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======De novo Assembly II====== **Guest lecturer: Stefan Prost,** =====Illumina Paired-end Sequencing Libraries===== * 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) =====> end_1 ________________________ end_2 <===== * Problem: not really sufficient for repetitive regions * ends can't be very far apart because Illumina can't handle big molecules * not enough info for scaffolding =====Illumina Mate-Pair Sequencing Libraries==== * 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. <===== end_1 ________________________ end_2 =====> * Genomic DNA > Fragment (2-5 kb) > biotinylate ends > Circularize > Fragment (400-600 bp) > Enrich biotinylated fragments > Ligate adaptors > … * 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===== * Uncommon and expensive, but the gold standard * Bacterial F-plasmid takes< 40 kb insert size * Fragment query DNA > DNA into BAC > Transform E. coli with BAC > ~300 kb long reads * =====Read Quality Assessment===== * Base quality: Phred scores reported by sequencer. * 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 * **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 ====Tools==== * 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 pn = proportion of correct reads k = kmer length λ_k= mode of the k-kmer count histogram Simpson 2013, arXiv * To estimate genome size need to know:i) total number of reads; ii) length of reads; and iii) kmer distribution =====Error Correction===== * High amount of small kmers are usually errors **Simulated contig length in the k-de Brujin graph can estimate the best kmer to use for assembly. Based on contig length N50**

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lecture_notes/04-08-2015.1429307582.txt.gz · Last modified: 2015/04/17 21:53 by sihussai