2019
DOI: 10.1101/601468
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Ribo-seQC: comprehensive analysis of cytoplasmic and organellar ribosome profiling data

Abstract: Ribosome profiling enables genome-wide analysis of translation with unprecedented resolution. We present Ribo-seQC, a versatile tool for the comprehensive analysis of Ribo-seq data, providing in-depth insights on data quality and translational profiles for cytoplasmic and organelle ribosomes. Ribo-seQC automatically generates platform-independent HTML reports, offering a detailed and easy-to-share basis for collaborative Ribo-seq projects.Availability: Ribo-seQC is available at https://github.com/ohlerlab/Ribo… Show more

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Cited by 36 publications
(37 citation statements)
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“…Reads aligned to rRNA or a collection of snoRNAs, tRNAs and miRNAs were discarded. The filtered reads were mapped to the hg38 version of the human genome using STAR (Dobin et al, 2013) and count matrices built as described previously (Calviello et al, 2020) using Ribo-seQC (Calviello et al, 2019). Only reads above 24 nucleotides long were included in the count.…”
Section: Pre-processing and Alignment Of Ngs Datamentioning
confidence: 99%
“…Reads aligned to rRNA or a collection of snoRNAs, tRNAs and miRNAs were discarded. The filtered reads were mapped to the hg38 version of the human genome using STAR (Dobin et al, 2013) and count matrices built as described previously (Calviello et al, 2020) using Ribo-seQC (Calviello et al, 2019). Only reads above 24 nucleotides long were included in the count.…”
Section: Pre-processing and Alignment Of Ngs Datamentioning
confidence: 99%
“…Count matrices for Ribo-seq and RNA-seq were built using reads mapping uniquely to CDS regions of protein-coding 3 genes, using the Bioconductor packages GenomicFeatures, GenomicFiles and GenomicAlignments. Genomic and transcript regions where extracted using Ribo-seQC (Calviello et al 2019). Only reads mapping for more than 25nt were used.…”
Section: Differential Expression Analysismentioning
confidence: 99%
“…We combined both types of uORFs into a single uORF category as we detect no differential impact of each uORF category on the primary ORF TE, in accordance with previous work (Heesch et al, 2019). For the visualization of P-site tracks ( Figure S3C) we used plots generated by Ribo-seQC (Calviello et al, 2019).…”
Section: Identifying Translated Open Reading Framesmentioning
confidence: 67%
“…We used STAR to align the previous datasets mapped with Tophat2 and we found Pearson correlations > 0.99 across both methods, supporting the reproducibility of the data regardless of the mapping algorithm. Data QC of all Ribo-seq libraries was performed using Ribo-seQC v1.1 (Calviello et al, 2019).…”
Section: Sequencing Data Alignmentmentioning
confidence: 99%
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