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NCBI Molecular Biology Resources

A Field Guidepart 2

September 30, 2004 ICGEB

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Genomes

Taxonomy

Links Between and Within Nodes

PubMed abstracts

Nucleotide sequences

Protein sequences

3-D Structure

3 -D Structures

Word weight

VAST

BLASTBLAST

Phylogeny

ComputationalComputational

Computational

Computational

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BLAST

VAST

Pubmed

Text

Sequence

Structure

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ePubmed: Computation of Related

Articles

The neighbors of a document are those documents in the database that are the most similar to it. The similarity between documents is measured by the words they have in common, with some adjustment for document lengths.

The value of a term is dependent on global and local types of information:

1) the number of different documents in the database that contain the term;

2) the number of times the term occurs in a particular document;

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Global and local weights

• The global weight of a term is greater for the less frequent terms. The presence of a term that occurred in most of the documents would really tell one very little about a document. On the other hand, a term that occurred in only 100 documents of one million would be very helpful in limiting the set of documents of interest.

• The local weight of a term is the measure of its importance in a particular document. Generally, the more frequent a term is within a document, the more important it is in representing the content of that document. However, this relationship is saturating, i.e., as the frequency continues to go up, the importance of the word increases less rapidly and finally comes to a finite limit.

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How we define similar documents

• The similarity between two documents is computed by adding up the weights (local wt1 × local wt2 × global wt) of all of the terms the two documents have in common. This provides an indication of how related two documents are.

• Once the similarity score of a document in relation to each of the other documents in the database has been computed, that document's neighbors are identified as the most similar (highest scoring) documents found. These closely related documents are pre-computed for each document in PubMed.

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Related articles: difficult task

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E-utilities: Top Level of Entrez

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E-utilities course

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E-utilities

• A set of seven server-side programs.

• Support a uniform URL syntax.

• Translate a standard set of URL-encoded input parameters for the array of programs comprising the Entrez system.

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Entrez Functions and E-utilities

• Searches: esearch.fcgi

• DocSums: esummary.fcgi

• Links: elink.fcgi

• Uploads: epost.fcgi

• Downloads: efetch.fcgi

• Global Query: egquery.fcgi

• Information: einfo.fcgi

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eA Docsum via esummary.fcgi and via the Web

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eA Simple Eutilities Pipeline

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eSearch for upstream regions of

homologous genes

• #!/usr/local/bin/perl #where the Perl is located

• use LWP::Simple; # we use LWP:Simple to get the content of URLs

• $ebase="http://eutils.ncbi.nlm.nih.gov/entrez/eutils/"; # this is a base URL we will add details to

• while(<>){ # we are reading file of gene names; file name is read from the command line;• chomp;$gene=$_;• $term=$gene."[gene+name]+AND+human[orgn]"; # we are interested in human genes only

#1. Search in Homologene

• $url=$ebase."esearch.fcgi?db=homologene&term=$term"; #search Entrez Gene with gene name• $result=get($url); #with the help of LWP's "get" command we download the content of the corresponding URL

• while($result=~/<Id>(\d+)<\/Id>/sg) #parsing out the content, reading gi's from Id lines• {$id.="$1,";} #...and concatenating them in one string, with commas as delimiters• chop $id;

#2. Link Homologene -> Nucleotide

• $url=$ebase."elink.fcgi?db=nucleotide&id=$id&dbfrom=homologene";#link back to nucleotides to get list of homolog NM gi's• $result=get($url);• $id="";• while($result=~/<Link>[^<]+<Id>(\d+)<\/Id>/sg){$id.="$1,";} chop $id;

#3. Link Nucleotide -> Gene

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eLots of precomputed data and a little bit of

parsing

• $url=$ebase."elink.fcgi?db=gene&id=$id&dbfrom=nucleotide"; #link to Entrez Gene again to get the genomic coordinates• $result=get($url);$id="";• while($result=~/<Link>[^<]+<Id>(\d+)<\/Id>/sg){push @ids,$1;} chop $id;• print @ids;• foreach $id (@ids){ #foreach NM accession gi

