1. NGS Bioinformatics Workshop 1
NGS Bioinformatics Workshop 1.2 Sequence Formats, Databases and Visualization Tools
March 14th, 2012
IRMACS 10900, SFU
Facilitator: Richard Bruskiewich
Adjunct Professor, MBB
Deeply Grateful Acknowledgment:
This week’s slides mainly courtesy of Professor Fiona Brinkman, MBB

2. Fiona Brinkman
Overview
Understand the purpose of, and use of, bioinformatics databases resources, such as GenBank,UniProt/Swiss-Prot, Entrez and Ensembl.
Be able to recognize common database data formats and sequence identifiers and know what their primary use is.
What kind of tools are available to visualize sequence data?
Appreciate the issues surrounding bioinformatic database updating.
Lecture 2

3. Biological Databases and Data Models
Fiona Brinkman
Biological Databases and Data Models
Lecture 2

4. Also check out the annual “web-software” issue of NAR every July
Great resource: The annual “January Database issue” of Nucleic Acids Research and associated “database of databases”
Also check out the annual “web-software” issue of NAR every July

5. Databases Organized array of information
Place where you put things in, and (if all goes well!) you should be able to get them out again.
Allows you to make discoveries.

6. Database Examples in Bioinformatics
Fiona Brinkman
Database Examples in Bioinformatics
Primary (archival)
GenBank/EMBL/DDBJ (seqs)
PDB (protein structures)
Medline (literature)
IMEx databases (protein interactions)
Secondary (curated)
RefSeq (seqs)
UniProt - SwissProt (seqs)
Taxon (taxonomy)
PROSITE (binding sites)
OMIM (genetics literature/reviews)
IMEx databases (protein interactions)
Lecture 2

7. UniProt: Swiss-Prot – An example of curated, reviewed annotation
Fiona Brinkman
UniProt: Swiss-Prot – An example of curated, reviewed annotation
Incorporates:
Function of the protein
Subcellular localization of protein
Post-translational modification
Domains and sites
Secondary structure
Quaternary structure
Similarities to other proteins
Diseases associated with deficiencies in the protein
Sequence conflicts, variants, etc.
Includes some PIR sequences
Combines/merges records from different organisms
Lecture 2

8. NIH NIG EMBL GenBank EMBL DDBJ
Fiona Brinkman
INSDC - International Nucleotide Sequence Database Collaboration
NIH
Entrez
NCBI
GenBank
Submissions
Updates
Submissions
Updates
EMBL
DDBJ
EBI
CIB
NIG
Submissions
Updates
SRS
EMBL
getentry
Lecture 2

9. Sequence Databases DNA Protein NCBI: GenBank -> RefSeq EBI: EMBL
Fiona Brinkman
Sequence Databases
DNA
NCBI: GenBank -> RefSeq
EBI: EMBL
Protein
NCBI: GenPept
EBI: UniProt: TrEMBL -> UniProt: Swiss-Prot
TrEMBL= “translated EMBL”
Others includes
PDB
GSDB
REFGenes
other sequences
National Center for Biotechnology Information
European Bioinformatics Institute
Lecture 2

