The document discusses various mass spectrometry file formats used in proteomics workflows, including the advantages of XML-based formats like mzML and mzIdentML that support metadata and can be read by different software. It also describes challenges with proprietary binary formats and efforts to develop common data standards and APIs through projects like ProteoWizard, PRIDE, and the ms-core-api library. Standard file formats are important for sharing and reusing proteomics data over time as instrumentation and software evolve.
make is a basic tool to define pipelines of shell commands.
It is useful if you have many shell scripts and commands, and you want to organize them.
Even if it has been written to automatize the build of compiled language programs, make is also useful in bioinformatics and other fields.
Generate a database of hexapeptides using the combination of all 15 amino acids: No included: Metionine (M), Triptophan (W), Cysteine (C), Isoleucine (I), Aspártic (D).
Index the database in Mascot for searching of peptide archives.
make is a basic tool to define pipelines of shell commands.
It is useful if you have many shell scripts and commands, and you want to organize them.
Even if it has been written to automatize the build of compiled language programs, make is also useful in bioinformatics and other fields.
Generate a database of hexapeptides using the combination of all 15 amino acids: No included: Metionine (M), Triptophan (W), Cysteine (C), Isoleucine (I), Aspártic (D).
Index the database in Mascot for searching of peptide archives.
ARCHIVED: new version available. 2016 - Bioinformatics of METASPACEMETASPACE
These slides present the bioinformatics for metabolite annotation of HR imaging MS. The bioinformatics was developed in the framework of the METASPACE project.
METASPACE is a European Horizon2020 project on Bioinformatics for Spatial Metabolomics. Specifically, it aims at developing an engine for metabolite annotation of HR imaging mass spectrometry data. The project was funded in the Personalizing Health and Care program for 3 years (2015-2018) and is coordinated by the European Molecular Biology Laboratory.
The presentation was given at the METASPACE Training Course at OurCon'16 on 17.10.2016.
For more information on METASPACE, please visit the project website http://metaspace2020.eu, twitter @metaspace2020, or email us at contact@metaspace2020.eu.
ARCHIVED: new version available. 2016 - METASPACE Training CourseMETASPACE
These are the slides from the METASPACE Training Course given at OurCon'16.
METASPACE is a European Horizon2020 project on Bioinformatics for Spatial Metabolomics. Specifically, it aims at developing an engine for metabolite annotation of HR imaging mass spectrometry data. The project was funded in the Personalizing Health and Care program for 3 years (2015-2018) and is coordinated by the European Molecular Biology Laboratory.
The slides are organized into two parts.
Part 1: Introduction of the project, the bioinformatics behind it, and the online engine.
Part 2: Step-by-step tutorial on how to use the online engine for annotating metabolites from HR imaging mass spectrometry data.
For more information on METASPACE, please visit the project website http://metaspace2020.eu, twitter @metaspace2020, or email us at contact@metaspace2020.eu.
Market Research Reports, Inc. has announced the addition of “Competitor Analysis: Glucagon-Like Peptide-1 (GLP-1) Receptor Agonists” research report to their offering. See more at- http://mrr.cm/Z7R
Insilico Analysis towards Infuenza Virus- A Homology modelling and molecular ...Subhasree Pal
An Insilico attempt was made to characterize a newly sequenced Interferon induced GTP-binding protein MX1protein (organism: Mus musculus (mouse)) of influenza- A virus (H1N1) to deduce its structural information and to identify the potential drug to inhibit the protein. But, due to unavailability of further sequences a ‘template’ 3LJB (Interferon induced GTP-binding protein MX1protein (organism: Homo sapiens) is selected. For that an effort was taken to deduce the 3-D structure of this template protein and to identify and bind the active site of Interferon induced GTP-binding protein MX1protein with docking technique.
ARCHIVED: new version available. 2016 - Bioinformatics of METASPACEMETASPACE
These slides present the bioinformatics for metabolite annotation of HR imaging MS. The bioinformatics was developed in the framework of the METASPACE project.
METASPACE is a European Horizon2020 project on Bioinformatics for Spatial Metabolomics. Specifically, it aims at developing an engine for metabolite annotation of HR imaging mass spectrometry data. The project was funded in the Personalizing Health and Care program for 3 years (2015-2018) and is coordinated by the European Molecular Biology Laboratory.
The presentation was given at the METASPACE Training Course at OurCon'16 on 17.10.2016.
For more information on METASPACE, please visit the project website http://metaspace2020.eu, twitter @metaspace2020, or email us at contact@metaspace2020.eu.
