Bioinformatics Data Analysis: InterPro

EBI is an Outstation of the European Molecular Biology Laboratory.

9 April 2008

InterPro data pipelinesAntony Quinn

Outline

• Onion • Main InterPro pipeline: predict protein families, domains, sites • A lot

• CluSTr• Automatic classification of proteins based on sequence similarity• A little

InterPro data pipelines: What‘s in it for me?9 April 2008

Not this...



Mission: to explore strange new proteins…

Onion + Protein Sequence

=Prediction of Functional Annotation

InterPro

Protein families, domains, repeats and sites


Requirements

• Handle all member databases and algorithms• HMMER (eg. Gene3D, PANTHER)• Regular expressions (PROSITE)• SignalP• TMHMM• BLAST (PIRSF)• FingerPRINTScan (PRINTS)

• Fast• Wide and deep coverage


Design

• UniParc• Solves mapping problem• Sequential IDs• Comprehensive – many DBs, all sequences

• Method Archive• Minimise calculations• Read flat files once

• Decoupled analysis and post-processing


The Trinity

Onion

UniParcMethod Archive

Member database methods

Protein sequences


New sequences

UniParc

Onion

New sequences

Run against all methods

UniParcMethod Archive


Member database release

Onion

UniParcMethod archive

Method Archive

Run new and changed methods

against all sequences

Advantages:

• If only post-processing or cut-off changed – only run that part

• No change – no need to rerun

Methods added,

changed or deleted


“Deluge” mode (manual)

UniParc

HMMflatfile

Profileflatfile

FPrintflatfile

New release of model database – search new and

changed models against all of UniParc

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FASTAfile

1000s of model files

anthill

UniParc

HMMflatfile

Profileflatfile

FPrintflatfile



FASTAfile


anthill

bsub sumissio

n cmds

UniParc

HMMflatfile

Profileflatfile

FPrintflatfile



FASTAfile


LSF

anthill

bsub sumissio

n cmds

UniParc

HMMflatfile

Profileflatfile

FPrintflatfile



FASTAfile


LSF

anthill

bsub sumissio

n cmds

output files (raw results)

SQL*Loader file

Parse, reformatUniParc

HMMflatfile

Profileflatfile

FPrintflatfile



FASTAfile


LSF

anthill

bsub sumissio

n cmds


SQL*Loader file

Parse, reformat

Load

ONION

Raw results table

UniParc

HMMflatfile

Profileflatfile

FPrintflatfile



FASTAfile


LSF

anthill

bsub sumissio

n cmds


SQL*Loader file

Parse, reformat

Load

ONION

Raw results table

post-processing

Final results table

UniParc

HMMflatfile

Profileflatfile

FPrintflatfile


“Drip” mode (automatic)

UniParc

New sequences– search all models every 4 minutes

anthillextract new sequences

HMMflatfile

Profileflatfile

FPrintflatfile



UniParc

LSF

anthill

bsub sumissio

n cmds


extract new sequences

HMMflatfile

Profileflatfile

FPrintflatfile


UniParc

LSF

anthill

bsub sumissio

n cmds


Parse, reformatand load

extract new sequences

ONION

Raw results table

post-processing

Final results table

HMMflatfile

Profileflatfile

FPrintflatfile



pirsf

pantherScoreassignment

HMMER

Pfam TIGRFAM SMART SUPERFAMILYGENE3D PIRSF PANTHER

GA cut-off

TC cut-off

E-value cut-off

E-value cut-off

AM filter

clan

nested

threshold

(kinase)

domainFinder

sequence

Oracle (raw data)

Oracle (refined data)

The refinery


Onion vs InterProScan

• Similarities• Software: HMMER, TMHMM, SignalP• Models: Pfam, Gene3D, PRINTS …etc

• Differences• Internal use only• Decoupled analysis and post-processing• Java + database• Faster


Limitations

• Database design• Inflexible – single member DB version• Redundant

• Tight coupling• Internal

• Difficult to test/debug• External

• Oracle• LSF• File system


Plans

• Merge InterProScan• Single code base = reduced maintenance cost• Java (Java 5? Spring? Maven?)• Database (Oracle, Derby?, Hibernate, Java stored procs?)

• Testable• JUnit• Continous integration?

• API• Java (web services?)• Oracle: views, stored procs


What’s in it for me?

• UniProt curators• On-demand sequence analysis?

• Ensembl production• InterPro hits• Pre- or post-UniParc?


CluSTr

• Input: UniProtKB, IPI, Ensembl Human – 6 million sequences• Output:

• Similiarity scores (Smith-Waterman) – 3.5 billion• Clusters (single linkage, aka nearest neighbour)• Orthologues (best reciprocal hit) – 627 species• Every 3 weeks (UniProt cycle)

• Availability: Oracle, web app, FTP (sims + GO mappings)• Customers

• integr8 (orthologues)• Druggable Genome (similarities)

• Potential• Set-based analyses• Similarities on-demand


Acknowledgements

• InterPro• Robert Petryszak (Dark Side)• Craig McAnulla (Onion)• John Maslen (CluSTr)• Beat Ramseier (Method Archive)• Sarah Hunter (Management)

• integr8• Paul Kersey (CluSTr)

• A Team• Tracy Mumford• Kerry Smith

Thank you

Bioinformatics Data Analysis: InterPro

Data & Analytics

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