Tweet
I am trying to build a comprehensive database of prokaryotic (bacteria and archea) and fungal genomes to be used for screening ancient DNA reads for contamination. What I found unfortunately that many of genomes in NCBI or EMBL databases have a lot of poly-N inserts, which obviously need to be eliminated. This can be done either by removing inserts from each FASTA record, which may be difficult, or by splitting records at poly-N inserts and trimming Ns from the ends. Is there a tool/sctipt to do this? Alternatively, I may have to abandon genomes and just concatenate GenBank relevant records, but I first will have to extract FASTA from them. Any advice?
-
-
It's not obvious to me that they have to be taken out. Reads just won't align there, that's all.
You could use sed to get rid of all the N's, or write a script in something to trim out sequences of Ns that are more than a certain length. -
Well, dowloaded data files need to be preprocessed by BWA-SW to make databases for local install of DeconSeq, and the author removed Ns by splitting, as BWA-SW replaces Ns with either of A, G, C, T at random (citing the paper). But it does not say in the paper how this was done...Comment
-
Why do you need to remove the poly-N regions? These are gap regions that could be usefulComment
-
I am also not convinced that you should remove N's, but if you must, you can with Biopieces (www.biopieces.org):
Code:read_fasta -i in.fna | transliterate_seq -d 'nN' | write_fasta -o out.fna -x
Comment
-
Wow, i did not think biopieces can do this. Gotta try!Comment
-
@yaximik
For more finegrained control you can use substitute_vals to remove blocks of N's longer than 25:
Code:read_fasta -i in.fna | substitute_vals -k SEQ -s 'N{25,}' -r '' -ig | write_fasta -o out.fna -xComment
-
Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
...07-09-2026, 11:10 AM -
Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.
There is no single reason why many patients don’t respond to treatment as expected. Cancer is...07-08-2026, 05:17 AM -
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
Comment