#4. Fetch XML document with gene information from Gene

• $url=$ebase."efetch.fcgi?db=gene&id=$id&retmode=xml";• #fetch the gene report that gives the genomic sequence and coordinates• $result=get($url);• $result=~/<Gene-commentary_type value=.genomic.>.+?<Seq-id_gi>(\d+)/s;• $id=$1;• $result=~/<Seq-interval_from>(\d+)/;$from=$1;• $result=~/<Seq-interval_to>(\d+)/;$to=$1;• $result=~/<Na-strand value="(\w+)"/;$strand=$1;if($strand eq "minus"){$strand=2;}else{$strand=1;}

• if($strand==1){ $to=$from;$from-=1000; }else{ $from=$to;$to+=1000; }

#5. Fetch upstream sequence from Nucleotide

• $url=$ebase."efetch.fcgi?db=nucleotide&id=$id&retmode=text&rettype=fasta&seq_start=$from&seq_stop=$to&strand=$strand";• #fetch sequence• $result=get($url);$result=~s/>ref/>lcl|$gene|/;• print "$result";• }

• }

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A General Design Approach

• Know what you want before you begin– Do I need the full record? (EFetch)– Will a DocSum be sufficient? (ESummary)

• Know what Entrez database contains the data you want– If it’s not in Entrez, the eUtils can’t access it

• Try your pipeline in interactive web Entrez first– Some Entrez queries may surprise you– Some Entrez data may surprise you– Some Entrez links may surprise you

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Others use E-utilities too: PubCrawler

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eMedBlast: searching for articles related

to a sequence.

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Fairness issue. Gate is only so wide. Scripts use the resources of many to satisfy a few.

Why Regulate?

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Scripts are like “fat” bunnies!!!

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Web Servers and Browsers

• Your browser makes one connection.

• Each server has an finite number of slots.

• A slot is allotted to a connection 1st come 1st served.

• Connections are (typically) not persistent.

• Scripts use more slots, and approach “persistent” connection.

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Normal Use

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Scripting

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Detection

• Weblogs are monitored by a script. • Alarm e-mails are sent hourly and a daily

encapsulation once a day.• Analysis – copyright versus volume. Not

automatic!• Blocking occurs.

– Copyrighted material can be very light volume.– Blast is “sensitive” can also be light in volume.– Entrez and PubMed mostly a “fairness” issue.

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How you are blocked.

• The IP address is blacklisted from the main NCBI web servers.

• You get a very obvious error message.

• Remember Spock: “The needs of the many outweigh the needs of the few.”

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How to avoid blockage.

• Plan your project.• Can I use other methods?

– FTP– Batch Entrez

• Write good scripts.– Expect errors– Multiple UIDs

• Follow the E-utils recommendations. • Ask us for advice.

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Recommendations.

• Use ‘eutils.ncbi.nlm.nih.gov’.

• Use the &tool and &email fields.

• Do not submit more than once every 3 seconds.

• Limit to 9 PM – 5 AM EST (our time).

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BLAST

VAST

Pubmed

Text

Sequence

Structure

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BLAST®

Basic Local Alignment Search Tool

• Why align sequences ? - because it is the best way to infer structure-function relationships for the

unknown biomolecules • Global vs local alignments• BLAST basics• MegaBLAST• Discontiguous MegaBLAST

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Basic Local Alignment Search Tool

Calculates similarity for biological sequences Finds best local alignments Heuristic approach based on Smith-Waterman

algorithm Searches for matching “words” and then extends the

hits Uses statistical theory to determine if a match might

have occurred by chance

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eGlobal

Alignment

Human: 15 IAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFH 84 +A + + + DL F K D+L I+ T+ W+ GR G IP+NYV + + +++ PW+ Worm: 63 VALFQYDARTDDDLSFKKDDILEILNDTQGDWWFARHKATGRTGYIPSNYVAREKSIES------QPWYF 125

Human: 85 GKITREQAERLLYPP--ETGLFLVRESTNYPGDYTLCVSCDGKVEHYRI-MYHASKLSIDEEVYFENLMQ 151 GK+ R AE+ L E G FLVR+S + D +L V + V+HYRI + H I F L Worm: 126 GKMRRIDAEKCLLHTLNEHGAFLVRDSESRQHDLSLSVRENDSVKHYRIQLDHGGYF-IARRRPFATLHD 194