ARCHIVED: new version available. 2016 - METASPACE Training CourseMETASPACE
These are the slides from the METASPACE Training Course given at OurCon'16.
METASPACE is a European Horizon2020 project on Bioinformatics for Spatial Metabolomics. Specifically, it aims at developing an engine for metabolite annotation of HR imaging mass spectrometry data. The project was funded in the Personalizing Health and Care program for 3 years (2015-2018) and is coordinated by the European Molecular Biology Laboratory.
The slides are organized into two parts.
Part 1: Introduction of the project, the bioinformatics behind it, and the online engine.
Part 2: Step-by-step tutorial on how to use the online engine for annotating metabolites from HR imaging mass spectrometry data.
For more information on METASPACE, please visit the project website http://metaspace2020.eu, twitter @metaspace2020, or email us at contact@metaspace2020.eu.
Market Research Reports, Inc. has announced the addition of “Competitor Analysis: Glucagon-Like Peptide-1 (GLP-1) Receptor Agonists” research report to their offering. See more at- http://mrr.cm/Z7R
Insilico Analysis towards Infuenza Virus- A Homology modelling and molecular ...Subhasree Pal
An Insilico attempt was made to characterize a newly sequenced Interferon induced GTP-binding protein MX1protein (organism: Mus musculus (mouse)) of influenza- A virus (H1N1) to deduce its structural information and to identify the potential drug to inhibit the protein. But, due to unavailability of further sequences a ‘template’ 3LJB (Interferon induced GTP-binding protein MX1protein (organism: Homo sapiens) is selected. For that an effort was taken to deduce the 3-D structure of this template protein and to identify and bind the active site of Interferon induced GTP-binding protein MX1protein with docking technique.
Next-generation sequencing data format and visualization with ngs.plot 2015Li Shen
An introduction to the commonly used formats for the next-generation sequencing data. ngs.plot is a popular tool for the visualization and data mining of the NGS data.
Geared towards bioinformatics students and taking a somewhat humoristic point of view, this presentation explains what bioinformaticians are and what they do.
ProFET - Protein Feature Engineering ToolkiDan Ofer
Summary of the ProFET project.
This is a newly developed toolkit for end to end machine learning and feature extraction from proteins.
The Code can be freely downloaded here:
https://github.com/ddofer/ProFET
Dan Ofer
Talk during the Annual Meeting of the EU PRIME-XS project in Avila. Highlights of ProteomeXchange in the last year in the context of the PRIME-XS project (JRA 1: Bioinformatics).
How Machine Learning and AI Can Support the Fight Against COVID-19Databricks
In this session, we show how to leverage CORD dataset, containing more than 400000 scientific papers on COVID and related topics, and recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease.
The idea explored in our talk is to apply modern NLP methods, such and named entity recognition (NER) and relation extraction to article’s abstracts (and, possibly, full text), to extract some meaningful insights from the text, and to enable semantically rich search over the paper corpus. We first investigate how to train NER model using Medical NER dataset from Kaggle, and specialized version of BERT (PubMedBERT) as a feature extractor, to allow automatic extraction of such entities as medical condition names, medicine names and pathogens. Entity extraction alone can provide us with some interesting findings, such as how approaches to COVID treatment evolved with time, in terms of mentioned medicines. We demonstrate how to use Azure Machine Learning for training the model.
To take this investigation one step further, we also investigate the usage of pre-trained medical models, available as Text Analytics for Health service on the Microsoft Azure cloud. In addition to many entity types, it can also extract relations (such as the dosage of medicine provisioned), entity negation, and entity mapping to some well-known medical ontologies. We investigate the best way to use Azure ML at scale to score large paper collection, and to store the results.
SBML (the Systems Biology Markup Language)Mike Hucka
Morning tutorial given at the COMBINE/ERASysApp day of tutorials on "Modelling and Simulation of Biological Models" on Sunday, September 14, ahead of ICSB 2014 in Melbourne, Australia.
Accelerate Pharmaceutical R&D with Big Data and MongoDBMongoDB
Introduction of disruptive technologies, including use of unstructured data, is critical to Pharmaceutical R&D. We will explore how MongoDB can be used to accelerate this. We will also have an open discussion with panel members who are using MongoDB in this space. This session will be 30 minutes and will be followed by a 20 minute panel discussion led by Jason Tetrault and Deniz Kural.
