1. Molecular Biology Databases
NCBI, DDBL, EMBL and others
These slides are obtained and/or modified from the ncbi website “field guide to ncbi” clides and from

2. What is a Database?
A database can be defined as "a collection of data arranged for ease and speed of search and retrieval.“
A DNA database contains individual records or data entries of the DNA sequences as well as information about the sequences.
A DNA database often contains flat-files. These are relatively simple database systems in which each database is contained in a single table.
In contrast, relational database systems can use multiple tables to store information, and each table can have a different record format.
This is the link to Omni, a guide to other databases in biology and medicine.

3. GenBank as a Database
GenBank is the National Institute of Health (NIH) genetic sequence database, an annotated collection of all publicly available DNA sequences.
It is maintained by the National Center for Biotechnology Information (NCBI) within the National Institute of Health (NIH).
Sequences are said to be annotated when a researcher ascribes a function to some or all of the sequences. For example, If I know a sequence of 1000 nucleotides contains a gene beginning at nucleotide 200 and ending at nucleotide 800 and say so in the record, then it is considered annotated.

4. Anatomy of a Genome InfoSystem
Information structure
– Records of hierarchical, complex documents; Tables of rows and columns of numbers, letters, words
– Table of contents, Reports, Indexing (as a reference book)
– Browse thru available structure.
– Search and retrieve according to biological questions
– Bulk data selection & retrieval for other uses
Information content
– Primary: Literature (referenced, abstracted and curated), Sequence and feature analyses, maps, controlled vocabulary/ontologies relevant to biology, people, research methods, contacts, etc.
– Metadata describing primary data, along with protocols, notes, sources
Informatics / software
– “Back-end” database, data collection, management, with some analyses
– “Front-end” information services (hypertext web, document search/retrieval methods); ease of understanding and usage (HCI)
– “Middleware” glue code, software, etc.
– Specialized application for genome data: maps, BLAST searches, ontologies

5. History of Sequence Databases
The first bioinformatics databases were constructed a few years after the first protein sequences began to become available.
The first protein sequence reported was that of bovine insulin in 1956, consisting of 51 residues.
Nearly a decade later, the first nucleic acid sequence was reported, that of yeast alanine tRNA with 77 bases.
Just a year later, Dayhoff gathered all the available sequence data to create the first bioinformatic database.
The Protein DataBank followed in 1972 with a collection of ten X-ray crystallographic protein structures, and the SWISSPROT protein sequence database began in 1987.

6. GenBank History
DNA databases began in the early 1980s with a database called GenBank, which was originated by the U.S. Department of Energy to hold the short stretches of DNA sequence that scientists were just beginning to obtain from a range of organisms.
In the early days of GenBank, rooms of technicians sat at keyboards consisting of only the four letters A, C, T and G, tediously entering the DNA-sequence information published in academic journals.

7. The National Center for Biotechnology Information
Created as a part of NLM in 1988
Establish public databases
U.S. National DNA Sequence Database
Perform research in computational biology
Develop software tools for sequence analysis
Disseminate biomedical information

8. GenBank History
Newer communication technologies enabled researchers to dial up GenBank and dump in their sequence data directly.
The administration of GenBank was transferred to National Institutes of Health's National Center for Biotechnology Information (NCBI).
With the advent of the World Wide Web, researchers could access the data in GenBank for free from around the globe.
Once the Human Genome Project (HGP) began in 1990, DNA-sequence data in GenBank began to grow exponentially.
With the introduction in the 1990s of high-throughput sequencing additions to GenBank skyrocketed.

9. An Interesting Metaphor
For Bioinformatics Information Flow and Databases
Cooks generate and enter the data.
Data Management makes it into a stew of blended information.
The waiters take the data from the servers to the public.
The diners are placing orders for the information they wish to consume.

11. Molecular Databases Primary Databases Derivative Databases
Original submissions by experimentalists
Database staff organize but don’t add additional information
Example: GenBank,SNP, GEO
Derivative Databases
Human curated
compilation and correction of data
Example: SWISS-PROT, NCBI RefSeq mRNA
Computationally Derived
Example: UniGene
Combinations
Example: NCBI Genome Assembly

12. What, the scientists submit their own DNA sequences?
Who checks for error?
Who makes people actually send their data to the database so all can share it?
Learn from success, failure of GenBank/EMBL extensive publicly shared bio-data
Carrot/stick approach. Granting agencies and journals began requiring scientists to publish sequence data. Patented sequences must be entered in the databases too.
However, there is significant public databank error due to data ownership by scientists; no inducements to update or go back and correct errors.

