Analyzing Targeted Resequencing Data With Galaxy

7.7 years ago by

Penn State

Lali: Please, always CC mailing list when you reply. Which browser/OS are your using? Thanks, anton galaxy team Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org

ADD COMMENT • link written 7.7 years ago by Anton Nekrutenko ♦ 1.7k

Ohh sorry about that! I am using both Windows XP and Ubuntu and I usually use Google Chrome.

ADD REPLY • link written 7.7 years ago by Lali • 50

Hi all, Like many people on this e-mail chain, I have been looking for advice on how to process Exome data. Below, I have described in detail what I have done with the hope of getting some clarification. Hopefully it will be helpful to many of us! I have SureSelect Exome captured data. The data was delivered to me as two separate files (/1) & (/2). Each file has ~33 million reads; 7.2 GB each. I am looking for SNPs from a family with cancer. Eventually I plan to compare the date from multiple members of the same family to find a related disease SNP. Below is the workflow that I used to process my data. I adapted it from the Screencast titles: "Mapping Illumina Reads: Paired Ends Example." I used all of the same default parameters as in the screencast. At the end of step 13, I had ~4,700,000 SNPs. This seemed like a lot so in step 14, I filtered on column 7 (c7) which I believe is the Quality SNP value. I set the filter as C7>=1 to remove all of the 0 (zero) values for Quality SNP. I figured that if they have a value of zero, they must not be real SNPs. This left me with ~180,000 SNPs. 1: Get Data: Illumina 1.3+ file (/1) 2: Get Data: Illumina 1.3+ file (/2) 3: FASTQ Groomer on data 1 4: FASTQ Groomer on data 2 5: FASTQ Summary Statistics on data 3 6: FASTQ Summary Statistics on data 4 7: Box plot on data 5 8: Box plot on data 6 9: Map with Bowtie for Illumina on data 4 and data 3: mapped reads 10: Filter Sam on data 9 11: SAM-to-BAM on data 10: converted to BAM 12: Generate pileup on data 11: converted pileup 13: Filter pileup on data 12 14: Filter data on 13 (c7>=1) 15: Sort on data 15 (C7; descending order) First, if anyone has ideas on how to improve the workflow, I would be open to suggestions; especially from people experienced with Galaxy. Second, I am concerned that many/most of the SNPs are known. Should I filter my data against the known SNPdb? If so, how can I do this in Galaxy (in Bowtie?) Third, as suggested in the screencast, I did not trim or filter my FASTQ Groomed data because I was interested in SNPs and I could filter on Quality later in the workflow. Would implementing a filtering step on phred quality (~20) at this step save me the step of filtering later on. Currently it takes multiple hours (~16) to process the data from start to finish, would filtering at this step reduce the amount of time that it takes to process my data? Presumably, there would be less data to process. I do this on the AWS Cloud and time is money! Fifth, when using Galaxy on the AWS cloud, does adding additional cores or adding High CPU ( or both) shorten the time to process the data? When I set up extra cores, it appeared that some of them are idle and I don't want to pay for idle cores. If anyone could share information on how best to manage the cloud, it would be appreciated. Finally, what is the difference between “stopping” an instance and “terminating” an instance on the cloud? Would I still get charged by AWS if I just stop an instance? Any clarification in this area would also