Human: 152 LVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGN-KVA 220 L+ HY +ADGLC L P Y W ++ + ++L++ IG G+FG+V G + N VAWorm: 195 LIAHYQREADGLCVNLGAPCAKSEAPQTTTFTYDDQWEVDRRSVRLIRQIGAGQFGEVWEGRWNVNVPVA 264

Human: 221 VKCIK-NDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGD 289 VK +K A FLAEA +M +LRH L+ L V ++ + IVTE M + +L+ +L+ RGR Worm: 265 VKKLKAGTADPTDFLAEAQIMKKLRHPKLLSLYAVCTRDE-PILIVTELMQE-NLLTFLQRRGRQCQMPQ 332 Human: 290 CLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLT----KEASSTQDTG-KLPVKWTA 353 L++ S V M YLE NF+HRDLAARN+L++ K++DFGL KE TG + P+KWTA Worm: 333 -LVEISAQVAAGMAYLEEMNFIHRDLAARNILINNSLSVKIADFGLARILMKENEYEARTGARFPIKWTA 401

Human: 354 PEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWH 423 PEA +F+TKSDVWSFGILL EI +FGR+PYP + +V+ +V+ GY+M P GCP +Y++M+ CW Worm: 402 PEAANYNRFTTKSDVWSFGILLTEIVTFGRLPYPGMTNAEVLQQVDAGYRMPCPAGCPVTLYDIMQQCWR 471

Human: 424 LDAAMRPSFLQLREQLEHI 443 D RP+F L+ +LE +Worm: 472 SDPDKRPTFETLQWKLEDL 492

human M--------------SAIQ----------------------AAWPSGT------------ECIAKYNFHG M S .. AA SG. . .A ... .worm MGSCIGKEDPPPGATSPVHTSSTLGRESLPSHPRIPSIGPIAASSSGNTIDKNQNISQSANFVALFQYDA 1 20 40 60

440 450human REQLEHI--------KTHELHL . .:: . : ...worm QWKLEDLFNLDSSEYKEASINF 500

Align program (Lipman and Pearson)

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eHow BLAST

Works

Make a lookup table of all “words” in the query

Scan the database for matching words

Initiate extensions from these matches

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eWord

sGTQITVEDLFYNIATRRKALKNQuery:

Word Size = 3

Word size is adjustable 2 or 3 for protein ( 3 default) > 7 for blastn ( 11 default )

Neighborhood Words

LTV, MTV, ISV, LSV, etc.

GTQ TQI QIT ITV TVE VED EDL DLF LFY …

Make a lookuptable of words

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eScan Database…Initiate

Extensions Protein BLAST requires two hits

GTQITVEDLFYNI

<------ TVE FFN ------>

two neighborhood words(threshold score)

Nucleotide BLAST requires exact matches

exact word match

ATCGCCATGCTTAATTGGGCTT<------ CATGCTTAATT ------>

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eAn Alignment That BLAST Can’t

Find…

1 GAATATATGAAGACCAAGATTGCAGTCCTGCTGGCCTGAACCACGCTATTCTTGCTGTTG || | || || || | || || || || | ||| |||||| | | || | ||| |

1 GAGTGTACGATGAGCCCGAGTGTAGCAGTGAAGATCTGGACCACGGTGTACTCGTTGTCG

61 GTTACGGAACCGAGAATGGTAAAGACTACTGGATCATTAAGAACTCCTGGGGAGCCAGTT

| || || || ||| || | |||||| || | |||||| ||||| | |

61 GCTATGGTGTTAAGGGTGGGAAGAAGTACTGGCTCGTCAAGAACAGCTGGGCTGAATCCT

121 GGGGTGAACAAGGTTATTTCAGGCTTGCTCGTGGTAAAAAC

|||| || ||||| || || | | |||| || |||

121 GGGGAGACCAAGGCTACATCCTTATGTCCCGTGACAACAAC

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e…but the corresponding amino acid

sequences are conserved much better

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Protein alignment looks good

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e…and they have the same domains,

too

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eLocal Alignment Statistics

High scores of local alignments between two random sequencesfollow the Extreme Value Distribution

Score

Alig

nm

en

ts

(applies to ungapped alignments)

E = Kmne-S E = mn2-S’

K = scale for search space = scale for scoring system S’ = bitscore = (S - lnK)/ln2