13. Primary vs. Derivative Databases
ATTGACTA
Primary vs. Derivative Databases
ACGTGC
TTGACA
CGTGA
TATAGCCG
GenBank AT GA C
ATT
Sequencing
Centers
UniGene
RefSeq
Genome
Assembly
Labs
Curators
Algorithms
AGCTCCGATA
CCGATGACAA

14. GenBank is NCBI’s Primary Sequence Database
Nucleotide only sequence database
Archival in nature
GenBank Data
Direct submissions (traditional records )
Batch submissions (EST, GSS, STS)
ftp accounts (genome data)
Three collaborating databases
GenBank
DNA Database of Japan (DDBJ)
European Molecular Biology Laboratory (EMBL) Database

15. Why use Bioinformatics Databases?
Speed of information retrieval
Increasing size of data sets
Amount of information available
Save time and money by simulating experiments prior to actual experiment (a.k.a. in silico)

16. How do you access Databases?
Search engines
Programs that allow you to search the database
Links from other sites to the search engines
Programs that directly link to the search engines

17. Boolean Logic Why do we use Boolean operators
To narrow your search
get fewer superfluous results
What are the Boolean Operators
AND-looks for entries with both terms
OR-looks for entries with one term or the other
NOT (or BUTNOT)-looks for entries with one term but not the other
* (Wildcard) -looks for ALL entries that contain the term with the * after it

18. Citations that contain the descriptors Food ‘AND’ Allergy only.

19. OR
Food
Allergy
Citations that contain the descriptors Food ‘OR’ Allergy. This is a bigger set.

20. Citations that contain the descriptors Allergy ‘NOT’ Food

21. * (Wildcard) Allerg* Food Citations that contain the descriptors
Allerg* (Allergies, Allergy, Allergen

22. GenBank as a Database
GenBank identifiers are unique combination of numbers and letters used to index GenBank sequence entries.
They can be used to retrieve information about a particular gene or DNA sequence from the GenBank database.
This information also includes links to similar sequence entries and other public databases, making it a relational database as well as a flat file database.
Sequences are said to be annotated when a researcher ascribes a function to some or all of the sequences. For example, If I know a sequence of 1000 nucleotides contains a gene beginning at nucleotide 200 and ending at nucleotide 800 and say so in the record, then it is considered annotated.

23. What is GenBank? NCBI’s Primary Sequence Database
Nucleotide only sequence database
Archival in nature
GenBank Data
Direct submissions individual records (BankIt, Sequin)
Batch submissions via (EST, GSS, STS)
ftp accounts sequencing centers
Data shared three collaborating databases
GenBank
DNA Database of Japan (DDBJ).
European Molecular Biology Laboratory Database
(EMBL) at EBI.

24. The International Sequence Database Collaboration
EBI
GenBank
DDBJ
EMBL
Entrez
SRS
getentry
NIG
CIB
NCBI
NIH
Submissions
Updates

25. GenBank: NCBI’s Primary Sequence Database
full release every two months
incremental and cumulative updates daily
available only through internet
ftp://ftp.ncbi.nih.gov/genbank/
Release 131 August 2002
18,197,119 Records
22,616,937,182 Nucleotides
110,000 + Species
Lots of sequences in the databases!
83.65 Gigabytes of data

26. GenBank: NCBI’s Primary Sequence Database
full release every two months
incremental and cumulative updates daily
available only through internet
ftp://ftp.ncbi.nih.gov/genbank/
Release 135 April 2003
24,027,936 Records
31,099,264,455 Nucleotides
120,000 + Species
Increased dramatically since August 2002
114 Gigabytes

27. GenBank: NCBI’s Primary Sequence Database
Release 139 December 2003
30,968,418 Records
36,553,368,485 Nucleotides
>140,000 Species
138 Gigabytes files
full release every two months
incremental and cumulative updates daily
available only through internet
Increased dramatically since April!
ftp://ftp.ncbi.nih.gov/genbank/

28. The Growth of GenBank Release 139: 31.0 million records5 10 15 20 25 30 35 5 10 15 20 25 30 35 40
Sequence records
Total base pairs
Release 139: million records
36.6 billion nucleotides
Average doubling time ≈ 12 months
Sequence Records
(millions)
Total Base Pairs
(billions)
Doubling time is currently less than 1 year and still accelerating.
'82
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'90
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'95
'96
'97
'98
'00
'01
'02
'03

