DNA sequencing methods

Dr. L.Yatawara

Dept.of MLS

FAHS,

University of Peradeniya Dr.L.Yatawara 1

DNA sequencing: methods

I. Brief history of sequencing

II. Sanger dideoxy method for sequencing

III. Sequencing large pieces of DNA

Dr.L.Yatawara 2

Why sequence DNA?

• All genes available for an organism to use -- a

very important tool for biologists

• Not just sequence of genes, but also positioning

of genes and sequences of regulatory regions

• New recombinant DNA constructs must be

sequenced to verify construction or positions of

mutations

• Etc.

Dr.L.Yatawara 3

History of DNA sequencing

Dr.L.Yatawara 4

MC chapter 12

History of DNA sequencing

Dr.L.Yatawara 5

Methods of sequencing

A. Sanger dideoxy (primer extension/chain-termination)

method: most popular protocol for sequencing, very

adaptable, scalable to large sequencing projects

B. Maxam-Gilbert chemical cleavage method: DNA is

labelled and then chemically cleaved in a sequence-

dependent manner. This method is not easily scaled and

is rather tedious

C. Pyrosequencing: measuring chain extension by

pyrophosphate monitoring

Dr.L.Yatawara 6

for dideoxy sequencing you need:

1) Single stranded DNA template

2) A primer for DNA synthesis

3) DNA polymerase

4) Deoxynucleoside triphosphates and

dideoxynucleotide triphosphates

Dr.L.Yatawara 7

Primers for DNA sequencing

• Oligonucleotide primers can be synthesized by phosphoramidite chemistry--usually designed manually and then purchased

• Sequence of the oligo must be complimentary to DNA flanking sequenced region

• Oligos are usually 15-30 nucleotides in length

Dr.L.Yatawara 8

DNA templates for sequencing:

• Single stranded DNA isolated from

recombinant M13 bacteriophage containing

DNA of interest

• Double-stranded DNA that has been

denatured

• Non-denatured double stranded DNA (cycle

sequencing)

Dr.L.Yatawara 9

One way for obtaining single-stranded DNA from a double

stranded source--magnets

Dr.L.Yatawara 10

Reagents for sequencing:

DNA polymerases

• Should be highly processive, and incorporate ddNTPs efficiently

• Should lack exonuclease activity

• Thermostability required for “cycle sequencing”

Dr.L.Yatawara 11

Single stranded DNA 5‟ 3‟

5‟ 3‟

Sanger dideoxy sequencing--basic method

a) Anneal the primer

Dr.L.Yatawara 12

Sanger dideoxy sequencing: basic method

b) Extend the

primer with DNA

polymerase in the

presence of all four

dNTPs, with a

limited amount of a

dideoxy NTP

(ddNTP)

5‟

3‟

Direction of

DNA

polymerase

travel

Dr.L.Yatawara 13

Sanger dideoxy sequencing: basic method

5‟ 3‟

5‟ 3‟

T T T T

ddA

ddA

ddA

ddA

ddATP in the

reaction: anywhere

there‟s a T in the

template strand,

occasionally a ddA

will be added to the

growing strand

Dr.L.Yatawara 14

How to visualize DNA fragments?

• Radioactivity

– Radiolabeled primers (kinase with 32P)

– Radiolabelled dNTPs (gamma 35S or 32P)

• Fluorescence

– ddNTPs chemically synthesized to contain fluors

– Each ddNTP fluoresces at a different wavelength allowing identification

Dr.L.Yatawara 15

Analysis of sequencing products:

Polyacrylamide gel electrophoresis--good

resolution of fragments differing by a single

dNTP

– Slab gels: as previously described

– Capillary gels: require only a tiny amount of

sample to be loaded, run much faster than

slab gels, best for high throughput

sequencing

Dr.L.Yatawara 16

DNA sequencing gels: old school

Analyze sequencing

products by gel

electrophoresis,

autoradiography

Different ddNTP used in

separate reactions

Radioactively labelled primer or

dNTP in sequencing reaction

Dr.L.Yatawara 17

Dr.L.Yatawara 18

cycle sequencing: denaturation

occurs during temperature cycles

94°C:DNA denatures

45°C: primer anneals

60-72°C: thermostable DNA

pol extends primer

Repeat 25-35 times

Advantages: don‟t need a lot

of template DNA

Disadvantages: DNA pol

may incorporate ddNTPs

poorly Dr.L.Yatawara 19

An automated sequencer

The output Dr.L.Yatawara 20

Current trends in sequencing:

It is rare for labs to do their own sequencing:

--costly, perishable reagents

--time consuming

--success rate varies

Instead most labs send out for sequencing:

--You prepare the DNA (usually plasmid, M13, or PCR product),

supply the primer, company or university sequencing center

does the rest

--The sequence is recorded by an automated sequencer as an

“electropherogram”

Dr.L.Yatawara 21

• Next generation sequencing- whole genome

sequencing

• GWAS

• SNP studies

Dr.L.Yatawara 22

~160 kbp

~1 kbp

Assemble sequences by

matching overlaps

BAC sequence

BAC overlaps give genome sequence

BREAK UP THE GENOME,

PUT IT BACK TOGETHER

Dr.L.Yatawara 23

Sequencing large pieces of DNA:

the “shotgun” method

• Break DNA into small pieces (typically sizes of around 1000 base pairs is preferable)

• Clone pieces of DNA into M13

• Sequence enough M13 clones to ensure complete coverage (eg. sequencing a 3 million base pair genome would require 5x to 10x 3 million base pairs to have a reliable representation of the genome)

• Assemble genome through overlap analysis using computer algorithms, also “polish” sequences using mapping information from individual clones, characterized genes, and genetic markers

• This process is assisted by robotics Dr.L.Yatawara 24

Sequencing strategy

A whole chromosome shotgun sequencing

strategy was used to determine the genome

sequence of P. falciparum clone 3D7. This approach

was taken because a whole genome shotgun

strategy was not feasible or cost-effective with the

technology that was available at the beginning of the

project. Also, high-quality large insert libraries of (A -

T)-rich P. falciparum DNA have never been

constructed in Escherichia coli, which ruled out a

clone-by-clone sequencing strategy. The

chromosomes were separated on pulsed field gels,

and chromosomal DNA was extracted…

Dr.L.Yatawara 25

The shotgun sequences were assembled into

contiguous DNA sequences (contigs), in some cases with

low coverage shotgun sequences of yeast artificial

chromosome (YAC) clones to assist in the ordering of

contigs for closure. Sequence tagged sites (STSs)10,

microsatellite markers11,12 and HAPPY mapping7 were

also used to place and orient contigs during the gap

closure process. The high (A /T) content of the genome

made gap closure extremely difficult7–9.

Chromosomes 1–5, 9 and 12 were closed,

whereas chromosomes 6–8, 10, 11, 13 and 14 contained

3–37 gaps (most less than 2.5 kb) per chromosome at the

beginning of genome annotation. Efforts to close the

remaining gaps are continuing.

Dr.L.Yatawara 26

Methods: Sequencing, gap closure and annotation

The techniques used at each of the three participating

centres for sequencing, closure and annotation are described in

the accompanying Letters.

To ensure that each centres‟ annotation procedures produced

roughly equivalent results, the Wellcome Trust Sanger Institute

(„Sanger‟) and the Institute for Genomic Research („TIGR‟)

annotated the same100-kb segment of chromosome 14.

Thus 88% of the exons predicted by the two centres in the 100-

kb fragment were identical or overlapped.

Dr.L.Yatawara 27

Previous sequencing techniques: one DNA molecule at a time

Needed: many DNA molecules at a time -- arrays

One of these: “pyrosequencing”

Cut a genome to DNA fragments 300 - 500 bases long

Immobilize single strands on a very small plastic bead (one

piece of DNA per bead)

Amplify the DNA on each bead to cover each bead to boost the

signal

Separate each bead on a plate with up to 1.6 million wells

Dr.L.Yatawara 28

Sequence by DNA polymerase -dependent chain extension,

one base at a time in the presence of a reporter (luciferase)

Luciferase is an enzyme that will emit a photon of light in

response to the pyrophosphate (PPi) released upon nucleotide

addition by DNA polymerase

Flashes of light and their intensity are recorded

Dr.L.Yatawara 29

Height of peak indicates the number of

dNTPs added

This sequence: TTTGGGGTTGCAGTT Dr.L.Yatawara 30

Introduction to bioinformatics 1) Making biological sense of DNA

sequences

2) Online databases: a brief survey

3) Database in depth: NCBI

4) What is BLAST?

5) Using BLAST for sequence analysis

6) “Biology workbench”, etc.

www.ncbi.nlm.nih.gov

www.tigr.org

http://workbench.sdsc.edu Dr.L.Yatawara 31

There‟s plenty of DNA to make sense of

http://www.genomesonline.org/

(2006) Dr.L.Yatawara 32

Making sense of genome sequences:

1) Genes

a) Protein-coding

• Where are the open reading frames?