10. GenBank Flat File Header Features (AA seq) DNA Sequence Title Taxonomy
LOCUS AF bp DNA linear BCT 19-AUG-1999
DEFINITION Pseudomonas fluorescens ECF sigma factor SigX (sigX) gene, complete
cds.
ACCESSION AF115338
VERSION AF GI:
KEYWORDS .
SOURCE Pseudomonas fluorescens.
ORGANISM Pseudomonas fluorescens
Bacteria; Proteobacteria; gamma subdivision; Pseudomonadaceae;
Pseudomonas.
REFERENCE 1 (bases 1 to 591)
AUTHORS Brinkman,F.S., Schoofs,G., Hancock,R.E. and De Mot,R.
TITLE Influence of a putative ECF sigma factor on expression of the major
outer membrane protein, OprF, in Pseudomonas aeruginosa and
Pseudomonas fluorescens
JOURNAL J. Bacteriol. 181 (16), (1999)
MEDLINE
PUBMED
REFERENCE 2 (bases 1 to 591)
AUTHORS De Mot,R.
TITLE Direct Submission
JOURNAL Submitted (04-DEC-1998) F.A. Janssens Laboratory of Genetics,
Applied Plant Sciences, K. Mercierlaan 92, Heverlee B-3001, Belgium
FEATURES Location/Qualifiers
source
/organism="Pseudomonas fluorescens"
/strain="M114"
/db_xref="taxon:294"
gene
/gene="sigX"
CDS
/codon_start=1
/transl_table=11
/product="ECF sigma factor SigX"
/protein_id="AAD "
/db_xref="GI: "
/translation="MNKAQTLSTRYDPRELSDEELVARSHTELFHVTRAYEELMRRYQ
RTLFNVCARYLGNDRDADDVCQEVMLKVLYGLKNLEGKSKFKTWLYSITYNECITQYR
KERRKRRLMDALSLDPLEEASEEKALQPEEKGGLDRWLVYVNPIDRGILVLRFVAELE
FQEIADIMHMGLSATKMRYKRALDKLREKFAGETET"
BASE COUNT a c g t
ORIGIN
1 atgaataaag cccaaacgct atccacgcgc tacgaccccc gcgagctctc tgatgaggag
61 ttggtcgcgc gctcgcatac cgagcttttt cacgtaacgc gcgcctatga agaactgatg
121 cggcgttacc agcgaacatt atttaacgtt tgtgcgagat atcttgggaa cgatcgcgac
181 gcagacgatg tctgtcagga agtcatgttg aaggtgctgt atggcctgaa gaacctcgag
241 gggaaatcga agttcaaaac gtggctctac agcatcacgt acaacgaatg tattacgcag
301 tatcggaagg aacggcgaaa gcgtcgcttg atggacgcat tgagtcttga ccccctcgag
361 gaagcgtccg aagaaaaggc gcttcaaccc gaggagaagg gcgggcttga tcgctggctg
421 gtgtatgtga acccgattga ccgtggaatt ctggtgcttc gatttgtcgc agagctggaa
481 tttcaggaga tcgcagacat catgcacatg ggtttgagtg cgacaaaaat gcgttacaaa
541 cgtgctctag ataaattgcg tgagaaattt gcaggcgaga ctgaaactta g
GenBank Flat File
Header
Title
Taxonomy
Citation
Features (AA seq)
DNA Sequence

11. EMBL Flat File Header Features (AA seq) DNA Sequence Title Taxonomy
ID AF standard; DNA; PRO; 591 BP.
AC AF115338;
SV AF
DT 03-JUN-1999 (Rel. 59, Created)
DT 23-AUG-1999 (Rel. 60, Last updated, Version 2)
DE Pseudomonas fluorescens ECF sigma factor SigX (sigX) gene, complete cds.
KW .
OS Pseudomonas fluorescens
OC Bacteria; Proteobacteria; gamma subdivision; Pseudomonadaceae; Pseudomonas.
RN [1] RP
RX MEDLINE;
RA Brinkman F.S., Schoofs G., Hancock R.E., De Mot R.;
RT "Influence of a putative ECF sigma factor on expression of the major outer
RT membrane protein, OprF, in Pseudomonas aeruginosa and Pseudomonas
RT fluorescens";
RL J. Bacteriol. 181(16): (1999).
RN [2]
RA De Mot R.;
RT ;
RL Submitted (04-DEC-1998) to the EMBL/GenBank/DDBJ databases.
RL F.A. Janssens Laboratory of Genetics, Applied Plant Sciences, K.
RL Mercierlaan 92, Heverlee B-3001, Belgium
DR SPTREMBL; Q9X4L7; Q9X4L7.
FH Key Location/Qualifiers FH
FT source
FT /db_xref="taxon:294"
FT /organism="Pseudomonas fluorescens"
FT /strain="M114"
FT CDS
FT /codon_start=1
FT /db_xref="SPTREMBL:Q9X4L7"
FT /transl_table=11
FT /gene="sigX"
FT /product="ECF sigma factor SigX"
FT /protein_id="AAD "
FT /translation="MNKAQTLSTRYDPRELSDEELVARSHTELFHVTRAYEELMRRYQR
FT TLFNVCARYLGNDRDADDVCQEVMLKVLYGLKNLEGKSKFKTWLYSITYNECITQYRKE
FT RRKRRLMDALSLDPLEEASEEKALQPEEKGGLDRWLVYVNPIDRGILVLRFVAELEFQE
FT IADIMHMGLSATKMRYKRALDKLREKFAGETET"
SQ Sequence 591 BP; 157 A; 133 C; 170 G; 131 T; 0 other;
atgaataaag cccaaacgct atccacgcgc tacgaccccc gcgagctctc tgatgaggag
ttggtcgcgc gctcgcatac cgagcttttt cacgtaacgc gcgcctatga agaactgatg
cggcgttacc agcgaacatt atttaacgtt tgtgcgagat atcttgggaa cgatcgcgac
gcagacgatg tctgtcagga agtcatgttg aaggtgctgt atggcctgaa gaacctcgag
gggaaatcga agttcaaaac gtggctctac agcatcacgt acaacgaatg tattacgcag
tatcggaagg aacggcgaaa gcgtcgcttg atggacgcat tgagtcttga ccccctcgag
gaagcgtccg aagaaaaggc gcttcaaccc gaggagaagg gcgggcttga tcgctggctg
gtgtatgtga acccgattga ccgtggaatt ctggtgcttc gatttgtcgc agagctggaa
tttcaggaga tcgcagacat catgcacatg ggtttgagtg cgacaaaaat gcgttacaaa
cgtgctctag ataaattgcg tgagaaattt gcaggcgaga ctgaaactta g //
EMBL Flat File
Header
Title
Taxonomy
Citation
Features (AA seq)
DNA Sequence

