Author Correction: Parent-of-origin-specific signatures of de novo mutations.

In the version of this article published, the P values for the enrichment of single mutation categories were inadvertently not corrected for multiple testing. After multiple-testing correction, only two of the six mutation categories mentioned are still statistically significant. To reflect this, the text "More specifically, paternally derived DNMs are enriched in transitions in A[.]G contexts, especially ACG>ATG and ATG>ACG (Bonferroni-corrected P = 1.3 × 10-2 and P = 1 × 10-3, respectively). Additionally, we observed overrepresentation of ATA>ACA mutations (Bonferroni-corrected P = 4.28 × 10-2) for DNMs of paternal origin. Among maternally derived DNMs, CCA>CTA, GCA>GTA and TCT>TGT mutations were significantly overrepresented (Bonferroni-corrected P = 4 × 10-4, P = 5 × 10-4, P = 1 × 10-3, respectively)" should read "More specifically, CCA>CTA and GCA>GTA mutations were significantly overenriched on the maternal allele (Bonferroni-corrected P = 0.0192 and P = 0.048, respectively)." Additionally, the last sentence to the legend for Fig. 3b should read "Green boxes highlight the mutation categories that differ significantly" instead of "Green boxes highlight the mutation categories that differ more than 1% of mutation load with a bootstrapping P value <0.05." Corrected versions of Fig. 3b and Supplementary Table 25 appear with the Author Correction.

ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions.

Summary: Gap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organism's genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy. Availability and implementation: Our tools are available as a web service with no installation needed (ProbAnnoWeb) at probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy) at github.com/PriceLab/probannopy. Contact: evangelos.simeonidis@systemsbiology.org or nathan.price@systemsbiology.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Tiered Human Integrated Sequence Search Databases for Shotgun Proteomics.

The results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstances-a problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ∼20,000 primary isoforms plus contaminants to a very large database that includes almost all nonredundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/ .

Parent-of-origin-specific signatures of de novo mutations.

De novo mutations (DNMs) originating in gametogenesis are an important source of genetic variation. We use a data set of 7,216 autosomal DNMs with resolved parent of origin from whole-genome sequencing of 816 parent-offspring trios to investigate differences between maternally and paternally derived DNMs and study the underlying mutational mechanisms. Our results show that the number of DNMs in offspring increases not only with paternal age, but also with maternal age, and that some genome regions show enrichment for maternally derived DNMs. We identify parent-of-origin-specific mutation signatures that become more pronounced with increased parental age, pointing to different mutational mechanisms in spermatogenesis and oogenesis. Moreover, we find DNMs that are spatially clustered to have a unique mutational signature with no significant differences between parental alleles, suggesting a different mutational mechanism. Our findings provide insights into the molecular mechanisms that underlie mutagenesis and are relevant to disease and evolution in humans.

Identification of copy number variants in whole-genome data using Reference Coverage Profiles.

The identification of DNA copy numbers from short-read sequencing data remains a challenge for both technical and algorithmic reasons. The raw data for these analyses are measured in tens to hundreds of gigabytes per genome; transmitting, storing, and analyzing such large files is cumbersome, particularly for methods that analyze several samples simultaneously. We developed a very efficient representation of depth of coverage (150-1000× compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing (WGS) data frequently miss copy number variants (CNVs), particularly hemizygous deletions in the 1-100 kb range. To fill this gap, we developed a method to identify CNVs in individual genomes, based on comparison to joint profiles pre-computed from a large set of genomes. We analyzed depth of coverage in over 6000 high quality (>40×) genomes. The depth of coverage has strong sequence-specific fluctuations only partially explained by global parameters like %GC. To account for these fluctuations, we constructed multi-genome profiles representing the observed or inferred diploid depth of coverage at each position along the genome. These Reference Coverage Profiles (RCPs) take into account the diverse technologies and pipeline versions used. Normalization of the scaled coverage to the RCP followed by hidden Markov model (HMM) segmentation enables efficient detection of CNVs and large deletions in individual genomes. Use of pre-computed multi-genome coverage profiles improves our ability to analyze each individual genome. We make available RCPs and tools for performing these analyses on personal genomes. We expect the increased sensitivity and specificity for individual genome analysis to be critical for achieving clinical-grade genome interpretation.

A standardized framing for reporting protein identifications in mzIdentML 1.2.

