Tweet
I have five Bam files which has data of two to three samples togather as the samples were run in same channel. Is there any easiest way of seprating Bam file for each samples i.e can I split Bam files and combine per samples.
Thanks.
Thanks.
-
-
I have just started this kind o analysis and as far as I could figure out people have suggested aligning all of your samples separately and denoting each with a unique group header, then merging your alignments. -
Bam per sample
JackieBadger, Thanks.
The problem which I have is each bam file is having two to three samples in it. So that means alignments are alreday performed? Any suggestion please how I can get it around to get bam per sample?Comment
-
Hi,
You should be able to use samtools to split your bam file into multiple bam files.
Here is how:
# get the list of all sample names in INPUT.bam
> samtools view -H INPUT.bam | awk '$0 ~ /^@RG/' | cut -f 2 | sed 's/ID://' > sample.list
# for each sample 's' in sample.list; extract reads belong to this sample and save it as a bam file
>for s in `cat sample.list ` ; do echo $s > sample.txt ; samtools view -bo ${s}.bam -hR sample.txt INPUT.bam ; done
Hope this helps,
QComment
-
Bam per sample
Thanks qtrinh,Comment
-
Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.Yesterday, 11:43 AM
-
I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before... -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM
Comment