be much appreciated. Again, time is money! I hope this helps many of us! Unfortunatly, I will not be in Pitt to ask these questions in person. Thanks in advance!!! Mike Subject: Re: [galaxy-user] Analyzing Targeted Resequencing data with Galaxy To: "Anton Nekrutenko" <anton@bx.psu.edu> Cc: "galaxy-user" <galaxy-user@lists.bx.psu.edu> Date: Tuesday, April 5, 2011, 11:50 AM Ohh sorry about that! I am using both Windows XP and Ubuntu and I usually use Google Chrome. Lali: Please, always CC mailing list when you reply. My only problem with Galaxy is that I have to keep on clearing my cache in order to get the history to display correctly, is there another way of solving this issue? Which browser/OS are your using? Thanks, anton galaxy team Thanks so much for the tips Anton! I am very excited about the newer developments. I did watch the quickies and they were very useful for a beginner like me, I actually did my first try at the alignment by following the Illumina single-end tutorial video step by step, but you need to watch the paired-end too, for some of the first steps, which are explained better on that one. I have been playing around a lot with Galaxy, and I have several workflows, my department just started doing sequencing, so we don't have standard procedures set in place. I was assigned to evaluate Galaxy and CLC, and so far CLC has not impressed me, except for the fact that it can generate reports easily. I think Galaxy is the way to go for me (us, if I can convince them to run a local server), since I am not a bioinformatician, and just the fact that you can queue up actions and just walk away is fantastic (amongst other things). But because I am a beginner, I am not 100% of the settings I have chosen and my data is not looking too good so far, but I am having a bioinformatician come over and help me on Thursday and I think your tips will be of help. My only problem with Galaxy is that I have to keep on clearing my cache in order to get the history to display correctly, is there another way of solving this issue? Best regards, L Lali: In your case the workflow for capture re-sequencing should look like this: 1. QC data (groom fastq files and plot quality distribution) 2. Map the reads (use bwa) 3. Generate and filter pileup 4. Intersect pileup with coordinates of sure select bates. However, before you dive in please understand basic Galaxy functionality by taking a look at http://usegalaxy.org/galaxy101 and watching *all* Illumina-related Galaxy quickies (black boxes on the front page on Galaxy). Next, take a look at http://usegalaxy.org/heteroplasmy. Note, that we are working on bringing "industrial-strength" diploid genotyping functionality in Galaxy in the next two-three months that will include more sophisticated genotypers, recalibration and realignment tools, and novel visualization approaches. Thank for using Galaxy. anton galaxy team Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org ___________________________________________________________ The Galaxy User list should be used for the discussion of Galaxy analysis and other features on the public server at usegalaxy.org. Please keep all replies on the list by using "reply all" in your mail client. For discussion of local Galaxy instances and the Galaxy source code, please use the Galaxy Development list: http://lists.bx.psu.edu/listinfo/galaxy-dev To manage your subscriptions to this and other Galaxy lists, please use the interface at: http://lists.bx.psu.edu/

