Body: <p>I was wondering if any one has had success merging pre-generated reference index files for BWA, bowtie2 from multiple references? </p>
  
  <p>For a concrete example I have hg19 and a bunch of viral and bacterial genomes that all have been index separately but I want to do a single alignment to all of them.  Is there a way I could say combine hg19, virus_A, virus_D and bacteria_X into a single reference without having to regenerate the indexes?  Since the problem could get combinatorial quickly and indexing anything with hg19 is time consuming I was wondering if there was a way around it.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-04T15:34:58.960Z
Comments.Text: this will likely depend on the tool no? i doubt there is a *generic* why to merge indexes? for `BLAST`, there is `blastdb_aliastool`
Comments.UserDisplayName: Chris_Rands
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-04T16:09:53.620Z
Comments.Text: Yeah, I would split this into separate questions for each tool. I don't actually know how the blast index works, but I would be very surprised if the same approach will work for all three tools. It might be the same for bowtie and bwa, but blast will likely be different.
Comments.UserDisplayName: terdon
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-04T19:59:58.160Z
Comments.Text: Agreed I wasn't looking for a generic tools but the question does read that way.  I was looking for a way for each of those tools.  Thanks @Chris_Rands ill check out `blastdb_aliastool`
Comments.UserDisplayName: Bioathlete
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-05T08:34:27.670Z
Comments.Text: What would you be aligning? Short reads? Why would you want to align the same data against three so different genomes? Surely the time you would save by not re-indexing would be lost again while wasting time aligning to the wrong genome, right?
Comments.UserDisplayName: terdon
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-05T12:12:44.440Z
Comments.Text: @terdon I imagine they have metagenomic data or similar where the sample composition is not known a priori (reads could be 90% human, 8% E. coli etc.)
Comments.UserDisplayName: Chris_Rands
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-05T12:28:56.117Z
Comments.Text: @Chris_Rands duh, indeed, should have thought of that, thanks.
Comments.UserDisplayName: terdon
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-05T14:17:31.767Z
Comments.Text: @terdon Correct we don't know the make up of the sample and want to have the reads complete for alignments
Comments.UserDisplayName: Bioathlete
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-05T14:24:46.527Z
Comments.Text: That makes perfect sense for [your BLAST question](https://bioinformatics.stackexchange.com/q/7386/298) but I would be wary of using it for short reads. Even between bacteria and humans there are bound to be regions of homology. I would be very curious to know how well this works if you do manage to do it! Maybe by assigning the viral and bacterial genomes as extra "chromosomes" in a single multi-fasta file along with hg19 and indexing that?
Comments.UserDisplayName: terdon
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-05T14:53:21.283Z
Comments.Text: @terdon The multi-fasta is the base case I was hoping not to have to reindex the different combinations but will either do that or create one giant reference
Comments.UserDisplayName: Bioathlete
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-05T14:57:19.690Z
Comments.Text: Could some of these help? [1](https://f1000researchdata.s3.amazonaws.com/manuscripts/17815/b3840e4e-8d44-4cf8-891b-2aa7a1c32122_15931_-_steven_wingett_v2.pdf?doi=10.12688/f1000research.15931.2&numberOfBrowsableCollections=15&numberOfBrowsableGateways=23), [2](https://academic.oup.com/bioinformatics/article/29/13/i361/189733), [3](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2009-10-9-r98)?
Comments.UserDisplayName: terdon
Tags: <read-mapping><genome-index>
Title: Merging/concatenating index files
ThreadText: # Merging/concatenating index files
  
  (Asked by: username_0 on 2019-04-04)
  
  I was wondering if any one has had success merging pre-generated reference index files for BWA, bowtie2 from multiple references?
  
  For a concrete example I have hg19 and a bunch of viral and bacterial genomes that all have been index separately but I want to do a single alignment to all of them. Is there a way I could say combine hg19, virus\_A, virus\_D and bacteria\_X into a single reference without having to regenerate the indexes? Since the problem could get combinatorial quickly and indexing anything with hg19 is time consuming I was wondering if there was a way around it.
  
  **Tags:** read-mapping, genome-index
  
  > **Comments:**
  > **username_1**: this will likely depend on the tool no? i doubt there is a *generic* why to merge indexes? for `BLAST`, there is `blastdb_aliastool`  
  > **username_2**: Yeah, I would split this into separate questions for each tool. I don't actually know how the blast index works, but I would be very surprised if the same approach will work for all three tools. It might be the same for bowtie and bwa, but blast will likely be different.  
  > **username_0**: Agreed I wasn't looking for a generic tools but the question does read that way.  I was looking for a way for each of those tools.  Thanks @username_1 ill check out `blastdb_aliastool`  
  > **username_2**: What would you be aligning? Short reads? Why would you want to align the same data against three so different genomes? Surely the time you would save by not re-indexing would be lost again while wasting time aligning to the wrong genome, right?  
  > **username_1**: @username_2 I imagine they have metagenomic data or similar where the sample composition is not known a priori (reads could be 90% human, 8% E. coli etc.)  
  > **username_2**: @username_1 duh, indeed, should have thought of that, thanks.  
  > **username_0**: @username_2 Correct we don't know the make up of the sample and want to have the reads complete for alignments  
  > **username_2**: That makes perfect sense for [your BLAST question](https://bioinformatics.stackexchange.com/q/7386/298) but I would be wary of using it for short reads. Even between bacteria and humans there are bound to be regions of homology. I would be very curious to know how well this works if you do manage to do it! Maybe by assigning the viral and bacterial genomes as extra "chromosomes" in a single multi-fasta file along with hg19 and indexing that?  
  > **username_0**: @username_2 The multi-fasta is the base case I was hoping not to have to reindex the different combinations but will either do that or create one giant reference  
  > **username_2**: Could some of these help? [1](https://f1000researchdata.s3.amazonaws.com/manuscripts/17815/b3840e4e-8d44-4cf8-891b-2aa7a1c32122_15931_-_steven_wingett_v2.pdf?doi=10.12688/f1000research.15931.2&numberOfBrowsableCollections=15&numberOfBrowsableGateways=23), [2](https://academic.oup.com/bioinformatics/article/29/13/i361/189733), [3](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2009-10-9-r98)?  
  
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  ## 0 answers
  
  