g. paolella napoli, 21/2/ 2008 1 progetto s.co.p.e. – wp4 bioinformatica nel progetto scope g....
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G. Paolella Napoli, 21/2/ 2008 1
Progetto S.Co.P.E. – WP4
Bioinformatica nel progetto SCOPE
G. Paolella, M. Petrillo, L.Cozzuto, A. Boccia, C. Cantarella, L.Sepe
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Our role within SCOPE
Nodes Nodes Nodes Ns Nodes
GRID software
High level middleware
SCOPE web siteAstronomy Chemistry
Physics Bioinformatics
Hardware
Middleware
Application
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Tasks
• Provide a large number of users with general purpose bioinformatic service, which take advantage of high performance hardware, allowing:– Web access for quick operations, performed by the vast majority
of users– Unix level access in the form of an integrated problem solving
environment
• Set up an automatic annotation system to be used in specific computational or experimental projects, based on the available services two specific applications:– CST analysis by comparative genomics– Mining for regulatory RNA within completely sequenced
genomes
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Bioinfo portal
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Available services
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Programs
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Graphic interface to programs
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Various operations in a row:Complement ->Translation -> Isoelectric point of the resulting protein.
DNA
Complement
Translation
Isoelectric point
CAPRI workflow
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SRS: the database tool
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SRS
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WEB SERVER
CAPRI SRSPISE
Other Emboss Fasta Blast
UserData DB
Primary remotedatabases
ENSEMBL
Services organization
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Sito periferico medicina
HD attached to the system:• 112 processor cluster• Two 8-processor servers, several 2-processor servers• Storage center (SCOPE)• Campus GRID and beyond (SCOPE)
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Broker
virtualnode
virtualnode
DB
DB
Grid
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de
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Low latency scheduler
High level scheduler
500 tasks/sec
20-50 ms delay
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Joining the GRID
HD attached to the system:• 1 Cluster Element (CE)• 5 Worker nodes (WN) biproc (expandable up to 40)• 1 Storage Element (SE) with 50 Gb• 1 User Interface (UI)
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Available at:lfn:/grid/scope/bioinfo/
programs/ (executables)dbs/ (datasets)
Currently installed tools
• Blast• Randfold• Infernal package
Support databases
• RFAM• Blast (human, rat, dog, chicken and macaca genomes)
GRID bioinformatic tools
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• Blastz• Clustalw• Dialignt• Emboss package• FASTA package• Genscan• Hmmer package• MCL package• Pcma• Primer3• RNAz• Vienna package• Multiz-tba
Ready to be installed tools
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Tasks
• Provide a large number of users with general purpose bioinformatic service, which take advantage of high performance hardware, allowing:– Web access for quick operations, performed by the vast majority
of users– Unix level access in the form of an integrated problem solving
environment
• Set up an automatic annotation system to be used in specific computational or experimental projects, based on the available services two specific applications:– CST analysis by comparative genomics– Mining for regulatory RNA within completely sequenced
genomes
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Due esempi
Due esempi di sistemi di annotazione automatica, utilizzati per la identificazione e caratterizzazione di sequenze di DNA con possibile ruolo funzionale:
– sequenze di piccole dimensioni, conservate tra uomo ed altre specie CST;
– sequenze in grado di codificare per RNA strutturati.
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• Obiettivo: Sistema di annotazione automatica di sequenze
• Motivazioni: Analisi computazionale di sequenze non codificanti permette l’identificazione di nuovi elementi funzionali
• Descrizione del problema e sua risoluzione. Diversi tipi di test predittivi applicati su larga scala ad un gran numero di dati sperimentali, estratti da banche dati pubblicamente disponibili o provenienti da dati sperimentali.
• Esigenza per l’uso dell’HPC: dato l’elevato numero di test, in genere si utilizzano cluster multiprocessore. L’uso di GRID permette di estendere l’analisi a set di dati di dimensioni ancora maggiori.
• Descrizione della soluzione del problema nell’ambiente HPC
Obiettivi e modalita’
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Identificazione e caratterizzazione di sequenze nucleotidiche conservate tra uomo e topo (CSTs) in altre specie.
H. Sapiens
M. Musculus
CSTs
CST identificate in geni associati a malattie: 64.495.Analisi da effettuare mediante BLAST contro altri genomi (ratto, cane, scimmia, pollo, etc).
Identificazione di CST
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Annotation is carried out through a pipeline which goes through the various phases wit hout requiring human assistance. Tasks requiring intensive CPU usage, such as BLAST homology search, are spread on several collaborating servers using a system specifically developed for load distribution and monitoring.
CST ANNOTATIONCSTs- chromosome position- type (i.e. intergenic, intronic, exonic, etc.)- coding %- closest gene and relative distances- .......
ENSEMBL gene and gene structure data- Max L-Score- Avg L-Score- .......
UCSC Log Score dataMatches with:- EST- Other genomes- Proteins (BlastX)
BLAST- repeats type- repeats %Repeat MaskerCoding Potential ScoreCPS - Redundancy- Overlapping- ........
PHP ScriptsDBRemote Servers Remote Servers
CST annotation
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DG-CST
1022 genes related to
genetically transmitted
disease
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KinWeb
500 genes coding for
human protein
kinases
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(a)
(b)
(c)
(d)
(e)
KinWeb DB
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BLAST
• Eseguibile submitted da un repository locale di programmi • Librerie di dati genomici conservate su SE locale e registrate sull'SE
centrale scopelfc01.dsf.unina.it:/grid/scope/bioinfo
• Esempio Blast delle 65597 CST contro genomi di cane, gallo, scimmia e ratto.
