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).
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Clustering of phosphorylation site recognition motifs can be exploited to predict the targets of cyclin-dependent kinase.
PMID 17316440 · PMC1852407 · Genome biology · 2007 · 8 claims · 6 setups
CDK consensus motifs are frequently clustered (closely spaced) in known CDK substrate proteins rather than uniformly distributed
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Simple models of genomic variation in human SNP density.
PMID 17553150 · PMC1919371 · BMC genomics · 2007 · 6 claims · 4 setups
Hierarchical Poisson model B, which allows both the mutation-rate proxy (Beta-distributed Λ) and the ARG-size proxy (Gamma-distributed T) to vary, fits the observed SNP density distribution significantly better than models with only one or neither varying.
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Has reproduction · 99
Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing.
PMID 34103501 · PMC8187371 · Nature communications · 2021 · 7 claims · 4 setups
Nanopore methylation detection tools exhibit a tradeoff between false positives and false negatives and high dispersion relative to expected per-site methylation frequencies.
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Has reproduction · 76
The genome and development-dependent transcriptomes of Pyronema confluens: a window into fungal evolution.
PMID 24068976 · PMC3778014 · PLoS genetics · 2013 · 8 claims · 8 setups
The 50 Mb P. confluens genome with 13,369 predicted protein-coding genes is more characteristic of higher filamentous ascomycetes than of the large, repeat-rich Tuber melanosporum genome, showing that the truffle's expanded genome is not typical of the Pezizales.
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Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.
PMID 19654296 · PMC2763410 · Cancer research · 2009 · 7 claims · 7 setups
CHASM, a Random Forest-based computational method, was developed to identify and prioritize missense mutations likely to be functional drivers of tumor cell proliferation.