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Old 02-14-2014, 06:00 AM   #1
super0925
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Smile RNA-seq pipeline on Ion-Proton data

Hi I am a rookie bioinformatics who will process the rna-seq from Ion-proton(Single-ended).
The pipeline for processing which I know until now is
Bowtie, Tophat, Cufflinks, and cummeRbund for RNA-seq alignment, assembly, DE analysis, and .visualization.
Any other options?
Or What is this advantage or disadvantage of this pipeline?
Thank you!

Last edited by super0925; 02-14-2014 at 06:39 AM.
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Old 02-14-2014, 11:14 AM   #2
BrianJames
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There is a link a the Ion Community that describes a pipeline. You have to register, but it is free.

http://ioncommunity.lifetechnologies.com/docs/DOC-7062
It uses a combination of Bowtie2 and Tophat2


I added a step to filter reads with Bowtie2. I used a filter file that I used to use with SOLiD data (ribosomes, adapters, tRNAs and such).

Brian
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Old 02-15-2014, 11:01 AM   #3
super0925
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Thank you! I got it.
But this pipeline which is suitable for Proton data is only reach the mapping and assemble. So after that steps which pipeline is more suitable? I mean for the differential analysis?
Cufflinks or Deseq or EdgeR? Do you have any recommendation ?
Cheers
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Old 02-15-2014, 11:01 AM   #4
super0925
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Quote:
Originally Posted by BrianJames View Post
There is a link a the Ion Community that describes a pipeline. You have to register, but it is free.

http://ioncommunity.lifetechnologies.com/docs/DOC-7062
It uses a combination of Bowtie2 and Tophat2


I added a step to filter reads with Bowtie2. I used a filter file that I used to use with SOLiD data (ribosomes, adapters, tRNAs and such).

Brian
Thank you! I got it.
But this pipeline which is suitable for Proton data is only reach the mapping and assemble. So after that steps I got the bam file, then which pipeline is more suitable? I mean for the differential analysis?
Cufflinks or Deseq or EdgeR? Do you have any recommendation ?
Cheers

Last edited by super0925; 02-15-2014 at 11:11 AM.
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Old 02-15-2014, 08:30 PM   #5
BrianJames
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I use Cuffdiff in cufflinks2. It's pretty straight forward, but I can see if I can find the command I ran on Tuesday, if you need it.
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Old 02-16-2014, 01:59 AM   #6
super0925
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Quote:
Originally Posted by BrianJames View Post
I use Cuffdiff in cufflinks2. It's pretty straight forward, but I can see if I can find the command I ran on Tuesday, if you need it.
Brian
Thank you Brian! Cuffdiff is really good but so far I don't know hot to compare the achievement between different methods (i.e. which is more suitable for the Proton data)

Last edited by super0925; 02-16-2014 at 02:04 AM.
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Old 02-16-2014, 10:58 AM   #7
BrianJames
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You might want to take a look at this thread.
http://seqanswers.com/forums/showthread.php?t=17678

I know I will look at it more when I get the chance. I must admit that I like Cuffdiff because since I've already installed cufflinks2 it is a simple linux command. To address your question (not answer it), once you have your read data aligned, I don't think the Proton platform is going to impact performance of differential expression options. I would like to work with the best package out there, but they all work pretty good, and are mostly impact statistical significance calculations. INMHO, good starting material (RNA quality), a well designed assay, and good biological replicates will have a much bigger impact on the quality of the eventual results.

Brian
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Old 02-17-2014, 08:15 AM   #8
super0925
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Quote:
Originally Posted by BrianJames View Post
You might want to take a look at this thread.
http://seqanswers.com/forums/showthread.php?t=17678

I know I will look at it more when I get the chance. I must admit that I like Cuffdiff because since I've already installed cufflinks2 it is a simple linux command. To address your question (not answer it), once you have your read data aligned, I don't think the Proton platform is going to impact performance of differential expression options. I would like to work with the best package out there, but they all work pretty good, and are mostly impact statistical significance calculations. INMHO, good starting material (RNA quality), a well designed assay, and good biological replicates will have a much bigger impact on the quality of the eventual results.

Brian
Thank you Brian! So nice you are!
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Old 05-26-2014, 10:59 AM   #9
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For DE analysis I would definitely recommend using multiple methods, e.g. cuffdiff, EdgeR, etc. BitSeq looks nice and I heard a talk about CASPAR which looks nice. They each use a different model to approximate the distribution of read counts, so they each give a different answer. It is worth trying several and taking the union/intersection.
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Old 05-29-2014, 10:37 AM   #10
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Our initial attempts at using Tophat2/Bowtie2 to map Ion Torrent RNAseq data were pretty disappointing (~30% aligned) and turned out to be biased against alignment of the longer reads (see attached image). Our average read length was ~75 bases, and longest ~150+ bases. This bias may be due to fixed penalties applied for gaps/mismatches per read rather than as a fraction of read length.

We had much better success with GSNAP (~70% alignment) and have used HTseq-count and DESeq for our differential expression analysis. Gene lengths and RPKMs can easily be calculated separately if required.

It is also worth noting that the second stage of the protocol at http://ioncommunity.lifetechnologies.com/docs/DOC-7062 will not add back any reads that should align over exon junctions. One might therefore get an underestimate of these positions.
Attached Images
File Type: png Sample 1562 Tophat mapping distirbution.png (113.7 KB, 30 views)

Last edited by mbzmg1; 05-29-2014 at 10:42 AM.
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Old 05-29-2014, 11:38 AM   #11
Brian Bushnell
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BBMap should be ideal for mapping Ion RNA-seq data. In my testing, it vastly outperforms GSNAP in all cases, and produces Tuxedo-compatible output.

If your reads are all shorter than 500bp, you can run it in normal mode; otherwise, you can run it in PacBio mode (with the script mapPacBio8k.sh).

Last edited by Brian Bushnell; 05-29-2014 at 11:54 AM.
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Old 05-29-2014, 11:45 AM   #12
BrianJames
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Quote:
Originally Posted by Brian Bushnell View Post
BBMap should be ideal for mapping Ion RNA-seq data. In my testing, it vastly outperforms GSNAP in all cases, and produces Tuxedo-compatible output.

If your reads are all shorter than 500bp, you can run it in normal mode; otherwise, you can run it in PacBio mode (with the script mapPacBio8k.sh).
Hi Brian,
I think this is the link you want http://seqanswers.com/forums/showthr...568#post133568

Brian
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Old 05-29-2014, 11:54 AM   #13
Brian Bushnell
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Thanks, that's exactly what I wanted!
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