SlideShare une entreprise Scribd logo
1  sur  24
Bioinformatics Resources and Tools
on the Web: A Primer
Joel H. Graber
Center for Advanced Biotechnology
Boston University
Outline
• Introduction: What is bioinformatics?
• The basics
– The five sites that all biologists should know
• Some examples
– Using the tools in a somewhat less-than-naïve manner
• Questions/comments are welcome at all points
• Much of this material comes from the Boston
University course: BF527 Bioinformatic
Applications (http://matrix.bu.edu/BF527/)
What is bioinformatics?
Examples of Bioinformatics
• Database interfaces
– Genbank/EMBL/DDBJ, Medline, SwissProt, PDB, …
• Sequence alignment
– BLAST, FASTA
• Multiple sequence alignment
– Clustal, MultAlin, DiAlign
• Gene finding
– Genscan, GenomeScan, GeneMark, GRAIL
• Protein Domain analysis and identification
– pfam, BLOCKS, ProDom,
• Pattern Identification/Characterization
– Gibbs Sampler, AlignACE, MEME
• Protein Folding prediction
– PredictProtein, SwissModeler
Things to know and remember about
using web server-based tools
• You are using someone else’s computer
• You are (probably) getting a reduced set of
options or capacity
• Servers are great for sporadic or proof-of-
principle work, but for intensive work, the
software should be obtained and run locally
Five websites that all biologists
should know
• NCBI (The National Center for Biotechnology Information;
– http://www.ncbi.nlm.nih.gov/
• EBI (The European Bioinformatics Institute)
– http://www.ebi.ac.uk/
• The Canadian Bioinformatics Resource
– http://www.cbr.nrc.ca/
• SwissProt/ExPASy (Swiss Bioinformatics Resource)
– http://expasy.cbr.nrc.ca/sprot/
• PDB (The Protein Databank)
– http://www.rcsb.org/PDB/
NCBI (http://www.ncbi.nlm.nih.gov/)
• Entrez interface to databases
– Medline/OMIM
– Genbank/Genpept/Structures
• BLAST server(s)
– Five-plus flavors of blast
• Draft Human Genome
• Much, much more…
EBI (http://www.ebi.ac.uk/)
• SRS database interface
– EMBL, SwissProt, and many more
• Many server-based tools
– ClustalW, DALI, …
SwissProt (http://expasy.cbr.nrc.ca/sprot/)
• Curation!!!
– Error rate in the information is greatly reduced in
comparison to most other databases.
• Extensive cross-linking to other data sources
• SwissProt is the ‘gold-standard’ by which
other databases can be measured, and is the
best place to start if you have a specific
protein to investigate
A few more resources to be aware of
• Human Genome Working Draft
– http://genome.ucsc.edu/
• TIGR (The Institute for Genomics Research)
– http://www.tigr.org/
• Celera
– http://www.celera.com/
• (Model) Organism specific information:
– Yeast: http://genome-www.stanford.edu/Saccharomyces/
– Arabidopis: http://www.tair.org/
– Mouse: http://www.jax.org/
– Fruitfly: http://www.fruitfly.org/
– Nematode: http://www.wormbase.org/
• Nucleic Acids Research Database Issue
– http://nar.oupjournals.org/ (First issue every year)
Example 1: Searching a new
genome for a specific protein
• Specific problem: We want to find the closest
match in C. elegans of D. melanogaster protein
NTF1, a transcription factor
• First- understanding the different forms of blast
The different versions of BLAST
1st Step: Search the proteins
• blastp is used to search for C. elegans
proteins that are similar to NTF1
• Two reasonable hits are found, but the hits
have suspicious characteristics
– besides the fact that they weren’t included in the
complete genome!
2nd Step: Search the nucleotides
• tblastn is used to search for translations of C.
elegans nucleotide that are similar to NTF1
• Now we have only one hit
– How are they related?
Conclusion: Incorrect gene
prediction/annotation
• The two predicted proteins have essentially
identical annotation
• The protein-protein alignments are disjoint
and consecutive on the protein
• The protein-nucleotide alignment includes
both protein-protein alignments in the proper
order
• Why/how does this happen?
Final(?) Check: Gene prediction
• Genscan is the best available ab initio gene
predictor
– http://genes.mit.edu/GENSCAN.html
• Genscan’s prediction spans both protein-
protein alignments, reinforcing our conclusion
of a bad prediction
Ab initio vs. similarity vs. hybrid
models for gene finding
• Ab initio: The gene looks like the average of
many genes
– Genscan, GeneMark, GRAIL…
• Similarity: The gene looks like a specific
known gene
– Procrustes,…
• Hybrid: A combination of both
– Genomescan (http://genes.mit.edu/genomescan/)
A similar example: Fruitfly homolog
of mRNA localization protein VERA
• Similar procedure as just described
– Tblastn search with BLOSUM45 produces an unexpected exon
• Conclusion: Incomplete (as opposed to incorrect)
annotation
– We have verified the existence of the rare isoform through RT-PCR
Another example: Find all genes with
pdz domains
• Multiple methods are possible
• The ‘best’ method will depend on many things
– How much do you know about the domain?
– Do you know the exact extent of the domain?
– How many examples do you expect to find?
Some possible methods if the domain
is a known domain:
• SwissProt
– text search capabilities
– good annotation of known domains
– crosslinks to other databases (domains)
• Databases of known domains:
– BLOCKS (http://blocks.fhcrc.org/)
– Pfam (http://pfam.wustl.edu/)
– Others (ProDom, ProSite, DOMO,…)
Determination of the nature of
conservation in a domain
• For new domains, multiple alignment is your
best option
– Global: clustalw
– Local: DiAlign
– Hidden Markov Model: HMMER
• For known domains, this work has largely
been done for you
– BLOCKS
– Pfam
If you have a protein, and want to
search it to known domains
• Search/Analysis tools
– Pfam
– BLOCKS
– PredictProtein
(http://cubic.bioc.columbia.edu/predictprotein/predictprotein.html)
Different representations of
conserved domains
• BLOCKS
– Gapless regions
– Often multiple blocks for one domain
• PFAM
– Statistical model, based on HMM
– Since gaps are allowed, most domains have only
one pfam model
Conclusions
• We have only touched small parts of the
elephant
• Trial and error (intelligently) is often your best
tool
• Keep up with the main five sites, and you’ll
have a pretty good idea of what is happening
and available