Expect ValueE = number of database hits you expect to find by

chancesize of database

your score

expected number of

random hits

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eScoring Systems -

Nucleotides

A G C T

A +1 –3 –3 -3

G –3 +1 –3 -3

C –3 –3 +1 -3

T –3 –3 –3 +1

Identity matrix

CAGGTAGCAAGCTTGCATGTCA

|| |||||||||||| ||||| raw score = 19-9 = 10

CACGTAGCAAGCTTG-GTGTCA

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eScoring Systems -

ProteinsPosition Independent Matrices

PAM Matrices (Percent Accepted Mutation)• Derived from observation; small dataset of alignments• Implicit model of evolution• All calculated from PAM1• PAM250 widely used

BLOSUM Matrices (BLOck SUbstitution Matrices)• Derived from observation; large dataset of highly

conserved blocks• Each matrix derived separately from blocks with a

defined percent identity cutoff• BLOSUM62 - default matrix for BLAST

Position Specific Score Matrices (PSSMs)PSI- and RPS-BLAST

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A 4R -1 5 N -2 0 6D -2 -2 1 6C 0 -3 -3 -3 9Q -1 1 0 0 -3 5E -1 0 0 2 -4 2 5G 0 -2 0 -1 -3 -2 -2 6H -2 0 1 -1 -3 0 0 -2 8I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 A R N D C Q E G H I L K M F P S T W Y V X

BLOSUM62

Common amino acids have low weights

Rare amino acids have high weights

Negative for less likely substitutionsPositive for more likely substitutions

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eOptions for Advanced Blast:

Protein

Matrix Selection•PAM30 -- most stringent•BLOSUM45 -- least stringent

Example Entrez queriesproteins all[Filter] NOT mammalia[Organism]green plants[Organism]srcdb refseq[Properties]Other advanced-W 2 word size–e 10000 expect value-v 2000 descriptions-b 2000 alignments

Limit by taxonMus musculus[Organism]Mammalia[Organism]Viridiplantae[Organism]

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eOptions for Advanced Blasting:

Nucleotide

Example Entrez Queriesnucleotide all[Filter] NOT mammalia[Organism]green plants[Organism]biomol mrna[Properties]biomol genomic[Properties]

OtherAdvanced-W 7 word size–e 10000 expect value-v 2000 descriptions-b 2000 alignments

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eFind a homolog of human CSK in C. elegans

Query = c-src tyrosine kinase (CSK) NP_004374 (450 aa) [Homo sapiens]

Database = NCBI protein nr Entrez limit: Caenorhabditis elegans [ORGN]

Program = BLASTP

Homology Searches

Hits to the Conserved Domain Database:

Query=>gi|4758078|ref|NP_004374.1| c-src tyrosine kinase [Homo sapiens]MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL

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BLAST Graphical Overview

SH3 SH2 tyr kinase domain

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eBLAST

Alignments

gi|7160701|emb|CAB04427.2| C. elegans KIN-22 protein (corresponding sequence F49B2.5) [Caenorhabditis elegans]

gi|17508235|ref|NP_493502.1| Tyrosine kinase with SH2, SH3 and N myristoylation domains, Drosophila suppressor of pole hole homolog (57.5 kD) (kin-22) [Caenorhabditis elegans] Length = 507

Score = 290 bits (742), Expect = 1e-78 Identities = 170/440 (38%), Positives = 245/440 (55%), Gaps = 21/440 (4%)

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e3D

Domains

TyrKc

SH3SH2

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sp|P27476|NSR1_YEAST NUCLEAR LOCALIZATION SEQUENCE BINDING PROTEIN (P67) Length = 414 Score = 40.2 bits (92), Expect = 0.013 Identities = 35/131 (26%), Positives = 56/131 (42%), Gaps = 4/131 (3%)

Query: 362 STTSLTSSSTSGSSDKVYAHQMVRTDSREQKLDAFLQPLSKPLS---SQPQAIVTEDKTD 418 S++S SSS+S SS + + ++S + + S S S+ + E K Sbjct: 29 SSSSSESSSSSSSSSESESESESESESSSSSSSSDSESSSSSSSDSESEAETKKEESKDS 88

FilteredUnfiltered

Low Complexity Filtering

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PSI-BLAST

Position-Specific Iterated BLAST

• Mining for protein domains• Confirming relationships among related proteins

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ePosition Specific Substitution

