Ballgown bridges the gap between transcriptome assembly and expression analysis

Frazee, Alyssa C.; Pertea, Geo; Jaffe, Andrew E.; Langmead, Ben; Salzberg, Steven L.; Leek, Jeffrey T.

doi:10.1038/nbt.3172

Cited by 644 publications

(518 citation statements)

References 34 publications

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“…The variancePartition software is an open source R package and is freely available on Bioconductor. The software can easily be applied to RNA-seq quantifications from featureCounts [46], HTSeq [47], kallisto [48], sailfish [49], salmon [50], RSEM [51] and cufflinks [52] which have been processed in R with limma/voom [15], DESeq2 [16], tximport [53] and ballgown [54]. The software provides a user-friendly interface for analysis and visualization with extensive documentation, and will enable routine application to a range of genomics datasets.…”

Section: Resultsmentioning

confidence: 99%

variancePartition: Interpreting drivers of variation in complex gene expression studies

Hoffman

Schadt

2016

Preprint

118

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Background: As large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. Results: We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets. Conclusions:Our open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition.

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Section: Resultsmentioning

confidence: 99%

variancePartition: Interpreting drivers of variation in complex gene expression studies

Hoffman

Schadt

2016

Preprint

118

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“…Reads were aligned to the 12X.2 version of the grapevine reference genome PN40024 using HISAT2 (Kim et al 2015) and the transcriptome was assembled using StringTie with the CRIBI functional annotation (Vitulo et al 2014). Transcribed genes were analyzed by fitting a linear model y = Xb + using Ballgown (Frazee et al 2015) where y was a vector holding the log 2 (FPKM + 1) values of genes with FPKM (Fragments Per Kilobase of transcript per Million reads sequenced) variances greater than one for each genotype; X was a design matrix that contained the indicator variables for the intercept and the genotypephase grouping; b was a vector holding the mean and the effect of the group value; and ∼ N(0, I 2 ) was a vector holding the error. For each gene, the full model was compared to a model without the grouping covariate to derive an F statistic.…”

Section: Rna-seq Experimental Design and Analysismentioning

confidence: 99%

Construction of a dense SNP map of a highly heterozygous diploid potato population and QTL analysis of tuber shape and eye depth

Prashar

Hornyik

Young³

et al. 2014

Theor Appl Genet

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Key message Downy mildew resistance across days post-inoculation, experiments, and years in two interspecific grapevine F 1 families was investigated using linear mixed models and Bayesian networks, and five new QTL were identified. Abstract Breeding grapevines for downy mildew disease resistance has traditionally relied on qualitative gene resistance, which can be overcome by pathogen evolution. Analyzing two interspecific F 1 families, both having ancestry derived from Vitis vinifera and wild North American Vitis species, across 2 years and multiple experiments, we found multiple loci associated with downy mildew sporulation and hypersensitive response in both families using a single phenotype model. The loci explained between 7 and 17% of the variance for either phenotype, suggesting a complex genetic architecture for these traits in the two families studied. For two loci, we used RNA-Seq to detect differentially transcribed genes and found that the candidate genes at these loci were likely not NBS-LRR genes. Additionally, using a multiple phenotype Bayesian network analysis, we found effects between the leaf trichome density, hypersensitive response, and sporulation phenotypes. Moderate-high heritabilities were found for all three phenotypes, suggesting that selection for downy mildew resistance is an achievable goal by breeding for either physical-or non-physical-based resistance mechanisms, with the combination of the two possibly providing durable resistance.

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“…Intron junction counts for annotations in GENCODE version 19 were obtained from aligned BAM files using Tablemaker and Ballgown 32 and normalized to the total number of junction reads observed 3 10 6 . The linear regression between normalized snoRNA expression (RPM) and the normalized expression for "host gene" junctions spanning each snoRNA were assembled in R. 33 Correlations between all snoRNAs and junction expressions were similarly performed.…”

Section: Splicing Analysismentioning

confidence: 99%

Expression profiling of snoRNAs in normal hematopoiesis and AML

Warner¹,

Spencer

Trissal³

et al. 2018

Blood Advances

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Key Points• A subset of snoRNAs is expressed in a developmental-and lineage-specific manner during human hematopoiesis.• Neither host gene expression nor alternative splicing accounted for the observed differential expression of snoRNAs in a subset of AML.Small nucleolar RNAs (snoRNAs) are noncoding RNAs that contribute to ribosome biogenesis and RNA splicing by modifying ribosomal RNA and spliceosome RNAs, respectively. We optimized a next-generation sequencing approach and a custom analysis pipeline to identify and quantify expression of snoRNAs in acute myeloid leukemia (AML) and normal hematopoietic cell populations. We show that snoRNAs are expressed in a lineage-and development-specific fashion during hematopoiesis. The most striking examples involve snoRNAs located in 2 imprinted loci, which are highly expressed in hematopoietic progenitors and downregulated during myeloid differentiation. Although most snoRNAs are expressed at similar levels in AML cells compared with CD34 1 , a subset of snoRNAs showed consistent differential expression, with the great majority of these being decreased in the AML samples. Analysis of host gene expression, splicing patterns, and whole-genome sequence data for mutational events did not identify transcriptional patterns or genetic alterations that account for these expression differences. These data provide a comprehensive analysis of the snoRNA transcriptome in normal and leukemic cells and should be helpful in the design of studies to define the contribution of snoRNAs to normal and malignant hematopoiesis.

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Ballgown bridges the gap between transcriptome assembly and expression analysis

Cited by 644 publications

References 34 publications

variancePartition: Interpreting drivers of variation in complex gene expression studies

variancePartition: Interpreting drivers of variation in complex gene expression studies

Construction of a dense SNP map of a highly heterozygous diploid potato population and QTL analysis of tuber shape and eye depth

Expression profiling of snoRNAs in normal hematopoiesis and AML

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