Tweet
I have a fasta file with gene sequences - are there existing tools / pipelines that calculate & quantify the variable and conserved regions? Assuming that there has to be an accepted protocol / method for this but haven't found anything...
-
-
Are the sequences from one gene (or multiple genes)? Generally a multiple sequence alignment (MSA) will give you this information. -
the sequences are all from one gene...Comment
-
Look into clustalW (or clustal omega if these are protein sequences). T-coffee/MUSCLE/MAFFT are all programs that can be used (http://www.ebi.ac.uk/Tools/msa/) for alignments as well. You may need to download and run the programs locally if you have a large number of sequences.
What exactly are you looking for in terms of "calculate and quantify"?Comment
-
what I'm looking to do is take the reads from a sequencing run and 'ensure' (as best I can) that the correct target section was in fact amplified... I have run the reads through usearch against a database of target gene sequences - but I am trying to see what kind of variability may be expected by calculating the existing variability in the known / published gene sequences...Comment
-
If your data is still in the form of individual reads then you may want to use an NGS aligner (BBMap, bowtie2, BWA) to align them the gene (or genes?) region and create a BAM. Then you can use samtools/bcftools to call SNP's/get a consensus sequence for the gene(s).Comment
-
yeah, I aligned them w/ bwa, created a bam file and then viewed them in igv and the reads - or 54% of them - are aligning along the bacterial genome where the gene is located...
but since it is coming from a clinical sample I am trying to see what the variability is along the existing db of sequenced genes to have something to help put the alignment score into perspective... I think I'll just write a simple script to calculate the base abundance once muscle finishes aligning the existing sequences...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.07-01-2026, 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...
Comment