pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

Marini, Fédérico; Binder, Harald

doi:10.1186/s12859-019-2879-1

Cited by 199 publications

(136 citation statements)

References 38 publications

(29 reference statements)

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“…In transcriptomics studies, genes and samples are often clustered to identify subgroups with similar transcriptional profiles (Liu and Si, 2014;Marini and Binder, 2019). While gene clustering helps identify coexpressed genes, sample clustering is instrumental to detect broad transcriptional similarities between samples, as well as to identify potential technical artifacts and mislabeled samples.…”

Section: Resultsmentioning

confidence: 99%

Systematic analysis of 1298 RNA‐Seq samples and construction of a comprehensive soybean (Glycine max) expression atlas

et al. 2020

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SUMMARY Soybean (Glycine max [L.] Merr.) is a major crop in animal feed and human nutrition, mainly for its rich protein and oil contents. The remarkable rise in soybean transcriptome studies over the past 5 years generated an enormous amount of RNA‐seq data, encompassing various tissues, developmental conditions and genotypes. In this study, we have collected data from 1298 publicly available soybean transcriptome samples, processed the raw sequencing reads and mapped them to the soybean reference genome in a systematic fashion. We found that 94% of the annotated genes (52 737/56 044) had detectable expression in at least one sample. Unsupervised clustering revealed three major groups, comprising samples from aerial, underground and seed/seed‐related parts. We found 452 genes with uniform and constant expression levels, supporting their roles as housekeeping genes. On the other hand, 1349 genes showed heavily biased expression patterns towards particular tissues. A transcript‐level analysis revealed that 95% (70 963 of 74 490) of the assembled transcripts have intron chains exactly matching those from known transcripts, whereas 3256 assembled transcripts represent potentially novel splicing isoforms. The dataset compiled here constitute a new resource for the community, which can be downloaded or accessed through a user‐friendly web interface at http://venanciogroup.uenf.br/resources/. This comprehensive transcriptome atlas will likely accelerate research on soybean genetics and genomics.

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Section: Resultsmentioning

confidence: 99%

Systematic analysis of 1298 RNA‐Seq samples and construction of a comprehensive soybean (Glycine max) expression atlas

et al. 2020

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“…To categorize the expression patterns of each tissue analyzed a principal component analysis (PCA) was performed with the pcaExplorer package in R (Marini and Binder, 2019) with superTranscripts read counts normalized to fragments per kilobase of exon per million reads mapped (FPKM) (Trapnell et al, 2010). Only superTranscripts with ≥1 FPKM were considered.…”

Section: Patterns Of Gene Tissue Expressionmentioning

confidence: 99%

De novo Assembly and Characterization of Patagonian Toothfish Transcriptome and Develop of EST-SSR Markers for Population Genetics

Touma

Garcia²,

Bravo³

et al. 2019

Front. Mar. Sci.

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Patagonian toothfish (Dissostichus eleginoides) is a hefty notothenioid fish and a key species within the marine ecosystem with a high migratory capacity across sub-Antarctic and south American Pacific oceans. Transcriptome characterization and molecular markers associated with micro and macro-evolutionary studies are not available, which in turn limits the gain of knowledge about the genetic basis of this species. Therefore, in the present study, a de novo transcriptome from eight tissue and an embryonic state of Patagonian toothfish was developed, using the Illumina Hiseq 4000 platform. A total of 233,424 superTranscripts were assembled and 37,446 annotated against public databases. Moreover, we identified 71,107 expressed sequence tag-simple sequence repeats (EST-SSRs), with an average number of 0.3 SSRs per superTranscripts and one SSR per 1.12 kB. The most abundant SSR type was repeated dinucleotide (53.67%), followed by trinucleotide (13.73%) repeats. From the total of EST-SSRs identified, 34,196 primer pairs were properly designed and a subset of 25 immune loci were selected for its evaluation as potential EST-SSR population markers. Of this subset, 11 proved to have good technical features and were evaluated in 64 animals from four Patagonian toothfish populations. A number of 63 alleles were identified, with a mean of 4.9 alleles per locus and a polymorphism information content ranging from 0.224 to 0.591, with a mean of 0.50. Significant (F ST , range 0.082-0.117 and G ST , range 0.069-0.291) genetic differentiation (P < 0.05) was determined among the populations analyzed. Therefore, the results presented here represent a relevant genetic resource for biological studies on evolution, conservation, genetic diversity, population structure, and genetic management of breeding stocks of this important species.

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“…Since the current workflows build on established methods for high-dimensional data analysis, it is possible to adapt the code to use other algorithms by exchanging a few lines in the R code. New tools such as pcaExplorer may also help by providing an interactive interface for using PCA to explore and interpret gene or protein expression patterns (Marini and Binder, 2019). Also, as new plasticity mechanisms are identified, those can be explored to find novel associations between visual plasticity and V1 neurobiology.…”

Section: Discussionmentioning

confidence: 99%

Constructing plasticity phenotypes to classify experience-dependent development of the visual cortex

Balsor

Jones²,

2020

Preprint

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Many neural mechanisms regulate experience-dependent plasticity in the visual cortex (V1) and new techniques for quantifying large numbers of proteins or genes are transforming how plasticity is studied into the era of big data. With those large data sets comes the challenge of extracting biologically meaningful results about visual plasticity from data-driven analytical methods designed for high-dimensional data. In other areas of neuroscience, high-information content methodologies are revealing more subtle aspects of neural development and individual variations that give rise to a richer picture of brain disorders. We have developed an approach for studying V1 plasticity that takes advantage of the known functions of many synaptic proteins for regulating visual plasticity and using that to rebrand the results of high-dimensional analyses into a plasticity phenotype. Here we provide a primer for analyzing experience-dependent plasticity in V1 using example R code to identify high-dimensional changes in a group of proteins. We describe using PCA to classify high-dimensional plasticity features and use them to construct a plasticity phenotype. In the examples, we show how the plasticity phenotype can be visualized and used to identify neurobiological features in V1 that change during development or after different visual rearing conditions. We include an R package "v1hdexplorer" that aggregates the various coding packages and custom visualization scripts written in R Studio.

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pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

Cited by 199 publications

References 38 publications

Systematic analysis of 1298 RNA‐Seq samples and construction of a comprehensive soybean (Glycine max) expression atlas

Systematic analysis of 1298 RNA‐Seq samples and construction of a comprehensive soybean (Glycine max) expression atlas

De novo Assembly and Characterization of Patagonian Toothfish Transcriptome and Develop of EST-SSR Markers for Population Genetics

Constructing plasticity phenotypes to classify experience-dependent development of the visual cortex

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