ADD REPLY • link written 7.7 years ago by Mike Dufault • 270

Mike: Which parameters did you use at step 13 (if you used main site to perform these analyses you can share your history with me). Thanks, anton Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org

ADD REPLY • link written 7.7 years ago by Anton Nekrutenko ♦ 1.7k

Hi Anton, The conditions are give below. Currently, I don't have access to the AWS cloud so I can not share my history at the moment. Select dataset: which contains: Pileup with ten columns (with consensus) See "Types of pileup datasets" below for examples Do not consider read bases with quality lower than: 20 No variants with quality below this value will be reported Do not report positions with coverage lower than: 3 Pileup lines with coverage lower than this value will be skipped Only report variants?: Yes See "Examples 1 and 2" below for explanation Convert coordinates to intervals?: Yes See "Output format" below for explanation Print total number of differences?: No See "Example 3" below for explanation Print quality and base string?: No See "Example 4" below for explanation I did save the output from step 15 to my USB stick and I have provided a bit of it below for what it is worth. chr1 100316588 100316589 A G 255 255 60 141 0 0 137 0 137 chr1 100575932 100575933 G A 255 255 60 89 89 0 0 0 89 chr1 100617886 100617887 C T 255 255 60 113 0 0 0 111 111 chr1 100672059 100672060 T C 255 255 60 225 1 220 0 0 221 chr1 101203826 101203827 G A 255 255 60 106 105 0 0 0 105 chr1 103461507 103461508 T A 255 255 60 87 82 0 0 0 82 chr1 104166495 104166496 T C 255 255 60 168 0 157 0 5 162 chr1 104256477 104256478 T A 255 255 60 84 82 0 0 0 82 Thanks for your help! Mike Subject: Re: [galaxy-user] Analyzing Targeted Resequencing data with Galaxy To: "Mike Dufault" <dufaultm@yahoo.com> Cc: "Lali" <laurafe@gmail.com>, "galaxy-user" <galaxy- user@lists.bx.psu.edu=""> Date: Tuesday, April 5, 2011, 2:33 PM Mike: Which parameters did you use at step 13 (if you used main site to perform these analyses you can share your history with me). Thanks, anton Hi all, Like many people on this e-mail chain, I have been looking for advice on how to process Exome data. Below, I have described in detail what I have done with the hope of getting some clarification. Hopefully it will be helpful to many of us! I have SureSelect Exome captured data. The data was delivered to me as two separate files (/1) & (/2). Each file has ~33 million reads; 7.2 GB each. I am looking for SNPs from a family with cancer. Eventually I plan to compare the date from multiple members of the same family to find a related disease SNP. Below is the workflow that I used to process my data. I adapted it from the Screencast titles: "Mapping Illumina Reads: Paired Ends Example." I used all of the same default parameters as in the screencast. At the end of step 13, I had ~4,700,000 SNPs. This seemed like a lot so in step 14, I filtered on column 7 (c7) which I believe is the Quality SNP value. I set the filter as C7>=1 to remove all of the 0 (zero) values for Quality SNP. I figured that if they have a value of zero, they must not be real SNPs. This left me with ~180,000 SNPs. 1: Get Data: Illumina 1.3+ file (/1) 2: Get Data: Illumina 1.3+ file (/2) 3: FASTQ Groomer on data 1 4: FASTQ Groomer on data 2 5: FASTQ Summary Statistics on data 3 6: FASTQ Summary Statistics on data 4 7: Box plot on data 5 8: Box plot on data 6 9: Map with Bowtie for Illumina on data 4 and data 3: mapped reads 10: Filter Sam on data 9 11: SAM-to-BAM on data 10: converted to BAM 12: Generate pileup on data 11: converted pileup 13: Filter pileup on data 12 14: Filter data on 13 (c7>=1) 15: Sort on data 15 (C7; descending order) First, if anyone has ideas on how to improve the workflow, I would be open to suggestions; especially from people experienced with Galaxy. Second, I am concerned that many/most of the SNPs are known. Should I filter my data against the known SNPdb? If so, how can I do this in Galaxy (in Bowtie?) Third, as suggested in the screencast, I did not trim or filter my FASTQ Groomed data because I was interested in SNPs and I could filter on Quality later in the workflow. Would implementing a filtering step on phred quality (~20) at this step save me the step of filtering later on. Currently it takes multiple