Hello everyone
I am trying to analyze RNA-Seq data. I am a beginner in this process and trying to learn software's used for analyzing RNA-Seq data. I have 10 tumor cancer samples with matching normal samples and I need to find gene fusions in these samples as a part of my school exercise. Could any one please suggest any process of how to proceed?
Thanks
Tools capable of detecting fusion genes:
Most of these use RNA-seq data, some use WGS data, and some use both. They are listed alphabetically. I will add to the list when I discover more.
1. Barnacle: http://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-14-550
2. Bellerophontes: http://bioinformatics.oxfordjournals.org/content/28/16/2114.long
3. BreakDancer: http://www.nature.com/nmeth/journal/v6/n9/abs/nmeth.1363.html
4. BreakFusion: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3389765/
5. BreakPointer: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3561864/
6. ChimeraScan: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3187648/
7. Comrad: http://bioinformatics.oxfordjournals.org/content/27/11/1481.long
8. CRAC: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053775/
9. deFuse: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1001138
10. Dissect: http://bioinformatics.oxfordjournals.org/content/28/12/i179.abstract
11. EBARDenovo: http://bioinformatics.oxfordjournals.org/content/early/2013/03/01/bioinformatics.btt092
12. EricScript: http://bioinformatics.oxfordjournals.org/content/28/24/3232
13. FusionAnalyser: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3439881/
14. FusionCatcher: http://biorxiv.org/content/early/2014/11/19/011650.full-text.pdf+html
15. FusionFinder: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384600/
16. FusionHunter: http://bioinformatics.oxfordjournals.org/content/27/12/1708.long
17. FusionMap: http://bioinformatics.oxfordjournals.org/content/27/14/1922
18. FusionQ: http://www.biomedcentral.com/1471-2105/14/193
19. FusionSeq: http://www.genomebiology.com/2010/11/10/R104
20. IDP-fusion: http://nar.oxfordjournals.org/content/early/2015/06/03/nar.gkv562.full
21. iFUSE: http://bioinformatics.oxfordjournals.org/content/29/13/1700.long
22. InFusion: https://bitbucket.org/kokonech/infusion/wiki/Home
23. INTEGRATE: http://www.ncbi.nlm.nih.gov/pubmed/26556708
24. JAFFA: http://www.genomemedicine.com/content/7/1/43
25. LifeScope: http://www.thermofisher.com/no/en/home/life-science/sequencing/next-generation-sequencing/solid-next-generation-sequencing/solid-next-generation-sequencing-data-analysis-solutions/lifescope-data-analysis-solid-next-generation-sequencing/lifescope-genomic-analysis-software-solid-next-generation-sequencing.html
26. MapSplice: http://www.ncbi.nlm.nih.gov/pubmed/20802226
27. MOJO https://github.com/cband/MOJO
28. nFuse: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3483554/
29. Pegasus: http://www.biomedcentral.com/1752-0509/8/97
30. PRADA: http://www.ncbi.nlm.nih.gov/pubmed/24695405
31. ShortFuse: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3072550/
32. SnowShoes-FTD: http://nar.oxfordjournals.org/content/39/15/e100
33. SOAPFuse: http://www.genomebiology.com/2013/14/2/R12
34. SOAPFusion: http://www.ncbi.nlm.nih.gov/pubmed/24123671
35. STAR: http://bioinformatics.oxfordjournals.org/content/29/1/15
36. STAR-Fusion: https://github.com/STAR-Fusion/STAR-Fusion/wiki
37. TopHat-Fusion: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245612/
38. TRUP: http://www.genomebiology.com/2015/16/1/7
39. ViralFusionSeq: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3582262/
Other useful programs:
Chimeraviz: https://bioconductor.org/packages/devel/bioc/html/chimeraviz.html (disclaimer: I created this)
Chimera: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4253834/
OncoFuse: http://bioinformatics.oxfordjournals.org/content/29/20/2539.long
FuMa: http://bioinformatics.oxfordjournals.org/content/early/2015/12/09/bioinformatics.btv721.abstract
Articles comparing gene fusion finders:
The structure of state of art gene fusion-finder algorithms
Application of next generation sequencing to human gene fusion detection: computational tools, features and perspectives
Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data
You might be interested in the question I just posted: Which program, tool, or strategy do you use to visualize genomic rearrangements?
You could try Tophat-Fusion. But, if you are really just getting started with RNA-Seq analysis I would start with simple expression level and differential expression analysis. To do that, you could start by installing and learning how to use the Tuxedo suite of software (Bowtie, Tophat, cufflinks, cuffdiff, CummeRbund). Once you have mastered those you can proceed to the slightly more advanced tophat-fusion (which now comes together with tophat2). They provide a tutorial on their website.
I strongly recommend a workshop/course on the subject. However, to get you started, why not work through last year's Canadian Bioinformatics Workshop (CBW) tutorial on RNA Sequence Analysis. You can find it on the 2011 course page under the "Informatics on High Throughput Sequencing Data" course. There are probably several other lectures there that will be helpful as well.
