Experiments
Searchable full-text extractions: founding hypothesis, core claims, experimental setups, key results and statistics — pulled out of each paper as structure. Search a cell line, an assay or an entity (e.g. HUH7) and find every paper that worked with it. This corpus stands on its own: most entries carry no reproduction assessment (yet).
-
Has reproduction · 74
Transcriptome profiling of Giardia intestinalis using strand-specific RNA-seq.
PMID 23555231 · PMC3610916 · PLoS computational biology · 2013 · 8 claims · 8 setups
Most of the G. intestinalis genome is transcribed in in vitro-grown trophozoites, but at vastly different expression levels.
-
Has reproduction · 87
High-resolution mapping of transcriptional dynamics across tissue development reveals a stable mRNA-tRNA interface.
PMID 25122613 · PMC4216921 · Genome research · 2014 · 8 claims · 7 setups
mRNA codon and amino acid pools are highly stable across mouse development and across tissues, simply reflecting the genomic background distribution of any possible transcriptome.
-
Has reproduction · 68
Bayesian transcriptome assembly.
PMID 25367074 · PMC4397945 · Genome biology · 2014 · 8 claims · 8 setups
Bayesembler, a probabilistic transcriptome assembler built on a Bayesian model of the RNA sequencing process with Gibbs sampling over expressed candidates, abundances and read assignments, is introduced.
-
Has reproduction · 59
Nucleosome regulatory dynamics in response to TGFβ.
PMID 24771338 · PMC4066760 · Nucleic acids research · 2014 · 8 claims · 7 setups
SuMMIt, a Bayesian strand-based mixture model requiring support from both ends of sequenced fragments, enables precise nucleosome mid-position calling, fuzziness scoring and between-condition change detection.
-
Has reproduction · 73
Vespucci: a system for building annotated databases of nascent transcripts.
PMID 24304890 · PMC3936758 · Nucleic acids research · 2014 · 8 claims · 7 setups
Existing ChIP-seq and RNA-seq analysis platforms (e.g. Cufflinks, peak callers) are unsuited to GRO-seq because they assume spliced/exonic reads, uniform density and paired-end data, and cannot identify transcriptional units de novo across the whole genome.