1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass...
-
Upload
aldous-bryan -
Category
Documents
-
view
214 -
download
0
Transcript of 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass...
![Page 1: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/1.jpg)
1
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
![Page 2: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/2.jpg)
2
Outline
∙ Introduction & Motivation - The Differential Proteomics Pipeline
∙ Computational Proteomics- Signal Processing and Feature Detection- The Isotope Wavelet Transform
∙ Parallelization via GPUs
∙ Results & Discussion
![Page 3: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/3.jpg)
3
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.
![Page 4: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/4.jpg)
4
Principle of Biological Mass Spectrometry
digest
intensity
mass
Fingerprint
Proteins Peptides
Peptides are
ionized and
accelerated
![Page 5: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/5.jpg)
5
Principle of Biological Mass Spectrometry
digest
intensity
mass
Fingerprint
mass of a single neutron
![Page 6: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/6.jpg)
6
Principle of Biological Mass Spectrometry
digest
intensity
mass
Fingerprint
mass of a single neutron
![Page 7: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/7.jpg)
7
(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
![Page 8: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/8.jpg)
8
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)
![Page 9: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/9.jpg)
9
Results – Myoglobin PMF
![Page 10: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/10.jpg)
10
Parallelization via CUDA
![Page 11: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/11.jpg)
11
Parallelization via CUDA
![Page 12: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/12.jpg)
12
Parallelization via CUDA
b-th data point
![Page 13: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/13.jpg)
13
Parallelization via CUDA
b-th data point
![Page 14: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/14.jpg)
14
Parallelization via CUDA
b-th data point
![Page 15: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/15.jpg)
15
Parallelization via CUDA
b-th data point
![Page 16: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/16.jpg)
16
Parallelization via CUDA
T0
b-th data point
Tn
![Page 17: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/17.jpg)
17
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
![Page 18: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/18.jpg)
18
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
![Page 19: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/19.jpg)
19
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
![Page 20: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/20.jpg)
20
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.
![Page 21: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/21.jpg)
21
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
![Page 22: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/22.jpg)
22
The Isotope Wavelet Transform
MS spectrum (charge state 3)
charge-1-transform
charge-2-transform
charge-3-transform
![Page 23: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/23.jpg)
23
The Sweep Line Idea
m/z [Th]
RT [s]
2 additional parameters:RT_cutoffRT_interleave
2 additional parameters:RT_cutoffRT_interleave
![Page 24: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/24.jpg)
24
digest
intensity
mass/charge
Fingerprintcharge state 1
Open Issues – Future Work
Fragment Fingerprint
![Page 25: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/25.jpg)
25
Open Issues – Future Work
∙ Separating overlapping patterns
![Page 26: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/26.jpg)
26
The Retention Time
![Page 27: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/27.jpg)
27
Results – 2D noisy data
![Page 28: 1 InCoB 2009, Singapore Ren é Hussong et al. Highly accelerated feature detection in mass spectrometry data using modern graphics processing units Bioinformatics.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d195503460f949eeab5/html5/thumbnails/28.jpg)
28
The Adaptive Isotope Wavelet Kernel
- denotes the Heaviside step function- λ(m) is a linear function fit to the averagine model