12. UniProt: Swiss-Prot (a curated DB)
ID CYS3_YEAST STANDARD; PRT; AA.
AC P31373;
DT 01-JUL-1993 (REL. 26, CREATED)
DT 01-JUL-1993 (REL. 26, LAST SEQUENCE UPDATE)
DT 01-NOV-1995 (REL. 32, LAST ANNOTATION UPDATE)
DE CYSTATHIONINE GAMMA-LYASE (EC ) (GAMMA-CYSTATHIONASE).
GN CYS3 OR CYI1 OR STR1 OR YAL012W OR FUN35.
OS SACCHAROMYCES CEREVISIAE (BAKER'S YEAST).
OC EUKARYOTA; FUNGI; ASCOMYCOTA; HEMIASCOMYCETES; SACCHAROMYCETALES;
OC SACCHAROMYCETACEAE; SACCHAROMYCES.
RN [1]
RP SEQUENCE FROM N.A., AND PARTIAL SEQUENCE.
RX MEDLINE; [NCBI, ExPASy, Israel, Japan]
RA ONO B.-I., TANAKA K., NAITO K., HEIKE C., SHINODA S., YAMAMOTO S.,
RA OHMORI S., OSHIMA T., TOH-E A.;
RT "Cloning and characterization of the CYS3 (CYI1) gene of
RT Saccharomyces cerevisiae.";
RL J. BACTERIOL. 174: (1992).
CC -!- CATALYTIC ACTIVITY: L-CYSTATHIONINE + H(2)O = L-CYSTEINE +
CC NH(3) + 2-OXOBUTANOATE.
CC -!- COFACTOR: PYRIDOXAL PHOSPHATE.
CC -!- PATHWAY: FINAL STEP IN THE TRANS-SULFURATION PATHWAY SYNTHESIZING
CC L-CYSTEINE FROM L-METHIONINE.
CC -!- SUBUNIT: HOMOTETRAMER.
CC -!- SUBCELLULAR LOCATION: CYTOPLASMIC.
CC -!- SIMILARITY: BELONGS TO THE TRANS-SULFURATION ENZYMES FAMILY. CC
CC This SWISS-PROT entry is copyright. It is produced through a collaboration
CC between the Swiss Institute of Bioinformatics and the EMBL outstation -
CC the European Bioinformatics Institute. There are no restrictions on its
CC use by non-profit institutions as long as its content is in no way
CC modified and this statement is not removed. Usage by and for commercial
CC entities requires a license agreement (See
CC or send an to
DR EMBL; L05146; AAC ; -. [EMBL / GenBank / DDBJ] [CoDingSequence]
DR EMBL; L04459; AAA ; -. [EMBL / GenBank / DDBJ] [CoDingSequence]
DR EMBL; D14135; BAA ; -. [EMBL / GenBank / DDBJ] [CoDingSequence]
DR PIR; S31228; S31228.
DR YEPD; 5280; -.
DR SGD; L ; CYS3. [SGD / YPD]
DR PFAM; PF01053; Cys_Met_Meta_PP; 1.
DR PROSITE; PS00868; CYS_MET_METAB_PP; 1.
DR DOMO; P31373.
