1 / 7

Denovo Sequencing Practical

Denovo Sequencing Practical. Overview. V ery small dataset from Staphylococcus aureus 4 million x 75 base-pair, paired end reads Cover basic aspects of de-novo  assembly from Illumina reads Does not cover Mixing other data types ( 454, Sanger, etc )

kalea
Download Presentation

Denovo Sequencing Practical

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Denovo Sequencing Practical

  2. Overview • Very small dataset from Staphylococcus aureus • 4 million x 75 base-pair, paired end reads • Cover basic aspects of de-novo assembly from Illumina reads • Does not cover • Mixing other data types (454, Sanger, etc) • Gap filling techniques for “finishing” • Measuring the accuracy of assemblies • It’s really just an ‘introduction to VELVET’

  3. Steps • Run files thru FastQC and examine ONLY the quality by read position graph and determine if the sequencing run was good ‘overall’ • Then run the sequences through Trimmomatic • Clip Illumina sequencing adapter • Allow clipping of leading and trailing ends • Use sliding window (size 4) trimming and a minimum length of 35 reads to be kept

  4. Look at the resultant FASTQ files using ‘more’ or ‘less’ - notice the read length differences • Merge and ‘sort’ trimmed reads (velvet needs one file with pairs following each other) • shuffleSequences_fastq.pla.fastqb.fastqall.fastq

  5. 5. Run velveth • velveth auto 29,69,10-shortPaired–fastqall.fastq • Kmers of length 29 to 69 in increments of 10 • reads in these sequence file and simply produces a hashtableand • two output files • Roadmaps • Sequences • Needed by next program velvetg

  6. Run velvetg to determine best k of the various options • velvetgauto_<YOUR-KMER> -exp_cov auto -cov_cutoff auto • Example: • velvetg auto_39 -exp_cov auto -cov_cutoff auto • velvetg auto_69 -exp_cov auto -cov_cutoff auto • Runfasta_stats_N50.pl on the contigs • compare output logs between groups • Which k_mer length is the ‘best’? We will assume that the highest n50 reflects the optimal k_mer length In practice, we would use a finer granularity for the range tested

  7. Bonus • Have a look at the velvet log and identify a long contig with highest coverage • Grab it in FASTA format and BLAST it against the nr protein database • What is the top hit? Is there any biological reason why it would have such high coverage?

More Related