Contenu connexe

Similaire à using_webbased_tools.ppt

GLBIO/CCBC Metagenomics Workshop
GLBIO/CCBC Metagenomics WorkshopGLBIO/CCBC Metagenomics Workshop
GLBIO/CCBC Metagenomics WorkshopMorgan Langille
 
Plant Pathogen Genome Data: My Life In Sequences
Plant Pathogen Genome Data: My Life In SequencesPlant Pathogen Genome Data: My Life In Sequences
Plant Pathogen Genome Data: My Life In SequencesLeighton Pritchard
 
Genome resource databases in horticutural crops
Genome resource databases in horticutural cropsGenome resource databases in horticutural crops
Genome resource databases in horticutural cropsPulipati Gangadhara Rao
 
2018 02 20_biological_databases_part1_v_upload
2018 02 20_biological_databases_part1_v_upload2018 02 20_biological_databases_part1_v_upload
2018 02 20_biological_databases_part1_v_uploadProf. Wim Van Criekinge
 
Bioinformatics__Lecture_1.ppt
Bioinformatics__Lecture_1.pptBioinformatics__Lecture_1.ppt
Bioinformatics__Lecture_1.pptsirwansleman
 
University of Manchester Symposium 2012: Extraction and Representation of in ...
University of Manchester Symposium 2012: Extraction and Representation of in ...University of Manchester Symposium 2012: Extraction and Representation of in ...
University of Manchester Symposium 2012: Extraction and Representation of in ...geraintduck
 
Reproducible research - to infinity
Reproducible research - to infinityReproducible research - to infinity
Reproducible research - to infinityPeterMorrell4
 
Genome science intermine
Genome science intermineGenome science intermine
Genome science intermineELIXIR UK
 
