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).

• nf-core/circrna: a portable workflow for the quantification, miRNA target prediction and differential expression analysis of circular RNAs.

  PMID 36694127 · PMC9875403 · BMC bioinformatics · 2023 · 8 claims · 4 setups

  Has reproduction · 76

  Existing circRNA workflows are limited: none delineate circRNA-miRNA interactions and only one performs differential expression analysis, requiring users to supplement missing analysis types with in-house expertise

  paired-end rna-seq mature circrna read circrna mirna

• MirDIP 5.2: tissue context annotation and novel microRNA curation.

  PMID 36453996 · PMC9825511 · Nucleic acids research · 2023 · 7 claims · 6 setups

  Has reproduction · 89

  mirDIP 5.2 removed eight outdated resources, added miRNATIP, and ran five prediction algorithms against miRBase and mirGeneDB miRNAs to expand and improve interaction coverage

  mirnas disease novel type mirna small bulk

• Identification and Characterization of Small Noncoding RNAs in Genome Sequences of the Edible Fungus Pleurotus ostreatus.

  PMID 27703969 · PMC5040776 · BioMed research international · 2016 · 7 claims · 8 setups

  Has reproduction · 95

  Genome-scale identification detected 254 small noncoding RNAs (snRNAs, snoRNAs, tRNAs, miRNAs, and other Rfam-classified sncRNAs) in the P. ostreatus CCEF00389 genome assembly

  pleurotus ostreatus ccef00389 sncrnas whole-genome rna-seq rfam

• MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features.

  PMID 17553836 · PMC1933124 · Nucleic acids research · 2007 · 8 claims · 8 setups

  Full-text index only

  A hybrid feature combining local contiguous triplet structure-sequence composition, MFE of the secondary structure, and P-value of a randomization test improves classification of real vs pseudo pre-miRNAs

  real combined pre-mirna training random independent repeated