DR PRODOM [Domain structure / List of seq. sharing at least 1 domain]
DR PROTOMAP; P31373.
DR PRESAGE; P31373.
DR SWISS-2DPAGE; GET REGION ON 2D PAGE.
KW CYSTEINE BIOSYNTHESIS; LYASE; PYRIDOXAL PHOSPHATE.
FT INIT_MET
FT BINDING PYRIDOXAL PHOSPHATE (BY SIMILARITY).
SQ SEQUENCE AA; MW; 55BA2771 CRC32;
TLQESDKFAT KAIHAGEHVD VHGSVIEPIS LSTTFKQSSP ANPIGTYEYS RSQNPNRENL
ERAVAALENA QYGLAFSSGS ATTATILQSL PQGSHAVSIG DVYGGTHRYF TKVANAHGVE
TSFTNDLLND LPQLIKENTK LVWIETPTNP TLKVTDIQKV ADLIKKHAAG QDVILVVDNT
FLSPYISNPL NFGADIVVHS ATKYINGHSD VVLGVLATNN KPLYERLQFL QNAIGAIPSP
FDAWLTHRGL KTLHLRVRQA ALSANKIAEF LAADKENVVA VNYPGLKTHP NYDVVLKQHR
DALGGGMISF RIKGGAEAAS KFASSTRLFT LAESLGGIES LLEVPAVMTH GGIPKEAREA
SGVFDDLVRI SVGIEDTDDL LEDIKQALKQ ATN //
ID CYS3_YEAST STANDARD; PRT; AA.
AC P31373;
DT 01-JUL-1993 (REL. 26, CREATED)
DE CYSTATHIONINE GAMMA-LYASE (EC ) (GAMMA-CYSTATHIONASE).
GN CYS3 OR CYI1 OR STR1 OR YAL012W OR FUN35.
OS TAXONOMY
OC SACCHAROMYCETACEAE; SACCHAROMYCES.
RX CITATION
CC -!- CATALYTIC ACTIVITY: L-CYSTATHIONINE + H(2)O = L-CYSTEINE +
CC NH(3) + 2-OXOBUTANOATE.
CC -!- COFACTOR: PYRIDOXAL PHOSPHATE.
CC -!- PATHWAY: FINAL STEP IN THE TRANS-SULFURATION PATHWAY SYNTHESIZING
CC L-CYSTEINE FROM L-METHIONINE.
CC -!- SUBUNIT: HOMOTETRAMER.
CC -!- SUBCELLULAR LOCATION: CYTOPLASMIC.
CC -!- SIMILARITY: BELONGS TO THE TRANS-SULFURATION ENZYMES FAMILY. CC
CC Disclaimer CC
DR DATABASE cross-reference
KW CYSTEINE BIOSYNTHESIS; LYASE; PYRIDOXAL PHOSPHATE.
FT INIT_MET
FT BINDING PYRIDOXAL PHOSPHATE (BY SIMILARITY).
SQ SEQUENCE AA; MW; 55BA2771 CRC32;
TLQESDKFAT KAIHAGEHVD VHGSVIEPIS LSTTFKQSSP ANPIGTYEYS RSQNPNRENL
ERAVAALENA QYGLAFSSGS ATTATILQSL PQGSHAVSIG DVYGGTHRYF TKVANAHGVE
TSFTNDLLND LPQLIKENTK LVWIETPTNP TLKVTDIQKV ADLIKKHAAG QDVILVVDNT
FLSPYISNPL NFGADIVVHS ATKYINGHSD VVLGVLATNN KPLYERLQFL QNAIGAIPSP
FDAWLTHRGL KTLHLRVRQA ALSANKIAEF LAADKENVVA VNYPGLKTHP NYDVVLKQHR
DALGGGMISF RIKGGAEAAS KFASSTRLFT LAESLGGIES LLEVPAVMTH GGIPKEAREA
SGVFDDLVRI SVGIEDTDDL LEDIKQALKQ ATN //

