Near-optimal probabilistic RNA-seq quantification

By Nicolas L. Bray, Harold Pimentel, Páll Melsted, Lior S. Pachter. Nature Biotechnology, 2016

8,658 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/da09c3e8cc017aeaa4bca0ee71c0e4d4ba4b6515

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Citations in the corpus

Transcript expression-aware annotation improves rare variant discovery and interpretation (2019, neutral): “and transcript sequence complexity (15-17).”
Reproducibility of computational workflows is automated using continuous analysis (2017, methodology): “This analysis quantified transcript counts using kallisto 26 , limma 27,28 and sleuth 29 ; performed principal component analysis and ran a differential expression analysis.”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Addressing these shortcomings, kallisto relies on the use of pseudoalignments to directly compute the sufficient statistics of the equivalenceclassbased model of transcript abundance estimation [101] .”
CDK4/6 Inhibition Augments Anti-Tumor Immunity by Enhancing T Cell Activation (2017, methodology): “The digested NFATc3 and His-GB1 were separated and further purified using size exclusion chromatography (GE Healthcare Life Sciences “Superdex 75 10/300 GL”).”
Expression Atlas update: from tissues to single cells (2019, methodology): “Filtered reads are then quantified with Kallisto (12), and cell-wise quantifications subsequently combined to a final matrix.”
Next-Generation Sequencing Technology: Current Trends and Advancements (2023, methodology): “Alignment of reads to reference Splice-aware aligner such as TopHat2 [123], HISAT2 [124], and STAR [125] Transcript quantiﬁcation featureCounts [126], HTSeq-count [127], Salmon [128], Kallisto [129] Differential gene expression analysis enrichment of gene categories DESeq2 [130], EdgeR [131], DAVID ”
A subcellular map of the human proteome (2017, methodology): “Human reference genome GrCh38 and further quantified using the Kallisto software (57) to generate normalized Transcript Per Million (TPM) values.”
variancePartition: interpreting drivers of variation in complex gene expression studies (2016, neutral): “The software can easily be applied to RNA-seq quan-tifications 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].”