E value
Workshop for module 2: Perform a BLASTsearch of different databases using a peptide sequence.