Rates

Active site serineWeakly conserved serine

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ePosition Specific Score Matrix

(PSSM)

A R N D C Q E G H I L K M F P S T W Y V 206 D 0 -2 0 2 -4 2 4 -4 -3 -5 -4 0 -2 -6 1 0 -1 -6 -4 -1 207 G -2 -1 0 -2 -4 -3 -3 6 -4 -5 -5 0 -2 -3 -2 -2 -1 0 -6 -5 208 V -1 1 -3 -3 -5 -1 -2 6 -1 -4 -5 1 -5 -6 -4 0 -2 -6 -4 -2 209 I -3 3 -3 -4 -6 0 -1 -4 -1 2 -4 6 -2 -5 -5 -3 0 -1 -4 0 210 D -2 -5 0 8 -5 -3 -2 -1 -4 -7 -6 -4 -6 -7 -5 1 -3 -7 -5 -6 211 S 4 -4 -4 -4 -4 -1 -4 -2 -3 -3 -5 -4 -4 -5 -1 4 3 -6 -5 -3 212 C -4 -7 -6 -7 12 -7 -7 -5 -6 -5 -5 -7 -5 0 -7 -4 -4 -5 0 -4 213 N -2 0 2 -1 -6 7 0 -2 0 -6 -4 2 0 -2 -5 -1 -3 -3 -4 -3 214 G -2 -3 -3 -4 -4 -4 -5 7 -4 -7 -7 -5 -4 -4 -6 -3 -5 -6 -6 -6 215 D -5 -5 -2 9 -7 -4 -1 -5 -5 -7 -7 -4 -7 -7 -5 -4 -4 -8 -7 -7 216 S -2 -4 -2 -4 -4 -3 -3 -3 -4 -6 -6 -3 -5 -6 -4 7 -2 -6 -5 -5 217 G -3 -6 -4 -5 -6 -5 -6 8 -6 -8 -7 -5 -6 -7 -6 -4 -5 -6 -7 -7 218 G -3 -6 -4 -5 -6 -5 -6 8 -6 -7 -7 -5 -6 -7 -6 -2 -4 -6 -7 -7 219 P -2 -6 -6 -5 -6 -5 -5 -6 -6 -6 -7 -4 -6 -7 9 -4 -4 -7 -7 -6 220 L -4 -6 -7 -7 -5 -5 -6 -7 0 -1 6 -6 1 0 -6 -6 -5 -5 -4 0 221 N -1 -6 0 -6 -4 -4 -6 -6 -1 3 0 -5 4 -3 -6 -2 -1 -6 -1 6 222 C 0 -4 -5 -5 10 -2 -5 -5 1 -1 -1 -5 0 -1 -4 -1 0 -5 0 0 223 Q 0 1 4 2 -5 2 0 0 0 -4 -2 1 0 0 0 -1 -1 -3 -3 -4 224 A -1 -1 1 3 -4 -1 1 4 -3 -4 -3 -1 -2 -2 -3 0 -2 -2 -2 -3

Active site nucleophile

Serine scored differently in these two positions

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e>gi|113340|sp|P03958|ADA_MOUSE ADENOSINE DEAMINASE (ADENOSINEMAQTPAFNKPKVELHVHLDGAIKPETILYFGKKRGIALPADTVEELRNIIGMDKPLSLPGFVIAGCREAIKRIAYEFVEMKAKEGVVYVEVRYSPHLLANSKVDPMPWNQTEGDVTPDDVVDEQAFGIKVRSILCCMRHQPSWSLEVLELCKKYNQKTVVAMDLAGDETIEGSSLFPGHVEAYRTVHAGEVGSPEVVREAVDILKTERVGHGYHTIEDEALYNRLLKENMHFEVCPWSSYLTGAVRFKNDKANYSLNTDDPLIFKSTLDTDYQMTKKDMGFTEEEFKRLNINAAKSSFLPEEEKK

PSI-BLAST

e value cutoff for PSSM

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RESULTS: Initial BLASTPSame results as protein-protein BLAST

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eResults of First PSSM Search

Other purine nucleotide metabolizing enzymes not found by ordinary BLAST

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Third PSSM Search: Convergence