13. PDB Protein Data Bank Protein and Nucleic acid 3D structures
Xray, NMR, Computationally predicted
Sequence present

14. PDB HEADER COMPND SOURCE AUTHOR DATE JRNL REMARK SECRES
HEADER LEUCINE ZIPPER JUL DGC DGC 2
COMPND GCN4 LEUCINE ZIPPER COMPLEXED WITH SPECIFIC DGC 3
COMPND 2 ATF/CREB SITE DNA DGC 4
SOURCE GCN4: YEAST (SACCHAROMYCES CEREVISIAE); DNA: SYNTHETIC DGC 5
AUTHOR T.J.RICHMOND DGC 6
REVDAT JUN-94 1DGC DGC 7
JRNL AUTH P.KONIG,T.J.RICHMOND DGC 8
JRNL TITL THE X-RAY STRUCTURE OF THE GCN4-BZIP BOUND TO DGC 9
JRNL TITL 2 ATF/CREB SITE DNA SHOWS THE COMPLEX DEPENDS ON DNA 1DGC 10
JRNL TITL 3 FLEXIBILITY DGC 11
JRNL REF J.MOL.BIOL V DGC 12
JRNL REFN ASTM JMOBAK UK ISSN DGC 13
REMARK DGC 14
REMARK DGC 15
REMARK 2 RESOLUTION ANGSTROMS DGC 16
REMARK DGC 17
REMARK 3 REFINEMENT DGC 18
REMARK PROGRAM X-PLOR DGC 19
REMARK AUTHORS BRUNGER DGC 20
REMARK R VALUE DGC 21
REMARK RMSD BOND DISTANCES ANGSTROMS DGC 22
REMARK RMSD BOND ANGLES DEGREES DGC 23
REMARK DGC 24
REMARK NUMBER OF REFLECTIONS DGC 25
REMARK RESOLUTION RANGE ANGSTROMS DGC 26
REMARK DATA CUTOFF SIGMA(F) DGC 27
REMARK PERCENT COMPLETION DGC 28
REMARK DGC 29
REMARK NUMBER OF PROTEIN ATOMS DGC 30
REMARK NUMBER OF NUCLEIC ACID ATOMS DGC 31
REMARK DGC 32
SEQRES 1 A ILE VAL PRO GLU SER SER ASP PRO ALA ALA LEU LYS ARG 1DGC 60
SEQRES 2 A ALA ARG ASN THR GLU ALA ALA ARG ARG SER ARG ALA ARG 1DGC 61
SEQRES 3 A LYS LEU GLN ARG MET LYS GLN LEU GLU ASP LYS VAL GLU 1DGC 62
SEQRES 4 A GLU LEU LEU SER LYS ASN TYR HIS LEU GLU ASN GLU VAL 1DGC 63
SEQRES 5 A ALA ARG LEU LYS LYS LEU VAL GLY GLU ARG DGC 64
SEQRES 1 B T G G A G A T G A C G T C 1DGC 65
SEQRES 2 B A T C T C C DGC 66
HELIX 1 A ALA A LYS A DGC 67
CRYST P DGC 68
ORIGX DGC 69
ORIGX DGC 70
ORIGX DGC 71
SCALE DGC 72
SCALE DGC 73
SCALE DGC 74
ATOM N PRO A DGC 75
ATOM CA PRO A DGC 76
ATOM C5 C B DGC 916
ATOM C6 C B DGC 917
TER C B DGC 918
MASTER DGC 919
END DGC 920
PDB
HEADER
COMPND
SOURCE
AUTHOR
DATE
JRNL
REMARK
SECRES
ATOM COORDINATES

15. Data Formats Flat Files √
Many other formats for particular uses… XML, Clustal (for multiple sequence alignments), GFF (for sequence annotation), etc…
FASTA – simplest!
High throughput data file formats: BAM, etc.

16. Fiona Brinkman
FASTA
>
>gi|121066|sp|P03069|GCN4_YEAST GENERAL CONTROL PROTEIN GCN4
MSEYQPSLFALNPMGFSPLDGSKSTNENVSASTSTAKPMVGQLIFDKFIKTEEDPI
IKQDTPSNLDFDFALPQTATAPDAKTVLPIPELDDAVVESFFSSSTDSTPMFEYEN
LEDNSKEWTSLFDNDIPVTTDDVSLADKAIESTEEVSLVPSNLEVSTTSFLPTPVL
EDAKLTQTRKVKKPNSVVKKSHHVGKDDESRLDHLGVVAYNRKQRSIPLSPIVPES
SDPAALKRARNTEAARRSRARKLQRMKQLEDKVEELLSKNYHLENEVARLKKLVGE R
>gi|121066|sp|P03069|GCN4_YEAST GENERAL CONTROL PROTEIN GCN4
Lecture 2

17. FASTA > Your favourite gene 1 - yfg1
Fiona Brinkman
FASTA
> Your favourite gene 1 - yfg1
MSEYQPSLFALNPMGFSPLDGSKSTNENVSASTSTAKPMVGQLIFDKFIKTEEDPI
IKQDTPSNLDFDFALPQTATAPDAKTVLPIPELDDAVVESFFSSSTDSTPMFEYEN
LEDNSKEWTSLFDNDIPVTTDDVSLADKAIESTEEVSLVPSNLEVSTTSFLPTPVL
EDAKLTQTRKVKKPNSVVKKSHHVGKDDESRLDHLGVVAYNRKQRSIPLSPIVPES
SDPAALKRARNTEAARRSRARKLQRMKQLEDKVEELLSKNYHLENEVARLKKLVGE R
> Your favourite gene 2 - yfg2
MQPSLFALNPMGFSPLDGSKSTNENVSASTSTAKPMVGQLIFDKFIKTEEDPIVIV
DTPSNLDFDFALPQTATAPDAKTVLPIPELDDAVVESFFSSSTDSTPMFEYENWTI
TSLFDNDIPVTTDDVSLADKAIESTEEVSLVPSNLEVSTTSFLPTPVLLEDNSKEW
EDAKLTQTRKVKKPNSVVKKSHHVGKDDESRLDHLGVVAYNRKQRSIPLSPIV
Lecture 2

