Proteomics - created from Mind Map

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The main understanding of the proteomics

The comprehensive scientific papers

Genome-Scale metabolic model

the illustrating playlists

this channel is the best channel illustrating the proteomics and mass spectrometry in details and in very short periods:- https://www.youtube.com/channel/UCDyS5AiNAvhWGXyIhI-IY2g/videos this is very informative, helpful and beneficial playlist for proteomics but in details and long-period-videos:- https://www.youtube.com/playlist?list=PL0-DJZZ1LXw4r6Mmm4nb2TOV2grt86lZQ

some Powerpoints and comprehensive articles

this data show is dipicting the main point in proteomics:-

https://www.slideshare.net/RaviKumar1027/proteomics-42922830

and this one:-

https://www.slideshare.net/NaghmehPoorinmohamma/techniques-in-proteomics

Quantitative Proteomics of the Cancer Cell Line Encyclopedia

Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data.

Mass spectrometry technics are used mainly

in proteomics' discipline for identification and quantification of proteins and

the main idea in these methods is each amino acid has a specific mass

distinguishing it from the other amino acids, so that tandem mass spectrometers

are concerned with cutting or digesting a given protein into smaller pieces

(peptides) by proteolytic enzymes such as trypsin then ionizing these peptides into positive ions to undergo a high voltage

difference that is required for separation of peptides according to its mass to

be detected by a detector at the end of that process to show us a graph gives

us the mass of each peptide and its amount or its abundance in the sample. So

that the key word here is the various methods that are used for quantification

which are divided into two main categories Labeling approaches and

Labeling-free approaches. The first approaches are concerned with labeling the

peptide by radioactive isotopes such as Isobaric tags for relative and absolute

quantitation (iTRAQ), Isotope-Coded Affinity Tag (ICAT), Quantitative Approaches Labeling approaches

Metabolic (SILAC) and others but those sort of technics involve several

drawbacks such as ignoring sequencing depth' value, giving coverage values

100%, not expressing on the protein size, and ignoring the antecedent observing probabilities of any given

peptide in the experiment. The ability of Mass Spectrometer in detection is

slightly inaccurate for various peptides because of contribution of chemical

properties of measured peptides in alteration of its ionization.

So that the second category which is

Labeling-free approaches are treating these antecedent imperfections such as

peak intensities, spectral counting, Protein Abundance Index (PAI), Exponentially modified Protein Abundance

Index (emPAI), and finally Absolute Protein Expression (APEX) that will be

presented in the following lines.),

Absolute Protein Expression (APEX), which improves on

basic spectral counting methods by including a correction factor for each

protein (called Oi value) that accounts for variable peptide detection by MS

techniques. The technique uses machine learning classification to derive

peptide detection probabilities that are used to predict the number of tryptic

peptides expected to be detected for one molecule of a particular protein (Oi

). This predicted spectral count is compared to the protein's observed MS total

spectral count during APEX computation of protein abundances.

The APEX Quantitative Proteomics Tool,

introduced here, is a free open source Java application that supports the APEX

protein quantitation technique. The APEX tool uses data from standard tandem

mass spectrometry proteomics experiments and provides computational support for

APEX protein abundance quantitation through a set of graphical user interfaces

that partition the parameter controls for the various processing tasks. The

tool also provides a Z-score analysis for identification of significant

differential protein expression, a utility to assess APEX classifier

performance via cross validation, and a utility to merge multiple APEX results

into a standardized format in preparation for further statistical analysis.

An integrated landscape of protein expression in human cancer

Quantification of 375 cell lines proteomes

Correlated expression of proteins across many pathways

Downregulation of multiple protein complexes in microsatellite instability

Protein complexes associated with sensitivity to gene knockdown and mutation

The APEX method of protein quantification

the various cancer cell lines databases

protein expression is the main cause of tumour

and there is a Correlation between mRNA and protein expression in cancer cell lines

191 cancer cell lines proteome quantification

Introduction

Genome-Scale Metabolic Modeling and Its Application to Microbial Communities

introducing article in wikipedia "Metabolic network modelling"

https://en.wikipedia.org/wiki/Metabolic_network_modelling

illustrating chapter in a book