Just below threshold, another nucleotide metabolism enzyme

Check to add to PSSM

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eMegaBLAS

T

AI217550AI251192AI254381BE645079

C:\seq\hs.4.fsa

> 1133045 gnl|UG|Hs#S1133045 qd43b11.x1 Homo sapiens cDNA, 3' end CATGTAAGCCATTTATTGGTTTGTTTTAAAAATATGTATTTTATTTATACATGAAGTTTGGTGAGAAGTGCTCGATTAGTTCAGACAACATCTGGCACTTGATGTCTGTCCTTCCCTCCTTTTTCCTACTCTCTTCTCCCCTCCTGCTGGTCATTGTGCAGTTCTGGAAATTAAAAAGGTGACAGCCAGGCTAAAAGCTAAGGGTTGGGTCTAGCTCACCTCCCACCCCCAACCACACCGTCTGCAGCCAGCCCCAGGCACCTGTCTCAAAGCTCCCGGGCTGTCCACACACACAAAAACCACAGTCTCCTTCCGGCCAGCTGGGCTGGCAGCCCGACCTGC> 1141828 gnl|UG|Hs#S1141828 qv37f11.x1 Homo sapiens cDNA, 3' end GAGAAGACGACAGAAGGGGAGAAGAGAGTAGGAAAAAGGAGGGAAGGACAGACATCAAGTGCCAGATGTTGTCTGAACTAATCGAGCACTTCTCACCAAACTTCATGTATAAATAAAATACATATTTTTAAAACAAACCAATAAATGGCTTACATCAAAAAAAAAAAAAAAAAAAAAAAAGTCGTATCGATGT> 1145899 gnl|UG|Hs#S1145899 qv33c06.x1 Homo sapiens cDNA, 3' endGAGAAGACGACAGAAGGGGAGAAGAGAGTAGGAAAAAGGAGGGAAGGACAGACATCAAGTGCCAGATGTTGTCTGAACTAATCGAGCACTTCTCACCAAACTTCATGTATAAATAAAATACATATTTTTAAAACAAACCAATAAATGGCTTACATCAAAAAAAAAAAAAAAAAAAAAAAAGTCGTATCGATGT> 2291670 gnl|UG|Hs#S2291670 7e65f04.x1 Homo sapiens cDNA, 3' end TTTCATGTAAGCCATTTATTGGTTTGTTTTAAAAATATGTATTTTATTTATACATGAAGTTTGGTGAGAAGTGCTCGATTAGTTCAAACAACATCTGGCACTTGATGTCTGTCCTTCCCTCCTTTTTCCTACTCTCTTCTCCCCTCCTGCTGGTCATTGTGCAGTTCTGGAAATTAAAAAGGTGACAGCCAGGCTAAAAGCTAAGGGTTGGGTCTAGCTCACCTCCCACCCCCAACCACACCGTCTGCAGCCAGCCCCAGGCACCTGTCTCAAAGCTCCCGGGCTGTCCACACACACAAAAACCACAGTCTCCTTCCGGCCAGCTGGGCTGGCAGCCCGACCTGCCTCCCAACCGCATTCCTGCCTGTGTAGCAGGCGGTGAGCACCCAGAAGGGGCACATACCTCTCCAAGCCTTGAAAGCAAAGCATGGAGATCTACAAAAATAGGATTTCCACTTGGAGAAATGTCGCTGGGACAGT

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eWhat is Discontiguous (Cross-species)

MegaBLAST?

W = 11, t = 16, coding: 1101101101101101W = 11, t = 16, non-coding: 1110010110110111W = 12, t = 16, coding: 1111101101101101W = 12, t = 16, non-coding: 1110110110110111W = 11, t = 18, coding: 101101100101101101W = 11, t = 18, non-coding: 111010010110010111W = 12, t = 18, coding: 101101101101101101W = 12, t = 18, non-coding: 111010110010110111W = 11, t = 21, coding: 100101100101100101101W = 11, t = 21, non-coding: 111010010100010010111W = 12, t = 21, coding: 100101101101100101101W = 12, t = 21, non-coding: 111010010110010010111

Ma, B., Tromp, J., Li, M., "PatternHunter: faster and more sensitive homology search", Bioinformatics 2002 Mar;18(3):440-5

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Neighbors: Precomputed BLAST

Nucleotide

Protein

Entrez Related Sequences produces a list of sequences sorted by BLAST score, but with no alignment details.

