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Molecular Biology Databases

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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 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 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!

28 The Growth of GenBank Release 139: 31.0 million records
5 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 '84 '85 '86 '87 '88 '90 '91 '92 '93 '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

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 .
/************************************************************************ * * 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> cd toolbox ftp> cd 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

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

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
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

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