Tweet
Can any one give me some tips about genome anotation for big genome, which tools or pipelines are good
Thank you in advance,
Thank you in advance,
-
-
You might want to give more details on your "big genome". Large genomic data? new species? what species? De-novo assembled? -
General advice: Read the supplementary material from published genome papers, compare their methods and find which one would be better for you project.Comment
-
Thank you both for your reaction :-)
@Richard, it's size is 3 GB, de novo assembled from short read. I am trying to follow the ensemble gene build pipeline, but it is not very clear. I was just wondering how other people do it. I have also seen in literature that people follow the ensemble pipeline. If there is any other tip, that will be nice
@FredericRaymond, thank you, I have done that already, but it is very vague.
Thank you again
ashuComment
-
one program I know of is AUGUSTUS, but i've never used it myself
Comment
-
I've used Augustus for the annotation of the Leishmania tarentolae genome. The software is built for prokaryotes, but we collaborated with the creator of the software to adapt it for Leishmania, which do not have introns. It performed pretty well.Comment
-
Ehhh, i see a contradiction in your reply FredericRaymond, i think you meant that the software is for eukaryotes. The website (http://augustus.gobics.de/) saysAUGUSTUS is a program that predicts genes in eukaryotic genomic sequencesComment
-
Thank you all for the tip, Augustus was in my list, but I was focused on the ensemble Pipeline. I will give it a try and see if it is a good tool.
Thank you again,
cheers
AshuComment
-
You right boetsie. My mistake. However, the intron-less models can be built for Augustus.
FRComment
-
how do you guys find possible introns on a gene? Which software do you use?Comment
-
Genewise is used very frequentlyComment
-
bbsinfo, there are (as far as I know) three ways to find introns.
1) You could do a spliced alignment of some kind of assembled transcripts/ESTs using for example Blat (if you are working in nucleotide space that is)
2) Get junctions from mapped transcripts using for example Tophat
3) Align known proteins to the genome using Genewise, Exonerate or perhaps Scipio.
Check out the suggested pipeline on the PASA homepage, it works well:
Comment
-
Hello,Originally posted by Ashu View Post
Did you use the Ensemble pipeline?
If yes can you provide a few details!
Regards,
--
pgComment
-
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