Name: Quality control metrics for LC-MS feature detection tools demonstrated in yeast
Keywords: Not available

Dataset Information

Quality control metrics for LC-MS feature detection tools demonstrated in yeast

ABSTRACT: Not available

INSTRUMENT(S): Fourier Transform Ion Cyclotron Resonance Mass Spectrometer, instrument model

ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Attila Csordas

PROVIDER: PRD000464 | Pride | 2012-07-26

REPOSITORIES: Pride

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Publications

Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.

Piening Brian D BD Wang Pei P Bangur Chaitanya S CS Whiteaker Jeffrey J Zhang Heidi H Feng Li-Chia LC Keane John F JF Eng Jimmy K JK Tang Hua H Prakash Amol A McIntosh Martin W MW Paulovich Amanda A

Journal of proteome research 20060701 7

Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Beca ...[more]

PMID: 16823959

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Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data. We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.. Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with TPM > 4). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples. We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.

Dataset Information

Quality control metrics for LC-MS feature detection tools demonstrated in yeast

Publications

Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.

Similar Datasets

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