Evidence map›Paper›PMID 38668970›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

Characterization of the Mitochondrial Proteome in the Ctenophore Mnemiopsis leidyi Using MitoPredictor.

Viraj Muthye, Dennis V Lavrov

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Viraj MuthyeDepartment of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA, USA.
Dennis V LavrovDepartment of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA, USA. dlavrov@iastate.edu.

Funding

Neuron-SELEX: Development of neuron-specific nanoscale toolkits for single-cell recognitionR01NS114491 · NINDS · UNIVERSITY OF FLORIDA · PI MOROZ, LEONID L · 2020 to 2024
$2.0M
NINDS NIH HHS R01 NS114491
6 · The paper itself

Abstract

Mitochondrial proteomes have been experimentally characterized for only a handful of animal species. However, the increasing availability of genomic and transcriptomic data allows one to infer mitochondrial proteins using computational tools. MitoPredictor is a novel random forest classifier, which utilizes orthology search, mitochondrial targeting signal (MTS) identification, and protein domain content to infer mitochondrial proteins in animals. MitoPredictor's output also includes an easy-to-use R Shiny applet for the visualization and analysis of the results. In this article, we provide a guide for predicting and analyzing the mitochondrial proteome of the ctenophore Mnemiopsis leidyi using MitoPredictor.

Indexed as

CtenophoraMitochondrial ProteinsProteomeAnimalsComputational BiologyMitochondriaProteomicsSoftwareMitochondrial ProteinsProteomeCtenophoraMachine learningMitochondriaMitoPredictorMnemiopsisProteomeRandom Forest

Identifiers

What OpenQuestion 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 OpenQuestion graph.