EUGM 2014 - Alfonso Pozzan (Aptuit): Expanding the scope of “literature data”...ChemAxon
Data associated to chemical structures reported in literature are very important in drug design as they expand the scope of in-house generated knowledge. Public and commercially available databases allow scientist to access an increasing number of such information. However, alternative sources of data are still contained in document format and difficult to extract like in patents. Document to structure tools can be very helpful in this area; the objective of the talk will be to highlight some of these aspects in particular from a drug discovery angle.
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...Bonnie Hurwitz
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to microbes. Overview of work underway to add applications and computational analysis pipelines to iPlant for metagenomics and microbial ecology.
Neuroscience core lecture given at the Icahn school of medicine at Mount Sinai. This is the version 2 of the same topic. I have made some modifications to give a more gentle introduction and add a new example for ngs.plot.
Metagenomic Data Provenance and Management using the ISA infrastructure --- o...Alejandra Gonzalez-Beltran
Metagenomic Data Provenance and Management using the ISA infrastructure - overview, implementation patterns & software tools
Slides presented at EBI Metagenomics Bioinformatics course: http://www.ebi.ac.uk/training/course/metagenomics2014
Environment Canada's Data Management ServiceSafe Software
A brief history in TimeSeries data at Environment Canada. An Enterprise view of how FME can be integrated into departmental data management activities.
DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...Felipe Albrecht
Short description and updates about DeepBlue Epigenomic Data Server that I presented during the last Blueprint (http://www.blueprint-epigenome.eu/) Jamboree in Madrid (June 2016)
BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters).
Systematic integration of millions of peptidoform evidences into Ensembl and ...Yasset Perez-Riverol
There is an increasing demand for approaches to integrate proteomics data with other ‘omics’ data types, especially genomics. In fact, current resources to integrate proteomics in a genome context are insufficient for large-scale studies. To bring the genomics and proteomics results into coherence, novel resources must be developed that provide simple links across previously acquired datasets with minimal preprocessing and hassle.
PRIDE (http://www.ebi.ac.uk/pride) and Ensembl (http://www.ensembl.org) are world-leading resources for proteomics and genomics data. We have developed a new resource and framework to enable a systematic integration of mass spectrometry (MS) based proteomics evidences into genome browsers. We automatically integrate every high-quality MS-based peptidoform reported in the PRIDE database, into genome coordinates through Ensembl, and other genome browsers.
IPG (Immobilized pH Gradient) based separations are frequently
used as the first step in shotgun proteomics methods; it yields an
increase in both the dynamic range and resolution of peptide
separation prior to the LC-MS analysis. Experimental isoelectric
point (pI) values can improve peptide identifications in conjunction
with MS/MS information. Our group has previously reported the
possibility of identifying theoretically peptides and proteins based
on different experimental properties. Thus, accurate estimation
of the pI value based on the amino acid sequence becomes critical
to perform these kinds of experiments. Nowadays, pI is commonly
predicted using the charge-state model [3], and/or the co-factor
algorithm. However, none of these methods is capable of
calculating the pI value for basic peptides accurately. In this
manuscript, we present an new approach that can significant
improve the pI estimation, by using Support Vector Machines
(SVM), an experimental amino acid descriptor taken from the
AAIndex database and the isoelectric point predicted by the
charge-state model.
SintCompound: A Small Compound Database for Virtual ScreeningYasset Perez-Riverol
A critical barrier to entry into structure-based virtual screening is the lack of a suitable, easy to access database of purchasable compounds. We have therefore prepared a library of (8’682’858) unique molecules, each one with 3D structure, using catalogs of compounds from vendors. We used a protocol based on free and academics chemoinformatics tools.
Our protocol included four steps (redundancy elimination, 3D structure generation, calculus of the atomic partial charge and molecular conformational generation). Each molecule in the library contains vendor and purchasing information and is ready for docking using a number of popular docking programs.
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Basavarajeeyam - Ayurvedic heritage book of Andhra pradesh
Standarization in Proteomics: From raw data to metadata files
1. From Raw Data to MetaData
Files
Yasset
Perez
Riverol
Proteomics
&
Bioiforma4cs
CIGB
2. Common Proteomic Workflow
Mixture/
Sample
Separa4on
Techniques
(1D,
2D)
LC
MS/MS
Iden4fica4on
OMSSA
– Different providers: (annotations,
software converters & viewers)
– For Raw data formats, there is also
the very real problem of “aging”.
Different:
– Protocols.
– Outputs.
– Providers.
Different:
– Strategies.
– Search Engines.
– Post-Processing
Analysis.
– File Outputs.
3. LC-MS/MS (different
instruments)
Raw File Raw File Raw File Raw File
Raw
data
is
binary!!!…
It
means
you
can’t
read
it
with
Notepad
but
also
without
their
programs
and
libraries.
Peaks without processing!!!
4. LC-MS/MS (“aging” problem.)
Thermo XCalibur MassLynx Trapper Compass
FrameWork
Next
the
problem
with
proprietary
raw
data
formats,
there
is
also
the
very
real
problem
of
“aging”
that
comes
with
any
binary
formaSed
data.
As
4me
goes
by,
support
for
certain
formats
tends
to
evaporate
and
within
the
space
of
several
years,
readers
can
no
longer
be
found
for
the
format.
Martens
and
co.
Proteomics
2005,
5,
3501–3505
5. Information inside Raw files
• Raw files contain all the individual peaks as registered
by the instrument detector.
• Peaks without processing!!!
• For LC-MS machines, can store elution profiles and
times for the LC part.
• Depending on the vendor and make of the machine,
other useful instrument-related information can be
stored in these files as well.
8. mzXML
mzXML
Parent FileList
MsInstrument
SeparationTechnique
dataProcessing
spooting
scanList
scan
scanDescription
msLevel
PrecursorList
binaryDataArray
binaryDataArray
• • •
scan
scan
scanOrigin
deisotoped
centroided
deconvoluted
mzXML was the first xml
based file format developed
for proteomics experiments.
It was developed by the
System Biology Group, USA.
The annotations in the file
are string based. It means,
they are in this way: (Name
Attribute, Value).
D o n o t s u p p o r t
chromatograms information.
Is very difficult to extend. The structure of the file
don’t allow to define new parameter or features for
each elements. For example, msInstrument are defined
only by the name of the instrument. Also, if the
spectrum is preprocessing with any program, is difficult
to incorporate the information.
Actually exist more than 4 versions
of the schema. The schema is
supported by the System Biology
Group, USA-Zurich.
10. OLS
• Is a web service oriented system
developed in Java.
• It was developed and is maintained by
the PRIDE Team!!!
• We have the service installed in a local
machine!!!!
• I know the library and the source
code. We have an strong collaboration
with the developers of the Service!!!
11. mzML
mzML
cvList
referenceableParamGroupList
sampleList
instrumentConfigurationList
softwareList
dataProcessingList
acquisitionSettingsList
run
spectrum
spectrumDescription
precursorList
scan
binaryDataArray
binaryDataArray
• • •
spectrumList
spectrum
spectrum
• • •
chromatogramList
chromatogram
chromatogram
• • •
chromatogram
binaryDataArray
binaryDataArray
Meta data about the spectra
plus all the spectra themselves.
The header at the top of the
file encodes information about:
the source of the data as well as
information about the sample,
instrument and software that
processed the data.
Cvterms are used to define the
metadata and the properties of
each element (software,
instrument, sample, scansetting,
etc.
Chromatograms may be encoded in mzML in a special element that contains one or
more cvParams to describe the type of chromatogram, followed by two base64-
encoded binary data arrays.
12. Comparison table
Metadata/fileformat mzml mzData mzXml mgf pkl ms2 dta
Species X X - - - - -
Tissue X X - - - - -
Instrument X X X - - - -
Experiment Description X - - - - - -
References X - - - - - -
Contacts X X X - - - -
X (FileContent /
Additional
creationDate) X X - - - -
Samples X X - - - - -
Instrument Configuration X X X - - - -
Data Processing X X X - - - -
mzML is supported by:
- Institute for Systems Biology , Seattle.
- Swiss Institute for Bioinformatics and Geneva Bioinformatics,
Switzerland.
- European Bioinformatics Institute, Hinxton, UK.
- Thermo Fisher, San Jose, CA.
- Indigo Biosystems, Carmel, IN.
mzML and mzXML is comatible with:
- Mascot!!!!, X! Tandem, OMSSA.
- PeptideProphet
Is
not
binary!!!…
It
means
you
can
read
it
with
Notepad
but
also
with
your
libraries
and
own
code…
13. ProteoWizard
msConvert
API
Thermo
API
Bruker
API
Agilent
API
Waters
API
File Input Supported:
– Thermo
– Bruker
– Agilent
– Waters
– Pkl
– mgf,
– dta
– ms2
File Output Supported:
– mzML
– mzXML
– mzData
– Pkl
– mgf
Cross-platform !!!!
