ArticleBioinformatics and biology insights2015
A Computational Pipeline for Cross-Species Analysis of RNA-seq Data Using R and Bioconductor.
Article in Bioinformatics and biology insights, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Epigenetics and In Silico Transcriptome Analysis of Pediatric Acute Myeloid Leukemia.European journal of haematology · 2026Review
- ABC Efflux Transporters and Solute Carriers in the Early Developing Brain of a Marsupial Monodelphis domestica (South American Gray Short-Tailed Opossum).The Journal of comparative neurology · 2024Article
- CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis.Bioinformatics (Oxford, England) · 2023Article
- CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis.bioRxiv : the preprint server for biology · 2023Article
- A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data.Molecular biology and evolution · 2023Article
- Annotation depth confounds direct comparison of gene expression across species.BMC bioinformatics · 2021Article
- Transcriptome analysis and comparison reveal divergence between the Mediterranean and the greenhouse whiteflies.PloS one · 2020Article
- Article
- Targeted Capture of Complete Coding Regions across Divergent Species.Genome biology and evolution · 2017Article
- Cross-species Transcriptomic Comparison of In Vitro and In Vivo Mammalian Neural Cells.Bioinformatics and biology insights · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
RNA sequencing (RNA-seq) has revolutionized transcriptome analysis through profiling the expression of thousands of genes at the same time. Systematic analysis of orthologous transcripts across species is critical for understanding the evolution of gene expression and uncovering important information in animal models of human diseases. Several computational methods have been published for analyzing gene expression between species, but they often lack crucial details and therefore cannot serve as a practical guide. Here, we present the first step-by-step protocol for cross-species RNA-seq analysis with a concise workflow that is largely based on the free open-source R language and Bioconductor packages. This protocol covers the entire process from short-read mapping, gene expression quantification, differential expression analysis to pathway enrichment. Many useful utilities for data visualization are included. This complete and easy-to-follow protocol provides hands-on guidance for users who are new to cross-species gene expression analysis.
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Identifiers
What OpenQuestion holds
Registered trials
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.