lecture_notes:04-08-2015
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lecture_notes:04-08-2015 [2015/04/09 22:42] sihussai |
lecture_notes:04-08-2015 [2015/04/17 22:34] sihussai fixing capitalization |
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| - | =====De novo Assembly II===== | + | ======De novo assembly II====== |
| **Guest lecturer: Stefan Prost, stefan.prost@berkley.edu** | **Guest lecturer: Stefan Prost, stefan.prost@berkley.edu** | ||
| - | ====Illumina Paired-end Sequencing Libraries==== | + | =====Illumina paired-end sequencing libraries===== |
| * MiSeq has 300 bp reads | * MiSeq has 300 bp reads | ||
| * Paired ends read from both directions, so you get one read for each end (may or may not overlap depending on molecule and read size) | * Paired ends read from both directions, so you get one read for each end (may or may not overlap depending on molecule and read size) | ||
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| end_2 <===== | end_2 <===== | ||
| - | * Can’t sequence repeat regions with paired-end reads | + | * Problem: not really sufficient for repetitive regions |
| * ends can't be very far apart because Illumina can't handle big molecules | * ends can't be very far apart because Illumina can't handle big molecules | ||
| * not enough info for scaffolding | * not enough info for scaffolding | ||
| - | ====Illumina Mate-Pair Sequencing Libraries=== | + | =====Illumina mate-pair sequencing libraries==== |
| * Idea: get paired reads that are much farther away (for more scaffolding info) | * Idea: get paired reads that are much farther away (for more scaffolding info) | ||
| * Basically, the same idea as paired-ends, except the middle section is missing and the ends are oriented the opposite way. | * Basically, the same idea as paired-ends, except the middle section is missing and the ends are oriented the opposite way. | ||
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| * Genomic DNA > Fragment (2-5 kb) > biotinylate ends > Circularize > Fragment (400-600 bp) > Enrich biotinylated fragments > Ligate adaptors > … | * Genomic DNA > Fragment (2-5 kb) > biotinylate ends > Circularize > Fragment (400-600 bp) > Enrich biotinylated fragments > Ligate adaptors > … | ||
| - | * Dependent on inferring insert size | + | * Cut DNA, attach a biotin tag to both ends of the target molecule |
| + | * Circularize target molecule | ||
| + | * This step is hard | ||
| + | * Circularized molecule is fragmented, fragment with the biotin tag (a.k.a the ends of the original target stuck to each other backwards, with the tags in between) is enriched | ||
| + | * Then end repair, A-tailing, adapters added, amplification, sequencing | ||
| + | * Dependent on inferring insert size (can be tricky) | ||
| + | * Most companies can get you 8 kb inserts, with skill you can get up to 20 kb | ||
| + | * Complicated process, weird stuff can happen in between | ||
| + | * Important difference between paired ends and mate-pairs: ends are oriented the opposite way. | ||
| - | ====BAC (Bacterial Artificial Chromosome) and Fosmid Libraries==== | + | =====BAC (Bacterial Artificial Chromosome) and fosmid libraries===== |
| + | * Uncommon and expensive, but the gold standard | ||
| * Bacterial F-plasmid takes< 40 kb insert size | * Bacterial F-plasmid takes< 40 kb insert size | ||
| * Fragment query DNA > DNA into BAC > Transform E. coli with BAC > ~300 kb long reads | * Fragment query DNA > DNA into BAC > Transform E. coli with BAC > ~300 kb long reads | ||
| * http://www.scq.ubc.ca/wp-content/plasmidtext.gif | * http://www.scq.ubc.ca/wp-content/plasmidtext.gif | ||
| - | ====Read Quality Assessment Tools==== | + | =====Read quality assessment===== |
| - | * FastQC (Most popular tool to tell you about the read library) | + | * Base quality: Phred scores reported by sequencer. |
| - | * Preqc | + | * Fastq files: fasta files, plus encoded phred scores |
| + | * Need to know if your file has phred33 or phred64 encoding | ||
| + | * Quality for each individual base is not the whole story, the context matters to the signal processing too | ||
| * Reads decrease in quality further down the read | * Reads decrease in quality further down the read | ||
| + | * **Depending on the assembler, you might need to trim lower quality reads off the end. Others want untrimmed data** | ||
| * Pacific Bio doesn’t have GC|AT bias | * Pacific Bio doesn’t have GC|AT bias | ||
| - | ====Estimating Genome Size from Read Data==== | + | ====Tools==== |
| - | . G = (pn(1-k+1))/(λ_k) | + | * FastQC (Most popular tool to tell you about the read library) |
| + | * FastQC reported an issue with our data with kmer count (related to adapter content) | ||
| + | * **This needs to be checked out and diagnosed!** | ||
| + | * Preqc | ||
| + | * Estimates how difficult the assembly will be | ||
| + | |||
| + | =====Estimating genome size from read data===== | ||
| + | G = (pn(1-k+1))/(λ_k) | ||
| G = Genome size | G = Genome size | ||
| pn = proportion of correct reads | pn = proportion of correct reads | ||
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| * To estimate genome size need to know:i) total number of reads; ii) length of reads; and iii) kmer distribution | * To estimate genome size need to know:i) total number of reads; ii) length of reads; and iii) kmer distribution | ||
| - | ====Error Correction==== | + | =====Error correction===== |
| * High amount of small kmers are usually errors | * High amount of small kmers are usually errors | ||
| - | ***Simulated contif length in the k-de Brujin graph can estimate the best kmer to use for assembly. Based on contif length N50 | + | **Simulated contig length in the k-de Brujin graph can estimate the best kmer to use for assembly. Based on contig length N50** |
lecture_notes/04-08-2015.txt · Last modified: 2015/04/17 22:34 by sihussai
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