• What are the ORFs most similar to? (What is

the function/structure/evolution history?)

b) RNA

2) Non-genes

a) Regulation: promoters and factor-binding sites

b) Transactions: replication, repair, and

segregation, DNA packaging (nucleosomes)

Dr.L.Yatawara 33

Sequence output

Computer calls

GNNTNNTGTGNCGGATACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGCACCACCAC

CACCACCACCCCATGGGTATGAATAAGCAAAAGGTTTGTCCTGCTTGTGAATCTGCGGAACTTATTTATGATCCAGAAAG

GGGGGAAATAGTCTGTGCCAAGTGCGGTTATGTAATAGAAGAGAACATAATTGATATGGGTCCTAAGTGGCGTGCTTTTG

ATGCTTCTCAAAGGGAACGCAGGTCTAGAACTGGTGCACCAGAAAGTATTCTTCTTCATGACAAGGGGCTTTCAACTGCA

ATTGGAATTGACAGATCGCTTTCCGGATTAATGAGAGAGAAGATGTACCGTTTGAGGAAGTGGCANTCCANATTANGAGT

TAGTGATGCAGCANANAGGAACCTAGCTTTTGCCCTAAGTGAGTTGGATAGAATTNCTGCTCAGTTAAAACTTCCNNGAC

ATGTAGAGGAAGAAGCTGCAANGCTGNACANAGANGCAGNGNGANAGGGACTTATTNGANGCAGATCTATTGAGAGCGTT

ATGGCGGCANGTGTTTACCCTGCTTGTAGGTTATTAAAAGNTCCCGGGACTCTGGATGAGATTGCTGATATTGCTAGAGC

Raw data

Dr.L.Yatawara 34

atgttgtatttgtctgaagaaaataaatccgtatccactccttgcc

ctcctgataagattatctttgatgcagagaggggggagtacattt

gctctgaaactggagaagttttagaagataaaattatagatca

agggccagagtggagggccttcacgccagaggagaaaga

aaagagaagcagagttggagggcctttaaacaatactattca

cgataggggtttatccactcttatagactggaaagataaggatg

ctatgggaagaactttagaccctaagagaagacttgaggcatt

gagatggagaaagtggcaaattaga

What does this sequence do?

Could it encode a protein?

Dr.L.Yatawara 35

Looking for ORFs (Open Reading Frames)

using “DNA Strider”

Dr.L.Yatawara 36

ORF map 1) Where are the potential starts (ATG)

and stops (TAA, TAG, TGA)?

2) Which reading frame is correct?

= ATG

= stop

codon

Reading frame #1 appears to encode a protein Dr.L.Yatawara 37

Cautions in ORF identification

• Not all genes initiate with ATG, particularly in certain microbes (archaea)

• What is the shortest possible length of a real ORF? 50 amino acids? 25 amino acids? Cut-off is somewhat arbitrary.

• In eukaryotes, ORFs can be difficult to identify because of introns

• Are there other sequences surrounding the ORF that indicate it might be functional? – promoter sequences for RNA polymerase binding

– Shine-Dalgarno sequences for ribosome binding?

Dr.L.Yatawara 38

What is the function of

the sequenced gene? Classical methods:

-- mutate gene, characterize phenotype for clues to function

(genetics)

-- purify protein product, characterize in vitro (biochemistry)

Comparison to previously characterized genes:

-- genes sequences that have high sequence similarity

usually have similar functions

-- if your gene has been previously characterized

(using classical methods) by someone else, you want

to know right away! (avoid duplication of labor) Dr.L.Yatawara 39

NCBI NCBI home page --Go to www.ncbi.nlm.nih.gov for the following

pages

Pubmed: search tool for literature--search by author, subject, title

words, etc.

All databases: “a retrieval system for searching several linked

databases”

BLAST: Basic Local Alignment Sequence Tool

OMIM: Online Mendelian Inheritance in Man

Books: many online textbooks available

Tax Browser: A taxonomic organization of organisms and their

genomes

Structure: Clearinghouse for solved molecular structures Dr.L.Yatawara 40

What does BLAST do?