18. Fiona Brinkman
In GenBank, records are organized for various reasons. Understanding the rationale behind “groupings” and “numbering” systems for such databases is the key to fully taking advantage of database resources - appropriately!
Lecture 2

19. LOCUS vs Accession vs PID vs protein_id: What’s the difference?
Fiona Brinkman
LOCUS vs Accession vs PID vs protein_id: What’s the difference?
LOCUS: Unique string of 10 letters and numbers in
the database. Not maintained amongst databases.
ACCESSION: A unique identifier to that record (particular sequence) in GenBank/EMBL/DDBJ that does not change when record is updated.
Nucleotide gi: Geninfo identifier (gi), a unique integer specific for GenBank which will change every time the sequence changes.
VERSION: System started in 1999 for GenBank/EMBL/DDBJ where the accession and version play the same function as the accession and gi number. Format: accession.version
PID: Protein Identifier: g, e or d prefix to gi number.
Can have one or two on one CDS (coding sequence).
Protein gi: Geninfo identifier (gi), a GenBank unique integer which will change every time the sequence changes.
protein_id: Identifier which has the same structure and function as the nucleotide Accession with version numbers.
Lecture 2

20. LOCUS, Accession, NID, gi and PID
Fiona Brinkman
LOCUS, Accession, NID, gi and PID
LOCUS HSU bp mRNA PRI MAY-1998
DEFINITION Homo sapiens integrin-linked kinase (ILK) mRNA, complete cds.
ACCESSION U40282
VERSION U GI:
LOCUS: HSU40282
ACCESSION: U40282
VERSION: U
GI:
PID: g
Protein gi:
protein_id: AAC
CDS
/gene="ILK"
/note="protein serine/threonine kinase"
/codon_start=1
/product="integrin-linked kinase"
/protein_id="AAC "
/db_xref="PID:g "
/db_xref="GI: "
Lecture 2

21. Which of these would you use to cite a sequence in a paper
Which of these would you use to cite a sequence in a paper? Can you think of situations where you would use one over another?

22. Fiona Brinkman
Which of these would you use to cite a sequence? When would you use one over another?
LOCUS: Unique string of 10 letters and numbers in
the database. Not maintained amongst databases.
ACCESSION: A unique identifier to that record (particular sequence) in GenBank/EMBL/DDBJ that does not change when record is updated.
Nucleotide gi: Geninfo identifier (gi), a unique integer specific for GenBank which will change every time the sequence changes. (and can disappear!)
VERSION: System started in 1999 for GenBank/EMBL/DDBJ where the accession and version play the same function as the accession and gi number. Format: accession.version
PID: Protein Identifier: g, e or d prefix to gi number.
Can have one or two on one CDS (coding sequence).
Protein gi: Geninfo identifier (gi), a GenBank unique integer which will change every time the sequence changes.
protein_id: Identifier which has the same structure and function as the nucleotide Accession with version numbers.
Tomato graphics from
Lecture 2

23. Briefly…Examples of Functional Divisions
Fiona Brinkman
Briefly…Examples of Functional Divisions
PAT Patent
EST Expressed Sequence Tags
STS Sequence Tagged Site
GSS Genome Survey Sequence
HTG High Throughput Genome (unfinished)
HTC High throughput cDNA (unfinished)
Genbank overview:
Lecture 2

24. Other Sequence (& related) File Formats
Historically, a number of other sequence and annotation file formats have been proposed, see:
The demands of representing NGS data have given rise to additional file formats and data compression standards, some of which you will encounter in this course. The next few slides will present an overview of a few of these emergent NGS formats and standards. See:

25. Other Sequence (& Annotation) File Formats
FASTQ – FASTA with quality data
2bit – compressed DNA sequence format
SAM/BAM – Sequence Alignment Mapping
GFF/GTF – General Feature Format
BED/WIG – annotation track data formats

26. FASTQ http://maq.sourceforge.net/fastq.shtml
FASTQ – FASTA “with an attitude” (embedded quality scores). Originally developed at the Sanger to couple (Phred) quality data with sequence, it is now common to specify raw read output data from NGS machines in this format.
Various flavors:
fastq-sanger
fastq-illumina
fastq-solexa
Differing in the format of the sequence identifier and in the valid range of quality scores. See:
/2009/12/16/nar.gkp1137.full
“…the Sanger version of the FASTQ format has found the broadest acceptance, supported by many assembly and read mapping tools …Therefore, most users will do this conversion very early in their workflows…”
@EAS54_6_R1_2_1_443_348
GTTGCTTCTGGCGTGGGTGGGGGGG
+EAS54_6_R1_2_1_443_348
*-+*''))**55CCF>>>>>>CCCC

27.
Linux, MacOSX or Unix only

28. 2bit File Format
Highly compressed sequence file stores multiple DNA sequences (up to 4 Gb total) in a compact randomly-accessible format. The file contains masking information as well as the DNA itself.