29. The Entrez System

30. Entrez Nucleotides Primary GenBank / EMBL / DDBJ 35,116,960 Derivative
RefSeq ,219
Third Party Annotation ,182
PDB ,703
Total ,384,248

31. Entrez Protein GenPept (GB,EMBL, DDBJ) 3,442,298 RefSeq 856,191
Third Party Annotation ,834
Swiss Prot ,508
PIR ,821
PRF ,079
Total ,442,298
BLAST nr ,642,191

32. Organization of GenBank: GenBank Divisions
Records are divided into 17 Divisions.
1 Patent (11 files)
5 High Throughput
11 Traditional
EST (288) Expressed Sequence Tag
GSS (98) Genome Survey Sequence
HTG (61) High Throughput Genomic
STS (3) Sequence Tagged Site
HTC (3) High Throughput cDNA
PRI (27) Primate
PLN (10) Plant and Fungal
BCT (8) Bacterial and Archeal
INV (6) Invertebrate
ROD (11) Rodent
VRL (3) Viral
VRT (4) Other Vertebrate
MAM (1) Mammalian (ex. ROD and PRI)
PHG (1) Phage
SYN (1) Synthetic (cloning vectors)
UNA (1) Unannotated
Traditional Divisions:
Direct Submissions
(Sequin and BankIt)
Accurate
Well characterized
BULK Divisions:
Batch Submission
( and FTP)
Inaccurate
Poorly characterized
The traditional divisions are generally taxonomic.
1. PRI - primate sequences 2. ROD - rodent sequences 3. MAM - other mammalian sequences 4. VRT - other vertebrate sequences 5. INV - invertebrate sequences 6. PLN - plant, fungal, and algal sequences 7. BCT - bacterial sequences 8. VRL - viral sequences 9. PHG - bacteriophage sequences 10. SYN - synthetic sequences 11. UNA - unannotated sequences
The high throughput divisions are based on genome sequencing projects.
12. EST - EST sequences (expressed sequence tags) 14. STS - STS sequences (sequence tagged sites) 15. GSS - GSS sequences (genome survey sequences) 16. HTG - HTGS sequences (high throughput genomic sequences) 17. HTC - unfinished high-throughput cDNA sequencing
Patent division: 13. PAT - patent sequences
Entrez query: gbdiv_xxx[Properties]

33. Traditional GenBank Divisions
Direct Submissions (Sequin and BankIt)
Accurate
Well characterized
BCT Bacterial and Archeal
INV Invertebrate
MAM Mammalian (ex. ROD and PRI)
PHG Phage
PLN Plant and Fungal
PRI Primate
ROD Rodent
SYN Synthetic (cloning vectors)
VRL Viral
VRT Other Vertebrate

34. A Helpful Resource
This is a link to a sample annotated GenBank Record. Click on any of the underlined links to learn more about the file structure.

35. What is an Accession Number?
An accession number is label that used to identify a sequence in the various databases. It is a string of letters and/or numbers that corresponds to a molecular sequence.
Examples (all for retinol-binding protein, RBP4):
X02775 GenBank genomic DNA sequence
NT_ Genomic contig
Rs dbSNP (single nucleotide polymorphism)
N An expressed sequence tag (1 of 170)
NM_ RefSeq DNA sequence (from a transcript)
NP_ RefSeq protein
AAC02945 GenBank protein
Q28369 SwissProt protein
1KT7 Protein Data Bank structure record

36. GenBank Flat File Format
When you click on an entry, you have opened a GenBank Flat File
Information includes:
The Name of the gene
The Accession number
Journal articles

37. GenBank Flat File Format
Information (Cont)
Structural information of the gene (eg intron/exon boundaries, promoters,etc)
The code for the protein
The code for the DNA (RNA-if mRNA it is the cDNA for the mRNA sequenced)

38. A Traditional GenBank Record
LOCUS AF bp mRNA INV MAR-2000
DEFINITION Limulus polyphemus myosin III mRNA, complete cds.
ACCESSION AF062069
VERSION AF GI:
KEYWORDS .
SOURCE Atlantic horseshoe crab.
ORGANISM Limulus polyphemus
Eukaryota; Metazoa; Arthropoda; Chelicerata; Merostomata;
Xiphosura; Limulidae; Limulus.
REFERENCE 1 (bases 1 to 3808)
AUTHORS Battelle,B.-A., Andrews,A.W., Calman,B.G., Sellers,J.R.,
Greenberg,R.M. and Smith,W.C.
TITLE A myosin III from Limulus eyes is a clock-regulated phosphoprotein
JOURNAL J. Neurosci. (1998) In press
REFERENCE 2 (bases 1 to 3808)
TITLE Direct Submission
JOURNAL Submitted (29-APR-1998) Whitney Laboratory, University of Florida,
9505 Ocean Shore Blvd., St. Augustine, FL 32086, USA
REFERENCE 3 (bases 1 to 3808)
JOURNAL Submitted (02-MAR-2000) Whitney Laboratory, University of Florida,
REMARK Sequence update by submitter
COMMENT On Mar 2, 2000 this sequence version replaced gi:
Definition =Title
ACCESSION AF062069
VERSION AF GI:
Accession Number
Version Number
GI Number
NCBI’s Taxonomy

39. GenBank Record: Feature Table
FEATURES Location/Qualifiers
source
/organism="Limulus polyphemus"
/db_xref="taxon:6850"
/tissue_type="lateral eye"
CDS
/note="N-terminal protein kinase domain; C-terminal myosin
heavy chain head; substrate for PKA"
/codon_start=1
/product="myosin III"
/protein_id="AAC "
/db_xref="GI: "
/translation="MEYKCISEHLPFETLPDPGDRFEVQELVGTGTYATVYSAIDKQA
NKKVALKIIGHIAENLLDIETEYRIYKAVNGIQFFPEFRGAFFKRGERESDNEVWLGI
EFLEEGTAADLLATHRRFGIHLKEDLIALIIKEVVRAVQYLHENSIIHRDIRAANIMF
SKEGYVKLIDFGLSASVKNTNGKAQSSVGSPYWMAPEVISCDCLQEPYNYTCDVWSIG
ITAIELADTVPSLSDIHALRAMFRINRNPPPSVKRETRWSETLKDFISECLVKNPEYR
PCIQEIPQHPFLAQVEGKEDQLRSELVDILKKNPGEKLRNKPYNVTFKNGHLKTISGQ
BASE COUNT a c g t
ORIGIN
1 tcgacatctg tggtcgcttt ttttagtaat aaaaaattgt attatgacgt cctatctgtt
3781 aagatacagt aactagggaa aaaaaaaa //
/protein_id="AAC "
/db_xref="GI: "
GenPept Protein IDS

40. Multiple Formats are available for Sequence Data
Historically, all the DNA and Protein software was written concurrent with the establishment of the databases. So the formats needed in the databases and the software co-evolved.
Sequence analysis software needs simpler formats than databases for speed- or else the program must be allowed to ignore most of the excess information.

41. FastA format is a very popular solution
>gi|603218|gb|U |MSU18238 Medicago sativa glucose-6-phosphate dehyd
CCACCAGATATAATTAAGTAGATCAGAGTAGAAGAAGATGGGAACAAATGAATGGCATGTAGAAAGAAGA
GATAGCATAGGTACTGAATCTCCTGTAGCAAGAGAGGTACTTGAAACTGGCACACTCTCTATTGTTGTGC
TTGGTGCTTCTGGTGATCTTGCCAAGAAGAAGACTTTTCCTGCACTTTTTCACTTATATAAACAGGAATT
GTTGCCACCTGATGAAGTTCACATTTTTGGCTATGCAAGGTCAAAGATCTCCGATGATGAATTGAGAAAC
AAATTGCGTAGCTATCTTGTTCCAGAGAAAGGTGCTTCTCCTAAACAGTTAGATGATGTATCAAAGTTTT
TACAATTGGTTAAATATGTAAGTGGCCCTTATGATTCTGAAGATGGATTTCGCTTGTTGGATAAAGAGAT
TTCAGAGCATGAATATTTGAAAAATAGTAAAGAGGGTTCATCTCGGAGGCTTTTCTATCTTGCACTTCCT
CCTTCAGTGTATCCATCCGTTTGCAAGATGATCAAAACTTGTTGCATGAATAAATCTGATCTTGGTGGAT
GGACACGCGTTGTTGTTGAGAAACCCTTTGGTAGGGATCTAGAATCTGCAGAAGAACTCAGTACTCAGAT
TGGAGAGTTATTTGAAGAACCACAGATTTATCGTATTGATCACTATTTAGGAAAGGAACTAGTGCAAAAC
ATGTTAGTACTTCGTTTTGCAAATCGGTTCTTCTTGCCTCTGTGGAACCACAACCACATTGACAATGTGC
AGATAGTATTTAGAGAGGATTTTGGAACTGATGGTCGTGGTGGATATTTTGACCAATATGGAATTATCCG
AGATATCATTCCAAACCATCTGTTGCAGGTTCTTTGCTTGATTGCTATGGAAAAACCCGTTTCTCTCAAG
CCTGAGCACATTCGAGATGAGAAAGTGAAGGTTCTTGAATCAGTACTCCCTATTAGAGATGATGAAGTTG
TTCTTGGACAATATGAAGGCTATACAGATGACCCAACTGTACCGGACGATTCAAACACCCCGACTTTTGC
AACTACTATTCTGCGGATACACAATGAAAGATGGGAAGGTGTTCCTTTCATTGTGAAAGCAGGGAAGGCC
CTAAATTCTAGGAAGGCAGAGATTCGGGTTCAATTCAAGGATGTTCCTGGTGACATTTTCAGGAGTAAAA
AGCAAGGGAGAAACGAGTTTGTTATCCGCCTACAACCTTCAGAAGCTATTTACATGAAGCTTACGGTCAA
GCAACCTGGACTGGAAATGTCTGCAGTTCAAAGTGAACTAGACTTGTCATATGGGCAACGATATCAAGGG
ATAACCATTCCAGAGGCTTATGAGCGTCTAATTCTCGACACAATTAGAGGTGATCAACAACATTTTGTTC
GCAGAGACGAATTAAAGGCATCATGGCAAATATTCACACCACTTTTACACAAAATTGATAGAGGGGAGTT
GAAGCCGGTTCCTTACAACCCGGGAAGTAGAGGTCCTGCAGAAGCAGATGAGTTATTAGAAAAAGCTGGA
TATGTTCAAACACCCGGTTATATATGGATTCCTCCTACCTTATAGAGTGACCAAATTTCATAATAAAACA
AGGATTAGGATTATCAGGAGCTTATAAATAAGTCTTCAATAAGCTTGTGAAATTTTCGTTATAATCTCTC
TCATTTTGGGGTGTATATCAAGCATTTAAGCGCGTGTTTGACACAGTTTGTGTAATAGATTTGGCTCTGA
ATGAAAATAAACGGGAATTGTTTCTTTTTGTTTTA
FASTA Definition Line
>gi|603218|gb|U |MSU18238
gi number
Database Identifiers
gb GenBank
emb EMBL
dbj DDBJ
sp SWISS-PROT
pdb Protein Databank
pir PIR
prf PRF
ref RefSeq
Accession number
Locus Name
>

42. FASTA format

43. Graphics format

44. ASN.1 Format
ASN.1, or Abstract Syntax Notation One, is an International Standards Organization (ISO) data representation format used to achieve interoperability between platforms.
NCBI uses ASN.1 for the storage and retrieval of data such as nucleotide and protein sequences, structures, genomes, and MEDLINE records.
ASN.1 permits computers and software systems of all types to reliably exchange both the data structure and content.

45. NCBI Software Development Tool Kit
The "NCBI Toolbox" is a set of software and data exchange specifications used by NCBI to produce portable, modular software for molecular biology.
The software in the Toolbox is primarily designed to read ASN.1 format records.
It is available to the public in the toolbox/ncbi_tools directory of NCBI's ftp site, and can be used in its own right or as a foundation for building tools with similar properties.
The readme files in the toolbox and toolbox/ncbi_tools directories of the FTP site contain more information about the toolbox and ASN.1.

46. Abstract Syntax Notation: ASN.1
Seq-entry ::= set {
level 1 ,
class nuc-prot ,
descr {
title "Medicago sativa glucose-6-phosphate dehydrogenase mRNA, and
translated products" ,
source {
org {
taxname "Medicago sativa subsp. sativa" ,
db { {
db "taxon" ,
tag
id } } ,
orgname {
name
binomial {
genus "Medicago" ,
species "sativa" ,
subspecies "subsp. sativa" } ,
mod {
FASTA
Nucleotide
Protein
GenPept
GenBank
ASN.1

47. NCBI Toolbox Toolbox Sources ftp> open ftp.ncbi.nih.gov .
/************************************************************************ *
* asn2ff.c
* convert an ASN.1 entry to flat file format, using the FFPrintArray.
**************************************************************************/
#include <accentr.h>
#include "asn2ff.h"
#include "asn2ffp.h"
#include "ffprint.h"
#include <subutil.h>
#include <objall.h>
#include <objcode.h>
#include <lsqfetch.h>
#include <explore.h>
#ifdef ENABLE_ID1
#include <accid1.h>
#endif
FILE *fpl;
Args myargs[] = {
{"Filename for asn.1 input","stdin",NULL,NULL,TRUE,'a',ARG_FILE_IN,0.0,0,NULL},
{"Input is a Seq-entry","F", NULL ,NULL ,TRUE,'e',ARG_BOOLEAN,0.0,0,NULL},
{"Input asnfile in binary mode","F",NULL,NULL,TRUE,'b',ARG_BOOLEAN,0.0,0,NULL},
{"Output Filename","stdout", NULL,NULL,TRUE,'o',ARG_FILE_OUT,0.0,0,NULL},
{"Show Sequence?","T", NULL ,NULL ,TRUE,'h',ARG_BOOLEAN,0.0,0,NULL},
Toolbox Sources
ftp> open ftp.ncbi.nih.gov .
ftp> cd toolbox
ftp> cd ncbi_tools
ftp://ftp.ncbi.nlm.gov/toolbox/ncbi_tools

48. Database Tools aren’t keeping pace
Despite the huge progress in sequencing and expression analysis technologies, and the corresponding magnitude of more data that is held in the public, private and commercial databases, the tools used for storage, retrieval, analysis and dissemination of data in bioinformatics are still very similar to the original systems gathered together by researchers years ago.
Many are simple extensions of the original academic systems, which have served the needs of both academic and commercial users for many years.
These systems are now beginning to fall behind as they struggle to keep up with the pace of change in the pharma industry.

49. Database Tools aren’t keeping pace
Databases are still gathered, organized, disseminated and searched using flat files.
Relational databases are still few and far between, and object-relational or fully object oriented systems are rarer still in mainstream applications.
Interfaces still rely on command lines, fat client interfaces, which must be installed on every desktop, or HTML/CGI forms.
Whilst they were in the hands of bioinformatics specialists, pharmas have been relatively undemanding of their tools.
Now the problems have expanded to cover the mainstream discovery process, much more flexible and scalable solutions are needed to serve pharma R&D informatics requirements.

50. There are more than one type of DNA sequence in Genebank
Genomic sequences made from genomic DNA- these do contain introns and LOTS of DNA that never becomes messenger RNA. mRNA codes for proteins.
cDNA sequences made from mRNA- these don’t contain the introns
ESTS (short stretches of cDNA sequences that are sort of a “rough draft”
mtDNA from mitochondrial genomes
SNP single nucleotide polymorphisms with some DNA variation.

51. Quality of the Sequence is Variable
Some of the DNA is sequenced several times before it is added to the databases.
Some of the DNA is sequenced very quickly on automated equipment and is input directly from the computers.
Both are important types of information.
The “draft” is corrected by curators who assemble the pieces into the genome.

52. Genome Sequencing Whole BAC insert (or genome)
shredding
sequencing
cloning isolating
GSS division
or trace archive
assembly
Draft Sequence (HTG division)

53. Working Draft Sequence
gaps

54. Assembly Required.
All the data is still in the pieces used to assemble the genomes.
So, that means all the overlapping pieces are still in the databases.
So, searching comes up with many versions and shorter subclones: pieces which are used to assemble the “genomic contigs” or contiguous pieces which are assembled into whole chromosomes.
Sometimes you want to use the smaller pieces, since handling the whole chromosome is awkward in sequence analysis.

55. HTG Division: High Throughput Genome
phase 1
phase 2
phase 3
ROD
Acc = AC
Acc =AC
Acc = AC
HTG
40,000 to > 350,000 bp

56. HTG Division: High Throughput Genome
40,000 to > 350,000 bp

57. Whole Genome Shotgun

58. STS Division : Sequence Tagged Sites
Segment of gene, EST , mRNA or genomic DNA
of known position (microsatellite)
PCR with STS primers gives one product per genome
Basis of Radiation Hybrid Mapping
UniGene
Genome Assembly
Related resource: Electronic PCR

59. Be aware of errors in the databases
Sequence errors:
genome projects’ error rate is 1/10,000 nucleotides;
ESTs’ error rate is 1/100 nucleotides.
Annotation errors:
Many databases annotate their sequences using automated computer programs. These programs do not always give correct annotations.
SwissProt is a protein database curated and annotated manually by biologists. It’s regarded as the most reliable database, but does not have the most up-to-date sequence information.

