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Showing module(s) with keyword "riboseq"

Module Kind Keywords Description
nf-core/anota2seq/anota2seqrun Process riboseq rnaseq translation differential Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq
nf-core/dotseq/dotseq Process riboseq rnaseq translation differential orf Detect differential ORF usage (DOU) and ORF-level differential translation efficiency (DTE) from Ribo-seq with matched RNA-seq using DOTSeq. Wraps DOTSeqDataSetsFromSummarizeOverlaps() + DOTSeq() + getContrasts() and emits the package's native contrast tables plus plotDOT() visualisations.
nf-core/fasta_gtf_bam_rpbp Workflow rpbp orf bayesian translation riboseq End-to-end translated-ORF discovery from ribosome profiling (Ribo-seq) data with Rp-Bp (Malone et al. 2017). Ribo-seq sequences the short mRNA footprints protected by translating ribosomes; Rp-Bp uses a Bayesian model to score which open reading frames (ORFs) on the transcriptome show the characteristic 3-nucleotide periodicity of active translation. Given a genome FASTA, annotation GTF and one or more pre-aligned Ribo-seq BAMs, the subworkflow: 1. Builds the per-ORF reference (rpbp/preparegenome) once per run - enumerates candidate ORFs from the GTF. 2. For each sample, builds per-read-length metagene profiles around annotated start codons (extract-metagene-profiles), scores their three-nucleotide periodicity with Bayesian models (estimate-metagene-profile-bayes-factors), picks the best P-site offset per periodic read length (select-periodic-offsets), and filters down to the high-confidence read-length/offset pairs that will drive ORF-level scoring (get-periodic-lengths-offsets). 3. Builds per-ORF P-site count vectors with those selected offsets (extract-orf-profiles), scores each ORF as "translated" vs "untranslated" with a Bayesian model (estimate-orf-bayes-factors), and emits the final filtered set of predicted translated ORFs as BED + DNA FASTA + protein FASTA (select-final-prediction-set). Each Rp-Bp step is a separate Nextflow process so `-resume` only re-runs what changed, and STAR/bowtie alignment is supplied via the BAM input (this subworkflow does not realign).
nf-core/gedi/indexgenome Process riboseq index genome gedi price orf Build a GEDI genome index from a FASTA and GTF for downstream PRICE ORF prediction
nf-core/gedi/price Process riboseq orf price gedi translation Identify translated ORFs from Ribo-seq BAMs using the PRICE algorithm
nf-core/plastid/makewiggle Process genomics riboseq psite wiggle bedgraph Create wiggle or bedGraph files from alignment files after applying a read mapping rule (e.g. to map ribosome-protected footprints at their P-sites), for visualization in a genome browser
nf-core/plastid/metagenegenerate Process genomics riboseq psite Compute a metagene profile of read alignments, counts, or quantitative data over one or more regions of interest, optionally applying a mapping rule
nf-core/plastid/psite Process genomics riboseq psite Estimate position of ribosomal P-site within ribosome profiling read alignments as a function of read length
nf-core/ribotish/predict Process riboseq predict bam Quality control of riboseq bam data
nf-core/ribotish/quality Process riboseq quality bam Quality control of riboseq bam data
nf-core/ribotricer/detectorfs Process riboseq orf genomics Accurate detection of short and long active ORFs using Ribo-seq data
nf-core/ribotricer/prepareorfs Process riboseq orf genomics Accurate detection of short and long active ORFs using Ribo-seq data
nf-core/ribowaltz Process riboseq psite genomics Calculation of optimal P-site offsets, diagnostic analysis and visual inspection of ribosome profiling data
nf-core/rpbp/estimatemetagenebayesfactors Process rpbp metagene bayes orf riboseq Score how strongly each per-read-length metagene profile shows the 3-nucleotide periodicity expected of actively translating ribosomes. For each candidate (read length, P-site offset) pair, Rp-Bp fits two competing Bayesian models to the count window around annotated start codons: a "periodic" model whose signal repeats every three nucleotides, and a "non-periodic" background model. The Bayes factor (ratio of the two marginal likelihoods) quantifies how much the data prefer the periodic explanation. Returns one row per (length, offset) pair with the mean and variance of the log Bayes factor across MCMC samples. Downstream, `rpbp/selectperiodicoffsets` picks the best offset per length from this table, and `rpbp/getperiodiclengthsoffsets` filters to the high-confidence pairs that drive ORF-level scoring. Uses the Stan models bundled inside the rpbp Python package.
nf-core/rpbp/estimateorfbayesfactors Process rpbp orf bayes translation riboseq Score every candidate ORF for evidence of active translation. For each ORF, Rp-Bp fits two competing Bayesian models to its per-codon P-site count vector: a "translated" model that expects P-site density to concentrate at codon-start positions (the in-frame signal a translating ribosome produces), and an "untranslated" / noise model for the same data. The Bayes factor (ratio of marginal likelihoods) quantifies how much the data favour the translated hypothesis. Emits a BED-style table with one row per ORF carrying genomic coordinates plus the mean and variance of the log Bayes factor across MCMC samples. Downstream, `rpbp/selectfinalpredictionset` applies Bayes-factor, length and overlap rules to this table to produce the final filtered prediction set. Uses the Stan models bundled inside the rpbp Python package.
nf-core/rpbp/extractmetageneprofiles Process rpbp metagene orf riboseq Build per-read-length pileups of Ribo-seq read 5'-ends around annotated start codons - the "metagene profile". For each read length, the profile counts how many reads of that length have their 5' end at each position in a window around every annotated start codon, summed across all transcripts. Looking at the profile across the window reveals whether reads of that length show