Workshop: Graph compression approaches in assembly

Jason Pell,Arend Hintze,Rosangela Canino-Koning,Adina Howe,James M. Tiedje,C. Titus Brown

Computational Advances in Bio and Medical Sciences（2012）

引用 0|浏览0

暂无评分

摘要

Using a probabilistic data structure to store DNA assembly graphs results in a significant memory savings over other methods. As long as the Bloom filter remains below a specific false positive rate, it remains possible to traverse the graph. Using a Bloom filter has many applications in metagenomics, mRNAseq, read filtering, and error correction. We are currently exploring these possibilities and more.

查看译文

关键词

DNA,data structures,filtering theory,genomics,graph theory,molecular biophysics,probability,Bloom filter,DNA assembly graph,error correction,graph compression approach,mRNAseq,metagenomics,probabilistic data structure,read filtering,Bloom filters,de Bruijn graphs,k-mers,metagenomics,next-generation sequencing

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要