Prodigal
高效的原核基因识别工具，使用动态规划和自训练策略自动学习物种特异性编码偏好性进行基因预测。 该方法无需外部训练数据即可准确预测蛋白编码基因，支持宏基因组模式处理混合物种组装数据。
| Property | Value |
|---|---|
| Purpose | 原核生物蛋白编码基因的快速从头预测 |
| Time Complexity | O(n) |
| Space Complexity | O(n) |
| Year | 2010 |
| Difficulty | Beginner |
| Languages | C |
| Category | Gene Prediction |
Complexity Analysis
- Time Complexity:
O(n) - Space Complexity:
O(n)
Performance Insight: The time complexity of this algorithm is linear (O(n)), scales linearly to TB-scale data and is suitable for streaming pipelines. Linear space can often be reduced by constant factors via sliding-window techniques.
Note: Complexity analysis is based on theoretical models. Actual runtime is affected by input scale, hardware, and implementation optimizations. Benchmark for your specific workload.
Literature & Implementation
Related Tools
GeneMark · Glimmer · FragGeneScan