1. NCBI Molecular Biology Resources
A Field Guide
part 2
September 30, ICGEB

2. Database Searching with Entrez
Using limits and field restriction to find human MutL homolog
Linking and neighboring with MutL
Mapping SNPs onto structure and the genome

3. Global Entrez Search

4. Document Summaries: MutL[All Fields]

5. Entrez Nucleotides: Limits & Preview/Index
Tabs

6. Entrez Nucleotides: Limits
Accession
All Fields
Author Name
EC/RN Number
Feature key
Filter
Gene Name
Issue
Journal Name
Keyword
Modification Date
Organism
Page Number
Primary Accession
Properties
Protein Name
Publication Date
SeqID String
Sequence Length
Substance Name
Text Word
Title
Uid
Volume
Field Restriction
MutL
Exclude bulk sequences

7. Entrez Nucleotides: Limits
MutL
Title == Definition
Exclude Bulk Sequences

8. Document Summaries: Limits

9. Adding Terms: Preview/Index
Accession
All Fields
Author Name
EC/RN Number
Feature key
Filter
Gene Name
Issue
Journal Name
Keyword
Modification Date
Organism
Page Number
Primary Accession
Properties
Protein Name
Publication Date
SeqID String
Sequence Length
Substance Name
Text Word
Title
Uid
Volume

10. Human MutL Search Results

11. Human MutL RefSeq
GenBank Records

12. NM_000249: Links

13. Literature Links
PubMed
OMIM

14. NM_000249: PubMed
Books

15. Books Link

16. OMIM: Human Disease Genes
Conserved Domain

17. Sequence Links
Nucleotide Protein

18. NM_000249: Related Sequences
similarity
Original GenBank mRNAs
Original GenBank genomic
Genome Project BAC

19. The Tax Browser NCBI’s Taxonomy
Taxonomy Link
The Tax Browser
NCBI’s Taxonomy

20. Taxonomy Link

21. The Tax Browser
Nucleotide
Protein
Structures
Popset

22. Marsupial PopSets

23. Mammalian Phylogenetic Study

24. Batch Downloads

25. Batch Downloads: FASTA and GI list

26. Batch Entrez / Entrez-utilities

27. Links Between and Within Nodes
Word weight
Computational
PubMed abstracts
Taxonomy
3-D Structure
3 -D Structures
VAST
Genomes
Phylogeny
Computational
Nucleotide sequences
Protein sequences
BLAST
BLAST
Computational
Computational

28. Text
Pubmed
Sequence
BLAST
Structure
VAST

29. 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

30. Global vs Local Alignment
Seq 1
Seq 2
Global alignment
Seq 1
Seq 2
Local alignment

31. Global vs Local Alignment
Seq1: WHEREISWALTERNOW (16aa)
Seq2: HEWASHEREBUTNOWISHERE (21aa)
Global
Seq1: W--HEREISWALTERNOW 16
W HERE
Seq2: 1 HEWASHEREBUTNOWISHERE
Local
Seq1: 1 W--HERE Seq1: 1 W--HERE 5
W HERE W HERE
Seq2: 3 WASHERE Seq2: 15 WISHERE 21

32. 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

33. Align program (Lipman and Pearson)
Global Alignment
Align program (Lipman and Pearson)
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 VA
Worm: 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: 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
Global alignments force a full-length comparison. In this example, the important domains are picked up by both methods, but looking at the first 15 a.a. of the query (60 a.a. of worm) shows how forcing an alignment in this region is not very helpful.
human REQLEHI KTHELHL
. .:: : ...
worm QWKLEDLFNLDSSEYKEASINF
500

34. How BLAST Works Make a lookup table of all “words” in the query
Scan the database for matching words
Initiate extensions from these matches

35. Words Query: GTQITVEDLFYNIATRRKALKN GTQ TQI QIT ITV TVE VED
Word Size = 3
Word size is adjustable
2 or 3 for protein ( 3 default)
> 7 for blastn ( 11 default )
GTQ
TQI
QIT
ITV
TVE
VED
EDL
DLF
LFY …
Make a lookup
table of words
Neighborhood Words
LTV, MTV, ISV, LSV, etc.

