Thanks Ann for your comments and for the stuff you showed at IGB - looks very 
interesting. I agree that multihits may the equivalent of the problem you 
describe from microarrays. I think, for me anyway, knowing the scale if the 
issue is the key thing at this stage. As you imply from your email the next 
-and potentially very interesting step -   is to figure out how/where these 
multihits are and how they came to be. I guess it all comes dow to where do 
genes come from? Well, many of them come from other genes via duplications, 
transpositions etc etc!

I have made a slight alteration to this "bristol" workflow which now 
automatically creates a sorted sam file of the multihits (forgot to put it in 
1st time round!)

Cheers
David


On 24 Feb 2011, at 12:08, Ann Loraine wrote:

> 
> Hello,
> 
> I like your approach of running the alignment tools with liberal settings and 
> then filtering the results into different categories.
> 
> This discussion reminds me of how in expression microarray analysis, we face 
> uncertainty as to what molecules (exactly) are hybridizing to the probes on a 
> chip. 
> 
> Maybe the ambiguity of mapping short sequence reads introduces similar 
> uncertainty?  
> 
> I also like your idea of capturing the reads that map multiple times. 
> 
> It’s interesting to visualize the alignments for reads that map onto multiple 
> locations in a genome.
> 
> An example (from data expressed in “wiggle” format) is described here:
> 
> https://wiki.transvar.org/confluence/x/w4BJAQ
> 
> My apologies for posting another IGB citation, but I think it can be 
> interesting and informative to see the data in this way, and IGB makes it 
> easy to zoom in and out through the data and find patterns quickly.
> 
> One of the first things I noticed when I started looking at coverage graphs 
> made from multi-mapping reads is that (1) there are a lot of them and (2) 
> they expose tandemly duplicated genes.  
> 
> Here’s a link to an image that showing a particularly striking example from a 
> single-read, 75 bp RNA-Seq data set from Arabidopsis thaliana Col-0. The 
> pattern of read alignment is nearly identical between the two genes. 
> https://wiki.transvar.org/confluence/download/attachments/21594307/tandem-duplication.png
> 
> You can’t see it from the image, of course, but if I right-click one of the 
> genes, IGB links out to a Web page describing the gene at 
> www.arabidopsis.org, the main on-line database for Arabidopsis. (Human genes 
> link to NCBI.) 
> 
> -Ann
> 
> 
> On 2/23/11 11:05 PM, "Jeremy Goecks" <jeremy.goe...@emory.edu> wrote:
> 
>> Hi David,
>> 
>> This is a really interesting workflow. My comments:
>> 
>> (1) I encourage you to start a discussion about this idea on seqanswers.com 
>> <http://seqanswers.com> ; you'll reach more people and may have a better 
>> discussion there. Ideally, you'll get a Tophat developer to chime in on what 
>> I perceive to be the main issue, which is:
>> 
>>> This may seem similar to setting tophat to ignore non-unique reads. 
>>> However, it is not. This approach gives you 10-15% more reads. I think it 
>>> is because if tophat finds (for example) that the forward read maps to one 
>>> site but the reverse read maps to two sites it throws away the whole read.
>> 
>> Remember that Tophat uses Bowtie to map reads, so it would make sense to 
>> look carefully at the Bowtie documentation to see how it handles paired-end 
>> reads. I can't find anything that directly addresses your issue. The other 
>> thing to consider is how Tophat maps reads -- it breaks them up in order to 
>> find splice junctions -- and so I'm not sure that Tophat/Bowtie is really 
>> mapping paired reads; it may be doing some hybrid single/paired-end mapping. 
>> Also, at one time, you could specify Bowtie parameters when running Tophat, 
>> but I don't see that option anymore.
>> 
>> (2) It would be interesting to know whether you get qualitatively different 
>> results via Cufflinks (or another transcriptome analysis software package) 
>> using your method vs. just using Tophat w/ and w/o ignoring non-unique 
