Effective Leveraging of Targeted Search Spaces for Improving Peptide Identification in Tandem Mass Spectrometry Based Proteomics

In shotgun proteomics, peptides are typically identified using database searching, which involves scoring acquired tandem mass spectra against peptides derived from standard protein sequence databases such as Uniprot, Refseq, or Ensembl. In this strategy, the sensitivity of peptide identification is known to be affected by the size of the search space. Therefore, creating a targeted sequence database containing only peptides likely to be present in the analyzed sample can be a useful technique for improving the sensitivity of peptide identification. In this study, we describe how targeted peptide databases can be created based on the frequency of identification in the global proteome machine database (GPMDB), the largest publicly available repository of peptide and protein identification data. We demonstrate that targeted peptide databases can be easily integrated into existing proteome analysis workflows and describe a computational strategy for minimizing any loss of peptide identifications arising from potential search space incompleteness in the targeted search spaces. We demonstrate the performance of our workflow using several data sets of varying size and sample complexity

The Spatial Form of Houses Built by Italian Migrants in Post WWII Brisbane, Australia

The literature reveals that despite the study of the relationship between human behavior, activities and built form has focused on physical spatial environments at any scale, ranging from built environment to built form, the investigation of micro-scale housing has been neglected in the past. Namely, regardless of the interest to this relationship, direct assessment of the extent to which migrants’ human behavior and activities influence and are also influenced by the spatial form of their houses is still rare in the field. This paper focuses on the exploration of the relationship between human behavior, activities and the spatial form of houses built by Italian migrants in post WWII Brisbane. The paper argues that the spatial form of migrants’ houses was influenced by two factors: the need to perform working and social activities dictated by culture as a way of life; urbanization patterns present in migrants’ native and host built environment

Comparative Analysis of Different Label-Free Mass Spectrometry Based Protein Abundance Estimates and Their Correlation with RNA-Seq Gene Expression Data

An increasing number of studies involve integrative analysis of gene and protein expression data taking advantage of new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS) instrumentation. Thus, it becomes interesting to revisit the correlative analysis of gene and protein expression data using more recently generated data sets. Furthermore, within the proteomics community there is a substantial interest in comparing the performance of different label-free quantitative proteomic strategies. Gene expression data can be used as an indirect benchmark for such protein-level comparisons. In this work we use publicly available mouse data to perform a joint analysis of genomic and proteomic data obtained on the same organism. First, we perform a comparative analysis of different label-free protein quantification methods (intensity based and spectral count based and using various associated data normalization steps) using several software tools on the proteomic side. Similarly, we perform correlative analysis of gene expression data derived using microarray and RNA-Seq methods on the genomic side. We also investigate the correlation between gene and protein expression data, and various factors affecting the accuracy of quantitation at both levels. It is observed that spectral count based protein abundance metrics, which are easy to extract from any published data, are comparable to intensity based measures with respect to correlation with gene expression data. The results of this work should be useful for designing robust computational pipelines for extraction and joint analysis of gene and protein expression data in the context of integrative studies

Comparative Analysis of Different Label-Free Mass Spectrometry Based Protein Abundance Estimates and Their Correlation with RNA-Seq Gene Expression Data

An increasing number of studies involve integrative analysis of gene and protein expression data taking advantage of new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS) instrumentation. Thus, it becomes interesting to revisit the correlative analysis of gene and protein expression data using more recently generated data sets. Furthermore, within the proteomics community there is a substantial interest in comparing the performance of different label-free quantitative proteomic strategies. Gene expression data can be used as an indirect benchmark for such protein-level comparisons. In this work we use publicly available mouse data to perform a joint analysis of genomic and proteomic data obtained on the same organism. First, we perform a comparative analysis of different label-free protein quantification methods (intensity based and spectral count based and using various associated data normalization steps) using several software tools on the proteomic side. Similarly, we perform correlative analysis of gene expression data derived using microarray and RNA-Seq methods on the genomic side. We also investigate the correlation between gene and protein expression data, and various factors affecting the accuracy of quantitation at both levels. It is observed that spectral count based protein abundance metrics, which are easy to extract from any published data, are comparable to intensity based measures with respect to correlation with gene expression data. The results of this work should be useful for designing robust computational pipelines for extraction and joint analysis of gene and protein expression data in the context of integrative studies

Comparative Analysis of Different Label-Free Mass Spectrometry Based Protein Abundance Estimates and Their Correlation with RNA-Seq Gene Expression Data

An increasing number of studies involve integrative analysis of gene and protein expression data taking advantage of new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS) instrumentation. Thus, it becomes interesting to revisit the correlative analysis of gene and protein expression data using more recently generated data sets. Furthermore, within the proteomics community there is a substantial interest in comparing the performance of different label-free quantitative proteomic strategies. Gene expression data can be used as an indirect benchmark for such protein-level comparisons. In this work we use publicly available mouse data to perform a joint analysis of genomic and proteomic data obtained on the same organism. First, we perform a comparative analysis of different label-free protein quantification methods (intensity based and spectral count based and using various associated data normalization steps) using several software tools on the proteomic side. Similarly, we perform correlative analysis of gene expression data derived using microarray and RNA-Seq methods on the genomic side. We also investigate the correlation between gene and protein expression data, and various factors affecting the accuracy of quantitation at both levels. It is observed that spectral count based protein abundance metrics, which are easy to extract from any published data, are comparable to intensity based measures with respect to correlation with gene expression data. The results of this work should be useful for designing robust computational pipelines for extraction and joint analysis of gene and protein expression data in the context of integrative studies