The UCSC genome browser: A Neuroscience focused overview
The UCSC genome browser: A Neuroscience focused overviewThe UCSC genome browser: A Neuroscience focused overview
The UCSC genome browser: A Neuroscience focused overviewVictoria Perreau
 
Role of bioinformatics in life sciences research
Role of bioinformatics in life sciences researchRole of bioinformatics in life sciences research
Role of bioinformatics in life sciences researchAnshika Bansal
 
Browsing Genes, Variation and Regulation data with Ensembl
Browsing Genes, Variation and Regulation data with EnsemblBrowsing Genes, Variation and Regulation data with Ensembl
Browsing Genes, Variation and Regulation data with EnsemblDenise Carvalho-Silva, PhD
 
Computational biology bls 303
Computational biology bls 303Computational biology bls 303
Computational biology bls 303Bruno Mmassy
 
Data Base in Bioinformatics.ppt
Data Base in Bioinformatics.pptData Base in Bioinformatics.ppt
Data Base in Bioinformatics.pptBangaluru
 
Connecting life sciences data at the European Bioinformatics Institute
Connecting life sciences data at the European Bioinformatics InstituteConnecting life sciences data at the European Bioinformatics Institute
Connecting life sciences data at the European Bioinformatics InstituteConnected Data World
 
Giab jan2016 intro and update 160128
Giab jan2016 intro and update 160128Giab jan2016 intro and update 160128
Giab jan2016 intro and update 160128GenomeInABottle
 
Informal presentation on bioinformatics
Informal presentation on bioinformaticsInformal presentation on bioinformatics
Informal presentation on bioinformaticsAtai Rabby
 
DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...
DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...
DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...Felipe Albrecht
 

Similaire à using_webbased_tools.ppt (20)

GLBIO/CCBC Metagenomics Workshop
GLBIO/CCBC Metagenomics WorkshopGLBIO/CCBC Metagenomics Workshop
GLBIO/CCBC Metagenomics Workshop
 
Plant Pathogen Genome Data: My Life In Sequences
Plant Pathogen Genome Data: My Life In SequencesPlant Pathogen Genome Data: My Life In Sequences
Plant Pathogen Genome Data: My Life In Sequences
 
Genome resource databases in horticutural crops
Genome resource databases in horticutural cropsGenome resource databases in horticutural crops
Genome resource databases in horticutural crops
 
2018 02 20_biological_databases_part1_v_upload
2018 02 20_biological_databases_part1_v_upload2018 02 20_biological_databases_part1_v_upload
2018 02 20_biological_databases_part1_v_upload
 
Bioinformatics__Lecture_1.ppt
Bioinformatics__Lecture_1.pptBioinformatics__Lecture_1.ppt
Bioinformatics__Lecture_1.ppt
 
University of Manchester Symposium 2012: Extraction and Representation of in ...
University of Manchester Symposium 2012: Extraction and Representation of in ...University of Manchester Symposium 2012: Extraction and Representation of in ...
University of Manchester Symposium 2012: Extraction and Representation of in ...
 
Reproducible research - to infinity
Reproducible research - to infinityReproducible research - to infinity
Reproducible research - to infinity
 
proteomics.ppt
proteomics.pptproteomics.ppt
proteomics.ppt
 
Proteins databases
Proteins databasesProteins databases
Proteins databases
 
Genome science intermine
Genome science intermineGenome science intermine
Genome science intermine
 
The UCSC genome browser: A Neuroscience focused overview
The UCSC genome browser: A Neuroscience focused overviewThe UCSC genome browser: A Neuroscience focused overview
The UCSC genome browser: A Neuroscience focused overview
 
Role of bioinformatics in life sciences research
Role of bioinformatics in life sciences researchRole of bioinformatics in life sciences research
Role of bioinformatics in life sciences research
 
Browsing Genes, Variation and Regulation data with Ensembl
Browsing Genes, Variation and Regulation data with EnsemblBrowsing Genes, Variation and Regulation data with Ensembl
Browsing Genes, Variation and Regulation data with Ensembl
 
Computational biology bls 303
Computational biology bls 303Computational biology bls 303
Computational biology bls 303
 
