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).

• A response to Yu et al. "A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array", BMC Bioinformatics 2007, 8: 145.

  PMID 17939873 · PMC2222656 · BMC bioinformatics · 2007 · 8 claims · 4 setups

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  Yu et al.'s original comparison ran RJaCGH's MCMC sampler for a severely insufficient number of iterations (50 burn-in, 500 total)

  simulated same mcmc-based direct original

• A computational screen for type I polyketide synthases in metagenomics shotgun data.

  PMID 18953415 · PMC2568958 · PloS one · 2008 · 8 claims · 6 setups

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  Combining HMM domain searches with maximum-likelihood phylogenetic trees can discriminate true PKS I sequences from evolutionarily related but functionally different enzymes (e.g., FAS I) in metagenomic data.

  predicted hmm-identified reference hmm hmm maximum-likelihood robinson-foulds

• QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data.

  PMID 17341461 · PMC1874617 · Nucleic acids research · 2007 · 8 claims · 7 setups

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  QuantiSNP (OB-HMM) provides probabilistic quantification of copy number states and significantly improves accuracy of segmental aneuploidy identification and breakpoint mapping relative to existing tools (BeadStudio/Illumina)

  genomic same pseudo-normal artificially snp fish molecular

• Applications for protein sequence-function evolution data: mRNA/protein expression analysis and coding SNP scoring tools.

  PMID 16912992 · PMC1538848 · Nucleic acids research · 2006 · 7 claims · 8 setups

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  PANTHER HMMs built from family/subfamily multiple sequence alignments can classify novel protein sequences into functional groups based on statistically significant HMM match scores

  input gene genome-wide genes sequence gene numerical