BACKGROUND: Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements. RESULTS: In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges. CONCLUSIONS: Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM .

Recent improvements in mass spectrometry (MS) technology led to an explosive amount of MS data collected and shared. A typical liquid chromatography/mass spectrometry (LC/MS) "image" from the instrument used in this study consists of 4 GB of data. To reduce the bit rate required to code the MS data below that of the authors' previous (lossless) algorithm, we introduce a technique for near-lossless compression. It guarantees that each decompressed sample differs from its original value by no more than a user-specified quantity defined as the target maximum absolute distortion (MAD). We evaluate the proposed method by introducing feature-based metrics applied to the decompressed MS data and show that the MAD-based compression outperforms a traditional coding algorithm aimed at minimizing the mean squared error.

The unrelenting growth ofmass spectrometry (MS) based proteomic data to gigabytes per sample and terabytes per experiment motivates this investigation into compression methods suited to MS signal sources. The data for this study was derived from peptides of hand-mixed protein samples passed through a high performance liquid chromatography system (HPLC) and an electrospray ionization time-of-flight (ESI-TOF) mass spectrometer. Several lossless data compression methods were applied and yielded up to a 25:1 compression ratio relative to the original files containing base64 encoding of the data.

Summary form only given. The unrelenting growth of liquid chromatography-mass spectrometry (LC-MS) based proteomic data to gigabytes per sample and terabytes per experiment motivates this investigation into compression methods suited to MS signal sources. Compression is needed to facilitate storage, searching, archiving, retrieval, and communication of proteomic MS data. We demonstrate compression techniques that reduce the average file size by a factor of 25 without any loss of accuracy. We have designed two main methods to code the MS data. The first method predicts the mass-to-charge ratio based on the intensity values and encodes the residual with bzip2. The second algorithm maps the original intensity values onto a universal grid and either directly encodes them with bzip2 or applies an arithmetic coder to the results of run-length coding. The latter method achieves the highest compression ratios.

MOTIVATION: Comparing two or more complex protein mixtures using liquid chromatography mass spectrometry (LC-MS) requires multiple analysis steps to locate and quantitate natural peptides within a single experiment and to align and normalize findings across multiple experiments. RESULTS: We describe msInspect, an open-source application comprising algorithms and visualization tools for the analysis of multiple LC-MS experimental measurements. The platform integrates novel algorithms for detecting signatures of natural peptides within a single LC-MS measurement and combines multiple experimental measurements into a peptide array, which may then be mined using analysis tools traditionally applied to genomic array analysis. The platform supports quantitation by both label-free and isotopic labeling approaches. The software implementation has been designed so that many key components may be easily replaced, making it useful as a workbench for integrating other novel algorithms developed by a growing research community. AVAILABILITY: The msInspect software is distributed freely under an Apache 2.0 license. The software as well as a Zip file with all peptide feature files and scripts needed to generate the tables and figures in this article are available at http://proteomics.fhcrc.org/.