Evidence map›Paper›PMID 31321403›Full record

ArticleDNA research : an international journal for rapid publication of reports on genes and genomes2019

Uncovering the mouse olfactory long non-coding transcriptome with a novel machine-learning model.

Antonio P Camargo, Thiago S Nakahara, Luiz E R Firmino, Paulo H M Netto, João B P do Nascimento, Elisa R Donnard, Pedro A F Galante, Marcelo F Carazzolle, Bettina Malnic, Fabio Papes

Open access · goldAbstract read
In one paragraph

Article in DNA research : an international journal for rapid publication of reports on genes and genomes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-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

8 citing papers in PubMed, 11 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Antonio P CamargoDepartment of Genetics and Evolution, Institute of Biology, University of Campinas, Campinas, SP, Brazil.
Thiago S NakaharaDepartment of Genetics and Evolution, Institute of Biology, University of Campinas, Campinas, SP, Brazil.
Luiz E R FirminoDepartment of Biochemistry, Institute of Chemistry, University of Sao Paulo, Sao Paulo, SP, Brazil.
Paulo H M NettoDepartment of Genetics and Evolution, Institute of Biology, University of Campinas, Campinas, SP, Brazil.
João B P do NascimentoDepartment of Biochemistry, Institute of Chemistry, University of Sao Paulo, Sao Paulo, SP, Brazil.
Elisa R DonnardMolecular Oncology Center, Hospital Sirio-Libanes, Sao Paulo, SP, Brazil.
Pedro A F GalanteMolecular Oncology Center, Hospital Sirio-Libanes, Sao Paulo, SP, Brazil.
Marcelo F CarazzolleDepartment of Genetics and Evolution, Institute of Biology, University of Campinas, Campinas, SP, Brazil.
Bettina MalnicDepartment of Biochemistry, Institute of Chemistry, University of Sao Paulo, Sao Paulo, SP, Brazil.
Fabio PapesDepartment of Genetics and Evolution, Institute of Biology, University of Campinas, Campinas, SP, Brazil.
Universidade de São Paulo · BRUniversidade Estadual de Campinas (UNICAMP) · BRHospital Sírio-Libanês · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Very little is known about long non-coding RNAs (lncRNAs) in the mammalian olfactory sensory epithelia. Deciphering the non-coding transcriptome in olfaction is relevant because these RNAs have been shown to play a role in chromatin modification and nuclear architecture reorganization, processes that accompany olfactory differentiation and olfactory receptor gene choice, one of the most poorly understood gene regulatory processes in mammals. In this study, we used a combination of in silico and ex vivo approaches to uncover a comprehensive catalogue of olfactory lncRNAs and to investigate their expression in the mouse olfactory organs. Initially, we used a novel machine-learning lncRNA classifier to discover hundreds of annotated and unannotated lncRNAs, some of which were predicted to be preferentially expressed in the main olfactory epithelium and the vomeronasal organ, the most important olfactory structures in the mouse. Moreover, we used whole-tissue and single-cell RNA sequencing data to discover lncRNAs expressed in mature sensory neurons of the main epithelium. Candidate lncRNAs were further validated by in situ hybridization and RT-PCR, leading to the identification of lncRNAs found throughout the olfactory epithelia, as well as others exquisitely expressed in subsets of mature olfactory neurons or progenitor cells.

Indexed as

Machine LearningTranscriptomeAnimalsFemaleMaleMiceOlfactory Receptor NeuronsRNA, Long NoncodingVomeronasal OrganRNA, Long Noncodinglong non-coding RNAsmachine learningolfactiontranscriptome

Identifiers

PMID31321403
PMCPMC6704403
OpenAlexW2963986743

What OpenQuestion holds

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Registered trials

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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.