Evidence map›Paper›PMID 31609424›Full record

ArticleGenome biology and evolution2019

TreeExp2: An Integrated Framework for Phylogenetic Transcriptome Analysis.

Jingwen Yang, Hang Ruan, Wenjie Xu, Xun Gu

Abstract readLetter
In one paragraph

Article in Genome biology and evolution, 2019. 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. 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.

Jingwen YangMOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China.
Hang RuanDepartment of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston-McGovern Medical School.
Wenjie XuMOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China.
Xun GuDepartment of Genetics, Development and Cell Biology, Iowa State University.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent innovations of next-generation sequencing such as RNA-seq have generated an enormous amount of comparative transcriptome data, which have shed lights on our understanding of the complexity of transcriptional regulatory systems. Despite numerous RNA-seq analyses, statistical methods and computational tools designed for phylogenetic transcriptome analysis and evolution have not been well developed. In response to this need, we developed software TreeExp2 specifically for RNA-seq data. The R-package TreeExp2 has implemented a suite of advanced, recently developed methods for transcriptome evolutionary analysis. Its main functions include the ancestral transcriptome inference, estimation of the strength of expression conservation, new expression distance, and the relative expression rate test. TreeExp2 provides an integrated, statistically sound framework for phylogenetic transcriptome analysis. It will considerably enhance our analytical capability for exploring the evolution and selection at the transcriptome level. The current version of TreeExp2 is available under GPLv3 license at the Github developer site https://github.com/jingwyang/TreeExp; last accessed November 12, 2019, and its online tutorial which describes the biological theories in details and fully worked case studies with real data can be found at https://jingwyang.github.io/TreeExp-Tutorial; last accessed November 12, 2019.

Indexed as

Computational BiologyGene Expression ProfilingHigh-Throughput Nucleotide SequencingPhylogenySequence Analysis, RNAhigh-throughput analysisphylogenomicsR-packagetranscriptome evolution

Identifiers

PMID31609424
PMCPMC6934891

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.