Data Base in Bioinformatics.ppt
Data Base in Bioinformatics.pptData Base in Bioinformatics.ppt
Data Base in Bioinformatics.ppt
 
Connecting life sciences data at the European Bioinformatics Institute
Connecting life sciences data at the European Bioinformatics InstituteConnecting life sciences data at the European Bioinformatics Institute
Connecting life sciences data at the European Bioinformatics Institute
 
Giab jan2016 intro and update 160128
Giab jan2016 intro and update 160128Giab jan2016 intro and update 160128
Giab jan2016 intro and update 160128
 
Informal presentation on bioinformatics
Informal presentation on bioinformaticsInformal presentation on bioinformatics
Informal presentation on bioinformatics
 
DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...
DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...
DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...
 
gene prediction methods.pptx
gene prediction methods.pptxgene prediction methods.pptx
gene prediction methods.pptx
 

Dernier

GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)Areesha Ahmad
 
GBSN - Microbiology (Unit 2)
GBSN - Microbiology (Unit 2)GBSN - Microbiology (Unit 2)
GBSN - Microbiology (Unit 2)Areesha Ahmad
 
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...ssuser79fe74
 
Seismic Method Estimate velocity from seismic data.pptx
Seismic Method Estimate velocity from seismic  data.pptxSeismic Method Estimate velocity from seismic  data.pptx
Seismic Method Estimate velocity from seismic data.pptxAlMamun560346
 
Formation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksFormation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksSérgio Sacani
 
Unit5-Cloud.pptx for lpu course cse121 o
Unit5-Cloud.pptx for lpu course cse121 oUnit5-Cloud.pptx for lpu course cse121 o
Unit5-Cloud.pptx for lpu course cse121 oManavSingh202607
 
High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...
High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...
High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...chandars293
 
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)Joonhun Lee
 
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPirithiRaju
 
Forensic Biology & Its biological significance.pdf
Forensic Biology & Its biological significance.pdfForensic Biology & Its biological significance.pdf
Forensic Biology & Its biological significance.pdfrohankumarsinghrore1
 
Proteomics: types, protein profiling steps etc.
Proteomics: types, protein profiling steps etc.Proteomics: types, protein profiling steps etc.
Proteomics: types, protein profiling steps etc.Silpa
 
biology HL practice questions IB BIOLOGY
biology HL practice questions IB BIOLOGYbiology HL practice questions IB BIOLOGY
biology HL practice questions IB BIOLOGY1301aanya
 
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...dkNET
 
GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)Areesha Ahmad
 
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 60009654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000Sapana Sha
 
pumpkin fruit fly, water melon fruit fly, cucumber fruit fly
pumpkin fruit fly, water melon fruit fly, cucumber fruit flypumpkin fruit fly, water melon fruit fly, cucumber fruit fly
pumpkin fruit fly, water melon fruit fly, cucumber fruit flyPRADYUMMAURYA1
 
SCIENCE-4-QUARTER4-WEEK-4-PPT-1 (1).pptx
SCIENCE-4-QUARTER4-WEEK-4-PPT-1 (1).pptxSCIENCE-4-QUARTER4-WEEK-4-PPT-1 (1).pptx
SCIENCE-4-QUARTER4-WEEK-4-PPT-1 (1).pptxRizalinePalanog2
 

Dernier (20)

GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)
 
GBSN - Microbiology (Unit 2)
GBSN - Microbiology (Unit 2)GBSN - Microbiology (Unit 2)
GBSN - Microbiology (Unit 2)
 
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
 
Seismic Method Estimate velocity from seismic data.pptx
Seismic Method Estimate velocity from seismic  data.pptxSeismic Method Estimate velocity from seismic  data.pptx
Seismic Method Estimate velocity from seismic data.pptx
 
Formation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disksFormation of low mass protostars and their circumstellar disks
Formation of low mass protostars and their circumstellar disks
 
Unit5-Cloud.pptx for lpu course cse121 o
Unit5-Cloud.pptx for lpu course cse121 oUnit5-Cloud.pptx for lpu course cse121 o
Unit5-Cloud.pptx for lpu course cse121 o
 
High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...
High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...
High Class Escorts in Hyderabad ₹7.5k Pick Up & Drop With Cash Payment 969456...
 