>> reads. A skeptical view of your workflow would note that (a) multi-mapping 
>> reads may be legitimate and should not be filtered out and (b) 
>> Cufflinks/compare/diff assembly and quantitation may smooth out stray reads 
>> enough so that your method isn't necessary.
>> 
>> Thanks for the interesting post,
>> J.
>> 
>> On Feb 23, 2011, at 9:41 AM, David Matthews wrote:
>> 
>>> Hi Jeremy,
>>> 
>>> I thought I'd write to get a discussion of a workflow for people doing RNA 
>>> seq that I have found very useful and addresses some issues in mapping mRNA 
>>> derived RNA-seq paired end data to the genome using tophat. Here is the 
>>> approach I use (I have a human mRNA sample deep sequenced with a 56bp 
>>> paired end read on an illumina generating 29 million reads):
>>> 
>>> 1. Align to hg19 (in my case) using tophat and allowing up to 40 hits for 
>>> each sequence read
>>> 2. In samtools filter for "read is unmapped", "mate is mapped" and "mate is 
>>> mapped in a proper pair"
>>> 3. Use "group" to group the filtered sam file on c1 (which is the 
>>> "bio-sequencer" read number) and set an operation to count on c1 as well. 
>>> This provides a list of the reads and how many times they map to the human 
>>> genome, because you have filtered the set for reads that have a mate pair 
>>> there will be an even number for each read. For most of the reads the 
>>> number will be 2 (indicating the forward read maps once and the reverse 
>>> read maps once and in a proper pair) but for reads that map ambiguously the 
>>> number will be multiples of 2. If you count these up I find that 18 million 
>>> reads map once, 1.3 million map twice, 400,000 reads map 3 times and so on 
>>> until you get down to 1 read mapping 30 times, 1 read mapping 31 times and 
>>> so on...
>>> 4. Filter the reads to remove any reads that map more than 2 times.
>>> 5. Use "compare two datasets" to compare your new list of reads that map 
>>> only twice to pull out all the reads in your sam file that only map twice 
>>> (i.e. the mate pairs).
>>> 6. You'll need to sort the sam file before you can use it with other 
>>> applications like IGV.
>>> 
>>> What you end up with is a sam file where all the reads map to one site only 
>>> and all the reads map as a proper pair. This may seem similar to setting 
>>> tophat to ignore non-unique reads. However, it is not. This approach gives 
>>> you 10-15% more reads. I think it is because if tophat finds (for example) 
>>> that the forward read maps to one site but the reverse read maps to two 
>>> sites it throws away the whole read. By filtering the sam file to restrict 
>>> it to only those mappings that make sense you increase the number of unique 
>>> reads by getting rid of irrational mappings.
>>> 
>>> Has anyone else found this? Does this make sense to anyone else? Am I 
>>> making a huge mistake somewhere?
>>> 
>>> A nice aspect of this (or at least I think so!) is that by filtering in 
>>> this manner you can also create a sam file of non-unique mappings which you 
>>> can monitor. This can be useful if one or more genes has a problem of 
>>> generating a lot of non-unique maps which may give problems accurately 
>>> estimating its expression. Also, you also get a list of how many multi hits 
>>> you have in your data so you know the scale of the problem.
>>> 
>>> Best Wishes,
>>> David.
>>> 
>>> __________________________________
>>> Dr David A. Matthews
>>> 
>>> Senior Lecturer in Virology
>>> Room E49
>>> Department of Cellular and Molecular Medicine,
>>> School of Medical Sciences
>>> University Walk,
>>> University of Bristol
>>> Bristol.
>>> BS8 1TD
>>> U.K.
>>> 
>>> Tel. +44 117 3312058
>>> Fax. +44 117 3312091
>>> 
>>> d.a.matth...@bristol.ac.uk
>>> 
>>> 
>>> 
>>> 
>> 
>> 
>> _______________________________________________
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> 
> -- 
> Ann Loraine
> Associate Professor
> Dept. of Bioinformatics and Genomics, UNCC
> North Carolina Research Campus
> 600 Laureate Way
> Kannapolis, NC 28081
> 704-250-5750
> www.transvar.org
> 
> _______________________________________________
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