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Module 2: Lab Practical

Module 2: Lab Practical. Michael Brudno Estimated completion time: 1-2 hours. Objectives. Get a practical feel for using letter-space and color-space data Do some small assembly jobs See how various genomes are very different. Task 1: Get the Tools. Download Velvet

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Module 2: Lab Practical

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  1. Module 2: Lab Practical Michael Brudno Estimated completion time: 1-2 hours

  2. Objectives Get a practical feel for using letter-space and color-space data Do some small assembly jobs See how various genomes are very different

  3. Task 1: Get the Tools • Download Velvet http://www.ebi.ac.uk/~zerbino/velvet/ • Download SHRiMP: http://compbio.cs.toronto.edu/shrimp • Learn the wget command in Linux: wget http://something • Don’t be afraid of the source code: compiling is as simple as “make”

  4. Playing with Velvet • Read the README file (or at least scan it) • The real Solexa data from a human BAC is in ~brudno/human_bac/ • Run velvet: ../velvet_0.6.04/velveth . 19 split_reads/reads.fa ../velvet_0.6.04/velvetg . Try varying the k-mer parameter. what’s the biggest contig? What is the N50 score? What is it in your case? Which chromosome is this BAC from?

  5. SHRiMP Read the README file (or at least scan it) Get the corresponding chromosome (from ~brudno/chr.fa) Use SHRiMP to map the reads to the reference bactig. How long did it take? Try again the reference chromosome (use -B flag) Kill the job (Ctrl-C) Try only with a few reads (use the head command)

  6. SHRiMP • Use the probcalc program to get out only reads with top hits (with normalized odds>0.5) • What does this command do: sort -nk 4 tophits | awk '{if ($4 > s) print s,$4; s = $5}' How is this number related to the number of contigs from velvet? • Look at the alignment file. Find the alignment and try to understand it. Run the line through prettyprint to get human-readable alignments

  7. Colorspace • Going colorspace with SHRiMP is just replacing -ls with -cs :) • Go to the color-space directory, map the reads to the reference genome • Wait! Don’t decompress the genome • Compress the reads, if you want • Run probcalc. What are the rates produced? Why do we have separate error rates and SNP rates? What happened in letter space? • Go into the README file and use the SHRiMP parameters from there. Do the rates change?

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