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贝叶斯蛋白质组学分析×变异检测×
领域生物信息学生物信息学
方法族Process / pipelineProcess / pipeline
起源年份2000s (major developments 2003–2010)2009–2010 (modern high-throughput era)
提出者Multiple contributors; foundational statistical frameworks by Nesvizhskii, Kall, Choi, and colleaguesLi et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
类型Probabilistic inference pipelineComputational genomics pipeline
开创性文献Kall, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2008). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods, 5(11), 923–925. link ↗McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303. DOI ↗
别名Bayesian protein quantification, Bayesian peptide inference, probabilistic proteomics, Bayesian mass spectrometry analysisSNP calling, genotyping from sequencing, mutation detection, variant detection
相关66
摘要Bayesian proteomics analysis applies probabilistic models to mass spectrometry data to identify peptides, infer protein presence, and quantify differential protein abundance across conditions. By encoding prior knowledge and propagating uncertainty through each step of the pipeline, Bayesian approaches produce calibrated posterior probabilities of identification and quantification rather than simple point estimates, enabling more principled control of false discovery rates and more honest reporting of uncertainty than purely frequentist alternatives.Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications.
ScholarGate数据集
  1. v1
  2. 2 来源
  3. PUBLISHED
  1. v1
  2. 2 来源
  3. PUBLISHED

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ScholarGate方法对比: Bayesian Proteomics Analysis · Variant Calling. 于 2026-06-15 检索自 https://scholargate.app/zh/compare