1) Searches chosen sequence database

and identifies sequences with similarity

to test sequence

2) Ranks similar sequences by degree of

homology (E value)

3) Illustrates alignment between test

sequence and similar sequences

Dr.L.Yatawara 41

Alignment of sequences:

The principle: two homologous sequences derived from the

same ancestral sequence will have at least some identical

(similar) amino acid residues

Fraction of identical amino acids is called “percent identity”

Similar amino acids: some amino acids have similar

physical/chemical properties, and more likely to substitute for

each other--these give specific similarity scores in

alignments

Gaps in similar/homologous sequences are rare, and are

given penalty scores

Dr.L.Yatawara 42

Homology of proteins

Homology: similarity of biological structure, physiology,

development, and evolution, based on genetic inheritance

Homologous proteins: statistically similar sequence, therefore

similar functions (often, but not always…)

Alignment of TFB and TFIIB sequences

Pho TFB1 1 -- ----- ----- ----- MTKQK VCPVC GST-- EFIYD PERGE IVCAR CGYPab TFB 1 -- ----- ----- ----- MTKQR VCPVC GST-- EFIYD PERGE IVCAR CGYPfu TFB1 1 -- ----- ----- ----- MNKQK VCPAC ESA-- ELIYD PERGE IVCAK CGYTko TFB1 1 -- ----- ----- ----- MSGKR VCPVC GST-- EFIYD PSRGE IVCKV CGYTko TFB2 1 -- ----- ----- MRG-- ISPKR VCPIC GST-- EFIYD PRRGE IVCAK CGYPfu TFB2 1 -- ----- MSSTE PGGGW LIYPV KCPYC KSR-- DLVYD RQHGE VFCKK CGSPho TFB2_ deduc edNTD isfro mBLAS T_ 1 -- ----- ----- YGG-- --SKI RCPVC GSS-- KIIYD PEHGE YYCAE CGHSso TFB1 1 -- ----- ----- MLYLS EENKS VSTPC PPD-- KIIFD AERGE YICSE TGESso TFB2 1 -- ----- ----- ----- ----M KCPYC KTDN- AITYD VEKGM YVCTN CASSce TFIIB 1 MM TRESI DKRAG RRGPN LNIVL TCPEC KVYPP KIVER FSEGD VVCAL CGLcon sensu s 1 m k vcpvC gst eliyd perGe ivCar cgy

Pho TFB1 32 VI EENII DMGPE WRAFD ASQR- -EKRS RTGAP ESILL HDKGL STDIG IDRPab TFB 32 VI EENIV DMGPE WRAFD ASQR- -EKRS RTGAP ESILL HDKGL STDIG IDRPfu TFB1 32 VI EENII DMGPE WRAFD ASQR- -ERRS RTGAP ESILL HDKGL STEIG IDRTko TFB1 32 VI EENVV DEGPE WRAFD PGQR- -EKRA RVGAP ESILL HDKGL STDIG IDRTko TFB2 35 VI EENVV DEGPE WRAFE PGQR- -EKRA RTGAP MTLMI HDKGL STDID WRDPfu TFB2 42 IL ATNLV DSEL- ----- ----- ---SR KTKTN DIPRY -TKRI G---- ---Pho TFB2_ deduc edNTD isfro mBLAS T_ 33 VI KS--F DTRV- ----- ----- ---RT FSSP- --PKF RSKGT S---- ---Sso TFB1 37 VL EDKII DQGPE WRAFT PEEK- -EKRS RVGGP LNNTI HDRGL STLID WKDSso TFB2 29 VI EDSAV DPGPD WRAYN AKDR- -NEKE RVGSP STPKV HDWGF HTIIG YGRSce TFIIB 51 VL SDKLV DTRSE WRTFS NDDHN GDDPS RVGEA SNPLL DGNNL STRIG KGEcon sensu s 51 vi eeniv D gpe wrafd qr ekrs rtgap esill hdkgl stdig r