Comparative Analysis of Different Label-Free Mass Spectrometry Based Protein Abundance Estimates and Their Correlation with RNA-Seq Gene Expression Data

An increasing number of studies involve integrative analysis of gene and protein expression data taking advantage of new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS) instrumentation. Thus, it becomes interesting to revisit the correlative analysis of gene and protein expression data using more recently generated data sets. Furthermore, within the proteomics community there is a substantial interest in comparing the performance of different label-free quantitative proteomic strategies. Gene expression data can be used as an indirect benchmark for such protein-level comparisons. In this work we use publicly available mouse data to perform a joint analysis of genomic and proteomic data obtained on the same organism. First, we perform a comparative analysis of different label-free protein quantification methods (intensity based and spectral count based and using various associated data normalization steps) using several software tools on the proteomic side. Similarly, we perform correlative analysis of gene expression data derived using microarray and RNA-Seq methods on the genomic side. We also investigate the correlation between gene and protein expression data, and various factors affecting the accuracy of quantitation at both levels. It is observed that spectral count based protein abundance metrics, which are easy to extract from any published data, are comparable to intensity based measures with respect to correlation with gene expression data. The results of this work should be useful for designing robust computational pipelines for extraction and joint analysis of gene and protein expression data in the context of integrative studies

Comparative Analysis of Different Label-Free Mass Spectrometry Based Protein Abundance Estimates and Their Correlation with RNA-Seq Gene Expression Data

An increasing number of studies involve integrative analysis of gene and protein expression data taking advantage of new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS) instrumentation. Thus, it becomes interesting to revisit the correlative analysis of gene and protein expression data using more recently generated data sets. Furthermore, within the proteomics community there is a substantial interest in comparing the performance of different label-free quantitative proteomic strategies. Gene expression data can be used as an indirect benchmark for such protein-level comparisons. In this work we use publicly available mouse data to perform a joint analysis of genomic and proteomic data obtained on the same organism. First, we perform a comparative analysis of different label-free protein quantification methods (intensity based and spectral count based and using various associated data normalization steps) using several software tools on the proteomic side. Similarly, we perform correlative analysis of gene expression data derived using microarray and RNA-Seq methods on the genomic side. We also investigate the correlation between gene and protein expression data, and various factors affecting the accuracy of quantitation at both levels. It is observed that spectral count based protein abundance metrics, which are easy to extract from any published data, are comparable to intensity based measures with respect to correlation with gene expression data. The results of this work should be useful for designing robust computational pipelines for extraction and joint analysis of gene and protein expression data in the context of integrative studies

Utility of RNA-seq and GPMDB Protein Observation Frequency for Improving the Sensitivity of Protein Identification by Tandem MS

Tandem mass spectrometry (MS/MS) followed by database search is the method of choice for protein identification in proteomic studies. Database searching methods employ spectral matching algorithms and statistical models to identify and quantify proteins in a sample. In general, these methods do not utilize any information other than spectral data for protein identification. However, considering the wealth of external data available for many biological systems, analysis methods can incorporate such information to improve the sensitivity of protein identification. In this study, we present a method to utilize Global Proteome Machine Database identification frequencies and RNA-seq transcript abundances to adjust the confidence scores of protein identifications. The method described is particularly useful for samples with low-to-moderate proteome coverage (i.e., <2000–3000 proteins), where we observe up to an 8% improvement in the number of proteins identified at a 1% false discovery rate

SAINT-MS1: Protein–Protein Interaction Scoring Using Label-free Intensity Data in Affinity Purification-Mass Spectrometry Experiments

We present a statistical method SAINT-MS1 for scoring protein–protein interactions based on the label-free MS1 intensity data from affinity purification-mass spectrometry (AP-MS) experiments. The method is an extension of Significance Analysis of INTeractome (SAINT), a model-based method previously developed for spectral count data. We reformulated the statistical model for log-transformed intensity data, including adequate treatment of missing observations, that is, interactions identified in some but not all replicate purifications. We demonstrate the performance of SAINT-MS1 using two recently published data sets: a small LTQ-Orbitrap data set with three replicate purifications of single human bait protein and control purifications and a larger drosophila data set targeting insulin receptor/target of rapamycin signaling pathway generated using an LTQ-FT instrument. Using the drosophila data set, we also compare and discuss the performance of SAINT analysis based on spectral count and MS1 intensity data in terms of the recovery of orthologous and literature-curated interactions. Given rapid advances in high mass accuracy instrumentation and intensity-based label-free quantification software, we expect that SAINT-MS1 will become a useful tool allowing improved detection of protein interactions in label-free AP-MS data, especially in the low abundance range

Implementing the MSFragger Search Engine as a Node in Proteome Discoverer

Here, we describe the implementation of the fast proteomics search engine MSFragger as a processing node in the widely used Proteome Discoverer (PD) software platform. PeptideProphet (via the Philosopher tool kit) is also implemented as an additional PD node to allow validation of MSFragger open (mass-tolerant) search results. These two nodes, along with the existing Percolator validation module, allow users to employ different search strategies and conveniently inspect search results through PD. Our results have demonstrated the improved numbers of PSMs, peptides, and proteins identified by MSFragger coupled with Percolator and significantly faster search speed compared to the conventional SEQUEST/Percolator PD workflows. The MSFragger-PD node is available at https://github.com/nesvilab/PD-Nodes/releases/