29. SAM/BAM
SAM– a tab-delimited text file that contains a compact and index-able representation of nucleotide sequence alignments
BAM – binary version of SAM (preferred by IGV)
I/O format of several NGS tools, see:
See also:
Li H.*, Handsaker B.*, Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R. and 1000 Genome Project Data Processing Subgroup (2009) The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 25,

30. Gene/General/Generic Feature Formats (GFF)
A General Feature Format (GFF) file is a relatively simple tab-delimited text file for describing genomic features. Many genome browsers – gbrowse, IGV, etc. - take GFF as input for annotation data
There are several slightly but significantly different GFF file formats (GFF,GFF2, GFF3, GTF). The current primary standard is GFF3:

31. Excerpt of a GFF File
##gff-version 3 1 ##sequence-region ctg ctg123 . gene ID=gene00001;Name=EDEN ctg123 . mRNA ID=mRNA00001;Parent=gene00001;Name=EDEN.1 ctg123 . exon ID=exon00001;Parent=mRNA00003 ctg123 . CDS ID=cds00001;Parent=mRNA00001;Name=edenprotein.1

32. BED File Format
BED format provides a flexible way to define the data lines that are displayed in an annotation track in a genome browser.
If your data set is BED-like, but it is very large and you would like to keep it on your own server, you should use the bigBed data format.

33. WIGgle format
The Wiggle format is for display of dense, continuous data such as GC percent, probability scores, and transcriptome data.
If you need to display continuous data that is sparse or contains elements of varying size, use the BedGraph format instead.
If you have a very large data set and you would like to keep it on your own server, you should use the bigWig data format

34. EMBOSS Sequence Analysis Suite
emboss.sourceforge.net

35. Open Bioinformatics Foundation bioperl / biojava / biopython / bioruby / biosql etc.

36. Sequence Databases: “Roll your Own”?
GMOD BioSQL: a lightweight database schema for storing and retrieving (annotated) sequence records using OpenBio software tools.
GMOD “Chado”: a more complex database schema for storing sequence data, genome feature annotation and a host of other related biological data (initially inspired by Drosophila genome annotation and genetics; supported by many GMOD software tools)

37. Fiona Brinkman
Retrieving Sequence Information: Using integrated database resources such as Entrez
Lecture 2

38. What you may be looking for:
Heard on CBC about a disease gene that was recently discovered, and you want to know more about it.
Want to build a dataset of DNA sequences upstream of a set of co-expressed genes, to identify common regulatory element sequences
Evolutionary, functional, structural analyses, etc…

39. Entrez: Initial version of this “Pathway to Discovery”
Term frequency statistics
MEDLINE abstracts
Literature citations in sequence databases
Literature citations in sequence databases
Nucleotide sequences
Protein sequences
Nucleotide sequence similarity
Amino acid sequence similarity
Coding region features

40. PubMed Text Neighboring
Fiona Brinkman
PubMed Text Neighboring
Genetic Analysis of Cancer in Families
The Genetic Predisposition to Cancer
Common terms could indicate similar subject matter
Statistical method
Weights based on term frequencies within document and within the database as a whole
Some terms are better than others
Lecture 2

41. Entrez began to integrate more data…
Fiona Brinkman
Entrez began to integrate more data…
MEDLINE
Expression
Data
Accession
Numbers
PubMed
online Journals
Full text
ACGATGTGGTCGATG
TTCTCTATTATTATC
GGAAGCTAAGGATAT
CGCTGATGTGAGGTGA
TCGGTTCTATCTGCA
TAGCATGGATATTGA
TGGCTTATAGGCTAG
CGCTGATGTGAGGTG
MVILLVILAIVLISD
VTGREGSWQIPCMNV
KRKKGREGDHIVLIL
ILLNNAWASVLPESDS
SDSGPLIILHEREKR
LALAMAREENSPNCT
PLIKRESAEDSEDLR
KRKKTDEDDHIVLIL
GenBank
Protein Sequences
Links
SNP Data
Accession
Numbers - Map
MMDB
structure:function
VAST
Genomes
Structures
Lecture 2