Pho TFB1 80 -- ----S LTGLM REKMY RLRKW QSRLR VSDAA ERNLA FALSE LDRIT AQLPab TFB 80 -- ----S LTGLM REKMY RLRKW QSRLR VSDAA ERNLA FALSE LDRIT AQLPfu TFB1 80 -- ----S LSGLM REKMY RLRKW QSRLR VSDAA ERNLA FALSE LDRIT AQLTko TFB1 80 -- ----S LTGLM REKMY RLRKW QSRLR VSDAA ERNLA FALSE LDRLA SNLTko TFB2 83 KD IHGNQ ITGMY RNKLR RLRMW QRRMR INDAA ERNLA FALSE LDRMA AQLPfu TFB2 70 -- ----- --EFT REKIY RLRKW QKKI- ---SS ERNLV LAMSE LRRLS GMLPho TFB2_ deduc edNTD isfro mBLAS T_ 57 -- ----- --DMV REKIH RLKRL DS--- ---FG NKTEK LGVEE ISRIS SQLSso TFB1 85 KD AMGRT LDPKR RLEAL RWRKW QIRAR IQSSI DRNLA QAMNE LERIG NLLSso TFB2 77 -- ----- --AKD RLKTL KMQRM QNKIR VS-PK DKKLV TLLSI LNDES SKLSce TFIIB 1 01 -- ----- ---TT DMRFT KELNK AQGKN VMDKK DNEVQ AAFAK ITMLC DAAcon sensu s 1 01 s ltglm rekmy rlrkw qsrlr vsdaa ernla false ldrit aql

Pho TFB1 1 24 KL PKHVE EEAAR LYREA VRKGL IRGRS IESVI AACVY AACRL LKVPR TLDPab TFB 1 24 KL PKHVE EEAAR LYREA VRKGL IRGRS IESVI AACVY AACRL LKVPR TLDPfu TFB1 1 24 KL PRHVE EEAAR LYREA VRKGL IRGRS IESVM AACVY AACRL LKVPR TLDTko TFB1 1 24 SL PKHVE EEAAR LYREA VRKGL IRGRS IEAVI AACVY AACRL LKVPR TLDTko TFB2 1 33 RL PRHLK EVAAS LYRKA VMKKL IRGRS IEGMV SAALY AACRM EGIPR TLDPfu TFB2 1 07 KL PKYVE EEAAY LYREA AKRGL TRRIP IETTV AACIY ATCRL FKVPR TLNPho TFB2_ deduc edNTD isfro mBLAS T_ 92 CL PKHVE REAVR IYRKL IKSGV TKGRS IESVA AACIY ISCRL YKVPR TLDSso TFB1 1 35 NL PKSVK DEAAL IYRKA VEKGL VRGRS IESVV AAAIY AACRR MKLAR TLDSso TFB2 1 17 EL PEHVK ETASL IIRKM VETGL TKRID QYTLI VAALY YSCQV NNIPR HLQSce TFIIB 1 41 EL PKIVK DCAKE AYKLC HDEKT LKGKS MESIM AASIL IGCRR AEVAR TFKcon sensu s 1 51 kL Pkhve eeAar lyrea vrkgl irgrs iesvi aAcvy aaCrl lkvpR tld

Pho TFB1 1 74 EI SDIAR VEKKE IGRSY RFIAR NLN-- ----- ---LT PKKLF VKPTD YVNPab TFB 1 74 EI SDIAR VEKKE IGRSY RFIAR NLN-- ----- ---LT PKKLF VKPTD YVNPfu TFB1 1 74 EI ADIAR VDKKE IGRSY RFIAR NLN-- ----- ---LT PKKLF VKPTD YVNTko TFB1 1 74 EI ADVSR VDKKE IGRSF RFIAR HLN-- ----- ---LT PKKLF VKPTD YVNTko TFB2 1 83 EI ASVSK VSKKE IGRSY RFMAR GLG-- ----- ---LN LRP-- TSPIE YVDPfu TFB2 1 57 EI ASYSK TEKKE IMKAF RVIVR NLN-- ----- ---LT PKMLL ARPTD YVDPho TFB2_ deduc edNTD isfro mBLAS T_ 1 42 EI AKVAK EDKKV IARVY RLVVK KLG-- ----- ---LS SKDML IRPEY YIDSso TFB1 1 85 EI AQYTK ANRKE VARCY RLLLR ELD-- ----- ---VS VPVS- -DPKD YVTSso TFB2 1 67 EF KVRYS ISSSE FWSAL KRVQY VANS- ----- ---IP GFRPK IKPAE YIPSce TFIIB 1 91 EI QSLIH VKTKE FGKTL NIMKN ILRGK SEDGF LKIDT DNMSG AQNLT YIPcon sensu s 2 01 Ei a i r vekke igrsy rfiar ln lt pkkl vkptd Yv