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
 
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
 
Forensic Biology & Its biological significance.pdf
Forensic Biology & Its biological significance.pdfForensic Biology & Its biological significance.pdf
Forensic Biology & Its biological significance.pdf
 
Clean In Place(CIP).pptx .
Clean In Place(CIP).pptx                 .Clean In Place(CIP).pptx                 .
Clean In Place(CIP).pptx .
 
Proteomics: types, protein profiling steps etc.
Proteomics: types, protein profiling steps etc.Proteomics: types, protein profiling steps etc.
Proteomics: types, protein profiling steps etc.
 
biology HL practice questions IB BIOLOGY
biology HL practice questions IB BIOLOGYbiology HL practice questions IB BIOLOGY
biology HL practice questions IB BIOLOGY
 
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
 
Site Acceptance Test .
Site Acceptance Test                    .Site Acceptance Test                    .
Site Acceptance Test .
 
CELL -Structural and Functional unit of life.pdf
CELL -Structural and Functional unit of life.pdfCELL -Structural and Functional unit of life.pdf
CELL -Structural and Functional unit of life.pdf
 
GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)
 
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 60009654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
 
pumpkin fruit fly, water melon fruit fly, cucumber fruit fly
pumpkin fruit fly, water melon fruit fly, cucumber fruit flypumpkin fruit fly, water melon fruit fly, cucumber fruit fly
pumpkin fruit fly, water melon fruit fly, cucumber fruit fly
 
SCIENCE-4-QUARTER4-WEEK-4-PPT-1 (1).pptx
SCIENCE-4-QUARTER4-WEEK-4-PPT-1 (1).pptxSCIENCE-4-QUARTER4-WEEK-4-PPT-1 (1).pptx
SCIENCE-4-QUARTER4-WEEK-4-PPT-1 (1).pptx
 