36. Scan Database…Initiate Extensions
Protein BLAST requires two hits
GTQITVEDLFYNI
< TVE FFN >
two neighborhood words
(threshold score)
Nucleotide BLAST requires exact matches
ATCGCCATGCTTAATTGGGCTT
< CATGCTTAATT >
exact word match

37. An Alignment That BLAST Can’t Find…
1 GAATATATGAAGACCAAGATTGCAGTCCTGCTGGCCTGAACCACGCTATTCTTGCTGTTG
|| | || || || | || || || || | ||| |||||| | | || | ||| |
1 GAGTGTACGATGAGCCCGAGTGTAGCAGTGAAGATCTGGACCACGGTGTACTCGTTGTCG
61 GTTACGGAACCGAGAATGGTAAAGACTACTGGATCATTAAGAACTCCTGGGGAGCCAGTT
| || || || ||| || | |||||| || | |||||| ||||| | |
61 GCTATGGTGTTAAGGGTGGGAAGAAGTACTGGCTCGTCAAGAACAGCTGGGCTGAATCCT
121 GGGGTGAACAAGGTTATTTCAGGCTTGCTCGTGGTAAAAAC
|||| || ||||| || || | | |||| || |||
121 GGGGAGACCAAGGCTACATCCTTATGTCCCGTGACAACAAC
Hypera postica cysteine proteinase mRNA vs Boophilus microplus cathepsin L-like proteinase precursor
Reason:
no contiguous exact match of 7 bp.

38. …but the corresponding amino acid sequences are conserved much better

39. Protein alignment looks good

40. …and they have the same domains, too

41. Local Alignment Statistics
High scores of local alignments between two random sequences
follow the Extreme Value Distribution
Expect Value
E = number of database hits you expect to find by chance
size of database
(applies to ungapped alignments)
E = Kmne-S
E = mn2-S’
K = scale for search space
 = scale for scoring system
S’ = bitscore = (S - lnK)/ln2
your score
Alignments
expected number of random hits
Score

42. Scoring Systems - Nucleotides
Identity matrix
A G C T
A +1 –3 –3 -3
G –3 +1 –3 -3
C –3 –
T –3 –3 –3 +1
CAGGTAGCAAGCTTGCATGTCA
|| |||||||||||| ||||| raw score = 19-9 = 10
CACGTAGCAAGCTTG-GTGTCA

43. Scoring Systems - Proteins
Position 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

44. BLOSUM62 Common amino acids have low weights
R -1 5 N D C Q E G H I L K M F P S T W Y V X
A R N D C Q E G H I L K M F P S T W Y V X
Positive for more likely substitutions
Common amino acids have low weights
Rare amino acids have high weights
Negative for less likely substitutions

45. Options for Advanced Blast: Protein
Example Entrez queries
proteins all[Filter] NOT mammalia[Organism]
green plants[Organism]
srcdb refseq[Properties]
Other advanced
-W 2 word size
–e expect value
-v descriptions
-b alignments
Limit by taxon
Mus musculus[Organism]
Mammalia[Organism]
Viridiplantae[Organism]
Matrix Selection
PAM30 -- most stringent
BLOSUM45 -- least stringent

46. Options for Advanced Blasting: Nucleotide
Example Entrez Queries
nucleotide all[Filter] NOT mammalia[Organism]
green plants[Organism]
biomol mrna[Properties]
biomol genomic[Properties]
OtherAdvanced
-W 7 word size
–e expect value
-v descriptions
-b alignments

47. Homology Searches Find a homolog of human CSK in C. elegans
Query = c-src tyrosine kinase (CSK)
NP_ (450 aa) [Homo sapiens]
Database = NCBI protein nr
Entrez limit: Caenorhabditis elegans [ORGN]
Program = BLASTP
Query=
>gi| |ref|NP_ | c-src tyrosine kinase [Homo sapiens]
MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL
Hits to the Conserved Domain Database:

48. BLAST Graphical Overview
SH3
SH2
tyr kinase domain

49. BLAST Alignments
gi| |emb|CAB | C. elegans KIN-22 protein (corresponding sequence F49B2.5) [Caenorhabditis elegans]
gi| |ref|NP_ | 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%)
Pick one hit . . .

50. 3D Domains
SH2
SH3
TyrKc
In this example, 3D Domains and Conserved Domains are similar, with Tyr kinase catalytic domain (CD) composed of 2 3D domains. Yellow is the catalytic loop.

51. Low Complexity Filtering
Filtered
Unfiltered
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

52. Intermission?