hours (~16) to process the data from start to finish, would filtering at this step reduce the amount of time that it takes to process my data? Presumably, there would be less data to process. I do this on the AWS Cloud and time is money! Fifth, when using Galaxy on the AWS cloud, does adding additional cores or adding High CPU ( or both) shorten the time to process the data? When I set up extra cores, it appeared that some of them are idle and I don't want to pay for idle cores. If anyone could share information on how best to manage the cloud, it would be appreciated. Finally, what is the difference between “stopping” an instance and “terminating” an instance on the cloud? Would I still get charged by AWS if I just stop an instance? Any clarification in this area would also be much appreciated. Again, time is money! I hope this helps many of us! Unfortunatly, I will not be in Pitt to ask these questions in person. Thanks in advance!!! Mike Subject: Re: [galaxy-user] Analyzing Targeted Resequencing data with Galaxy To: "Anton Nekrutenko" <anton@bx.psu.edu> Cc: "galaxy-user" <galaxy-user@lists.bx.psu.edu> Date: Tuesday, April 5, 2011, 11:50 AM Ohh sorry about that! I am using both Windows XP and Ubuntu and I usually use Google Chrome. Lali: Please, always CC mailing list when you reply. My only problem with Galaxy is that I have to keep on clearing my cache in order to get the history to display correctly, is there another way of solving this issue? Which browser/OS are your using? Thanks, anton galaxy team Thanks so much for the tips Anton! I am very excited about the newer developments. I did watch the quickies and they were very useful for a beginner like me, I actually did my first try at the alignment by following the Illumina single-end tutorial video step by step, but you need to watch the paired-end too, for some of the first steps, which are explained better on that one. I have been playing around a lot with Galaxy, and I have several workflows, my department just started doing sequencing, so we don't have standard procedures set in place. I was assigned to evaluate Galaxy and CLC, and so far CLC has not impressed me, except for the fact that it can generate reports easily. I think Galaxy is the way to go for me (us, if I can convince them to run a local server), since I am not a bioinformatician, and just the fact that you can queue up actions and just walk away is fantastic (amongst other things). But because I am a beginner, I am not 100% of the settings I have chosen and my data is not looking too good so far, but I am having a bioinformatician come over and help me on Thursday and I think your tips will be of help. My only problem with Galaxy is that I have to keep on clearing my cache in order to get the history to display correctly, is there another way of solving this issue? Best regards, L Lali: In your case the workflow for capture re-sequencing should look like this: 1. QC data (groom fastq files and plot quality distribution) 2. Map the reads (use bwa) 3. Generate and filter pileup 4. Intersect pileup with coordinates of sure select bates. However, before you dive in please understand basic Galaxy functionality by taking a look at http://usegalaxy.org/galaxy101 and watching *all* Illumina-related Galaxy quickies (black boxes on the front page on Galaxy). Next, take a look at http://usegalaxy.org/heteroplasmy. Note, that we are working on bringing "industrial-strength" diploid genotyping functionality in Galaxy in the next two-three months that will include more sophisticated genotypers, recalibration and realignment tools, and novel visualization approaches. Thank for using Galaxy. anton galaxy team Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org ___________________________________________________________ The Galaxy User list should be used for the discussion of Galaxy analysis and other features on the public server at usegalaxy.org. Please keep all replies on the list by using "reply all" in your mail client. For discussion of local Galaxy instances and the Galaxy source code, please use the Galaxy Development list: http://lists.bx.psu.edu/listinfo/galaxy-dev To manage your subscriptions to this and other Galaxy lists, please use the interface at: http://lists.bx.psu.edu/ Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org