60. There is a Lot of Sequence in the Databases
One problem is finding what you are looking for in the database.
Try putting in the search term human beta hemoglobin into the nucleotide database. It won’t be easy to find the sequence in the 88 pages of hits!
RefSeq was invented to help you find some of the common sequences based on a human (or now, a computer) looking over all the similar submissions of the same sequence to the database.
RefSeq corrects some of those sequence errors by comparing lots of sequences.

61. RefSeq: NCBI’s Derivative Sequence Database
Curated transcripts and proteins
reviewed
human, mouse, rat, fruit fly, zebrafish, arabidopsis, C. elegans
Human model transcripts and proteins
Assembled Genomic Regions (contigs)
draft human genome
mouse genome
Chromosome records
microbial
organelle

62. RefSeq Benefits non-redundancy
explicitly linked nucleotide and protein sequences
updates to reflect current sequence data and biology
data validation
format consistency
distinct accession series
stewardship by NCBI staff and collaborators

63. The RefSeq Accession Numbers
NCBI Reference Sequences
mRNAs and Proteins
NM_ Curated mRNA
NP_ Curated Protein
NR_ Curated non-coding RNA
XM_ Predicted Transcript (human, mouse)
XP_ Predicted Protein (human, mouse)
XR_ Predicted non-coding RNA
Gene Records
NG_ Reference Genomic Sequence (human)
Assemblies
NT_ Contig (Mouse and Human)
NW_ WGS Supercontig (Mouse)
NC_ Chromosome (Microbial, Arabidopsis )
human
mouse
rat
fruit fly
zebrafish
Arabidopsis
Microbial

64. GenBank Sequences: Human Lipoprotein Lipase

65. Curated RefSeq Records: NM_, NP_

66. Alignment Based Models

67. Alignment Based Models
AA change
In alignment, computers are used to line up two similar sequences and notice where the matches and mimatches are.

68. Alignment GeneratedTranscripts: XM_,XP_

69. RefSeq Contig: NT_, NW_

70. RefSeq Chromosomes: NC_
LOCUS NC_ bp DNA circular BCT OCT-2001
DEFINITION Escherichia coli O157:H7, complete genome.
ACCESSION NC_002695
VERSION NC_ GI:
KEYWORDS .
SOURCE Escherichia coli O157:H7.
ORGANISM Escherichia coli O157:H7
Bacteria; Proteobacteria; gamma subdivision; Enterobacteriaceae;
Escherichia.
REFERENCE 1 (sites)
AUTHORS Makino,K., Yokoyama,K., Kubota,Y., Yutsudo,C.H., Kimura,S.,
Kurokawa,K., Ishii,K., Hattori,M., Tatsuno,I., Abe,H., Iida,T.,
Yamamoto,K., Ohnishi,M., Hayashi,T., Yasunaga,T., Honda,T.,
Sasakawa,C. and Shinagawa,H.
TITLE Complete nucleotide sequence of the prophage VT2-Sakai carrying the
verotoxin 2 genes of the enterohemorrhagic Escherichia coli O157:H7
derived from the Sakai outbreak
JOURNAL Genes Genet. Syst. 74 (5), (1999)
MEDLINE
PUBMED

71. Integrated WWW Access: BLAST and Entrez

72. Some Web Statistics -25 million hits per day
-150,000190,000240,000 users/per day
-1.2 million Entrez searches
-PubMed alone: 1 million searches
-BLAST alone: 80,000 searches per day
3 terabytes of data dowloaded daily via FTP
July 2001

73. Users per day
Christmas Day

74. Bulk GenBank Divisions
Batch Submission and htg ( and ftp)
Inaccurate
Poorly Characterized
EST Expressed Sequence Tag
STS Sequence Tagged Site
GSS Genome Survey Sequence
HTG High Throughput Genomic

