Evidence map›Paper›PMID 36952201›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Computational Evaluation of Peroxisomal Targeting Signals in Metazoa.

Markus Kunze

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. 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
0.8field-weighted citation impact, top 29% of its field
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 citations in OpenAlex.

  1. 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

1 author at 1 institution in 1 country.

Markus KunzeDepartment of Pathobiology of the Nervous System, Center for Brain Research, Medical University of Vienna, Vienna, Austria. markus.kunze@meduniwien.ac.at.
Medical University of Vienna · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most soluble proteins enclosed in peroxisomes encode either type-1 or type-2 peroxisomal targeting signals (PTS1 or PTS2), which act as postal codes and define the proteins' intracellular destination. Thus, various computational programs have been developed to evaluate the probability of specific peptide sequences for being a functional PTS or to scan the primary sequence of proteins for such signals. Among these prediction algorithms the PTS1-predictor ( https://mendel.imp.ac.at/pts1/ ) has been amply used, but the research logic of this and other PTS1 prediction tools is occasionally misjudged giving rise to characteristic pitfalls. Here, a proper utilization of the PTS1-predictor is introduced together with a framework of additional tests to increase the validity of the interpretation of results. Moreover, a list of possible causes for a mismatch between results of such predictions and experimental outcomes is provided. However, the foundational arguments apply to other prediction tools for PTS1 motifs as well.

Indexed as

AlgorithmsPeroxisomal Targeting SignalsPeroxisomesAnimalsConsensus SequenceProtein TransportPeroxisomal Targeting SignalsAlternative targeting signalsBilocalizationPeroxisomesPredictionProtein transportPTS1-predictorReceptor proteinTargeting signal

Identifiers

PMID36952201
OpenAlexW4360600846

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.