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consensus (qui)

Italian translation: spettri di consenso

GLOSSARY ENTRY (DERIVED FROM QUESTION BELOW)
English term or phrase:consensus spectra
Italian translation:spettri di consenso
Entered by: federica gagliardi

11:42 Jun 10, 2016
English to Italian translations [PRO]
Medical - Biology (-tech,-chem,micro-) / Peptidi riconosciuti dal sistema immunitario
English term or phrase: consensus (qui)
Un progetto di ricerca UE sull'immunopeptidoma:

Most cells in the body display fragments of proteins (including pathogen-derived proteins) on their surface through MHC class I molecules. These peptides are collectively referred to as the immunopeptidome and their presentation to immune cells shapes immunity. However, our understanding of the molecular composition and biogenesis of the immunopeptidome in health and disease is poor. Seeking to address this, scientists on the XXX project developed a mass spectrometry high-throughput workflow to quantitatively measure peptides presented by MHC/HLA molecules. (...)

Project members generated peptide libraries consisting of ***consensus*** fragment ion spectra, and analysed them to produce quantitative digital maps of MHC immunopeptidomes.

A cosa si riferisce il termine in oggetto? Agli ioni frammento? In che senso?
Grazie
federica gagliardi
Italy
Local time: 20:06
spettri di consenso
Explanation:
Ritengo che consensus si riferisca a spectra.
L'articolo seguente mi sembra illuminante. Ne ho riportato solo alcuni passaggi. Non trovo un corrispondente nella letteratura italiana ma non credo esistano molte possibilità di traduzione, per cui la resa proposta dovrebbe essere corretta.


Human Plasma PeptideAtlas, a collection of 40 contributed, heterogeneous shotgun proteomics datasets, and verified the effectiveness of the library building algorithm to generate high-quality, representative consensus spectra and to remove questionable spectra.

Creation of Consensus Spectra
The raw spectral library generated as described in the previous section contains non-unique entries resulting from multiple observations of the same peptide ion. Spectra with the same peptide identification are termed replicates. Where available, replicates are combined to create a “consensus” spectrum that is representative of the peptide ion through a series of steps:

1.Remove dissimilar replicates – Pairwise dot products among replicates are calculated, and replicates that do not resemble the rest of the replicates are discarded.


2.Rank the remaining replicates by quality -- The remaining replicates are then ranked by their signal-to-noise ratio (defined here as the average intensity of the 2nd to 6th highest peaks divided by the median intensity).


3.Align the replicates -- For each replicate, alignment is performed for each peak, starting from the base peak, by looking for matching peaks in all other replicate spectra within an adaptive m/z tolerance that is inversely proportional to the intensity rank of the matched peak (+/− 0.8 Th at maximum). This helps limit the undesirable matching of noise peaks while allowing significant peaks to be aligned easily. This process is repeated for each replicate, starting from the top-ranked (highest signal-to-noise ratio), and for each remaining unaligned peak.


4.Remove noise peaks -- A peak “voting” scheme is adopted, whereby the aligned peak will be included in the final consensus spectrum if and only if it is present in more than 60% of the replicate spectra. In other words, the resulting consensus spectrum only contains peaks that are consistently present in a majority of the replicates, and therefore should be largely devoid of random noise or spurious impurity peaks.


5.Average peak m/z and intensities -- The consensus m/z and intensity values are calculated as weighted averages of the respective values of the corresponding peaks in the replicates. The weight used is the signal-to-noise ratio of the replicate, so that the consensus spectrum resembles the higher-quality replicates more than the lower-quality ones.


6.Perform book-keeping – Various types of information, including the sample sources and sequence searching scores are combined and copied over to the consensus library entry, such that valuable information of the originating datasets is preserved for future reference.


