lecture_notes:04-09-2010
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lecture_notes:04-09-2010 [2010/04/11 22:30] learithe |
lecture_notes:04-09-2010 [2010/04/12 21:37] (current) cbrumbau Changed for reference format, italics, punctuation, spelling, etc. |
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| Take fix mode script from /projects/compbio/bin/scripts and replace protein user group with BME 235 user group. | Take fix mode script from /projects/compbio/bin/scripts and replace protein user group with BME 235 user group. | ||
| - | Next week will have a reference genome (//Pyrobaculum oguniense//, aka //"Pog"//) to use for testing the tools on. | + | Next week will have a reference genome (//Pyrobaculum oguniense//, aka "//Pog//") to use for testing the tools on. |
| - | For the most part Pog is done; however, there are still some uncertainty with 8 SNPs left. It is definitely past the MIAMI standard at this point. | + | For the most part //Pog// is done; however, there are still some uncertainty with 8 SNPs left. It is definitely past the MIAMI standard at this point. (//Pog// assembly is down to only 8 SNPs & one potentially variable insert.) |
| - | Note about sequencing platform quality scores: most platforms are trying to use the phred quality score((http://en.wikipedia.org/wiki/Phred_quality_score)), so the quality score is comparable between the platforms and runs | + | Note about sequencing platform quality scores: most platforms are trying to use the Phred quality score[(cite:phred>[[wp>Phred quality score]])], so the quality score is theoretically comparable between the platforms and runs (although calibration causes scores to vary between runs and instruments nonetheless). |
| It can be informative, once reads are mapped, to look at the quality scores for reads with observed errors. | It can be informative, once reads are mapped, to look at the quality scores for reads with observed errors. | ||
| - | //Pog// assembly is down to only 8 snps & one potentially variable insert | + | Lior Pachter (from UC Berkeley) is vising on Monday, to speak about the Bowtie/TopHat/CuffLinks algorithms. (Bowtie: mapping; TopHat/Cufflinks: find splice junctions, predicted spliced transcripts. Bowtie is used in a lot of the assembly algorithms.) |
| - | + | ||
| - | Lior Pachter (from UC Berkeley) is vising on Monday, to speak about the Bowtie/TopHat/CuffLinks algorithms. Bowtie: mapping; TopHat/Cufflinks: find splice junctions, predicted spliced transcripts. Bowtie is used in a lot of the assembly agorithms. | + | |
| ===== Main lecture: Assembler graphs ===== | ===== Main lecture: Assembler graphs ===== | ||
| Line 45: | Line 43: | ||
| * <- <- (B -> A) | * <- <- (B -> A) | ||
| * 3 different edge types: | * 3 different edge types: | ||
| - | * same dir: A to B / B to A | + | * Same dir: A to B / B to A |
| - | * tail-to-tail (convergent): A' to B | + | * Tail-to-tail (convergent): A' to B |
| - | * head-to-head (divergent): A to B' | + | * Head-to-head (divergent): A to B' |
| - | Need to have some tolerance for error because the reads are noisy. When creating read overlaps, if you require 100% pairing, you’ll miss a lot of data. plus these include the read ends, where quality falls off. so need a “overlap quality score”. | + | Need to have some tolerance for error because the reads are noisy. When creating read overlaps, if you require 100% pairing, you’ll miss a lot of data. Also these include the read ends, where quality falls off, so you need a “overlap quality score”. |
| - | Can’t do all-vs-all searches (n^2 algorithms not a good idea with billions of reads…). So how do you search what to overlap? Most algorithms do a blast-like filter before trying to align edges (~ nlogn) | + | Can’t do all-vs-all searches (n<sup>2</sup> algorithms not a good idea with billions of reads). So how do you search what to overlap? Most algorithms do a BLAST-like filter before trying to align edges (~nlogn). |
