fork:pmid:27508061:pmid-27508061-from-reads-to-genes-to-pathways-f804cb05 Public
https://scm.piranha.sh/git/ada56728063306072cb24f17/sci-fork-pmid-27508061-pmid-27508061-from-reads-to-genes-to-pathways-f804cb05 · at 65c2fc923fb2
fork:pmid:27508061:pmid-27508061-from-reads-to-genes-to-pathways-f804cb05 · revision 2 · F1000Research
In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling epithelial cell subsets in the mouse mammary gland. The workflow uses R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, including alignment of read sequences, data exploration, differential expression analysis, visualization and pathway analysis. Read alignment and count quantification is conducted using the Rsubread package and the statistical analyses are performed using the edgeR package. The differential expression analysis uses the quasi-likelihood functionality of edgeR.
Yunshun Chen, Aaron T. L. Lun, Gordon K. Smyth
Recomputed from the file: it matches.
manuscript · open · resolved
xml · 60,617 bytes · sha256:3bf8a3f4c0135bde16e66f7f232937e6a89868783968461f69da3b9c8795dceb
Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.
raw · open · resolved
geo-series ·
Except for the targets file targets.txt, all data analyzed in the workflow is read automatically from public websites as part of the code. All software used is publicly available as part of Bioconductor 3.3, except for the fastq-dump utility, which can be downloaded from NCBI website as described in the text. The article includes the complete code necessary to reproduce the analyses shown.
raw · open · resolved
sra-study ·
The twelve raw FASTQ runs the paper aligns, one per library, and the study they belong to.
supplementary · open · verified
geo-supplementary · 513,592 bytes · sha256:906b6ec4c33e95ca0ba454a3e0d07ad74e9cb7528ee38b89a9b122f5674a5f0d
The genewise read-count matrix the paper analyses: 27179 genes over twelve libraries, named by the repository. Fetched and hashed on 4 November 2026, and checked against the numbers the paper printed.
reference · open · verified
bioconductor-package · 63,495,324 bytes · sha256:5dc93707b7ca319b295b5b19963c573d5a3817d836f458a3e30d635ddd4e2365
The annotation release the paper's own sessionInfo names, fetched and hashed on 4 November 2026 from the Bioconductor 3.3 archive. It decides which genes keep a symbol, and the paper discards the ones that do not, so it is an input to the filtering stage.
code · open · verified
text/plain · 525 bytes · sha256:88ae42c0539403073f0bbbd53287dd9c9cf61ddd5b1a42856243d7d99113cb8b
The authors' own file, fetched and hashed on 3 November 2026. The publisher states no licence for the entry.
code · open · verified
R source · 10,298 bytes · sha256:3b11478ac314c4456af8b4ba7af7992e938488ffc7bec88d3f73f42aed4c0bba
The authors' own file, fetched and hashed on 3 November 2026. The publisher states no licence for the entry.
code · open · verified
Rnw source · 59,373 bytes · sha256:defd3acb73dcbfe885ca02209ac9437b785b4ccf12c7ba09a8a8bb311173902d
The authors' own file, fetched and hashed on 3 November 2026. The publisher states no licence for the entry.
code · open · verified
PDF · 904,232 bytes · sha256:76ea4da65b151b1bb78ba42b0d80c0e864632df0e13219fe968525a43efd2276
The authors' own file, fetched and hashed on 3 November 2026. The publisher states no licence for the entry.
reference · open · declared
fasta.gz ·
The mouse genome build the paper indexes and aligns to, as the paper names it.
In this file
As the paper states them
12 samples in total.
Reference group: B.pregnant.
There are therefore six groups of RNA samples, one for each combination of cell type and mouse status. Two biological replicates were collected for each group.
A fork of the exact Corpus revision, containing the five paper-defined assays in order. No assay has run until a recorded execution is bound to this fork.