42. Entrez Entrez Help http://www.ncbi.nlm.nih.gov/books/NBK3837/
Check out also What’s New
on Twitter
to keep up on new features added (like the Database of Genomic Structural Variation recently released)
SFU’s Cenk Sahinalp - international leader in structural variation bioinformatics research

43. BLink

44. Other Sequence Databases and Sequence Data Visualization
Fiona Brinkman
Other Sequence Databases and Sequence Data Visualization
Lecture 2

45. Fiona Brinkman
The Ensembl Genomes Database: Focuses on humans and select vertebrates (but a plant version is also available…)
Lecture 2

46. What is Ensembl?
Publicly available, automated annotation of selected eukaryotic genomes (initially with mammalian focus)
Open source software (but slightly complicated to set up…)
Multiple different ways to access data, including programmatic (Perl API)
Provides access to additional data from other groups (distributed annotation system or DAS)

47. ENSEMBL – Region in Detail
Check out the “Printable mini-course” at

48. Generic Model Organism Database (GMOD) Project

49. A powerful querying system
BioMart
(Ensmart)
A powerful querying system
(later: we’ll learn about Ensembl’s Perl API)

50. Distributed Annotation System (DAS)
Allows Third-Party annotation
Users choose the annotation they are interested in
Good for specialized feature annotation or for comparison of different methodologies
Allows you to view different data in a consistent user interface/display
Open source display focused on eukaryotes  Ensembl
Open source display for any dataset  Gbrowse

51. Another genome data viewer with DAS
Gbrowse:
Another genome data viewer with DAS
Gene track 
Protein track 
Metabolic pathways track 
Regulons track 
3D structures track 
Intergenic sequences track 
Terminators track 
DNA sequence track 
Translation track 

53. Gbrowse is used to display genomic data for many projects
Mouse, Rat, Fly, C. elegans and other animals
Rice and a number of other plants
S. cerevisiae and other yeasts
A number of unicellular eukaryotes
Many many prokaryotes
Other types of data: HapMap, Segmental Duplications, RNA-seq data-specific or other type-specific data
** Open source package ** (slightly simpler to set up than Ensembl)

54. Entrez, Ensembl, Gbrowse: What’s the difference?
Search and retrieval system for major databases, including PubMed, Sequences (including genomes), Structures, Taxonomy, etc.
NCBI (Maryland, USA) centrally hosts Entrez and they decide what to host and maintain
Not open source
Ensembl
Automated annotation of selected eukaryotic genomes
EMBL-EBI and the Sanger Institute (Cambridge/Hinxton, UK) centrally hosts most resources and they decide what data to host and maintain.
Open source and can obtain a local copy plus access other DAS data
Gbrowse
Genome/genomic data viewer
Very decentralized – anyone can set it up and publicly display any data
Open source and can set up a local copy plus access other DAS data

55. Entrez, Ensembl, Gbrowse: Benefits/Disadvantages of each?
Reputable institution – trust in the data
Maintained by well established group with a lot of capital
Perceived more consistency
Limited to what they make available
They make the call on how to display it, analyze it, and classify it
Some of the analyses are definitely a black box
Ensembl
Open source – can see how the data is analyzed/processed – NOT necessarily an issue with lower quality data – a lot of eyes are watching you (wooahh haa haa…)
Gbrowse
Easy to use and set up
Open source – can see how the data is analyzed/processed
Anybody can release their data to the world
Anybody can analyze the data in they want and release it to the world

56. Local Visualization of NGS Data

57. How do I update or correct errors in the Databases?
Example: For Gene names, citations, new protein name, sequencing errors in Genbank…
But most people don’t bother to correct things that they notice are wrong…
 increased need for more focused community-based projects

58. Community Assisted Curation of Subsets of Datasets
Core curators continually update annotation of a data subset (i.e. a genome)
Literature review
Input from the community
Updates sent in batches to centralized databases - > additional review -> becomes, for example, an NCBI RefSeq
Examples: WormBase.org, Pseudomonas.com

59. Ethical issues with bioinformatics databases
How public and/or open source should biomolecular data be?
How much should researchers be forced to release data as soon as possible?
How much analysis of a genome can a researcher publish before the genome sequence is published?
How do we best organize the data?  BIG issue! i.e. biomolecular pathway classifications can bias analyses of pathways are found to be upregulated or downregulated by gene expression analysis

60. Resources http://www.ncbi.nlm.nih.gov/ http://www.ebi.ac.uk/