DocumentCode
1809858
Title
Workshop: Graph compression approaches in assembly
Author
Pell, Jason ; Hintze, Arend ; Canino-Koning, Rosangela ; Howe, A. ; Tiedje, James M. ; Brown, C. Titus
Author_Institution
Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA
fYear
2012
fDate
23-25 Feb. 2012
Firstpage
1
Lastpage
2
Abstract
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.
Keywords
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; Assembly; Bioinformatics; DNA; Data structures; Educational institutions; Genomics; Soil; Bloom filters; de Bruijn graphs; k-mers; metagenomics; next-generation sequencing;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Advances in Bio and Medical Sciences (ICCABS), 2012 IEEE 2nd International Conference on
Conference_Location
Las Vegas, NV
Print_ISBN
978-1-4673-1320-9
Electronic_ISBN
978-1-4673-1319-3
Type
conf
DOI
10.1109/ICCABS.2012.6182675
Filename
6182675
Link To Document