ADD REPLY • link written 7.7 years ago by Mike Dufault • 270

Mike: You have a fairly deep coverage, so increasing quality cutoff to 25 - 30 and coverage to at least 20, will dramatically decrease the number of SNPs. To see which SNPs are from dbSNP simple obtains dbSNP data from UCSC (Get Data -> UCSC main) and join with the pileup you've generated (Operate on Genomic Intervals -> Join). To add to the excellent comments by Sean -> realignment and recalibration tools are coming by this Summer together with more sophisticated genotypers. Tx, anton galaxy team Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org

ADD REPLY • link written 7.7 years ago by Anton Nekrutenko ♦ 1.7k

Thanks for all the tips and advice, I will get back to this thread after I have tried it out :)

ADD REPLY • link written 7.7 years ago by Lali • 50

Btw Anton, you never answered anything about the bug with the history not loading properly until I clear my cache. I use Windows XP - Firefox Ubuntu - Google Chrome Any ideas?

ADD REPLY • link written 7.7 years ago by Lali • 50

Lali, we don't have an answer yet because we have never seen this and can't reproduce. Are you using a proxy server or anything unusual? -- jt (composed on my phone)

ADD REPLY • link written 7.7 years ago by James Taylor ♦ 470

No proxy, I have accessed Galaxy both from home and the office and it is the same thing. I clear the cache, relogin and things are fine for maybe 1 or 2 workflows and then it starts messing up again. It is not a problem that is there all the time, it happens sometimes only, and the solution I've found is clearing the cache. This is what I do: 1-Make a new history - Galaxy shows a new blank history 2-Select a saved data set and send to history 3-Old history loads (last history I worked on, maybe days ago) 4-Hit refresh 5-New history with newly loaded dataset appears 6- Select a workflow, set my loaded dataset as input 7-Click on send to new history and set a name for it 8-Click ok, history remains the same (step 5) 9-Hit refresh 10-Nothing happens, same history from step 5 11-Close browser 12-Wait a few hours 13-Open Galaxy again 14-Same history from step 5 15-Click on saved histories and click on the history made with the workflow 16-Loads ok or Clear cache and do 1 to 6, no problem history loads as it should. Also, when I save workflows, some steps get jumbled like: 1- Groom 2-filter artifacts 3-clip 4-another clip 5-quality trim 6-map saved workflow: 1-groom 2-filter artifacts 3-clip 4-quality trim 5-another clip 6-map -L

ADD REPLY • link written 7.7 years ago by Lali • 50

Regarding the workflows, step ordering should be consistent when re- saving unless you're moving things around on the screen. If you're finding this not to be the case, please share the workflow with me and I'll look into it. -Dannon

ADD REPLY • link written 7.7 years ago by Dannon Baker ♦ 3.7k

Hi, Mike. See my couple of comments below.... Sean This might not be the best choice, as bowtie does not allow gapped alignment. See here for a discussion of indels and SNV calling: http://bioinformatics.oxfordjournals.org/content/26/6/722.long You will probably also want to consider local realignment around indels and potentially quality score recalibration. Keep in mind that, depending on the version of dbSNP, there are many cancer-associated SNPs contaminating the database. Adding a gapped alignment algorithm, indel realignment, and quality recalibration can easily increase this time to a couple of days per sample.