15. Identification
X!Tandem
Mascot
Database
Search
Mascot
Percolator
PeptideProphet
Scaffold
X! Tandem OMSSA Fenyx
PeptideProphet
De Novo
Sequence
Peaks PepNovo
Spectral
Library
SpectraST NIST
Thousand
approaches!!!…
It
means
you
can
combine
different
programs,
with
different
parameters,
and
different
workflows..
16. File Formats?
AnalysisXML: v1.0 – candidate (Dic 08)
.dat
.dat
.dat
pepXML
protXML
AnalysisXML
Seattle Proteome Center at
the Institute for Systems
Biology
Programs with excel output
OMSSA
Programs with their output format
17. mzidentml
Collection of use cases agreed
to cover:
- e.g. PMF, MS/MS,
sequence tag, de novo,
spectral library
Pep
Evidence1
Ambiguity
Group1
Protein
Result Set
Protein
Hypothesis1
Pep
Evidence2
Pep
Evidence1
Protein
Hypothesis2
Pep
Evidence2
Pep
Evidence1
Ambiguity
Group2
Protein
Hypothesis1
…
…
…
…
… …
Pep
Evidence2
Mul9ple
Search
Engines!!!…
Protocol
Descrip4ons,
Database
Proper4es,
Search
Engines,
Parameters,
Modifica4ons..
Fully
compa4ble
with
Otology's!!!
Supported
by
Mascot!!!
18. mzidentml
• Results in mzIdentML format can be exported directly from Mascot (export of version 1.1
available in version 2.3)
• Converters are currently available for Sequest and Proteome Discoverer output (.msf
and .protXML) (e.g. within ProCon: http://www.medizinisches-proteom-center.de/ProCon),.
• OMSSA and X!Tandem (http://code.google.com/p/mzidentml-parsers/)
• The pipeline applications Scaffold (import into Scaffold PTM and export of mzIdentML
available in Scaffold version 3) and TPP (results can be exported to mzIdentML via the
ProteoWizard converter).
• A beta exporter is also available for Phenyx.
• OpenMS implements C++ code for reading (and as of release 1.9) writing mzIdentML.
• An open-source Java API for reading and writing mzIdentML has also been developed,
available from http://code.google.com/p/jmzidentml/!!!!!
19. Gels
(nobody
care)
― Only limited support for the storage of detailed descriptions of all stages of a
gel-based proteomics workflow.
― Information is mostly restricted to unstructured text paragraphs.
Different Scenarios:
OffeGel-electrophoresis
1D 2D
One of the reasons is the lack of widely accepted standards for
representing gel data and the difficulties encountered modelling the range
of workflows employed in different settings.
20. gelml
Gelml is basically a metadata file
that contains the URI of the image
file.
The structure of the schema is
complex !!!!. One of the reason is the
amount of different protocols
Not well documented, an small
community behind, and not really
extended in the community!!!
21. Before Technical Things!!!
• The number of tools based on XML
standard files is growing exponentially..
Why:
– Easy to read and write!!!
– They are standards!!!!
– Repositories Support (PRIDE,
PEPTIDEATLAS).
– Have enough information for most of the
programs.
22. APIs
• jmzml: Library to read/
write information from
mzml files.
• jmzidentml: Library to
read/write information
from mzidentml files.
• jgelml: Library to read/
write information from
gelml files. (current
development)
• Developed by
the PRIDE team.
• Java Libraries.
• Still growing.
• Open-Source
and Free.
23. ms-core-api
Applications
proteolims
N-terminal
Identification
Web services
ms-core-api
APIs
jmzml jmzxml jmzData jmzReader jmzidml jgelml
m s - c o r e - a p i i s a j a v a
framewrok, a common object
model to represent different
file formats.
Support now:
― mzidentml
― mzml, mzData, mzXML
― pride xml, pride database
― pkl, mgf, ms2, dta
― gelml (current work)
Cross-platform and well
documented!!!
The aim of ms-core-api library is to guarantee for our current
development tools a common language of objects and classes!!!!
24. The relevance of APIs concept
• Different programs can used to
implement the main functionalities.
• If you have APIs .. Then you just need
to think on integration, scalability and
presentation…
• Easy to maintain and to scale and to
share…
• They are the “MAIN CORE!”!!
27. conclusion
• mzml is the current standard for MS/MS storage.
• mzidentml will be the future standard on proteomics
community for peptide/protein identification storage.
• gelml is not very extended in the community but so
far the best option for gel information storage.
• ms-core-api support mzml,mzidentml, and in the near
future gelml.