Read the inputs and check them against the hashes the paper and its sources publish
computational
Run the calls the authors published, in the order the authors published them
computational
Compare every number with the number the paper printed, at the precision it printed it
computational
Write the tables and the figures, and hash each file the assay produced
computational
Package the result with the dataset hash, the parameters and the artefact hashes
computational
Read the inputs and check them against the hashes the paper and its sources publish
computational
Run the calls the authors published, in the order the authors published them
computational
Compare every number with the number the paper printed, at the precision it printed it
computational
Write the tables and the figures, and hash each file the assay produced
computational
Package the result with the dataset hash, the parameters and the artefact hashes
computational
Read the inputs and check them against the hashes the paper and its sources publish
computational
Run the calls the authors published, in the order the authors published them
computational
Compare every number with the number the paper printed, at the precision it printed it
computational
Write the tables and the figures, and hash each file the assay produced
computational
Package the result with the dataset hash, the parameters and the artefact hashes
computational
Read the inputs and check them against the hashes the paper and its sources publish
computational
Run the calls the authors published, in the order the authors published them
computational
Compare every number with the number the paper printed, at the precision it printed it
computational
Write the tables and the figures, and hash each file the assay produced
computational
Package the result with the dataset hash, the parameters and the artefact hashes
computational
Read the inputs and check them against the hashes the paper and its sources publish
computational
Run the calls the authors published, in the order the authors published them
computational
Compare every number with the number the paper printed, at the precision it printed it
computational
Write the tables and the figures, and hash each file the assay produced
computational
Package the result with the dataset hash, the parameters and the artefact hashes
computational
sequence_align
Read alignment as the workflow of the paper runs it.
assay:1 · data_generation · version 0.1.0
needs: rna_seq_read_alignment, sequence_align
sha256:781215e3b5e462988c0f0a214b7462f32b5e18c58a59ff0e7366fdf7059c67a9
assay:2 · data_generation · version 0.1.0
needs: rna_seq_gene_quantification
depends on: assay:1
sha256:a7731347e0ac054f8756d31ba6f586cb68a0e381b1946dfec53ef72bcddb1407
assay:3 · normalisation · version 0.1.0
needs: rna_seq_count_filtering_and_normalisation, normalise_counts
depends on: assay:2
sha256:029f48150fdbe3973df58cc1d9a63ea34bd5ba35c319469ee1cfd1249101c0fe
assay:4 · analysis · version 0.1.0
needs: rna_seq_differential_expression, differential_expression_fit
depends on: assay:3
sha256:dfda866165c72d6cd1517a23e85867cdaa0adc1df88f9977b642792bdcdc7278
assay:5 · analysis · version 0.1.0
needs: rna_seq_pathway_enrichment, pathway_enrich
depends on: assay:4
sha256:13560e312926f7eb0829d4fcf69d3ebb6bc2af081c63ea341def7d3ca69a1e27
can run: normalise_counts, differential_expression_fit, fdr_bh, pathway_enrich
Still to build
method
The DSL catalogue has no operation for: rna_seq_read_alignment, gene_count_quantification, normalise_counts_tmm, differential_expression_quasi_likelihood. The catalogue's normalise_counts is counts per million and its differential_expression_fit is a Welch t-test, so neither is edgeR's TMM normalisation nor its quasi-likelihood F-test.
capability
The corpus capability table owns computational analysis.
data
The GEO series resolves, its twelve samples are listed, and the twelve SRA runs resolve. No dataset was downloaded and no licence was read.
Runs
85f9fdf4-4c2e-4155-8daa-da11d28021ef
rna_seq_differential_expression · complete
cb681050-44e5-43c9-801c-87e82a09ed55
rna_seq_count_filtering_and_normalisation · complete
8573fa99-85cd-4f56-bdd5-3651d1cdfd58
rna_seq_gene_quantification · refused
7daff85e-0e95-4e29-b922-1bf66aa96252
rna_seq_read_alignment · refused
213bfb92-8ff7-4d1e-925c-19d9f7047551
rna_seq_count_filtering_and_normalisation · refused
c0d08ec6-0c1f-49db-b139-adcc2916656d
rna_seq_count_filtering_and_normalisation · refused
91daa9a9-2b77-45e0-8727-13a7643efe9d
rna_seq_pathway_enrichment · complete
a92cdcdd-e95c-4579-a4dd-7648d2c24eff
rna_seq_differential_expression · complete
fa44f761-7022-4c3f-975f-d8ce85abd2a8
rna_seq_count_filtering_and_normalisation · complete
Review carried by the file
absent
No person has reviewed this file. The reader's assessment is an opinion and not a decision.
data
readiness
review
No fork has been opened from this paper yet.
Files
| fork:pmid:27508061:pmid-27508061-from-reads-to-genes-to-pathways-f804cb05.json | 90,559 bytes |