75. EST Division: Expressed Sequence Tags
>IMAGE: ' mRNA sequence
GACAGCATTCGGGCCGAGATGTCTCGCTCCGTGGCCTTAGCTGTGCTCGCGCTACTCTCTCTTTCTGGCC
TGGAGGTATCCAGCGTACTCCAAAGATTCAGGTTTACTCACGTCATCCAGCAGAGAATGGAAAGTCAAAT
TTCCTGAATTGCTATGTGTCTGGGTTTCATCCATCCGACATTGAAGTTGACTTACTGAAGAATGGAGAGA
GAATTGAAAAAGTGGAGCATTCAGACTTGTCTTTCAGCAAGGACTGGTCTTTCTATCTCTTGTACTACAC
TGAATTCACCCCCACTGAAAAAGATGAGTATGCCTGCCGTGTTGAACCATGTNGACTTTGTCACAGNCCC
AAGTTNAGTTTAAGTGGGNATCGAGACATGTAAGGCAGGCATCATGGGAGGTTTTGAAGNATGCCGCNTT
TTGGATTGGGATGAATTCCAAATTTCTGGTTTGCTTGNTTTTTTAATATTGGATATGCTTTTG
nucleus
30,000
genes
gatccantgccatacg 5’ 3’
ctcgccaattcnntcg
>IMAGE: ', mRNA sequence
NNTCAAGTTTTATGATTTATTTAACTTGTGGAACAAAAATAAACCAGATTAACCACAACCATGCCTTACT
TTATCAAATGTATAAGANGTAAATATGAATCTTATATGACAAAATGTTTCATTCATTATAACAAATTTCC
AATAATCCTGTCAATNATATTTCTAAATTTTCCCCCAAATTCTAAGCAGAGTATGTAAATTGGAAGTTAA
CTTATGCACGCTTAACTATCTTAACAAGCTTTGAGTGCAAGAGATTGANGAGTTCAAATCTGACCAAGAT
GTTGATGTTGGATAAGAGAATTCTCTGCTCCCCACCTCTANGTTGCCAGCCCTC
- isolate unique clones
sequence once
from each end
RNA
gene products
make cDNA
library
80-100,000 unique
cDNA clones in library

76. Unigene A gene-oriented view of sequence entries
UniGene collects expressed sequence tags (ESTs) into clusters, in an attempt to form one gene per cluster.
Use UniGene to study where your gene is expressed in the body, when it is expressed, and see its abundance.

77. UniGene http://www.ncbi.nlm.nih.gov/UniGene/
MegaBlast based automated sequence clustering
Nonredundant set of gene oriented clusters
Each cluster a unique gene
Information on tissue types and map locations
Includes well-characterized genes and novel ESTs
Useful for gene discovery and selection of mapping reagents

78. EST hits A.t. serine protease mRNA
A.t. mRNA
5’ EST hits
3’ EST hits

79. Arabidopsis UniGene Statistics
39,855 mRNAs + gene CDSs
87, EST, 3'reads
42, EST, 5'reads
+ 32, EST, other/unknown
201, total sequences in clusters
Final Number of Clusters (sets)
===============================
sets total
25,474 sets contain at least one known gene
17,654 sets contain at least one EST
16,326 sets contain both genes and ESTs
UniGene Build 14
Apr. 9th, 2002
26,808
115,000,000 bp
25,498 expected genes
5% uncharacterized transcripts

80. Hs UniGene Statistics 1,181,855 EST, 3'reads 1,461,928 EST, 5'reads
73,419 mRNAs + gene CDSs
1,181, EST, 3'reads
1,461, EST, 5'reads
+ 616, EST, other/unknown
3,333, total sequences in clusters
Final Number of Clusters (sets)
===============================
sets total
22,431 sets contain at least one known gene
97,618 sets contain at least one EST
21,233 sets contain both genes and ESTs
UniGene Build 148
Apr. 8th, 2002
98,816
3,000,000 base pairs
30 K expected genes
80% uncharacterized transcripts

81. UniGene Collections Jul, 2002
Sequences Clusters
Homo sapiens human 3,569, ,602
Mus musculus mouse 2,332, ,247
Rattus norvegicus rat , ,220
Danio rerio zebrafish , ,404
Bos taurus cow , ,295
Xenopus laevis frog , ,984
D.melanogaster fruit fly , , 115
Anopholes gambiae mosquito , ,556
Plants
Arabidopsis thaliana thale cress , ,875
Oryzia sativa rice , ,802
Triticum aestivum wheat , ,575
Hordeum vulgare barley , ,324
Zea mays maize (corn) , ,301