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VISUALIZING THE PROTEIN SEQUENCE UNIVERSE L.STANBERRY 1 , R.HIGDON 1 , W.HAYNES 1 , N.KOLKER 1 , W.BROOMALL 1 , S.EKANAYAKE 2 , A.HUGHES 2 , Y.RUAN 2 , J.QIU 2 , E.KOLKER 1 , G.FOX 2 1 SEATTLE CHILDRENS, 2 INDIANA UNIVERSITY ECMLS 2012, 18 June

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VISUALIZING THE PROTEIN SEQUENCE UNIVERSE

L.STANBERRY1, R.HIGDON1, W.HAYNES1, N.KOLKER1, W.BROOMALL1, S.EKANAYAKE2, A.HUGHES2, Y.RUAN2, J.QIU2, E.KOLKER1, G.FOX2

1SEATTLE CHILDREN’S, 2INDIANA UNIVERSITY

ECMLS 2012, 18 June 2012

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Outline

Visualizing PSU

2  A 4th paradigm problem in biology

Assigning functions (annotating) proteins

 Challenge  Our goal  PSU: methods & initial results  Conclusions

ECMLS 2012

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Grand Challenge of Functional Genomics

Visualizing PSU

3  New technologies produce peta- and exabytes of

data

 Protein Sequence Universe (PSU), the protein

sequence space, expand exponentially

EMP

, i5K, iPlant, NEON

30% of existing sequenced proteins unannotated

 Existing resources overwhelmed, many unsupported:

COG, Systers, ClusTr, eggNOG.

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Ultimate Goal: Annotate All Proteins

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Our approach:

 Revitalize, expand & enhance protein annotation

resources.

 Develop sustainable software framework.  Use HPC and most powerful CI – grids & clouds.  Provide rigorous and reliable tools to annotate

protein sequences.

ECMLS 2012

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COG: Clusters of Orthologous Groups

Visualizing PSU

5  COG database was developed by NCBI.  Proteins classified into groups with common function

encoded in complete genomes.

 Prokaryotes (COG): 66 genomes, 200K proteins, 5K

clusters.

 Eukaryotes (KOG): 7 genomes, 113K proteins, 5K

clusters.

 Valuable scientific resource: 5K citations.  Last updated: 2006.

ECMLS 2012

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Clustering 10 million UniRef100

Visualizing PSU

6  UniRef100: 10M proteins including 5.3M bacterial &

archaeal

 BLAST - common sequence alignment approach  All vs. All alignment on Azure  475 eight-core virtual machines produced 3+

billion filtered records in 6 days

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Clustering 10 million UniRef100

Visualizing PSU

7  Use prokaryotic COG as a starting point.  Expand COGs ~20 fold (3.5 million proteins).  Cluster 2M proteins into 500K functional groups  Single linkage clustering with MapReduce

framework on Hadoop

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Promise and Challenge of Annotation

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 Clustering facilitates mass annotation

BUT

 Takes considerable efforts and expertise  Multiple cloud systems and compute solutions

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Public Resources

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 Struggle to cope with the influx of data  Provide limited interactive and analytic capabilities  Many no longer supported (SYSTERS, CluSTr, COG)  Biological community needs scalable, sustainable

and efficient approach to visualize, explore and annotate new data.

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Protein Sequence Universe

Visualizing PSU

10  PSU Goal: Enhance annotation resources with

analytic and visualization (browser) tools.

 Project sequence data into 3D using

multidimensional scaling (MDS).

 MDS interpolation allows expanding the universe

without time consuming all vs all O(N2)

 3D map allows much faster interpolation

ECMLS 2012

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Multi-Dimensional Scaling (MDS)

Visualizing PSU

11  Sammon‘s objective function 

is dissimilarity measure between sequences i and j

 d is Euclidean distance between projections xi and xj  Denominator: larger contribution from smaller

dissimilarities

 f is monotone transformation of dissimilarity measure

chosen “artistically”

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 

 

n j i ij j i ij

f x x d f H ) ( ) , ( ) (

2

 

ij

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Typical Metagenomic MDS

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MDS Details

ECMLS 2012 Visualizing PSU

13  f chosen heuristically to increase the ratio of standard

deviation to mean for and to increase the range of dissimilarity measures.

 O(n2) complexity to map n sequences into 3D.  MDS can be solved using EM (SMACOF – fastest but

limited) or directly by Newton's method (it’s just 2 )

 Used robust implementation of nonlinear 2 minimization

with Levenberg-Marquardt

 3D projections visualized in PlotViz

) (

ij

f 

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MDS Details

ECMLS 2012 Visualizing PSU

14  Input Data: 100K sequences from well-characterized

prokaryotic COGs.

