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Parallel Detection of Regulatory
Elements with gMP
Bertil Schmidt, Lin Feng, Amey Laud, Yusdi Santoso
Damayanti Gupta
CMSC 838 Presentation
Motivation
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Fundamental question
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How are expression levels of thousands of genes regulated ?
Very important
Understanding of gene function
 Response to environment
Understand genetic causes of diseases
 Evaluate effects of drus
Detect mutations
Remember
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Sets of genes -> Pathways -> Genetic Networks
Gene regulation
 Control decisions turn genes on/off
 Gene Regulation Network
CMSC 838T – Presentation
Talk Overview
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Overview of talk
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Motivation
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Technique
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Experiment
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Related work
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Conclusions
CMSC 838T – Presentation
Technique
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Motifs upstream of genes regulate gene expression
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Motifs are sites of regulatory activity
Identify regulatory motifs by combining
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Gene expression data
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Detect common motifs occuring upstream of genes
Huge datasets
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Utilise parallel computing
CMSC 838T – Presentation
Technique
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gRNA
 Java
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development framework
gMP
 Java
communication library
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REDUCE
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Algorithm to identify regulatory motifs
REDUCE parallelised with gMP
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Increase computing power
Get motifs ranked in statistical significance
CMSC 838T – Presentation
gRNA framework
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Consists of APIs
CMSC 838T – Presentation
gRNA - APIs
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Interact with data sources
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Provide functionality from biology
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Pipelines tasks into unified process
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Repository of resources
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Distributed programming
CMSC 838T – Presentation
gRNA environment
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gRNA Grid
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Clustered computing environment
Application written for gRNA
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Multiple-tier application
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Applications operate from client computer
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Communicates with cluster through single computer
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Hosts EJB server
Server identifies processing nodes
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each of these perform tasks
CMSC 838T – Presentation
gRNA Grid
CMSC 838T – Presentation
gMP
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Java based message passing tool
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Built on top of sockets
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Manages virtual processors to run on available
machines
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Scalable
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Machines added/removed easily
CMSC 838T – Presentation
gMP
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Processes are grouped
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Communication primitives provided for sending and
receiving data
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Collective communication to several nodes enabled
modularly and efficiently
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Enables functions to be implemented on data
CMSC 838T – Presentation
REDUCE algorithm
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Based on model
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Upstream motifs contribute additively to expression level of
each gene
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Quantify the extent to which these motifs contribute to
expression data
Fit log of expression ratio to sum of activating and
inhibitory terms
Find stastically most significant motifs
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Plots of fitting parameters suggest biological function
CMSC 838T – Presentation
REDUCE algorithm
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Terms
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Occurence vector
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Measure of how often a motif is found
Expression vector
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Measure of gene expression
CMSC 838T – Presentation
REDUCE method
Consists of
1) Motif frequency counter
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counts occurrences of DNA motifs upstream of each ORF
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motifs are about 7~11 nucleotides in length
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get occurence vectors
CMSC 838T – Presentation
REDUCE algorithm
2) Significant motif finder
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Use
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i) Normalised occurrence vector made for each motif nμ
ii) Normalised vector of logs of gene expression ratio vectorsa
Take dot product of these (a . nμ) ,and square.
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Can be considered as frequency of occurence X expressive
power of regulatory motif
It is squared to get rid of negatives
Correlate gene expression with occurence of motif
Largest dot product is most significant motif
CMSC 838T – Presentation
....
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a is modified to remove effect of this motif
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residual gene expression vector
Process repeated until motifs are ranked
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CMSC 838T – Presentation
Table: Finding significant motifs
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Uses a - (.5816,.2522,.2886,-.5947, -.1595, -.3683)
CMSC 838T – Presentation
REDUCE parallelised with gMP...
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Parallel motif frequency counter
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Split set of ORFs equally
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Distribute across available nodes
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Each node calculates in parallel to get occurence vectors
Matrix transposition
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Occurence vectors scattered across nodes
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Advantageous to store each vector in single node
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Transpose motif frequency matrix
For each ORF can only calculate fraction of occurence
frequencies for all motifs
But the entire occurence frequency is needed
CMSC 838T – Presentation
...
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Parallel significant motif finder
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Normalises occurence vector within each node
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At each node, most significant motif calculated
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Global most significant motif calculated
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Process iterated to rank occurence vectors
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Interface in gRNA allows ease of implementation
CMSC 838T – Presentation
Experiment
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Use Compaq Alpha system
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Consists of cluster of 8 AlphaServer SC/ES45
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Connected by high-speed Alpha SC 16-Port switch and
ELAN PCI adapter cards.
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Each server contains 4 Alpha EV68 processors
CMSC 838T – Presentation
Results
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Use 7090 gene expressions of yeast
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ORFs of length 600
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Motifs upto length 7
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Throughput (in MBytes/s) also shown
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20 most significant motifs computed.
CMSC 838T – Presentation
Analysis
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Runtime scales well with number of processing nodes
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Frequency counter scales perfectly
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Motif finder also scales
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Cannot achieve perfect scaling because of communication
overhead.
CMSC 838T – Presentation
Related work
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DiscoveryLink
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Provides configurable wrappers as interfaces to multiple data
sources
Kleisli system
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Systematically manages and integrates external databases
Uses functional query language to perform correlation
across databases
Toolkits designed with functionality for specialised
areas
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BioJava, BioPerl, PAL
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Sequence Analysis
Ensembl initiative, DAS
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provide extensible approach to issue of annotating genomic
data
CMSC 838T – Presentation
Related work
Previous approaches using Java for high performance
computing
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Bindings into native message-passing APIs(e.g.MPI)
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Does not allow easy integration into larger Java
applications
Pure Java message passing interfaces
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JMPI, CCJ
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Both implemented on top of Java RMI
– Slower than using raw sockets
CCJ tries to overcome
– optimised RMI implementation
– not portable
Both cannot handle integration
CMSC 838T – Presentation
Comparison
According to authors ...
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gRNA distinguishes itself
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Uses whole range of requirements for applications in
computational biology
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Provides decoupled, yet inter-related subsystems
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Ease of 3rd party implementation
CMSC 838T – Presentation
Observations
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REDUCE surpasses traditional clustering approach
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REDUCE algorithm has high runtime
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Complexity depends on product of number possible motifs and
that of genes.
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Grows exponentially with length of sequences
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So length of motif is restricted
REDUCE algorithm is greedy
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suboptimal
REDUCE is simplistic
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lacks parameters for interactions between motifs
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does not consider impact of other biological knowledge
CMSC 838T – Presentation
...
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Not clear that results of REDUCE are biologically
significant
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Experiment does not effectively show how higher
computation power helps results
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Only analysis from 9 to 16 processors, is this sufficient to
determine ‘good scaling’?
CMSC 838T – Presentation
Conclusions
Finally...
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gRNA demonstrates efficient mechanism for
development of genome-centric applications
Further...
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Extensions to REDUCE have been proposed
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require higher computing power
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more specialised programming interfaces required
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Identifying communication patterns
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Use of data structures e.g. sequences, trees, matrices
CMSC 838T – Presentation