ADD REPLY • link written 7.7 years ago by Sean Davis • 220

Sean, Anton and Jen, Thanks for all of the suggestions (in separate replies) on how to better analyze my SelectSure captured Exome data. My original work- flow is below in the e-mail string. Based on the suggestions, I plan to change my work-flow by increasing my quality filter from 20 to 25-30 and increasing my minimum coverage from 3x to ~20x. I will use the Join function to compare the SNPs that are in common with the samples from two family members to filter (narrow down) what they have in common, since I am looking for a hereditary disease. Then i will use the Join function again with the SNPs from build (131) to characterize the SNPs. Sean suggested realignment around indels and potentially quality score recalibration. Is that even possible with Galaxy at the moment? Where in the flow can I perform Indel analysis? Will I need to process my data separately for SNPs and Indel analysis, or can they be done sequentially in the same linear work-flow? I am still a little unsure of the best way to hand this. Please let me know if you have any more suggestions or comments before I re-launch the analysis later this evening. Once I get a flow that works, I hope to be able to publish it for everyone to benefit from. Thanks to the Galaxy team for an outstanding platform and support! Mike Subject: Re: [galaxy-user] Analyzing Targeted Resequencing data with Galaxy To: "Mike Dufault" <dufaultm@yahoo.com> Cc: "galaxy-user" <galaxy-user@lists.bx.psu.edu> Date: Tuesday, April 5, 2011, 4:39 PM Hi, Mike. See my couple of comments below.... Sean Hi all, Like many people on this e-mail chain, I have been looking for advice on how to process Exome data. Below, I have described in detail what I have done with the hope of getting some clarification. Hopefully it will be helpful to many of us! I have SureSelect Exome captured data. The data was delivered to me as two separate files (/1) & (/2). Each file has ~33 million reads; 7.2 GB each. I am looking for SNPs from a family with cancer. Eventually I plan to compare the date from multiple members of the same family to find a related disease SNP. Below is the workflow that I used to process my data. I adapted it from the Screencast titles: "Mapping Illumina Reads: Paired Ends Example." I used all of the same default parameters as in the screencast. At the end of step 13, I had ~4,700,000 SNPs. This seemed like a lot so in step 14, I filtered on column 7 (c7) which I believe is the Quality SNP value. I set the filter as C7>=1 to remove all of the 0 (zero) values for Quality SNP. I figured that if they have a value of zero, they must not be real SNPs. This left me with ~180,000 SNPs. 1: Get Data: Illumina 1.3+ file (/1) 2: Get Data: Illumina 1.3+ file (/2) 3: FASTQ Groomer on data 1 4: FASTQ Groomer on data 2 5: FASTQ Summary Statistics on data 3 6: FASTQ Summary Statistics on data 4 7: Box plot on data 5 8: Box plot on data 6 9: Map with Bowtie for Illumina on data 4 and data 3: mapped reads This might not be the best choice, as bowtie does not allow gapped alignment. See here for a discussion of indels and SNV calling: http://bioinformatics.oxfordjournals.org/content/26/6/722.long You will probably also want to consider local realignment around indels and potentially quality score recalibration. 10: Filter Sam on data 9 11: SAM-to-BAM on data 10: converted to BAM 12: Generate pileup on data 11: converted pileup 13: Filter pileup on data 12 14: Filter data on 13 (c7>=1) 15: Sort on data 15 (C7; descending order) First, if anyone has ideas on how to improve the workflow, I would be open to suggestions; especially from people experienced with Galaxy. Second, I am concerned that many/most of the SNPs are known. Should I filter my data against the known SNPdb? If so, how can I do this in Galaxy (in Bowtie?) Keep in mind that, depending on the version of dbSNP, there are many cancer-associated SNPs contaminating the database. Third, as suggested in the screencast, I did not trim or filter my FASTQ Groomed data because I was interested in SNPs and I could filter on Quality later in the workflow. Would implementing a filtering step on phred quality (~20) at this step save me the step of filtering later on. Currently it takes multiple hours (~16) to process the data from start to finish, would filtering at this step reduce the amount of time that it takes to process my data? Presumably, there would be less data to process. I do this on the AWS Cloud and time is money! Adding a gapped alignment algorithm, indel realignment, and quality recalibration can easily increase this time to a couple of days per sample. Fifth, when using Galaxy on the AWS cloud, does adding additional cores or adding High CPU ( or both) shorten the time to process the data? When I set up extra cores, it appeared that some of them are idle and I don't want to pay for idle cores. If anyone could share information on how best to manage the cloud, it would be appreciated. Finally, what is the difference between “stopping” an instance and “terminating” an instance on the cloud? Would I still get charged by AWS if I just stop an instance? Any clarification in this area would also be much appreciated. Again, time is money! I hope this helps many of us! Unfortunatly, I will not be in Pitt to ask these questions in person. Thanks in advance!!! Mike

ADD REPLY • link written 7.6 years ago by Mike Dufault • 270

Since you are looking only for variants in common, you can be more lenient (allow more false-positives per sample), so I would not increase the coverage that high and rely more on the snp quality filter. I do not think so. This depends on the software being used. Pileup can call both indels and SNVs.

ADD REPLY • link written 7.6 years ago by Sean Davis • 220

Mike: Realignment and recalibration is not yet possible on the main site. However, we are working on several re-sequencing projects in house where these tools are used and will bring them to Galaxy by ISMB conference in Vienna. The indel analysis at the moment is rather simplistic (yet still very useful) and is based on processing on CIGAR strings in aligned SAM files. You can simply run datasets generated by BWA through our indels tools. Thanks and let us know if you have more questions. anton galaxy team Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org

ADD REPLY • link written 7.6 years ago by Anton Nekrutenko ♦ 1.7k

Similar posts • Search »