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InCoB 2009, Singapore

René Hussong et al.

Highly accelerated feature detection in mass spectrometry data

using modern graphics processing unitsBioinformatics 25 (2009).

Junior Research Group for Protein-Protein-Interactions and Computational Proteomics

Saarland University, Saarbruecken, Germany

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Outline

∙ Introduction & Motivation - The Differential Proteomics Pipeline

∙ Computational Proteomics- Signal Processing and Feature Detection- The Isotope Wavelet Transform

∙ Parallelization via GPUs

∙ Results & Discussion

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The Differential Proteomics Pipeline

Two probes:e.g. sick vs. healthy Mass Spectrometer

List of differentiallyexpressed proteins

Applications range from basic pharmaceutical researchover medical diagnostics and therapy

to biotechnology and engineering.

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Principle of Biological Mass Spectrometry

digest

intensity

mass

Fingerprint

Proteins Peptides

Peptides are

ionized and

accelerated

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Principle of Biological Mass Spectrometry

digest

intensity

mass

Fingerprint

mass of a single neutron

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Principle of Biological Mass Spectrometry

digest

intensity

mass

Fingerprint

mass of a single neutron

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(Simple) Feature Finding

Typically done by simple thresholding:

Needs additional preprocessing steps, like e.g.: - Baseline elimination (e.g. by morphological filters) - Noise reduction and/or smoothing (Mostly) needs resampling

Needs additional postprocessing steps, like e.g.:- Peak clustering (so-called “deconvolution”)- Model fitting, charge prediction

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The Isotope Wavelet Transform

Convolution with a kernel function

- by construction robust against noise and baseline artifacts- also acts as a filter for chemical noise - predicts simultaneously the charge state- needs no explicit resampling - only a single parameter (threshold)

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Results – Myoglobin PMF

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Parallelization via CUDA

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Parallelization via CUDA

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Parallelization via CUDA

b-th data point

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Parallelization via CUDA

b-th data point

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Parallelization via CUDA

b-th data point

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Parallelization via CUDA

b-th data point

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Parallelization via CUDA

T0

b-th data point

Tn

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Parallelization via CUDA and TBB

2x NVIDIA Tesla C870 via Intel Threading Building Blocks

1x NVIDIA Tesla C870

1x CPU 2.3 GHz

>200x speedup>200x speedup

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Open Issues – Future Work

∙ Solutions for machine-specific ‘artifacts’, e.g.- Tailing effects in TOF-Analyzers- Severe mass discretization in high resolution data

∙ Separating overlapping patterns

∙ Tests for MSn spectra- Refined averagine model

GPU solutions

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Availability: OpenMS

∙ An open source C++ library for mass spectrometry

∙ Designed for “users” as well as for “developers”

∙ TOPP- “The OpenMS proteomics pipeline”- suite of independent software tools- include file handling / conversion- peak picking and feature detection - includes visualizer TOPPView…

http://www.openms.de

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References

Hussong, R, Gregorius, B, Tholey, A, and Hildebrandt, A (2009). Highly accelerated feature detection in proteomics data sets using modern graphics processing units. Bioinformatics 25.

Schulz-Trieglaff, O, Hussong, R, Gröpl, C, Leinenbach, A, Hildebrandt, A, Huber, C, and Reinert, K (2008). Computational Quantification of Peptides from LC-MS Data. Journal of Computational Biology 15(7).

Sturm, M, Bertsch, A, Gröpl, C, Hildebrandt, A, Hussong, R, Lange, E, Pfeifer, N, Schulz-Trieglaff, O, Zerck, A, Reinert, K, and Kohlbacher, O (2008).OpenMS - An open-source software framework for mass spectrometry, BMC Bioinformatics 9(163).

Hussong, R, Tholey, A, and Hildebrandt, A (2007).Efficient Analysis of Mass Spectrometry Data Using the Isotope WaveletIn: COMPLIFE 2007: The Third International Symposium on Computational Life Science. American Institute of Physics (AIP) 940.

Schulz-Trieglaff, O, Hussong, R, Gröpl, C, Hildebrandt, A, and Reinert, K (2007). A Fast and Accurate Algorithm for the Quantification of Peptides from Mass Spectrometry Data, In: Proceedings of the Eleventh Annual International Conference on Research in Computational Molecular Biology (RECOMB). Lecture Notes in Bioinformatics (LNBI) 4453.

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The Isotope Wavelet Transform

Kernel functioncharge state 1, mass 1000D

Kernel functioncharge state 1, mass 2000D

- by construction robust against noise and baseline artifacts- also acts as a filter for chemical noise - predicts simultaneously the charge state- needs no explicit resampling - only a single parameter (threshold)

Convolution with a kernel function

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The Isotope Wavelet Transform

MS spectrum (charge state 3)

charge-1-transform

charge-2-transform

charge-3-transform

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The Sweep Line Idea

m/z [Th]

RT [s]

2 additional parameters:RT_cutoffRT_interleave

2 additional parameters:RT_cutoffRT_interleave

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digest

intensity

mass/charge

Fingerprintcharge state 1

Open Issues – Future Work

Fragment Fingerprint

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Open Issues – Future Work

∙ Separating overlapping patterns

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The Retention Time

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Results – 2D noisy data

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The Adaptive Isotope Wavelet Kernel

- denotes the Heaviside step function- λ(m) is a linear function fit to the averagine model