Pho TFB1 1 -- ----- ----- ----- MTKQK VCPVC GST-- EFIYD PERGE IVCAR CGYPab TFB 1 -- ----- ----- ----- MTKQR VCPVC GST-- EFIYD PERGE IVCAR CGYPfu TFB1 1 -- ----- ----- ----- MNKQK VCPAC ESA-- ELIYD PERGE IVCAK CGYTko TFB1 1 -- ----- ----- ----- MSGKR VCPVC GST-- EFIYD PSRGE IVCKV CGYTko TFB2 1 -- ----- ----- MRG-- ISPKR VCPIC GST-- EFIYD PRRGE IVCAK CGYPfu TFB2 1 -- ----- MSSTE PGGGW LIYPV KCPYC KSR-- DLVYD RQHGE VFCKK CGSPho TFB2_ deduc edNTD isfro mBLAS T_ 1 -- ----- ----- YGG-- --SKI RCPVC GSS-- KIIYD PEHGE YYCAE CGHSso TFB1 1 -- ----- ----- MLYLS EENKS VSTPC PPD-- KIIFD AERGE YICSE TGESso TFB2 1 -- ----- ----- ----- ----M KCPYC KTDN- AITYD VEKGM YVCTN CASSce TFIIB 1 MM TRESI DKRAG RRGPN LNIVL TCPEC KVYPP KIVER FSEGD VVCAL CGLcon sensu s 1 m k vcpvC gst eliyd perGe ivCar cgy

Pho TFB1 32 VI EENII DMGPE WRAFD ASQR- -EKRS RTGAP ESILL HDKGL STDIG IDRPab TFB 32 VI EENIV DMGPE WRAFD ASQR- -EKRS RTGAP ESILL HDKGL STDIG IDRPfu TFB1 32 VI EENII DMGPE WRAFD ASQR- -ERRS RTGAP ESILL HDKGL STEIG IDRTko TFB1 32 VI EENVV DEGPE WRAFD PGQR- -EKRA RVGAP ESILL HDKGL STDIG IDRTko TFB2 35 VI EENVV DEGPE WRAFE PGQR- -EKRA RTGAP MTLMI HDKGL STDID WRDPfu TFB2 42 IL ATNLV DSEL- ----- ----- ---SR KTKTN DIPRY -TKRI G---- ---Pho TFB2_ deduc edNTD isfro mBLAS T_ 33 VI KS--F DTRV- ----- ----- ---RT FSSP- --PKF RSKGT S---- ---Sso TFB1 37 VL EDKII DQGPE WRAFT PEEK- -EKRS RVGGP LNNTI HDRGL STLID WKDSso TFB2 29 VI EDSAV DPGPD WRAYN AKDR- -NEKE RVGSP STPKV HDWGF HTIIG YGRSce TFIIB 51 VL SDKLV DTRSE WRTFS NDDHN GDDPS RVGEA SNPLL DGNNL STRIG KGEcon sensu s 51 vi eeniv D gpe wrafd qr ekrs rtgap esill hdkgl stdig r

Pho TFB1 80 -- ----S LTGLM REKMY RLRKW QSRLR VSDAA ERNLA FALSE LDRIT AQLPab TFB 80 -- ----S LTGLM REKMY RLRKW QSRLR VSDAA ERNLA FALSE LDRIT AQLPfu TFB1 80 -- ----S LSGLM REKMY RLRKW QSRLR VSDAA ERNLA FALSE LDRIT AQLTko TFB1 80 -- ----S LTGLM REKMY RLRKW QSRLR VSDAA ERNLA FALSE LDRLA SNLTko TFB2 83 KD IHGNQ ITGMY RNKLR RLRMW QRRMR INDAA ERNLA FALSE LDRMA AQLPfu TFB2 70 -- ----- --EFT REKIY RLRKW QKKI- ---SS ERNLV LAMSE LRRLS GMLPho TFB2_ deduc edNTD isfro mBLAS T_ 57 -- ----- --DMV REKIH RLKRL DS--- ---FG NKTEK LGVEE ISRIS SQLSso TFB1 85 KD AMGRT LDPKR RLEAL RWRKW QIRAR IQSSI DRNLA QAMNE LERIG NLLSso TFB2 77 -- ----- --AKD RLKTL KMQRM QNKIR VS-PK DKKLV TLLSI LNDES SKLSce TFIIB 1 01 -- ----- ---TT DMRFT KELNK AQGKN VMDKK DNEVQ AAFAK ITMLC DAAcon sensu s 1 01 s ltglm rekmy rlrkw qsrlr vsdaa ernla false ldrit aql