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eBlink – Protein BLAST Alignments

• Lists only 200 hits • List is nonredundant

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eBLAST Databases: Non-redundant

protein

nr (non-redundant protein sequences)– GenBank CDS translations– NP_ RefSeqs– Outside Protein

• PIR, Swiss-Prot, PRF

– PDB (sequences from structures)

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eBLAST Databases: Nucleic

Acid• nr (nt)

– Traditional GenBank Divisions– NM_ and XM_ RefSeqs

• dbest – EST Division

• htgs – HTG division

• gss – GSS division

• chromosome – NC_ RefSeqs

• wgs– whole genome shotgun

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Genomic BLAST

• These pages provide customized nucleotide and protein databases for each genome• If a Map Viewer is available, the BLAST hits can be viewed on the maps

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eWhat if Your Favorite Gene is not found

in the latest genome build?

POSSIBLE VARIANTS:

• The gene does not exist;

• It exists, but there is a problem with assembly;

• It exists, but there is a problem with annotation

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eAn example: finding prestin in Human

genome

• We start with rat prestin, BLAST it against the Human genome and look for evidences that human prestin exists as well.

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Searching the Human Genome

>gi|12188917|emb|AJ303372.1|RNO303372 Rattus norvegicus

ATGGATCATGCTGAAGAAAATGAAATTCCTGCAGAGATCAGAAGTACCTCGTGGAA

GTCATCCGGTCCTCCAGGAGAGGCTGCACGTCAAGGACAAAGTCACAGACTCCATC

GCAGGCATTCACGTGCACTCCTAAAAAAGTAAGAAACATCATCTACATGTTCTTGC

TTGCCAGCATATAAATTCAAGGAGTATGTGCTGGGTGACTTGGTCTCGGGCATAAG

AGCTCCCCCAAGGCTTAGCCTTCGCGATGCTGGCAGCTGTGCCTCCGGTGTTCGGC

On for same species comparisons

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eBLAST Results

16 hits to one contig

Human Genome Database953 contigs2.9 billion letters

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eMap Viewer: Genomic Context of BLAST Hits

Genes

Genome Scan

Models

Human EST hits

Contig

GenBank

Mouse EST hits

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eHuman prestin: now appears in Build

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Now we can compare genes

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Three prestin genes: finally together!

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Same prestin, different assemblies

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eDoes homology mean the common

biological function?

• Not always; the existence of the common ancestor does not guarantee that some function won’t be lost or acquired after the divergence.

An example: zeta-crystallin is a component of a transparent lens matrix

of the vertebrate eye. Its homolog in E.coli is the metabolic

enzyme quinone oxidoreductase.

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BLAST

VAST

Entrez

Text

Sequence

Structure

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eStructure similarity: No More

BLASTing!

• Three-dimensional structures are most conserved during the evolution;

• One still can detect the existence of the common ancestor based on the structure similarity;

• Spatial similarity is not calculated the same way we do it for sequences

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eVAST: Structure

NeighborsVector Alignment Search Tool

For each protein chain,

locate SSEs (secondarystructure elements),

and represent them asindividual vectors.

1

2

3

4

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Human IL-4

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VAST: Structure Neighbors

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eStructure Neighbors in Cn3D

SH3 SH2

C-Srckinase

Human vs.Chicken

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e3D Domain Neighbors

HumanC-SrcKinase(Tyr)

vs.

Chk1kinase(Ser/Thr)

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NCBI is changing

From sequence data storage facility to one-stop shop with integrated databases of various kind.

You can be part of the future – work with us! Your expertise and data are indispensable.

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GenBank

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Refseq

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Entrez Gene

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Homologene database

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New generation of databases: an example

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eProtein interaction database: a seed for

future precomputed resources

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New databases: GenSAT

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PubChem

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Headache? Take Aspirin

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Aspirin has 432 neighbors

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Link to 3D protein structures

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eFor More Information…

•General Help info@ncbi.nlm.nih.gov•BLAST blast-help@ncbi.nlm.nih.gov

E-mail addresses

The (free!) NCBI Newsletter

The NCBI Handbook

http://www.ncbi.nih.gov/Education/index.html

The NCBI Education Page

http://www.ncbi.nih.gov/About/newsletter.html

Follow the link from the NCBI Home Page