 Proximity measure: sequence alignment scores  Scores calculated using Needleman-Wunsch  Scores “sqrt 4D” transformed and fed into MDS

 Analytic form for transformation to 4D  ij

n decreases dimension n > 1; increases n < 1

 “sqrt 4D” reduced dimension of distance data from

244 for ij to14 for f(ij)

 Hence more uniform coverage of Euclidean space

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3D View of 100K COG Sequences

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Implementation

Visualizing PSU

16  NW computed in parallel on 100 node 8-core

system.

 Used Twister (IU) in the Reduce phase of MapReduce  MDS Calculations performed on 768 core MS HPC

cluster (32 nodes)

 Scaling, parallel MPI with threading intranode  Parallel efficiency of the code approximately 70%  Lost efficiency due memory bandwidth saturation  NW required 1 day, MDS job - 3 days.

ECMLS 2012

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Cluster Annotation

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COG Annotation Uniref100 COG1131 ABC-type multidrug transport system, ATPase component 14406 COG1136 ABC-type antimicrobial peptide transport system, ATPase component 7306 COG1126 ABC-type polar amino acid transport system, ATPase component 4061 COG3839 ABC-type sugar transport systems, ATPase component 4121 COG0444 ABC-type dipeptide/oligopeptide/nickel transport system ATPase comp 3520 COG4608 ABC-type oligopeptide transport system, ATPase component 3074 COG3842 ABC-type spermidine/putrescine transport systems, ATPase comp 3665 COG0333 Ribosomal protein L32 1148 COG0454 Histone acetyltransferase HPA2 and Related acetyltransferases 14085 COG0477 Permeases of the major facilitator superfamily 48590 COG1028 Dehydrogenases with different specificities 37461

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Heatmap of NW vs Euclidean Distances

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Dendrogram of Cluster Centroids

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Selected Clusters

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Heatmap for Selected Clusters

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Future Steps

 Comparison Needleman-Wunsch v. Blast v. PSIBlast

 NW easier as complete; Blast has missing distances

 Different Transformations distance  monotonic function(distance)

to reduce formal starting dimension (increase sigma/mean)

 Automate cluster consensus finding as sequence that minimizes

maximum distance to other sequences

 Improve O(N2) to O(N) complexity by interpolating new sequences

to original set and only doing small regions with O(N2)

 Successful in metagenomics  Can use Oct-tree from 3D mapping or set of consensus vectors  Some clusters diffuse?

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Blast6

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Full Data Blast6 Original run has 0.96 cut

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Cluster Data Blast6 Original run has 0.96 cut

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Use Barnes Hut OctTree

  • riginally developed to

make O(N2) astrophysics O(NlogN)

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OctTree for 100K sample of Fungi We use OctTree for logarithmic interpolation

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440K Interpolated

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Conclusions

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 Data Knowledge: protein annotation  Overwhelming influx of new sequences  Annotation is an immense challenge.  HPC and advanced analytics needed.  PSU as tool to facilitate annotation:  Interactive visualization and exploration  Integrates info on function, pathways, structure, and environment  MDS preserves grouping structure of protein space  MDS can use different proximities and biological data  Parallel MDS handles large-scale data  MDS interpolation quickly maps new sequences into existing space

ECMLS 2012

→

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DELSA: Data → Knowledge → Action

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Data-Enabled Life Sciences Alliance International

 Collective innovation to tackle modern biological

challenges through best computational practices and advanced cyberinfrastructure.

 Harness expertise and resources across disciplines  Promote accurate, sustainable, scalable approaches  Facilitate translation of data influx into tangible

innovations and groundbreaking discoveries

ECMLS 2012

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References and Resources

Visualizing PSU

31  COG data is available at the NCBI site

ftp://ftp.ncbi.nih.gov/pub/wolf/COGs/COG0303/

 MDS results are available at

http://manxcatcogblog.blogspot.com/

 All software used to analyze and visualize the

data is an open source.

 DELSA: http://www.delsaglobal.org

 Protein Global Atlas and Data Accessibility Projects

ECMLS 2012

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Acknowledgements

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Grant support

 NSF: under DBI: 0969929 (EK) and 0910818 (GF)  NIH: 5 RC2 HG 005806- 02 (GF); NIGMS grant

R01 GM-076680-04 (EK); NIDDK grants U01-DK- 089571 and U01-DK-072473 (EK)

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Thank you for your attention

ECMLS 2012