Evidence map›Paper›PMID 32442239›Full record

ArticleGenetics and molecular biology2019

The combined analysis as the best strategy for Dual RNA-Seq mapping.

Eliandro Espindula, Edilena Reis Sperb, Evelise Bach, Luciane Maria Pereira Passaglia

Abstract read
In one paragraph

Article in Genetics and molecular biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

4 authors.

Eliandro EspindulaUniversidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Porto Alegre, RS, Brazil.
Edilena Reis SperbUniversidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Porto Alegre, RS, Brazil.
Evelise BachUniversidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Porto Alegre, RS, Brazil.
Luciane Maria Pereira PassagliaUniversidade Federal do Rio Grande do Sul (UFRGS), Instituto de Biociências, Departamento de Genética, Porto Alegre, RS, Brazil.ORCID http://orcid.org/0000-0001-5011-9120

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In Dual RNA-Seq experiments the simultaneous extraction of RNA and analysis of gene expression data from both interacting organisms could be a challenge. One alternative is separating the reads during in silico data analysis. There are two main mapping methods used: sequential and combined. Here we present a combined approach in which the libraries were aligned to a concatenated genome to sort the reads before mapping them to the respective annotated genomes. A comparison of this method with the sequential analysis was performed. Two RNA-Seq libraries available in public databases consisting of a eukaryotic (Zea mays) and a prokaryotic (Herbaspirillum seropediceae) organisms were mixed to simulate a Dual RNA-Seq experiment. Libraries from real Dual RNA-Seq experiments were also used. The sequential analysis consistently attributed more reads to the first reference genome used in the analysis (due to cross-mapping) than the combined approach. More importantly, the combined analysis resulted in lower numbers of cross-mapped reads. Our results highlight the necessity of combining the reference genomes to sort reads previously to the counting step to avoid losing information in Dual RNA-Seq experiments. Since most studies first map the RNA-Seq libraries to the eukaryotic genome, much prokaryotic information has probably been lost.

Identifiers

PMID32442239
PMCPMC7249662

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.