It should be noted that the above procedure for creating the consensus spectrum is devised in the hope that it will work reasonably well under less than ideal circumstances, such as when the number of replicates is small or when some replicates are of poor quality. The details of the methodology were developed by trail-and-error and manual inspection of many consensus spectra created with different methods and parameters, and were found to be effective



Spectral library searching has been proposed as a useful complement, and in some cases, a promising alternative to sequence database searching (7). In this approach, the peptide identification is made by comparing the query MS/MS spectrum to a library of reference spectra for which the identifications are known. This method has been commonly practiced for mass spectrometric analysis of small molecules (8–10). Recently, thanks to the rapid accumulation of shotgun proteomics data from which spectral libraries could be compiled, spectral searching has become a reality for proteomics applications, with some preliminary demonstration of success (11–14). As discussed in these reports, the advantages of spectral library searching over traditional sequence searching are manifold. First, because the search space is confined to previously observed and identified peptides, the search engine does not waste computational time attempting to match the query spectra with putative peptide sequences that are never observed in practice. This results in a drastic increase in search speed and selectivity. Second, similarity scoring in spectral searching is more precise, in that one is comparing experimental spectra to experimental spectra, and not to simplistic theoretical spectra constructed from peptide sequences. Consequently, spectral searching is able to take full advantage of all spectral features, including actual peak intensities and the presence of uncommon fragment ions, to determine the best match.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2637392/

Consensus Spectra: Multiple spectra assigned to a single peptide ion (replicates) were combined to form a ‘consensus spectrum’. This involved the following series of steps for each identified peptide ion:
http://chemdata.nist.gov/mass-spc/ftp/mass-spc/PepLib.pdf

http://www.pnas.org/content/suppl/2007/03/22/0701130104.DC1/...


https://it.wikipedia.org/wiki/Libreria_spettrale
Le librerie di spettri o librerie spettrali sono raccolte di spettri utilizzate in spettrometria e spettroscopia nell'analisi qualitativa. Aiutano nel riconoscimento di composti o miscele incognite tramite confronto, possono essere generiche o specifiche per una classe di sostanze. Il confronto viene effettuato da programmi specifici.

Le librerie si trovano in vendita o di libero accesso su internet o sono prodotte dai laboratori di analisi per uso interno.

Gli algoritmi per la ricerca dello spettro possono basarsi sul confronto tra spettri (ricerca d'identità)[1]: semplice confronto tra spettri per ricerca in banca dati (library search) o confronto tra spettri semplificati e indici di ritenzione [2] (nel caso di accoppiamenti con la cromatografia, tipicamente GC-MS) o sul confronto per somiglianza, un metodo più complesso.
Selected response from:

texjax DDS PhD
Local time: 14:06
Grading comment
grazie
4 KudoZ points were awarded for this answer



Summary of answers provided
3 +2spettri di consenso
texjax DDS PhD
3consenso
daria fedele


Discussion entries: 6





  

Answers


53 mins   confidence: Answerer confidence 3/5Answerer confidence 3/5
consenso


Explanation:
frammenti consenso non ti va bene? forse non ho capito il tuo problema

daria fedele
Italy
Local time: 20:06
Specializes in field
Native speaker of: Italian
PRO pts in category: 32
Login to enter a peer comment (or grade)

2 hrs   confidence: Answerer confidence 3/5Answerer confidence 3/5 peer agreement (net): +2
spettri di consenso


Explanation:
Ritengo che consensus si riferisca a spectra.
L'articolo seguente mi sembra illuminante. Ne ho riportato solo alcuni passaggi. Non trovo un corrispondente nella letteratura italiana ma non credo esistano molte possibilità di traduzione, per cui la resa proposta dovrebbe essere corretta.


Human Plasma PeptideAtlas, a collection of 40 contributed, heterogeneous shotgun proteomics datasets, and verified the effectiveness of the library building algorithm to generate high-quality, representative consensus spectra and to remove questionable spectra.

Creation of Consensus Spectra
The raw spectral library generated as described in the previous section contains non-unique entries resulting from multiple observations of the same peptide ion. Spectra with the same peptide identification are termed replicates. Where available, replicates are combined to create a “consensus” spectrum that is representative of the peptide ion through a series of steps:

1.Remove dissimilar replicates – Pairwise dot products among replicates are calculated, and replicates that do not resemble the rest of the replicates are discarded.


2.Rank the remaining replicates by quality -- The remaining replicates are then ranked by their signal-to-noise ratio (defined here as the average intensity of the 2nd to 6th highest peaks divided by the median intensity).


