66 research outputs found
The PeptideAtlas of the Domestic Laying Hen
Proteomics-based biological research
is greatly expanded by high-quality
mass spectrometry studies, which are themselves enabled by access
to quality mass spectrometry resources, such as high-quality curated
proteome data repositories. We present a PeptideAtlas for the domestic
chicken, containing an extensive and robust collection of chicken
tissue and plasma samples with substantial value for the chicken proteomics
community for protein validation and design of downstream targeted
proteome quantitation. The chicken PeptideAtlas contains 6646 canonical
proteins at a protein FDR of 1.3%, derived from ā¼100āÆ000
peptides at a peptide level FDR of 0.1%. The rich collection of readily
accessible data is easily mined for the purposes of data validation
and experimental planning, particularly in the realm of developing
proteome quantitation workflows. Herein we demonstrate the use of
the atlas to mine information on common chicken acute-phase proteins
and biomarkers for cancer detection research, as well as their localization
and polymorphisms. This wealth of information will support future
proteome-based research using this highly important agricultural organism
in pursuit of both chicken and human health outcomes
jTraML: An Open Source Java API for TraML, the PSI Standard for Sharing SRM Transitions
We here present jTraML, a Java API for the Proteomics Standards Initiative TraML data standard. The library provides fully functional classes for all elements specified in the TraML XSD document, as well as convenient methods to construct controlled vocabulary-based instances required to define SRM transitions. The use of jTraML is demonstrated via a two-way conversion tool between TraML documents and vendor specific files, facilitating the adoption process of this new community standard. The library is released as open source under the permissive Apache2 license and can be downloaded from http://jtraml.googlecode.com. TraML files can also be converted online at http://iomics.ugent.be/jtraml
Decreased Gap Width in a Cylindrical High-Field Asymmetric Waveform Ion Mobility Spectrometry Device Improves Protein Discovery
High-field asymmetric waveform ion
mobility spectrometry (FAIMS)
is an atmospheric pressure ion mobility technique that separates gas
phase ions according to their characteristic dependence of ion mobility
on electric field strength. FAIMS can be implemented as a means of
automated gas-phase fractionation in liquid chromatography-tandem
mass spectrometry (LC-MS/MS) experiments. We modified a commercially
available cylindrical FAIMS device by enlarging the inner electrode,
thereby narrowing the gap and increasing the effective field strength.
This modification provided a nearly 4-fold increase in FAIMS peak
capacity over the optimally configured unmodified device. We employed
the modified FAIMS device for on-line fractionation in a proteomic
analysis of a complex sample and observed major increases in protein
discovery. NanoLC-FAIMS-MS/MS of an unfractionated yeast tryptic digest
using the modified FAIMS device identified 53% more proteins than
were identified using an unmodified FAIMS device and 98% more proteins
than were identified with unaided nanoLC-MS/MS. We describe here the
development of a nanoLC-FAIMS-MS/MS protocol that provides automated
gas-phase fractionation for proteomic analysis of complex protein
digests. We compare this protocol against prefractionation of peptides
with isoelectric focusing and demonstrate that FAIMS fractionation
yields comparable protein recovery while significantly reducing the
amount of sample required and eliminating the need for additional
sample handling
Mass Fingerprinting of Complex Mixtures: Protein Inference from High-Resolution Peptide Masses and Predicted Retention Times
In typical shotgun experiments, the
mass spectrometer records the
masses of a large set of ionized analytes but fragments only a fraction
of them. In the subsequent analyses, normally only the fragmented
ions are used to compile a set of peptide identifications, while the
unfragmented ones are disregarded. In this work, we show how the unfragmented
ions, here denoted MS1-features, can be used to increase the confidence
of the proteins identified in shotgun experiments. Specifically, we
propose the usage of in silico mass tags, where the observed MS1-features
are matched against de novo predicted masses and retention times for
all peptides derived from a sequence database. We present a statistical
model to assign protein-level probabilities based on the MS1-features
and combine this data with the fragmentation spectra. Our approach
was evaluated for two triplicate data sets from yeast and human, respectively,
leading to up to 7% more protein identifications at a fixed protein-level
false discovery rate of 1%. The additional protein identifications
were validated both in the context of the mass spectrometry data and
by examining their estimated transcript levels generated using RNA-Seq.
The proposed method is reproducible, straightforward to apply, and
can even be used to reanalyze and increase the yield of existing data
sets
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 LCāMS/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 ā¼12500
Swiss-Prot entries, leaving ā¼7500 gene products yet to be confidently
cataloged. We characterize these āPA-unseenā 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
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 LCāMS/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 ā¼12500
Swiss-Prot entries, leaving ā¼7500 gene products yet to be confidently
cataloged. We characterize these āPA-unseenā 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
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 LCāMS/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 ā¼12500
Swiss-Prot entries, leaving ā¼7500 gene products yet to be confidently
cataloged. We characterize these āPA-unseenā 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
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 LCāMS/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 ā¼12500
Swiss-Prot entries, leaving ā¼7500 gene products yet to be confidently
cataloged. We characterize these āPA-unseenā 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
Whole genome sequence and comparative analysis of <i>Borrelia burgdorferi</i> MM1
<div><p>Lyme disease is caused by spirochaetes of the <i>Borrelia burgdorferi</i> sensu lato genospecies. Complete genome assemblies are available for fewer than ten strains of <i>Borrelia burgdorferi</i> sensu stricto, the primary cause of Lyme disease in North America. MM1 is a sensu stricto strain originally isolated in the midwestern United States. Aside from a small number of genes, the complete genome sequence of this strain has not been reported. Here we present the complete genome sequence of MM1 in relation to other sensu stricto strains and in terms of its Multi Locus Sequence Typing. Our results indicate that MM1 is a new sequence type which contains a conserved main chromosome and 15 plasmids. Our results include the first contiguous 28.5 kb assembly of lp28-8, a linear plasmid carrying the <i>vls</i> antigenic variation system, from a <i>Borrelia burgdorferi</i> sensu stricto strain.</p></div
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 LCāMS/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 ā¼12500
Swiss-Prot entries, leaving ā¼7500 gene products yet to be confidently
cataloged. We characterize these āPA-unseenā 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
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