Difference between revisions of "Genome Assembly Project: Leland Taylor '12"
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#Kingsford, C., Schatz, M.C. & Pop, M. Assembly complexity of prokaryotic genomes using short reads. BMC Bioinformatics 11, 21 (2010). | #Kingsford, C., Schatz, M.C. & Pop, M. Assembly complexity of prokaryotic genomes using short reads. BMC Bioinformatics 11, 21 (2010). | ||
| − | + | *Use De Brujin graphs to estimate "completeness" of genomes | |
| − | + | **Find Eulerian path(s) in these graphs | |
| − | + | **Note the assumptions made in the paper | |
| − | + | *Lists compression techniques and the order to employ them | |
| − | + | *Can use this method to compute N50 | |
| − | + | **N50 = the length of the largest contig (m) such that at least 50% of genome covered by contigs of size >= m. | |
| − | + | **A higher N50 score usually correlates to a more "correct" genome | |
| − | + | *Regardless of correctness of genome, for nearly all read sizes (1000nt > size > 25nt), 85%+ of genes accurately identified (85% is for 25nt reads). | |
#Pop, M. Genome assembly reborn: recent computational challenges. Briefings in Bioinformatics 10, 354-366 (2009). | #Pop, M. Genome assembly reborn: recent computational challenges. Briefings in Bioinformatics 10, 354-366 (2009). | ||
Revision as of 14:24, 23 May 2011
November 21 2024
- Kingsford, C., Schatz, M.C. & Pop, M. Assembly complexity of prokaryotic genomes using short reads. BMC Bioinformatics 11, 21 (2010).
- Use De Brujin graphs to estimate "completeness" of genomes
- Find Eulerian path(s) in these graphs
- Note the assumptions made in the paper
- Lists compression techniques and the order to employ them
- Can use this method to compute N50
- N50 = the length of the largest contig (m) such that at least 50% of genome covered by contigs of size >= m.
- A higher N50 score usually correlates to a more "correct" genome
- Regardless of correctness of genome, for nearly all read sizes (1000nt > size > 25nt), 85%+ of genes accurately identified (85% is for 25nt reads).
- Pop, M. Genome assembly reborn: recent computational challenges. Briefings in Bioinformatics 10, 354-366 (2009).