Pho TFB1 1 24 KL PKHVE EEAAR LYREA VRKGL IRGRS IESVI AACVY AACRL LKVPR TLDPab TFB 1 24 KL PKHVE EEAAR LYREA VRKGL IRGRS IESVI AACVY AACRL LKVPR TLDPfu TFB1 1 24 KL PRHVE EEAAR LYREA VRKGL IRGRS IESVM AACVY AACRL LKVPR TLDTko TFB1 1 24 SL PKHVE EEAAR LYREA VRKGL IRGRS IEAVI AACVY AACRL LKVPR TLDTko TFB2 1 33 RL PRHLK EVAAS LYRKA VMKKL IRGRS IEGMV SAALY AACRM EGIPR TLDPfu TFB2 1 07 KL PKYVE EEAAY LYREA AKRGL TRRIP IETTV AACIY ATCRL FKVPR TLNPho TFB2_ deduc edNTD isfro mBLAS T_ 92 CL PKHVE REAVR IYRKL IKSGV TKGRS IESVA AACIY ISCRL YKVPR TLDSso TFB1 1 35 NL PKSVK DEAAL IYRKA VEKGL VRGRS IESVV AAAIY AACRR MKLAR TLDSso TFB2 1 17 EL PEHVK ETASL IIRKM VETGL TKRID QYTLI VAALY YSCQV NNIPR HLQSce TFIIB 1 41 EL PKIVK DCAKE AYKLC HDEKT LKGKS MESIM AASIL IGCRR AEVAR TFKcon sensu s 1 51 kL Pkhve eeAar lyrea vrkgl irgrs iesvi aAcvy aaCrl lkvpR tld

Pho TFB1 1 74 EI SDIAR VEKKE IGRSY RFIAR NLN-- ----- ---LT PKKLF VKPTD YVNPab TFB 1 74 EI SDIAR VEKKE IGRSY RFIAR NLN-- ----- ---LT PKKLF VKPTD YVNPfu TFB1 1 74 EI ADIAR VDKKE IGRSY RFIAR NLN-- ----- ---LT PKKLF VKPTD YVNTko TFB1 1 74 EI ADVSR VDKKE IGRSF RFIAR HLN-- ----- ---LT PKKLF VKPTD YVNTko TFB2 1 83 EI ASVSK VSKKE IGRSY RFMAR GLG-- ----- ---LN LRP-- TSPIE YVDPfu TFB2 1 57 EI ASYSK TEKKE IMKAF RVIVR NLN-- ----- ---LT PKMLL ARPTD YVDPho TFB2_ deduc edNTD isfro mBLAS T_ 1 42 EI AKVAK EDKKV IARVY RLVVK KLG-- ----- ---LS SKDML IRPEY YIDSso TFB1 1 85 EI AQYTK ANRKE VARCY RLLLR ELD-- ----- ---VS VPVS- -DPKD YVTSso TFB2 1 67 EF KVRYS ISSSE FWSAL KRVQY VANS- ----- ---IP GFRPK IKPAE YIPSce TFIIB 1 91 EI QSLIH VKTKE FGKTL NIMKN ILRGK SEDGF LKIDT DNMSG AQNLT YIPcon sensu s 2 01 Ei a i r vekke igrsy rfiar ln lt pkkl vkptd Yv

Dr.L.Yatawara 43

High sequence similarity correlates with functional similarity

40-20% identity: fold can be predicted by similarity but precise

function cannot be predicted (the 40% rule)

enzymes

Non-enzymes

Dr.L.Yatawara 44

Programs available for BLAST searches

Protein sequence (this is the best option)

blastp--compares an amino acid query sequence against a protein

sequence database

tblastn--compares a protein query sequence against a nucleotide

sequence database translated in all reading frames

DNA sequence

blastn--compares a nucleotide query sequence against a nucleotide

sequence database

blastx--compares a nucleotide query sequence translated in all reading

frames against a protein sequence database

tblastx--compares the six-frame translations of a nucleotide query

sequence against the six-frame translations of a nucleotide sequence

database. Dr.L.Yatawara 45

BLAST considers all possible combinations of

matches

mismatches

gaps

in any given alignment

Gives the “best” (highest scoring) alignment of sequences

Three scores

1) percent identity

2) similarity score

3) E-value--probability that two sequences will have

the similarity they have by chance (lower number, higher

probability of evolutionary homology, higher probability of

similar function) Dr.L.Yatawara 46

What is the E-value?

The E value represents the chance that the similarity is

random and therefore insignificant. Essentially, the E value

describes the random background noise that exists for

matches between sequences. For example, an E value of 1

assigned to a hit can be interpreted as meaning that in a

database of the current size one might expect to see 1

match with a similar score simply by chance.

You can change the Expect value threshold on most main

BLAST search pages. When the Expect value is increased

from the default value of 10, a larger list with more low-

scoring hits can be reported.

Dr.L.Yatawara 47

E values (continued)

From the BLAST tutorial:

Although hits with E values much higher than 0.1 are

unlikely to reflect true sequence relatives, it is useful

to examine hits with lower significance (E values

between 0.1 and 10) for short regions of similarity. In

the absence of longer similarities, these short

regions may allow the tentative assignment of

biochemical activities to the ORF in question. The

significance of any such regions must be assessed

on a case by case basis.

Dr.L.Yatawara 48

Relationship between E-value and function

Single domain proteins

Multi-domain proteins

E value greater than 10-10, similar structure but possibly

different functions Dr.L.Yatawara 49

Computer calls

GNNTNNTGTGNCGGATACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGCACCACCAC

CACCACCACCCCATGGGTATGAATAAGCAAAAGGTTTGTCCTGCTTGTGAATCTGCGGAACTTATTTATGATCCAGAAAG

GGGGGAAATAGTCTGTGCCAAGTGCGGTTATGTAATAGAAGAGAACATAATTGATATGGGTCCTAAGTGGCGTGCTTTTG

ATGCTTCTCAAAGGGAACGCAGGTCTAGAACTGGTGCACCAGAAAGTATTCTTCTTCATGACAAGGGGCTTTCAACTGCA

ATTGGAATTGACAGATCGCTTTCCGGATTAATGAGAGAGAAGATGTACCGTTTGAGGAAGTGGCANTCCANATTANGAGT

TAGTGATGCAGCANANAGGAACCTAGCTTTTGCCCTAAGTGAGTTGGATAGAATTNCTGCTCAGTTAAAACTTCCNNGAC

ATGTAGAGGAAGAAGCTGCAANGCTGNACANAGANGCAGNGNGANAGGGACTTATTNGANGCAGATCTATTGAGAGCGTT

ATGGCGGCANGTGTTTACCCTGCTTGTAGGTTATTAAAAGNTCCCGGGACTCTGGATGAGATTGCTGATATTGCTAGAGC

Raw data

What does this sequence do? Cue up BLAST…..

Dr.L.Yatawara 50

MKCPYCKSRDLVYDRQHGEVFCKKCGSILATNLVDSEL

SRKTKTNDIPRYTKRIGEFTREKIYRLRKWQKKISSERN

LVLAMSELRRLSGMLKLPKYVEEEAAYLYREAAKRGLT

RRIPIETTVAACIYATCRLFKVPRTLNEIASYSKTEKKEIM

KAFRVIVRNLNLTPKMLLARPTDYVDKFADELELSERVR

RRTVDILRRANEEGITSGKNPLSLVAAALYIASLLEGERR

SQKEIARVTGVSEMTVRNRYKELA

Find the open reading frame(s)

Translate it:

Dr.L.Yatawara 51

BLAST against (go to genomes page):

-- Microbial genomes

-- environmental sequences (genomes)

Results:

1) Distribution of hits: query sequence and positions in

sequence that gave alignments

2) Sequences producing significant alignments

1) Accession number (this takes you to the sequence that

yielded the hit: gene or contig)

2) Name of sequence (sometimes identifies the gene)

3) Similarity score

4) E-value

3) Alignments arranged by E value, with links to gene reports Dr.L.Yatawara 52

2) Large percentages of

coding proteins cannot be

assigned function based

on homology

1) Homology? the function is

only inferred (NOT known) Two problems with BLAST

Dr.L.Yatawara 53

For a current list of databases and bioinformatics

tools see: Nucleic Acids Research annual

bioinformatics issue (comes out every January).

List of all the databases described, by category:

http://www.oxfordjournals.org/nar/database/cap/

Guide to NCBI: see Webct

Dr.L.Yatawara 54

Bioinformatics:

making sense of biological sequence

• New DNA sequences are analyzed for ORFs (Open Reading Frames: protein)

• Any DNA or protein sequence can then be compared to all other sequences in databases, and similar sequences identified

• There is much more -- a great diversity of programs and databases are available

Dr.L.Yatawara 55

DNA

RNA

protein

genome

“transcriptome”

“proteome”

(we have this)

(we want these)

Dr.L.Yatawara 56