| - | + | Side Note: For transcriptome libraries, if done properly, reads should have known strandedness, so it can’t be run through algorithms which make strandedness arbitrary (story about problems with a prominent yeast microarray transcriptome analysis incorrectly finding a lot of “antisense” mRNAs due to library prep error). | |
| - | (Side Note: for transcriptome libraries, if done properly, reads should have known strandedness, so can’t be run through algorithms which make strandedness arbitrary (story about problems with a prominent yeast microarray transcriptome analysis incorrectly finding a lot of “antisense” mRNAs due to library prep error) | + | |
| Line 103: | Line 100: | ||
| Realistically, there are issues: | Realistically, there are issues: | ||
| - | == End of Contig boundaries: == | ||
| - | what if A->B and A->C and A->D BUT A->B and A->C are inconsistent with each other? | + | == End of contig boundaries == |
| - | … A becomes “end of contig”, because you aren’t sure where to go next | + | |
| - | also end of contig if there are no more edges from the node | + | What if A->B and A->C and A->D //BUT// A->B and A->C are inconsistent with each other? |
| + | … A becomes “end of contig”, because you aren’t sure where to go next. | ||
| + | Also end of contig if there are no more edges from the node. | ||
| + | |||
| + | == Spurs == | ||
| - | == Spurs: == | ||
| <code> | <code> | ||
| kmer -> kmer -> kmer -> kmer -> kmer | kmer -> kmer -> kmer -> kmer -> kmer | ||
| \-> kmer -> kmer -> kmer (off to nowhere) | \-> kmer -> kmer -> kmer (off to nowhere) | ||
| </code> | </code> | ||
| - | path diverges but does not reconverge, resulting in source/sink dead-ends (these are likely due to read errors) | + | Path diverges but does not reconverge, resulting in source/sink dead-ends (these are likely due to read errors). |
| + | |||
| + | == Bubbles == | ||
| - | == Bubbles: == | ||
| <code> | <code> | ||
| /-> kmer -> kmer -> kmer -\ | /-> kmer -> kmer -> kmer -\ | ||
| Line 122: | Line 122: | ||
| </code> | </code> | ||
| - | path splits due to a SNP but then converges. this can happen with real SNPs, read error SNPs, and real repeats which differ by a SNP or two | + | The path splits due to a SNP but then converges. This can happen with real SNPs, read error SNPs, and real repeats which differ by a SNP or two. |
| + | |||
| + | == Loop == | ||
| - | == Loop: == | ||
| <code> | <code> | ||
| kmer -> kmer -> kmer -> kmer -> kmer -> kmer -> kmer | kmer -> kmer -> kmer -> kmer -> kmer -> kmer -> kmer | ||
| \- kmers <-/ | \- kmers <-/ | ||
| </code> | </code> | ||
| - | tandem repeats will generate a circle, but have edges in and out; hard to disambiguate copy # though. If the data is really clean (ie, in/out edges are ~10 read-depth with low SD, and inside circle has ~20 read-depth with low SD), can guess that there might be 2 copies of the repeat, but not highly reliable | + | Tandem repeats will generate a circle, but have edges in and out; hard to disambiguate copy number though. If the data is really clean (i.e. in/out edges are ~10 read-depth with low SD, and inside circle has ~20 read-depth with low SD), we can guess that there might be 2 copies of the repeat, but this is not highly reliable. |
| + | |||
| + | == Multiple paths == | ||
| - | == Multiple paths: == | ||
| <code> | <code> | ||
| A B | A B | ||
| Line 145: | Line 147: | ||
| Largest bias usually comes from PCR for amplification. | Largest bias usually comes from PCR for amplification. | ||
| - | ===Assembly:=== | + | === Assembly === |
| - | algorithms (both overlap and de Bruijn) need to collapse bubbles and trim spurs.\\ | + | |
| - | spurs: discard if their read count is low\\ | + | |
| - | bubbles: tricky, because they can represent real, divergent paths | + | |
| + | Algorithms (both overlap and de Bruijn) need to collapse bubbles and trim spurs.\\ | ||
| + | Spurs: Discard if their read count is low.\\ | ||
| + | Bubbles: Tricky, because they can represent real, divergent paths. | ||
| + | ===== References ===== | ||
| + | <refnotes>notes-separator: none</refnotes> | ||
| + | ~~REFNOTES cite~~ | ||
lecture_notes/04-09-2010.1271025033.txt.gz · Last modified: 2010/04/11 22:30 by learithe
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