Evidence map›Paper›PMID 39436593›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

De Novo Sequencing of Peptides from Tandem Mass Spectra and Applications in Proteogenomics.

Savas Takan, Jens Allmer

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Deep learning in bioinformatics.Turkish journal of biology = Turk biyoloji dergisi · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Savas TakanDepartment of artificial intelligence and data engineering, Faculty of Engineering, Ankara University, Ankara, Turkey.
Jens AllmerMedical Informatics and Bioinformatics, Institute for Measurement Engineering and Sensor Technology, Hochschule Ruhr West, University of Applied Sciences, Mülheim adR., Germany. jens@allmer.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The changes in protein expression are hallmarks of development and disease. Protein expression can be established qualitatively and quantitatively using mass spectrometry (MS). Samples are prepared, proteins extracted and then analyzed using MS and MS/MS. The resulting spectra need to be processed computationally to assign peptide spectrum match. Database searches employ sequence databases or spectral libraries for matching possible peptides with the measured spectra. This route is well established but fails when peptides are not found in sequence repositories. In this case, de novo sequencing of MS/MS spectra can be employed. Many computational algorithms that establish the peptide sequence from MS/MS spectrum alone are available. While de novo sequencing assigns a sequence to an MS/MS spectrum, this assignment can be used in further processes for genome annotation. For example, novel exons can be assigned, known exons can be extended, and splice sites can be validated at the protein level. We compiled an extensive list of such algorithms, grouped them, and discussed the selected approaches. We also provide a roadmap of how de novo sequencing can enter mainstream proteogenomic analysis. In the future, de novo predictions can be added to sample-specific protein databases, including RNA-seq translations. These enriched databases can then be used for proteogenomics studies with existing pipelines.

Indexed as

AlgorithmsPeptidesProteogenomicsTandem Mass SpectrometryAmino Acid SequenceComputational BiologyDatabases, ProteinHumansSequence Analysis, ProteinSoftwarePeptidesDe novoMass spectrometryMS/MSProteogenomicsPSMSequencing

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.