Selecting the best alignment BAM file

[Rangika]

Thank you Michael for your answer.

Regards
Sumudu

[Michael.Ante]

The bam_stat.py was a suggestion since it also works for DNA-seq alignments (you'll hopefully don't see spliced reads).
You can also have a look at the QC-metrics from Picard tools, or have a look at GATK.
Or you can extract the aligned reads (samtools view) and count e.g. how often each read is aligned. Without trimming you might have a high %mapping rate given by samtools flagstat, but you don't know how many reads were aligned with a high confidence to a single or few positions.

Most of the library preps have also a small section of how to deal with the analysis. Additionally, there are a plethora of publications describing their approach to DNA-Seq analysis.

Cheers,
Michael

[Rangika]

Thank you Michael. My dataset is DNA-seq. Can I use RSeQC tools to check alignment for DNA data as well. Do you suggest RSeQC statistics would lead in to better BAM selection?

Appreciate if you would clarify this a bit more since I'm new to this.

Regards
Rangika

[Michael.Ante]

Hi Rangika,

2nd first:
You need to be aware of the fact that samtools flagstat produces statistics on alignments. Meaning, a read can align multiple time and will occur multiple times in the flagstat output. You may check your alignment file with e.g. bam_stat.py from the RSeQC tools.
Furthermore, I'd check the read files with FastQC before and after trimming.

So:
1) I'd check a set of different data sets to choose which way to go. Also, I would not rely on the %mapped from samtools flagstat.

Cheers,
Michael

[Rangika]

Hi,

I have a PE dataset 300bp inserts by illumina MiSeq. I aligned the raw data using BWA-mem. Mapping statistics generated using Samtools flagstat are below.

5541008 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 secondary
76008 + 0 supplementary
0 + 0 duplicates
5413610 + 0 mapped (97.70% : N/A)
5465000 + 0 paired in sequencing
2732500 + 0 read1
2732500 + 0 read2
5266140 + 0 properly paired (96.36% : N/A)
5319406 + 0 with itself and mate mapped
18196 + 0 singletons (0.33% : N/A)
32368 + 0 with mate mapped to a different chr
8821 + 0 with mate mapped to a different chr (mapQ>=5)

I also used Trimmomatic on the same dataset, ILLUMINACLIP to remove any adapter sequences, trimmed reads sliding window 4:10, leading & trailing bases <3, length <39bp. Aligned this set using BWA-mem and got the results as below.

5529752 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 secondary
65642 + 0 supplementary
0 + 0 duplicates
5396698 + 0 mapped (97.59% : N/A)
5464110 + 0 paired in sequencing
2732055 + 0 read1
2732055 + 0 read2
5263982 + 0 properly paired (96.34% : N/A)
5308488 + 0 with itself and mate mapped
22568 + 0 singletons (0.41% : N/A)
23856 + 0 with mate mapped to a different chr
4865 + 0 with mate mapped to a different chr (mapQ>=5)

1) Can I use this information to select a best alignment based on mapped %. Raw data gave 97.7% mapping which is higher than trimmed data. So can I select BAM I got from raw data as the best?

2) I used "samtools view -c -f 3 data.bam" to find the properly paired reads. But the value I got is different to the value for that parameter by flagstat for both datasets. I checked some other parameters like itself & mate mapped they too gave different results. What could be the reason.

Appreciate your answers.
Thanks in advance.

Regds
Rangika