Let me put my 5 cents.. actually I've been using Tophat-fusion (then Tophat2) for most of the RNA-seq analysis. I think this is a gold standard in the field. Unfortunately for me (and others, see e.g. http://seqanswers.com/forums/showthread.php?t=13096) tophat-fusion-post is really hard to get working. Tophat-fusion produces numerous false-positives (most of fusions are coming from the same transcript), as well as a lot of read-through events. I would like to recommend our post-filtering pipeline http://bioinformatics.oxfordjournals.org/content/early/2013/08/24/bioinformatics.btt445, hope you'll find it useful
It is important to note that the in-frame filter remains a complex question. Based on analysis of available manually mapped fusion junctions in literature [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0004805] there could be indels that could repair/break fusion frame thus having a very high impact on presence and function of fusion protein. I'm not sure if currently available fusion detection software could effectively handle this issue (correct me if I'm wrong).
Here's a list of fusion gene detection tools working with RNA-seq data
You could take the RNAseq datasets and map them against a reference genome using mapping tools that can handle split reads. Two tools, I can recommend are segemehl, and TopHat Fusion.
They use reads that overlap with splice sites and appear to be cleaved when mapping them back to a genome (one side to one exon, the other side to the next exon and intron in between), to predict splice sites and/or fusion transcripts. The difference is basically, that when you have a splice site, both ends are somewhat 'close' to each other. In a fusion transcript, these splits are far away, on different strands, or even on different chromosomes.
How to use segemehl for your problem (on the segemehl link, every step is explained in more detail):
segemehl is NOT a fusion gene finder! segemehl is a simple aligner exactly like Bowtie2, BWA, START, etc. This is stated by the segemehl people here: http://seqanswers.com/forums/showthread.php?t=40765&highlight=segemehl
Finding splice junctions is different than finding fusion genes!
The question is asking for fusion finder! TopHat-Fusion is in the same sentence with SEGEMHL in your answer when TopHat-Fusion is a fusion gene finder and SEGEMHL is not!
Also, the three steps from above are a method for finding splice junctions. Please note that finding splice junctions does not mean that one is finding fusion genes! Most of the things ( that is 98%) what will be found with those 3 steps will be readthroughs and readthroughs!
I see your point. Sorry for being somewhat off-topic then. I did not want to send anyone down the wrong track, nor upset you. Thanks for clarifying the difference of "fusion-junction" and "fusion-gene", of course it is something different and I should have been much more detailed in the first place. Just wanted to help though. :) Next time more precise! :)
I see your point also and I think that this confusion comes from the authors of SEGEMEHL which state in their paper that SEGEMEHL identifies readily fusion transcripts without need of separate post-processing.
Here is the paper:
Hoffmann et al. A multi-split mapping algorithm for circular RNA, splicing, trans-splicing, and FUSION DETECTION, Genome Biol. 2014. http://www.ncbi.nlm.nih.gov/pubmed/24512684
and here is the quote from the above article:
Implemented in the segemehl mapping tool, it readily identifies conventional splice junctions, collinear and non-collinear fusion transcripts, and trans-spliced RNAs, without the need for separate post-processing...
I believe that overall gene fusions with both breakpoints in introns will be simply the most frequent class of fusions. So if a software could be sensitive enough to spot a junction between exons of different genes and report it - it is capable of fusion detection
According to this definition then BLAST/BLAT/BOWTIE/BOWTIE2/BWA fusion finders too!
Out there are only known few hundreds of fusion genes known all together for all cancers! See for Mitelman database of gene fusions and COSMIC Catalog of somatic mutations in cancer!
If one just runs straight any aligner on one sample will "find" discover thousands of candidate fusion genes which makes one wonder how is this possible that one sample has more "gene fusions" than all known validated fusion genes in all cancers (which come from thousands of samples)?
So, you wanted to use it for fusion-gene detection, but it didn't work and now you are upset? I'm really sorry, if our statements confused you and you wasted your time. Perhaps we can help you running segemehl and haarz to get some fusion-junctions and downstream some fusion-genes out of it?
Also a note for the rest of interested people:
If any of you is interested in learning how to use segemehl to detect fusion transcripts and/or circularized RNAs, I can recommend you the following hands-on course:
Developers of the algorithm will explain you step-by-step how you can use segemehl to detect standard and non-standard transcripts. They will assure that all of you understand the difference between 'fusion-junctions' and 'fusion-genes' and what exactly you can do with segemehl and all its downstream analysis tools like (lack or haarz).
If you wish I may give a lecture/presentation at your course about finding fusion genes and fusion genes finders where a clear and easy to understand explanation will be given about:
Giving a lecture is actually a great idea! Unfortunately, the workshop is already prepared, announced and there are no free slots left. Nevertheless, I will ask around at the bioinformatics group of the University of Leipzig and I'm pretty sure there are more than enough interested people for a talk. Thanks for your offer! I will come back to you and then we can discuss about a date. Can you send me your contact information? (Is there a possibility for private messages here? If not, send your contact info to david.langenberger@ecseq.com)
Again, out there are known only a few hundred of gene fusions in all cancers! Tophat-fusion is finding ~130 000 candidate fusion genes in 4 samples (from: http://www.hindawi.com/journals/bmri/2013/340620/ )!!!! That is over 1000 times more than all gene fusion known in all cancers!!! For sure 99.99% of those 130 000 fusions do not exist and are just false positives! There are fusion finders which offer the full package and require no additional filtering!
