DOI: 10.18129/B9.bioc.seqpac  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see seqpac.

Seqpac: A Framework for smallRNA analysis in R using Sequence-Based Counts

Bioconductor version: 3.17

Seqpac provides functions and workflows for analysis of short sequenced reads. It was originally developed for small RNA analysis, but can be implemented on any sequencing raw data (provided as a fastq-file), where the unit of measurement is counts of unique sequences. The core of the seqpac workflow is the generation and subsequence analysis/visualization of a standardized object called PAC. Using an innovative targeting system, Seqpac process, analyze and visualize sample or sequence group differences using the PAC object. A PAC object in its most basic form is a list containing three types of data frames. - Phenotype table (P): Sample names (rows) with associated metadata (columns) e.g. treatment. - Annotation table (A): Unique sequences (rows) with annotation (columns), eg. reference alignments. - Counts table (C): Counts of unique sequences (rows) for each sample (columns). The PAC-object follows the rule: - Row names in P must be identical with column names in C. - Row names in A must be identical with row names in C. Thus P and A describes the columns and rows in C, respectively. The targeting system, will either target specific samples in P (pheno_target) or sequences in A (anno_target) and group them according to a target column in P and A, respectively (see vignettes for more details).

Author: Daniel Natt [aut, cre, fnd], Lovisa Örkenby [ctb], Signe Skog [ctb], Anita Öst [aut, fnd]

Maintainer: Daniel Natt <daniel.natt at>

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biocViews AnnotationWorkflow, BasicWorkflow, EpigeneticsWorkflow, GeneExpressionWorkflow, Workflow
Version 1.0.0
License GPL-3
Depends R (>= 4.2.0)
Imports Biostrings(>= 2.46.0), foreach (>= 1.5.1), GenomicRanges(>= 1.30.3), Rbowtie(>= 1.18.0), ShortRead(>= 1.36.1), tibble (>= 3.1.2), BiocParallel(>= 1.12.0), cowplot (>= 0.9.4), data.table (>= 1.14.0), digest (>= 0.6.27), doParallel (>= 1.0.16), dplyr (>= 1.0.6), factoextra (>= 1.0.7), FactoMineR (>= 1.41), ggplot2 (>= 3.3.3), IRanges(>= 2.12.0), parallel (>= 3.4.4), reshape2 (>= 1.4.4), rtracklayer(>= 1.38.3), stringr (>= 1.4.0), stats (>= 3.4.4), methods, S4Vectors
Suggests benchmarkme (>= 0.6.0), DESeq2(>= 1.18.1), GenomeInfoDb(>= 1.14.0), gginnards (>= 0.0.2), qqman (>= 0.1.8), rmarkdown, BiocStyle, knitr, testthat, UpSetR (>= 1.4.0), venneuler, R.utils, bigreadr, readr, vroom
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