What you have outlined below is perfect.
I wonder how hard it would be to design a few filters that only look a
certain genes and or filter model organisms out of the dataset.
For example, say you want only data for 16s or only gyrase, but no
/E.coli/ and no /Pseudomanas aeroginosa/
Senior Core Laboratory Research Staff
Advanced Genome Technologies Core
University of Vermont
Vermont Cancer Center
149 Beaumont ave
Health Science Research Facility 303/305
Burlington Vermont 05405
On 9/25/2013 12:06 AM, Jing Yu wrote:
> Hi Scott,
> My first thought is:
> 1. Remove rDNA sequences (and/or other well known highly-conserved
> sequences to reduce the workload in step 2).
> 2. Blast, then remove sequences with > (say 99%) match to > (say 5)
> genus. (Optional if step 1 is already good enough)
> For step 1:
> Build a fasta file of the chosen highly conserved sequences, and
> use it as a feed to blast against your MiSeq result.
> Remove positive hits.
> For step 2:
> Blast remaining MiSeq sequences against NCBI (or whatever) database.
> Remove if it hits more than n genus.
> On 24 Sep 2013, at 22:17, Scott Tighe <scott.tighe(a)uvm.edu
> <mailto:firstname.lastname@example.org>> wrote:
>> Jing et al
>> Thank you for the offer to write some code to help advance the
>> metagenomics arena. It is certainly needed.
>> So the problem is well known with megablast and shotgun metagenomics
>> and without proper understanding and correct software will yield very
>> misleading and in many cases incorrect data. For those of us who wish
>> NOT to move to a protein level of comparison for specific reasons, we
>> are stuck.
>> *The Problem:*
>> If I megablast 50 million sequences from a HiSeq run, millions of
>> rRNA sequences will have a 99% match to all microbes rRNA genbank
>> deposits. Not surprizing since the rRNA is highly conserved. The
>> difference between E.coli and Shigella is 1 to 2 bases for the full
>> 1540 bp 16s. So 16s is not useful for Genus level, and certainly not
>> *So what happens:*
>> The returned matches will have many hits to whatever model organism
>> is in Genbank. For example E coli has 13000 entries for rRNA and
>> Sphearotilus has 3 entries for rRNA. If the blasted sequence matches
>> both, the results will mislead the investigator to think they have
>> 13000 hits to E coli, EVEN if the microbe is Sphearotilus.
>> *The cure?:*
>> If there was a way to filter/ remove all hits ? Let say, for example,
>> that a result has a first match (say E. coli) at >99% a second match
>> (say Pseudomanas) at >99% and a third , forth and fifth match >99 for
>> three other organisms. This sequence _must_ be discarded because it
>> is a conserve sequence.
>> Basically conserved sequence is the enemy and invalidates the entire
>> **Another problem:*
>> If you have a reference sample with 19 non-model microbes, and you
>> run that by HiSeq Shotgun for metagenomics and then megablast, what
>> do you think you get? If E coli is not in the reference sample, how
>> many hits do you think you get? Yes, 10,000 of thousands. So without
>> removing conserved sequences, your data is wrong and you are much
>> better served by culturing and running a Biolog metabolic panel and
>> comparing to the sequence result.
>> So where do we start? I have some shotgun metagenomics data from the
>> reference sample which included the 19 microbes. That was data from a
>> Scott Tighe
>> Senior Core Laboratory Research Staff
>> Advanced Genome Technologies Core
>> University of Vermont
>> Vermont Cancer Center
>> 149 Beaumont ave
>> Health Science Research Facility 303/305
>> Burlington Vermont 05405
>> On 9/20/2013 9:17 PM, Jing Yu wrote:
>>> Hi Scott,
>>> I can do some perl programming, such as local/remote blasting. Can
>>> you specify your problem a little bit clearer, so that maybe I can
>>> write a program to do just that?
>>> 16s is basically useless for identification to genus. Since I
>>> started sequencing 16s in 1992, I have come to realize that without
>>> sequencing the full 1540 bases, it is generally misleading, and
>>> even than, it is not accurate enough to nail genus on more than 1/2
>>> the cases. However, what is your feeling on ITS and gyrase, They
>>> seem to be far more discriminating but those databases have been
>>> decommissioned sometime ago.
>>> The desirable thing would be that Galaxy or NCBI add a "filter
>>> conserved genes" [ ie any hit with a second choice greater than 3%
>>> distance]. Something such as that.
>>> If you (or others) are aware of such a thing, I'd love the here
>>> about it.