Browse
Submit Data
Databases
API
Help

Dataset Information

1116 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Data extraction from proteomics raw data:an evaluation of nine tandem MS tools using a large Orbitrap data set

ABSTRACT: Data extraction from proteomics raw data:an evaluation of nine tandem MS tools using a large Orbitrap data set

INSTRUMENT(S): LTQ Orbitrap Velos, instrument model

ORGANISM(S): Homo Sapiens (human) Escherichia Coli Hybrid Schizosaccharomyces Pombe

SUBMITTER: Francesco Mattia Mancuso

PROVIDER: PRD000711 | Pride | 2012-09-01

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	PRIDE_Exp_Complete_Ac_24258.pride.mgf.gz	Mgf
	PRIDE_Exp_Complete_Ac_24258.pride.mztab.gz	Mztab
	PRIDE_Exp_Complete_Ac_24258.xml.gz	Xml
	PRIDE_Exp_Complete_Ac_24259.pride.mgf.gz	Mgf
	PRIDE_Exp_Complete_Ac_24259.pride.mztab.gz	Mztab

Items per page:

1 - 5 of 285

Publications

Data extraction from proteomics raw data: an evaluation of nine tandem MS tools using a large Orbitrap data set.

Mancuso Francesco F Bunkenborg Jakob J Wierer Michael M Molina Henrik H

Journal of proteomics 20120620 17

In shot-gun proteomics raw tandem MS data are processed with extraction tools to produce condensed peak lists that can be uploaded to database search engines. Many extraction tools are available but to our knowledge, a systematic comparison of such tools has not yet been carried out. Using raw data containing more than 400,000 tandem MS spectra acquired using an Orbitrap Velos we compared 9 tandem MS extraction tools, freely available as well as commercial. We compared the tools with respect to ...[more]

PMID: 22728601

Similar Datasets

Whose data set is it anyway? Sharing raw data from randomized trials.

Project description:BackgroundSharing of raw research data is common in many areas of medical research, genomics being perhaps the most well-known example. In the clinical trial community investigators routinely refuse to share raw data from a randomized trial without giving a reason.DiscussionData sharing benefits numerous research-related activities: reproducing analyses; testing secondary hypotheses; developing and evaluating novel statistical methods; teaching; aiding design of future trials; meta-analysis; and, possibly, preventing error, fraud and selective reporting. Clinical trialists, however, sometimes appear overly concerned with being scooped and with misrepresentation of their work. Both possibilities can be avoided with simple measures such as inclusion of the original trialists as co-authors on any publication resulting from data sharing. Moreover, if we treat any data set as belonging to the patients who comprise it, rather than the investigators, such concerns fall away.ConclusionTechnological developments, particularly the Internet, have made data sharing generally a trivial logistical problem. Data sharing should come to be seen as an inherent part of conducting a randomized trial, similar to the way in which we consider ethical review and publication of study results. Journals and funding bodies should insist that trialists make raw data available, for example, by publishing data on the Web. If the clinical trial community continues to fail with respect to data sharing, we will only strengthen the public perception that we do clinical trials to benefit ourselves, not our patients.

| S-EPMC1489946 | biostudies-literature

Efficient data extraction from neutron time-of-flight spin-echo raw data.

Project description:Neutron spin-echo spectrometers with a position-sensitive detector and operating with extended time-of-flight-tagged wavelength frames are able to collect a comprehensive set of data covering a large range of wavevector and Fourier time space with only a few instrumental settings in a quasi-continuous way. Extracting all the information contained in the raw data and mapping them to a suitable physical space in the most efficient way is a challenge. This article reports algorithms employed in dedicated software, DrSpine (data reduction for spin echo), that achieves this goal and yields reliable representations of the intermediate scattering function S(Q,?t) independent of the selected 'binning'.

| S-EPMC6782076 | biostudies-literature

Normalization and extraction of interpretable metrics from raw accelerometry data.

Project description:We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.

| S-EPMC4072911 | biostudies-literature

raw data

Project description:raw data Raw sequence reads

| PRJNA943615 | ENA

raw data

Project description:raw data Raw sequence reads

| PRJNA539994 | ENA

raw data

Project description:raw data Raw sequence reads

| PRJNA943614 | ENA

Colib'read on galaxy: a tools suite dedicated to biological information extraction from raw NGS reads.

Project description:BackgroundWith next-generation sequencing (NGS) technologies, the life sciences face a deluge of raw data. Classical analysis processes for such data often begin with an assembly step, needing large amounts of computing resources, and potentially removing or modifying parts of the biological information contained in the data. Our approach proposes to focus directly on biological questions, by considering raw unassembled NGS data, through a suite of six command-line tools.FindingsDedicated to 'whole-genome assembly-free' treatments, the Colib'read tools suite uses optimized algorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of a de Bruijn graph and bloom filter, such analyses can be performed in a few hours, using small amounts of memory. Applications using real data demonstrate the good accuracy of these tools compared to classical approaches. To facilitate data analysis and tools dissemination, we developed Galaxy tools and tool shed repositories.ConclusionsWith the Colib'read Galaxy tools suite, we enable a broad range of life scientists to analyze raw NGS data. More importantly, our approach allows the maximum biological information to be retained in the data, and uses a very low memory footprint.

| S-EPMC4750246 | biostudies-literature

RawTools Parsing of Orbitrap Raw Data Files - HeLa subset analysis

Project description:RawTools is a software that provides parsing and quantification of raw Thermo Orbitrap mass spectrometer data. RawTools software was used to process a subset of injections (n = 10) from a prepared HeLa digest that were analyzed on an Orbitrap Velos to get instrument performance metrics.

2018-09-18 | PXD011070 | Pride

RawTools Parsing of Orbitrap Raw Data Files -Quality control analysis

Project description:RawTools is a software that provides parsing and quantification of raw Thermo Orbitrap mass spectrometer data. RawTools software was used to process a set of injections (n = 140) from a prepared HeLa digest that were analyzed on an Orbitrap Velos to get summarized instrument performance metrics for quality control.

2018-09-18 | PXD011069 | Pride

CS4_UL20_Stress_HepG2_WT_TempoSeq_24hTP raw data

Project description:CS4_UL20_Stress_HepG2_WT_TempoSeq_24hTP raw data

| PRJEB32393 | ENA