using_webbased_tools.ppt

  • 1. Bioinformatics Resources and Tools on the Web: A Primer Joel H. Graber Center for Advanced Biotechnology Boston University
  • 2. Outline • Introduction: What is bioinformatics? • The basics – The five sites that all biologists should know • Some examples – Using the tools in a somewhat less-than-naïve manner • Questions/comments are welcome at all points • Much of this material comes from the Boston University course: BF527 Bioinformatic Applications (http://matrix.bu.edu/BF527/)
  • 4. Examples of Bioinformatics • Database interfaces – Genbank/EMBL/DDBJ, Medline, SwissProt, PDB, … • Sequence alignment – BLAST, FASTA • Multiple sequence alignment – Clustal, MultAlin, DiAlign • Gene finding – Genscan, GenomeScan, GeneMark, GRAIL • Protein Domain analysis and identification – pfam, BLOCKS, ProDom, • Pattern Identification/Characterization – Gibbs Sampler, AlignACE, MEME • Protein Folding prediction – PredictProtein, SwissModeler
  • 5. Things to know and remember about using web server-based tools • You are using someone else’s computer • You are (probably) getting a reduced set of options or capacity • Servers are great for sporadic or proof-of- principle work, but for intensive work, the software should be obtained and run locally
  • 6. Five websites that all biologists should know • NCBI (The National Center for Biotechnology Information; – http://www.ncbi.nlm.nih.gov/ • EBI (The European Bioinformatics Institute) – http://www.ebi.ac.uk/ • The Canadian Bioinformatics Resource – http://www.cbr.nrc.ca/ • SwissProt/ExPASy (Swiss Bioinformatics Resource) – http://expasy.cbr.nrc.ca/sprot/ • PDB (The Protein Databank) – http://www.rcsb.org/PDB/
  • 7. NCBI (http://www.ncbi.nlm.nih.gov/) • Entrez interface to databases – Medline/OMIM – Genbank/Genpept/Structures • BLAST server(s) – Five-plus flavors of blast • Draft Human Genome • Much, much more…
  • 8. EBI (http://www.ebi.ac.uk/) • SRS database interface – EMBL, SwissProt, and many more • Many server-based tools – ClustalW, DALI, …
  • 9. SwissProt (http://expasy.cbr.nrc.ca/sprot/) • Curation!!! – Error rate in the information is greatly reduced in comparison to most other databases. • Extensive cross-linking to other data sources • SwissProt is the ‘gold-standard’ by which other databases can be measured, and is the best place to start if you have a specific protein to investigate
  • 10. A few more resources to be aware of • Human Genome Working Draft – http://genome.ucsc.edu/ • TIGR (The Institute for Genomics Research) – http://www.tigr.org/ • Celera – http://www.celera.com/ • (Model) Organism specific information: – Yeast: http://genome-www.stanford.edu/Saccharomyces/ – Arabidopis: http://www.tair.org/ – Mouse: http://www.jax.org/ – Fruitfly: http://www.fruitfly.org/ – Nematode: http://www.wormbase.org/ • Nucleic Acids Research Database Issue – http://nar.oupjournals.org/ (First issue every year)
  • 11. Example 1: Searching a new genome for a specific protein • Specific problem: We want to find the closest match in C. elegans of D. melanogaster protein NTF1, a transcription factor • First- understanding the different forms of blast
  • 13. 1st Step: Search the proteins • blastp is used to search for C. elegans proteins that are similar to NTF1 • Two reasonable hits are found, but the hits have suspicious characteristics – besides the fact that they weren’t included in the complete genome!
  • 14. 2nd Step: Search the nucleotides • tblastn is used to search for translations of C. elegans nucleotide that are similar to NTF1 • Now we have only one hit – How are they related?
  • 15. Conclusion: Incorrect gene prediction/annotation • The two predicted proteins have essentially identical annotation • The protein-protein alignments are disjoint and consecutive on the protein • The protein-nucleotide alignment includes both protein-protein alignments in the proper order • Why/how does this happen?
  • 16. Final(?) Check: Gene prediction • Genscan is the best available ab initio gene predictor – http://genes.mit.edu/GENSCAN.html • Genscan’s prediction spans both protein- protein alignments, reinforcing our conclusion of a bad prediction
  • 17. Ab initio vs. similarity vs. hybrid models for gene finding • Ab initio: The gene looks like the average of many genes – Genscan, GeneMark, GRAIL… • Similarity: The gene looks like a specific known gene – Procrustes,… • Hybrid: A combination of both – Genomescan (http://genes.mit.edu/genomescan/)
  • 18. A similar example: Fruitfly homolog of mRNA localization protein VERA • Similar procedure as just described – Tblastn search with BLOSUM45 produces an unexpected exon • Conclusion: Incomplete (as opposed to incorrect) annotation – We have verified the existence of the rare isoform through RT-PCR
  • 19. Another example: Find all genes with pdz domains • Multiple methods are possible • The ‘best’ method will depend on many things – How much do you know about the domain? – Do you know the exact extent of the domain? – How many examples do you expect to find?
  • 20. Some possible methods if the domain is a known domain: • SwissProt – text search capabilities – good annotation of known domains – crosslinks to other databases (domains) • Databases of known domains: – BLOCKS (http://blocks.fhcrc.org/) – Pfam (http://pfam.wustl.edu/) – Others (ProDom, ProSite, DOMO,…)
  • 21. Determination of the nature of conservation in a domain • For new domains, multiple alignment is your best option – Global: clustalw – Local: DiAlign – Hidden Markov Model: HMMER • For known domains, this work has largely been done for you – BLOCKS – Pfam
  • 22. If you have a protein, and want to search it to known domains • Search/Analysis tools – Pfam – BLOCKS – PredictProtein (http://cubic.bioc.columbia.edu/predictprotein/predictprotein.html)
  • 23. Different representations of conserved domains • BLOCKS – Gapless regions – Often multiple blocks for one domain • PFAM – Statistical model, based on HMM – Since gaps are allowed, most domains have only one pfam model
  • 24. Conclusions • We have only touched small parts of the elephant • Trial and error (intelligently) is often your best tool • Keep up with the main five sites, and you’ll have a pretty good idea of what is happening and available