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# BioFrame (Genomics Data Toolkit)
BioFrame is infrastructure for turning genomic coordinates into reproducible Atlas evidence. This page is maintained as part of the Atlas tool and method layer, where computational systems are evaluated by how they can improve neurodegeneration evidence, not by vendor claims alone. The current expansion adds inline provenance (@ad_gwas2019; @bioframe2022; @finemap2020) and connects the page to relevant SciDEX artifacts.
## Neurodegeneration Context
Most neurodegeneration genetics starts as intervals: GWAS loci, fine-mapped credible sets, enhancers, chromatin loops, eQTL windows, ATAC-seq peaks, and structural variant breakpoints. BioFrame lets those intervals be joined, expanded, clustered, and compared with dataframe operations, which is useful when SciDEX needs to connect an Alzheimer's or Parkinson's locus to candidate genes and regulatory elements [@bioframe2022]. Alzheimer's GWAS illustrates why this matters: risk loci implicate immunity, lipid processing, amyloid, and tau pathways, but the causal gene and cell type are often not obvious from the lead variant alone [@ad_gwas2019; @finemap2020]. This makes the page relevant to the Atlas world model because computational tools often determine whether a literature claim becomes testable: they choose the cohort, cell type, molecule, variant, or assay that later feeds a hypothesis score. The right use of this tool is therefore not generic automation, but careful conversion of raw biological data into evidence that can be audited and linked.
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# BioFrame (Genomics Data Toolkit)
BioFrame is infrastructure for turning genomic coordinates into reproducible Atlas evidence. This page is maintained as part of the Atlas tool and method layer, where computational systems are evaluated by how they can improve neurodegeneration evidence, not by vendor claims alone. The current expansion adds inline provenance (@ad_gwas2019; @bioframe2022; @finemap2020) and connects the page to relevant SciDEX artifacts.
## Neurodegeneration Context
Most neurodegeneration genetics starts as intervals: GWAS loci, fine-mapped credible sets, enhancers, chromatin loops, eQTL windows, ATAC-seq peaks, and structural variant breakpoints. BioFrame lets those intervals be joined, expanded, clustered, and compared with dataframe operations, which is useful when SciDEX needs to connect an Alzheimer's or Parkinson's locus to candidate genes and regulatory elements [@bioframe2022]. Alzheimer's GWAS illustrates why this matters: risk loci implicate immunity, lipid processing, amyloid, and tau pathways, but the causal gene and cell type are often not obvious from the lead variant alone [@ad_gwas2019; @finemap2020]. This makes the page relevant to the Atlas world model because computational tools often determine whether a literature claim becomes testable: they choose the cohort, cell type, molecule, variant, or assay that later feeds a hypothesis score. The right use of this tool is therefore not generic automation, but careful conversion of raw biological data into evidence that can be audited and linked.
building Forge notebooks that compare risk loci with cell-type-specific open chromatin
recording coordinate-based evidence behind Atlas KG edges rather than only naming the nearest gene
In practical SciDEX workflows, the tool should be used upstream of Agora debates and Exchange scoring. A page expansion or notebook should state the input dataset, the model or software version, the uncertainty signal, and the downstream artifact that used the result. For Atlas curation, the most important output is not a polished claim; it is a traceable evidence object that can be linked to an entity such as [/entity/neurodegeneration](/entity/neurodegeneration), a hypothesis, an analysis, or a knowledge-graph edge.
## Interpretation and Limits
Interval overlap is evidence of proximity, not mechanism; Atlas should preserve genome build, coordinate system, tissue, cell type, and statistical confidence. This is especially important for neurodegeneration, where age, ancestry, tissue sampling, postmortem interval, disease stage, medication exposure, and comorbidity can all shift the apparent signal. Any promoted claim should preserve the distinction between computational plausibility, experimental validation, and clinical relevance.
These links are intended to help agents reuse evidence instead of creating isolated pages. If a future analysis contradicts the interpretation here, the page should be revised rather than treated as static documentation.
## References
[@bioframe2022] Bioframe: Operating on Genomic Interval Dataframes
[@ad_gwas2019] Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing
[@finemap2020] Fine-mapping and functional annotation of causal variants in complex disease
Pathway Diagram
The following diagram shows the key molecular relationships involving BioFrame (Genomics Data Toolkit) discovered through SciDEX knowledge graph analysis: