Tweet
I have a NGS data of podophyllum, can somebody tell me how can I analyse my data and design possible pathway for phyllotoxin biosynthesis. I am totally new to NGS, so please answer me in details like what softwares I should use.
-
-
What kind of NGS data do you have? What do you estimate the genome coverage to be, based on the expected genome size? Is there a closely related genome available in the databases?
A project like this is not just about using "software". It will require some thought and a lot of work if you want to ultimately get anywhere close to the aim of "design possible pathway for phyllotoxin biosynthesis". -
What was sequenced (DNA or RNA)? In either case, assembly (since it is 454 data, Newbler is a reasonable assembler choice) would probably be the first step.
Are there any known genes/protein in that pathway? Then you'd probably want to predict genes (if you have DNA sequences) or predict proteins (if you have RNA sequences) and perform functional annotation or compare to known sequences from known parts of that pathway or similar pathways. Software choices will dependent a lot on what kind of data and previous knowledge you have.Comment
-
Start with doing some basic QC (or post QC results if they are already available) for your data. Is this DNA or RNA sequence? Secondary metabolite genes are often clustered in fungi and if you have an idea of what chemical functions are required by the phyllotoxin pathway then that could be a starting point for your hunt. All this would require enough sequence coverage. If there is not enough coverage you would need to convince your supervisor why that is important.Originally posted by Soumi
In any case before you start doing anything get up to speed on NGS applications that are relevant to your needs: http://www.nature.com/nrg/series/nex...ion/index.html. Specifically assembly (if that is what you will need to do): http://en.wikibooks.org/wiki/Next_Ge..._novo_assemblyComment
-
Its a DNA sequence. My senior has already performed denovo assembly by using Newbler.
He has also done Kyoto encyclopedia of genes and genomes (KEGG) analysis using KEGG Automatic Annotation Server (KAAS). He has proposed a probable pathway for phyllotoxin biosynthesis. But since the actual pathway is still unknown, I want to know what can I do with the data?Comment
-
If you are working in this area, you must have seen this paper: http://www.ncbi.nlm.nih.gov/pubmed/23161544
It appears that a model for the pathway has been described in the paper (http://www.ncbi.nlm.nih.gov/pmc/arti...044/figure/F2/). Finding remaining genes in the cluster may be the only bioinformatic component of this study. Additional studies would require functional genomics (doing knockouts to see what intermediate compound accumulates to nail the pathway steps down). I don't know if this is possible to do with P. hexandrum. You probably know that better.Comment
-
We have already told you the broad outline of what can/needs to be done.
Identify the two genes described in the JBC paper in your data. Look around the region to see if there are additional genes that seem to be present in a cluster (they always need not be, but in general, genes for secondary metabolites are in a cluster). You could do some bioinformatic analysis (blast, multiple sequence alignments (use protein sequence, if possible)) to see if you can identify a putative set of functions. This analysis will only get you so far as to come up with a hypothesis (e.g. gene A --> step 3 in pathway). Proving this hypothesis will require experimental work to ascribe a definitive function for any genes you may find.
Don't take the following the wrong way. I assume you are a graduate student just starting a project? If so, propose some definite steps/show us results of your analysis using the data you have. If you are not able to figure something out on your own then we can try to help. Don't expect to get a set of "steps" to complete your project.Comment
-
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