Inferring which protein species have been detected in bottom-up proteomics experiments has been a challenging problem for which solutions have been maturing over the past decade. While many inference approaches now function well in isolation, comparing and reconciling the results generated across different tools remains difficult. It presently stands as one of the greatest barriers in collaborative efforts such as the Human Proteome Project and public repositories such as the PRoteomics IDEntifications (PRIDE) database. Here we present a framework for reporting protein identifications that seeks to improve capabilities for comparing results generated by different inference tools. This framework standardizes the terminology for describing protein identification results, associated with the HUPO-Proteomics Standards Initiative (PSI) mzIdentML standard, while still allowing for differing methodologies to reach that final state. It is proposed that developers of software for reporting identification results will adopt this terminology in their outputs. While the new terminology does not require any changes to the core mzIdentML model, it represents a significant change in practice, and, as such, the rules will be released via a new version of the mzIdentML specification (version 1.2) so that consumers of files are able to determine whether the new guidelines have been adopted by export software.

Using PeptideAtlas, SRMAtlas, and PASSEL: Comprehensive Resources for Discovery and Targeted Proteomics.

PeptideAtlas, SRMAtlas, and PASSEL are Web-accessible resources to support discovery and targeted proteomics research. PeptideAtlas is a multi-species compendium of shotgun proteomic data provided by the scientific community; SRMAtlas is a resource of high-quality, complete proteome SRM assays generated in a consistent manner for the targeted identification and quantification of proteins; and PASSEL is a repository that compiles and represents selected reaction monitoring data, all in an easy-to-use interface. The databases are generated from native mass spectrometry data files that are analyzed in a standardized manner including statistical validation of the results. Each resource offers search functionalities and can be queried by user-defined constraints; the query results are provided in tables or are graphically displayed. PeptideAtlas, SRMAtlas, and PASSEL are publicly available freely via the Web site http://www.peptideatlas.org. In this protocol, we describe the use of these resources, we highlight how to submit, search, collate and download data.

State of the human proteome in 2013 as viewed through PeptideAtlas: comparing the kidney, urine, and plasma proteomes for the biology- and disease-driven Human Proteome Project.

The kidney, urine, and plasma proteomes are intimately related: proteins and metabolic waste products are filtered from the plasma by the kidney and excreted via the urine, while kidney proteins may be secreted into the circulation or released into the urine. Shotgun proteomics data sets derived from human kidney, urine, and plasma samples were collated and processed using a uniform software pipeline, and relative protein abundances were estimated by spectral counting. The resulting PeptideAtlas builds yielded 4005, 2491, and 3553 nonredundant proteins at 1% FDR for the kidney, urine, and plasma proteomes, respectively - for kidney and plasma, the largest high-confidence protein sets to date. The same pipeline applied to all available human data yielded a 2013 Human PeptideAtlas build containing 12,644 nonredundant proteins and at least one peptide for each of ∼14,000 Swiss-Prot entries, an increase over 2012 of ∼7.5% of the predicted human proteome. We demonstrate that abundances are correlated between plasma and urine, examine the most abundant urine proteins not derived from either plasma or kidney, and consider the biomarker potential of proteins associated with renal decline. This analysis forms part of the Biology and Disease-driven Human Proteome Project (B/D-HPP) and is a contribution to the Chromosome-centric Human Proteome Project (C-HPP) special issue.

The Mtb proteome library: a resource of assays to quantify the complete proteome of Mycobacterium tuberculosis.

Research advancing our understanding of Mycobacterium tuberculosis (Mtb) biology and complex host-Mtb interactions requires consistent and precise quantitative measurements of Mtb proteins. We describe the generation and validation of a compendium of assays to quantify 97% of the 4,012 annotated Mtb proteins by the targeted mass spectrometric method selected reaction monitoring (SRM). Furthermore, we estimate the absolute abundance for 55% of all Mtb proteins, revealing a dynamic range within the Mtb proteome of over four orders of magnitude, and identify previously unannotated proteins. As an example of the assay library utility, we monitored the entire Mtb dormancy survival regulon (DosR), which is linked to anaerobic survival and Mtb persistence, and show its dynamic protein-level regulation during hypoxia. In conclusion, we present a publicly available research resource that supports the sensitive, precise, and reproducible quantification of virtually any Mtb protein by a robust and widely accessible mass spectrometric method.

The state of the human proteome in 2012 as viewed through PeptideAtlas.

The Human Proteome Project was launched in September 2010 with the goal of characterizing at least one protein product from each protein-coding gene. Here we assess how much of the proteome has been detected to date via tandem mass spectrometry by analyzing PeptideAtlas, a compendium of human derived LC-MS/MS proteomics data from many laboratories around the world. All data sets are processed with a consistent set of parameters using the Trans-Proteomic Pipeline and subjected to a 1% protein FDR filter before inclusion in PeptideAtlas. Therefore, PeptideAtlas contains only high confidence protein identifications. To increase proteome coverage, we explored new comprehensive public data sources for data likely to add new proteins to the Human PeptideAtlas. We then folded these data into a Human PeptideAtlas 2012 build and mapped it to Swiss-Prot, a protein sequence database curated to contain one entry per human protein coding gene. We find that this latest PeptideAtlas build includes at least one peptide for each of ~12500 Swiss-Prot entries, leaving ~7500 gene products yet to be confidently cataloged. We characterize these "PA-unseen" proteins in terms of tissue localization, transcript abundance, and Gene Ontology enrichment, and propose reasons for their absence from PeptideAtlas and strategies for detecting them in the future.

Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics.

The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies.

A Bovine PeptideAtlas of milk and mammary gland proteomes.

Proteome information resources of farm animals are lagging behind those of the classical model organisms despite their important biological and economic relevance. Here, we present a Bovine PeptideAtlas, representing a first collection of Bos taurus proteome data sets within the PeptideAtlas framework. This database was built primarily as a source of information for designing selected reaction monitoring assays for studying milk production and mammary gland health, but it has an intrinsic general value for the farm animal research community. The Bovine PeptideAtlas comprises 1921 proteins at 1.2% false discovery rate (FDR) and 8559 distinct peptides at 0.29% FDR identified in 107 samples from six tissues. The PeptideAtlas web interface has a rich set of visualization and data exploration tools, enabling users to interactively mine information about individual proteins and peptides, their prototypic features, genome mappings, and supporting spectral evidence.

PASSEL: the PeptideAtlas SRMexperiment library.

Public repositories for proteomics data have accelerated proteomics research by enabling more efficient cross-analyses of datasets, supporting the creation of protein and peptide compendia of experimental results, supporting the development and testing of new software tools, and facilitating the manuscript review process. The repositories available to date have been designed to accommodate either shotgun experiments or generic proteomic data files. Here, we describe a new kind of proteomic data repository for the collection and representation of data from selected reaction monitoring (SRM) measurements. The PeptideAtlas SRM Experiment Library (PASSEL) allows researchers to easily submit proteomic data sets generated by SRM. The raw data are automatically processed in a uniform manner and the results are stored in a database, where they may be downloaded or browsed via a web interface that includes a chromatogram viewer. PASSELenables cross-analysis of SRMdata, supports optimization of SRMdata collection, and facilitates the review process of SRMdata. Further, PASSELwill help in the assessment of proteotypic peptide performance in a wide array of samples containing the same peptide, as well as across multiple experimental protocols.

A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas.

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.

Using the Human Plasma PeptideAtlas to study human plasma proteins.

PeptideAtlas is a web-accessible database of LC-MS/MS shotgun proteomics results from hundreds of experiments conducted in diverse laboratories, with all data processed via a uniform analysis pipeline. A total of 91 experiments on human plasma and serum are included. Using the PeptideAtlas web interface, users can browse and search the Human Plasma PeptideAtlas for identified peptides and identified proteins, view spectra, and select proteotypic peptides. Users can easily view supporting information such as chromosomal mapping, estimated abundances, and sequence alignments. Herein, the reader is instructed in the use of the Human Plasma PeptideAtlas through an illustrated exploration of cytokine receptors in plasma.

A guided tour of the Trans-Proteomic Pipeline.

The Trans-Proteomic Pipeline (TPP) is a suite of software tools for the analysis of MS/MS data sets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein-level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample data set, demonstrating that the setup and use of the tools are straightforward and well supported and do not require specialized informatic resources or knowledge.

Trans-Proteomic Pipeline supports and improves analysis of electron transfer dissociation data sets.

Electron transfer dissociation (ETD) is an alternative fragmentation technique to CID that has recently become commercially available. ETD has several advantages over CID. It is less prone to fragmenting amino acid side chains, especially those that are modified, thus yielding fragment ion spectra with more uniform peak intensities. Further, precursor ions of longer peptides and higher charge states can be fragmented and identified. However, analysis of ETD spectra has a few important differences that require the optimization of the software packages used for the analysis of CID data or the development of specialized tools. We have adapted the Trans-Proteomic Pipeline to process ETD data. Specifically, we have added support for fragment ion spectra from high-charge precursors, compatibility with charge-state estimation algorithms, provisions for the use of the Lys-C protease, capabilities for ETD spectrum library building, and updates to the data formats to differentiate CID and ETD spectra. We show the results of processing data sets from several different types of ETD instruments and demonstrate that application of the ETD-enhanced Trans-Proteomic Pipeline can increase the number of spectrum identifications at a fixed false discovery rate by as much as 100% over native output from a single sequence search engine.

High-throughput generation of selected reaction-monitoring assays for proteins and proteomes.

Selected reaction monitoring (SRM) uses sensitive and specific mass spectrometric assays to measure target analytes across multiple samples, but it has not been broadly applied in proteomics owing to the tedious assay development process for each protein. We describe a method based on crude synthetic peptide libraries for the high-throughput development of SRM assays. We illustrate the power of the approach by generating and applying validated SRM assays for all Saccharomyces cerevisiae kinases and phosphatases.