In addition to splice junction discovery, MapSplice is capable of detecting gene fusions in RNA-seq data. From their website:
MapSplice is an algorithm for mapping RNA-seq data to reference genome for splice junction discovery. Features of MapSplice include:
FusionCatcher is another option.
FusionCatcher searches for novel/known fusion genes, translocations, and chimeras in RNA-seq data (paired-end reads from Illumina NGS platforms like Solexa and HiSeq) from diseased samples. The aims of FusionCatcher are: very good detection rate for finding candidate fusion genes, very easy to use (i.e. no a priori knowledge of databases and bioinformatics is needed in order to run FusionCatcher), to be as automatic as possible (i.e. the FusionCatcher will choose automatically the best parameters in order to find candidate fusion genes, e.g. finding automatically the adapters, building the exon-exon junctions automatically based on the length of the input reads, etc.) while providing the best possible detection rate for finding fusion genes
Current citation for FusionCatcher:
D. Nicorici, M. Satalan, H. Edgren, S. Kangaspeska, A. Murumagi, O. Kallioniemi, S. Virtanen, O. Kilkku, FusionCatcher - a tool for finding somatic fusion genes in paired-end RNA-sequencing data, bioRxiv, Nov. 2014, DOI:10.1101/011650
You can take a look at using GSNAP for this purpose, also.
Trans-ABySS has been used to successfully identify gene-fusions from RNA-seq data by the group that developed it (full disclosure, I used to belong to that group).
Here is a Fusion Detection tool for supervised analysis.You have to provide your list of fusion genes to check if present. https://github.com/FusionInspector/FusionInspector/wiki Also,it gives the bam files that show fusion support.
BreakFusion is another option that was recently reported and released.
For Gene-fusion detection you can also try SnowShoes-FTD:
Article describing it: http://nar.oxfordjournals.org/content/early/2011/05/27/nar.gkr362.full
Here are some reviews of RNA fusion detection tools:
http://www.hindawi.com/journals/bmri/2013/340620/
http://www.oapublishinglondon.com/article/617
http://www.nature.com/articles/srep21597
Here is a comparison of several Fusion Genes Finders:
Precision and FDR is presented also there!
SOAPfuse has that kind of statistics. http://genomebiology.com/2013/14/2/R12
If this helps you we are using deFuse. ;-)
You can take a look at https://www.nature.com/articles/srep21597
Genomon Fusion is yet another option.
"A novel tool for fusion discovery with paired-end RNA-Seq reads. The tool follows a different strategy by “finding fusions directly and verifying them”, differentiating it from all other existing tools by “finding the candidate regions and searching for the fusions afterwards”. This enables the fusion discovery process to be more effective and sensitive, also with a specular performance under low coverage of sequencing far more better than other tools."
Not to be confused with:
"An open source tool developed for genome-wide detection of fusion transcripts from paired-end RNA-Seq data. By comparing with previously released tools, SOAPfuse has a good performance. It is developed in perl. So far, it is developed only for analysis on human being RNA-Seq data.
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A third vote for TopHat-Fusion, I recently used to see fusion in brain cancer samples, it predicts a lots of possible fusion (you can filter the results by coverage and a secondary analysis for mapping specificity with blat).
Is TopHat fusion included in Galaxy Server??
This should probably be asked as a comment on one of the existing answers suggesting tophat-fusion or, since multiple answers suggested tophat-fusion, as a comment to your original question. But, definitely not as an answer to your question.
have you ever used SOAPfuse,can you tell me how to use SOAPfuse?
the details of command.
Hello everyone,
I had a concern about the outputs from 2 gene fusion calling tools that I was able to get to work. I ran Tophar-fusion and STAR-fusion on one of my RNA-Seq samples and the output gene fusion list doesn't match at all.
Has anyone else faced a similar issue before ? Any thoughts on why this could be the case would be really appreciated.
Thanks a lot.
Please provide more info:
How the output was compared? Have you checked if the output is matched when allowing +/- shift in breakpoint coordinates? Are there any matches if fusion list is collapsed to gene pairs?
What is the average number of fusions you've got with each tool? What about the coverage: number of reads spanning the junction itself, etc?
Are you sure that there are any fusions in your sample? :)
Actually, Tophat-fusion and STAR-fusion need a lot of improvement. Tophat-fusion consistently in is the bottom of all comparisons of fusion finders. TopHat-fusion calls hundreds of thousands of fusions per sample when it is well known that fusions are very rare and one has one fusion for every 10 samples analyzed. Therefore is it is expected that the lists of fusions from TopHat-fusion and STAR-fusion do not match at all.