I ve aligned a list of sequence to a transcript to observe if this sequences are present in the transcript. I produced a BAM file. This is the result of my Alignement.
Left reads: Input : 27849344 Mapped : 16442 ( 0.1% of input) of these: 7259 (44.1%) have multiple alignments (56 have >100) Right reads: Input : 27849344 Mapped : 15211 ( 0.1% of input) of these: 8390 (55.2%) have multiple alignments (56 have >100) 0.1% overall read mapping rate. Aligned pairs: 5975 of these: 3776 (63.2%) have multiple alignments 194 ( 3.2%) are discordant alignments 0.0% concordant pair alignment rate. ~
Now I have to see that I don't count the same read twice, because I have changed the multiple alignment parameters. If a read maps on two different sequences, it is always the same read. I have to count the reads as unique. So how can I made this check?
Before doing that, I would worry about the
0.1% overall read mapping rate.... Do you think it is normal to have such a low alignment rate ?
This is likely because full dataset is being aligned to
a transcript. Not generally recommended but probable explanation for that
0.1%figure in this case.
I'VE aligned a set of transposon to a transcript to observe if these transposable elements are presents. It is a experimental test. I've used tophat
What exactly are you trying to show? Is the following interpretation correct.
Yes, it is correct. I want to check if the reads don't align 2 or more times the same sequence
Depends on the alignment tool you used and whether or it includes such tags.
Also please use the search function.
I'VE used tophat. I would make this analysis on R
There is no logical overlap between those two. One is a splice-aware alignment program and other is a statistical programming environment.
Sorry, I would to analize the bam file produced with tophat in r environment
What kind of analysis?
A kind of analysis that allows me to count the repetitive elements in the trascripts. Anyway I ve tried a solution. I opened the bam with samtools, and I made unique() to count the unique reads that drop in some elements of interest. For example I made unique to all the reads presents in SINEs