%0 Journal Article %1 Akella2009 %A Akella, Lakshmi Manohar %A Rejtar, Tomas %A Orazine, Christina %A Hincapie, Marina %A Hancock, William S. %C 1155 16TH ST, NW, WASHINGTON, DC 20036 USA %D 2009 %I AMER CHEMICAL SOC %J JOURNAL OF PROTEOME RESEARCH %K {LC-MS;proteomics;biomarkerdiscovery;hierarchicalclustering;heatmap;visualization;Multi-DimensionalScaling(MDS);PrincipalComponentAnalysis(PCA);Tanimotodistance} %N 10 %P 4732-4742 %R 10.1021/pr900427q %T CLUE-TIPS, Clustering Methods for Pattern Analysis of LC-MS Data %V 8 %X Liquid Chromatography Mass Spectrometry (LC-MS) based proteomics is an important tool in detecting changes in peptide/protein abundances in samples potentially leading to the discovery of disease biomarker candidates. We present CLUE-TIPS (Clustering Using Euclidean distance in Tanimoto Inter-Point Space), an approach that compares complex proteomic samples for similarity/dissimilarity analysis. In CLUE-TIPS, an intersample distance feature map is generated from filtered, aligned and binarized raw LC-MS data by applying the Tanimoto distance metric to obtain normalized similarity scores between all sample pairs for each m/z value. We developed clustering and visualization methods for the intersample distance map to analyze various samples for differences at the sample level as well as the individual m/z level. An approach to query for specific m/z values that are associated with similarity/dissimilarity patterns in a set of samples was also briefly described. CLUE-TIPS can also be used as a tool in assessing the quality of LC-MS runs. The presented approach does not rely on tandem mass-spectrometry (MS/MS), isotopic labels or gels and also does not rely on feature extraction methods. CLUE-TIPS suite was applied to LC-MS data obtained from plasma samples collected at various time points and treatment conditions from immunosuppressed mice implanted with MCF-7 human breast cancer cells. The generated raw LC-MS data was used for pattern analysis and similarity/dissimilarity detection. CLUE-TIPS successfully detected the differences/similarities in samples at various time points taken during the progression of tumor, and also recognized differences/similarities in samples representing various treatment conditions.

@article{Akella2009, abstract = {{Liquid Chromatography Mass Spectrometry (LC-MS) based proteomics is an important tool in detecting changes in peptide/protein abundances in samples potentially leading to the discovery of disease biomarker candidates. We present CLUE-TIPS (Clustering Using Euclidean distance in Tanimoto Inter-Point Space), an approach that compares complex proteomic samples for similarity/dissimilarity analysis. In CLUE-TIPS, an intersample distance feature map is generated from filtered, aligned and binarized raw LC-MS data by applying the Tanimoto distance metric to obtain normalized similarity scores between all sample pairs for each m/z value. We developed clustering and visualization methods for the intersample distance map to analyze various samples for differences at the sample level as well as the individual m/z level. An approach to query for specific m/z values that are associated with similarity/dissimilarity patterns in a set of samples was also briefly described. CLUE-TIPS can also be used as a tool in assessing the quality of LC-MS runs. The presented approach does not rely on tandem mass-spectrometry (MS/MS), isotopic labels or gels and also does not rely on feature extraction methods. CLUE-TIPS suite was applied to LC-MS data obtained from plasma samples collected at various time points and treatment conditions from immunosuppressed mice implanted with MCF-7 human breast cancer cells. The generated raw LC-MS data was used for pattern analysis and similarity/dissimilarity detection. CLUE-TIPS successfully detected the differences/similarities in samples at various time points taken during the progression of tumor, and also recognized differences/similarities in samples representing various treatment conditions.}}, added-at = {2011-01-17T12:51:32.000+0100}, address = {{1155 16TH ST, NW, WASHINGTON, DC 20036 USA}}, affiliation = {{Akella, LM (Reprint Author), Marine Biol Lab, Woods Hole, MA 02543 USA. {[}Akella, Lakshmi Manohar; Rejtar, Tomas; Orazine, Christina; Hincapie, Marina; Hancock, William S.] Northeastern Univ, Barnett Inst Chem \& Biol Anal, Boston, MA 02115 USA.}}, author = {Akella, Lakshmi Manohar and Rejtar, Tomas and Orazine, Christina and Hincapie, Marina and Hancock, William S.}, author-email = {{lakella@mbl.edu t.rejtar@neu.edu}}, biburl = {https://www.bibsonomy.org/bibtex/236cb60ef5a9ff08752caee46e74c4067/hkayabilisim}, doc-delivery-number = {{501BM}}, doi = {{10.1021/pr900427q}}, file = {:home/hkaya/Projeler/diagnus/Screener/doc/literature/Akella2009.pdf:PDF}, interhash = {121acba427dcec1d0cf036bc4e1b67e2}, intrahash = {36cb60ef5a9ff08752caee46e74c4067}, issn = {{1535-3893}}, journal = {{JOURNAL OF PROTEOME RESEARCH}}, journal-iso = {{J. Proteome Res.}}, keywords = {{LC-MS;proteomics;biomarkerdiscovery;hierarchicalclustering;heatmap;visualization;Multi-DimensionalScaling(MDS);PrincipalComponentAnalysis(PCA);Tanimotodistance}}, keywords-plus = {{QUANTITATIVE PROTEOMIC ANALYSIS; MASS-SPECTROMETRY; PROTEIN MIXTURES; BIOMARKER DISCOVERY; COMPLEX PEPTIDE; DATA SETS; RECOGNITION; ALGORITHM; ALIGNMENT; SEQUENCE}}, language = {{English}}, month = {{OCT}}, number = {{10}}, number-of-cited-references = {{31}}, pages = {{4732-4742}}, publisher = {{AMER CHEMICAL SOC}}, subject-category = {{Biochemical Research Methods}}, times-cited = {{1}}, timestamp = {2011-01-17T12:51:33.000+0100}, title = {{CLUE-TIPS, Clustering Methods for Pattern Analysis of LC-MS Data}}, type = {{Article}}, unique-id = {{ISI:000270353900031}}, volume = {{8}}, year = {{2009}} }