![]() Should bet contain any contigs that represent patches, or alternative haplotypes. Genome fasta files should include all primary chromosomes, unplaced sequences and un-localized sequences, as well as any organelles.Genome sequence fasta files and annotation (gff, gtf) files go together! These should be identified at the beginning of analysis. Otherwise, you get mis-mapping because its close enough.Need to provide a mapper with all possible places the read could have arisen from, which is best represented by the genome.More so, a mapper will try to map every read, somewhere, provided the result meets its minimum requirements.Shouldn’t map to all splice variants as these would show up as ‘multi-mappers’.Which transcript of a gene should you map to, canonical transcript?.May seem intuitive to map RNAseq data to transcriptome, but it is not that simple.Mapping against the genome vs transcriptome Experience suggests differences between “traditional” mappers are in the low abundance genes.Blazing FAST and can run on most laptops.Does transcript quantifications (or gene).Many alignment algorithms to choose from. In RNAseq data, you must also consider effect of splice junctions, reads may span an intron. Algorithms that use paired-end information => might prefer correct distance over correct alignment.Placing reads in repetitive regions: Some algorithms only return 1 mapping If multiple: map quality = 0.What if the closest fully sequenced genome is too divergent? (3% is a common alignment capability).Sequencing errors and variations: alignment between read and true source in genome may have more differences than alignment with some other copy of repeat.Placing reads in regions that do not exist in the reference genome (reads extend off the end).The goal then is to find the match(es) with either the “best” edit distance (smallest), or all matches with edit distance less than max edit dist. In mapping the question is more, given a small chunk of sequence, where in the genome did this piece most likely come from. Mapping tries to put together the puzzle pieces directly onto an image of the picture. Cp -r /share/biocore/workshops/2019_March_RNAseq/HTS_testing /share/workshop/$USER/rnaseq_example/.Ĭp -r /share/biocore/workshops/2019_March_RNAseq/01-HTS_Preproc /share/workshop/$USER/rnaseq_example/.Ĭp /share/biocore/workshops/2019_March_RNAseq/summary_hts.txt /share/workshop/$USER/rnaseq_example/.Īssembly seeks to put together the puzzle without knowing what the picture is. ![]()
0 Comments
Leave a Reply. |