2016
DOI: 10.1038/nbt0816-888d
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Erratum: Near-optimal probabilistic RNA-seq quantification

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Cited by 257 publications
(195 citation statements)
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“…Libraries were sequenced on Hiseq 4000 (50bp reads) Gene expression levels were quantitated using kallisto [39] . These data were converted to counts and summarized per gene using tximport [40] and differential expression was carried out using DESeq2 [41] using an FDR of 0.05 and no explicit fold change cutoff.…”
Section: Rna-seq Data Analysismentioning
confidence: 99%
“…Libraries were sequenced on Hiseq 4000 (50bp reads) Gene expression levels were quantitated using kallisto [39] . These data were converted to counts and summarized per gene using tximport [40] and differential expression was carried out using DESeq2 [41] using an FDR of 0.05 and no explicit fold change cutoff.…”
Section: Rna-seq Data Analysismentioning
confidence: 99%
“…Danio rerio Gene Stable IDs were translated to Mus musculus Gene Stable IDs using biomaRt 51 package and the overall gene expression was analyzed with Gene Set Enrichment Analysis (GSEA) 52 using the Hallmarks collection as biological insight. Only data with adjusted p-value and FDR < 0.05 were considered significant and represented.…”
Section: Methodsmentioning
confidence: 99%
“…In addition to the reconstruction of transcript sequences, RNA sequencing also allows the user to quantify transcript expression by counting the number of sequenced reads that map to a 220 given transcript. Paired reads for each library were pseudo-aligned on the Transcriptome de Bruijn Graph (T-DBG) using Kallisto [35]. We chose this method, based on a k-mer approach, because it is much faster while providing the same accuracy as the best mapping approaches [35].…”
Section: Differential Expression and Go Enrichmentmentioning
confidence: 99%
“…Paired reads for each library were pseudo-aligned on the Transcriptome de Bruijn Graph (T-DBG) using Kallisto [35]. We chose this method, based on a k-mer approach, because it is much faster while providing the same accuracy as the best mapping approaches [35]. This method produced raw counts and normalized count statistics (TPM, or transcripts per million reads) for each assembled contig.…”
Section: Differential Expression and Go Enrichmentmentioning
confidence: 99%
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