• Numero jobs sottomessi 67• Gruppo di sequenze di input: 1000 sequenze• Tempo totale di esecuzione dei 67 jobs: 4 ore• Tempo medio per job: 18 minuti (2 spesi per scaricare il dataset).
• Tempo CPU• Ricerca di 1 sequenza nel genoma di topo => 5 sec. • 64.495 sequenze => 3,75 giorni• 10 genomi => 37,5 giorni• MPIBLAST (soltanto installato)
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Bacterial SLSs
Pae-1 (Pseudomonas aeuruginosa)Eric (Escherichia coli)
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Identificazione e caratterizzazione in genomi batterici di famiglie di sequenze ripetute che condividono una struttura secondaria conservata.
Analisi da effettuare mediante INFERNAL su oltre 300 genomi batterici
EsempioRicerca di una famiglia in un genoma =====> 6 ore.Ricerca di 50 famiglie in un genoma =====> 12,5 giorniRicerca di 50 famiglie in 300 genomi =====> 10 anni
Ricerca Strutture secondarie
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DNA
Aim: find potential regulatory sequences acting as structured RNAs.
Pilot project: Analyses carried on chromosome 21.
Protein
Structured RNA
mRNA
Folding of the human genome
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Chromosome length 46,944,323 bp
Transcriptome length 14,609,025 bp
Sequences potentially transcribed has been split in overlapping fragments of 150 bp length.
Fragments 290,904 sequences
Total length 43,726,912 bp
Genome plan
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Length 46,944,323 bps
Total genes 392
> miRNA Genes 10
> rRNA Genes 3
> snRNA Genes 7
> snoRNA Genes 8
> miscRNA 8
Found known RNAs 9
Transcriptome length 14,609,025
Sequences potentially transcribed has been split in overlapping fragments of 150 bp length.
290,904 sequences
Results
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Valutazione dei risultati ottenuti
• RANDFOLD• Programma randfold• Eseguibile submitted da un repository locale di programmi di
bioinformatica• Gruppo di sequenze di input: 2500 sequenze di regioni trascritte del chr
21• Numero jobs sottomessi 117
• Tempo CPU richiesto• Sequenze derivate dai geni del cromosoma 21: 291.589• Predizione su 1 sequenza => 45 sec.• 291.589 sequenze => 152 giorni.
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Node number n_sequences seconds Day(s)
1 1 45 0
1 291,589 13,121,505 152
117 2,500 112,500 1,3
About 3 days
How long ?
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0
10000
20000
30000
40000
50000
60000
0 200 400 600 800 1000 1200
proc numbers
time (sec)
single nodegrid
Performance
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Some extra applications
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Assemble
…
Contigs Scaffolds
…
geneA tRNA prom oprA oprB
geneCluster A
Annotation
High throughput sequencing
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• Identification of genes and other genetic elements.• Protein functional annotation.• Cellular process annotation.
• Identification of ORFs, tRNAs, rRNAs• Scanning for signals, such as promoters and microRNAs• Identification of operons and gene clusters• Comparison with known genomes/proteins• Identification of orthologs and paralogs • Characterization of protein domains• Reconstruction of complete metabolic pathways• …• …
Annotation Steps
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Annotation
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IPROC
IPROC
IPROC
The image processing system: IPROC
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image in
iProcStep
iProcStepImageMagick
iProcStepPHP
iProcStepPerl
commandLine program
Image MagickPackage
PHPPackage
PERLPackage
Command LinePackages
adapter adapter
image out
adapter
Image processing modules
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HPCon
ClusternodesG
ateway
iPage
image
area
data + images
page
iPaneiPaneiPane
proc-steps
IPROC architecture
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Cluster Nodes
AccessServer
AccessServer
AccessServer
CLUSTER
IPROC
Parallel processing
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The group
Angelo BocciaGianluca BusielloMauro PetrilloConcita Cantarella*Luca CozzutoLeandra Sepe*
Vittorio LucignanoMarisa Passaro
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SPEED (μ /40 )m min
( )ANGLE degree R
FRONT MIDDLE FAR FRONT MIDDLE FAR FRONT MIDDLE FAR
3 3NIH T 7,27 4,92 5,25 194,95 181,82 212,620.85
( =0.49)coeff0.47
( =0.56)coeff0.09
( =0.30)coeff
NIHRas 11,57 6,88 7,57 160,74 188,16 87,60.83
( =0.51)coeff0.59
( =0.54)coeff0.34
( =0.30)coeff
NIHSrc 6,05 5.1 3,71 181,08 168,29 156,950.89
( =0.48)coeff0.74
( =0.49)coeff0.56
( =0.35)coeff
SPEED μm/40min R
average angle(degree)
NIH3t3 8.43 0.02 226.73
NIHRas 11.73 0.37 203.79
NIHSrc 4.87 0.24 251.07
middle
front
far
NIHRas, NIH3T3, NIHSrc wound NIHRas, NIH3T3, NIHSrc wound healinghealing
Three cell subpopulations: Three cell subpopulations:
front, middle, and far from front, middle, and far from
the woundthe wound
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Version number 1 features tab-delimited
Name filename
Depth size 16bit
wdim size 4 where files
cdim size 3 where files
pdim size n where files
tdim size n unit min scale 10 where files
ldim size n unit µm scale 0.4 where layers
Time 1 Time 2 Time n
well1 well2
well3 well4
Channel1Channel 2
Channel 3
Position 1
Position n
l1
ln
File format
Data input: text description
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Acquisition parameters Buttons to slide
the acquisition
Image processing menus
Info panel for each frame
hide/show control command
IPROC
Image processing
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Broker
virtualnode
virtualnode
DB
DB
Grid
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Hierarchical node organization
top related