3.Align the replicates -- For each replicate, alignment is performed for each peak, starting from the base peak, by looking for matching peaks in all other replicate spectra within an adaptive m/z tolerance that is inversely proportional to the intensity rank of the matched peak (+/− 0.8 Th at maximum). This helps limit the undesirable matching of noise peaks while allowing significant peaks to be aligned easily. This process is repeated for each replicate, starting from the top-ranked (highest signal-to-noise ratio), and for each remaining unaligned peak.


4.Remove noise peaks -- A peak “voting” scheme is adopted, whereby the aligned peak will be included in the final consensus spectrum if and only if it is present in more than 60% of the replicate spectra. In other words, the resulting consensus spectrum only contains peaks that are consistently present in a majority of the replicates, and therefore should be largely devoid of random noise or spurious impurity peaks.


5.Average peak m/z and intensities -- The consensus m/z and intensity values are calculated as weighted averages of the respective values of the corresponding peaks in the replicates. The weight used is the signal-to-noise ratio of the replicate, so that the consensus spectrum resembles the higher-quality replicates more than the lower-quality ones.


6.Perform book-keeping – Various types of information, including the sample sources and sequence searching scores are combined and copied over to the consensus library entry, such that valuable information of the originating datasets is preserved for future reference.


It should be noted that the above procedure for creating the consensus spectrum is devised in the hope that it will work reasonably well under less than ideal circumstances, such as when the number of replicates is small or when some replicates are of poor quality. The details of the methodology were developed by trail-and-error and manual inspection of many consensus spectra created with different methods and parameters, and were found to be effective



Spectral library searching has been proposed as a useful complement, and in some cases, a promising alternative to sequence database searching (7). In this approach, the peptide identification is made by comparing the query MS/MS spectrum to a library of reference spectra for which the identifications are known. This method has been commonly practiced for mass spectrometric analysis of small molecules (8–10). Recently, thanks to the rapid accumulation of shotgun proteomics data from which spectral libraries could be compiled, spectral searching has become a reality for proteomics applications, with some preliminary demonstration of success (11–14). As discussed in these reports, the advantages of spectral library searching over traditional sequence searching are manifold. First, because the search space is confined to previously observed and identified peptides, the search engine does not waste computational time attempting to match the query spectra with putative peptide sequences that are never observed in practice. This results in a drastic increase in search speed and selectivity. Second, similarity scoring in spectral searching is more precise, in that one is comparing experimental spectra to experimental spectra, and not to simplistic theoretical spectra constructed from peptide sequences. Consequently, spectral searching is able to take full advantage of all spectral features, including actual peak intensities and the presence of uncommon fragment ions, to determine the best match.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2637392/

Consensus Spectra: Multiple spectra assigned to a single peptide ion (replicates) were combined to form a ‘consensus spectrum’. This involved the following series of steps for each identified peptide ion:
http://chemdata.nist.gov/mass-spc/ftp/mass-spc/PepLib.pdf

http://www.pnas.org/content/suppl/2007/03/22/0701130104.DC1/...


https://it.wikipedia.org/wiki/Libreria_spettrale
Le librerie di spettri o librerie spettrali sono raccolte di spettri utilizzate in spettrometria e spettroscopia nell'analisi qualitativa. Aiutano nel riconoscimento di composti o miscele incognite tramite confronto, possono essere generiche o specifiche per una classe di sostanze. Il confronto viene effettuato da programmi specifici.

Le librerie si trovano in vendita o di libero accesso su internet o sono prodotte dai laboratori di analisi per uso interno.

Gli algoritmi per la ricerca dello spettro possono basarsi sul confronto tra spettri (ricerca d'identità)[1]: semplice confronto tra spettri per ricerca in banca dati (library search) o confronto tra spettri semplificati e indici di ritenzione [2] (nel caso di accoppiamenti con la cromatografia, tipicamente GC-MS) o sul confronto per somiglianza, un metodo più complesso.


texjax DDS PhD
Local time: 14:06
Works in field
Native speaker of: Native in ItalianItalian
PRO pts in category: 102
Grading comment
grazie

Peer comments on this answer (and responses from the answerer)
agree  Valentina LG
49 mins
  -> Grazie Valentina
agree  Paola Maria Agrati
2 days 18 hrs
  -> Grazie Paola, mi fa piacere sapere che concordi :))
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