Answers.Body: <p>FYI (non-OPs) it is very likely this sample has already been diagnosed and proven positive via a PCR test. I don't know the OP, but I am pretty familiar with oncovirus studies. To be clear, my guess is that PCR positive - doesn't mean cancer.</p>
  <ol>
  <li>What I think is occurring (if I'm wrong hopefully the OP will say) - is the  OP has a cancer patient(s) and has good evidence that the underlying cause is MCPyV: which is a very specific oncovirus.</li>
  <li>What they want to do is identify viral integration in the patient genome as evidence of oncogenesis. To put this <em>general</em> field in perspective I think this issue is literally the rise and fall of many investigators and companies in cancer (again not focusing on a singular virus <em>per se</em> but generically on the oncoviruses). Keep in mind I have an explanatory bias towards viruses.</li>
  <li>The molecular genetics of integration is likely to be  complex.</li>
  </ol>
  <p>In summary, the OP will particularly want to do (I suspect) is map the viral integration site. What is difficult is not diagnosis of the virus because at any time post-any sort of approximate assembly the genome can be locally <code>blasted</code> against the reference virus and its likely to have already been PCR'd. It's getting an assembly good enough to understanding the integration site - to demonstrate integration has occurred and thats is likely difficult (but I hope I'm wrong). Inverse PCR was used for this sort of thing - but thats a wetlab approach and PCR-based strategies with traditional Sanger sequencing can still be very useful here, but its laborious but was/is a  traditional approach.</p>
  <p>Thus, I suspect the OP is from a classical virological background venturing into NGS. I noted the OP used a human reference genome hg38 in a previous post.</p>
  <p>If thats correct then ... the big issue in my opinion is obtaining a correct assembly of the putative virus-human genome <strong>O</strong> Steps would be,</p>
  <ul>
  <li>QC</li>
  <li>Assembly e.g. via <code>bwa-mem2</code> ... <strong>O</strong> <strong>O</strong></li>
  <li>Construct a local <code>makeblastdb</code> database of the genome then ..</li>
  <li><code>blastn</code> between the human genome and a viral query to identify the integration site</li>
  <li>Perform something like a <code>gatk</code> analysis to have a different perspective on the integration site via VCF</li>
  <li>hone in on the contig via <code>IGV</code> for optical analysis and study how the virus has integrated</li>
  </ul>
  <p><strong>O</strong> My eukaryotic genomics is outdated and the above approach is summarised in Genomics in the Cloud by Geraldine <a href="https://www.oreilly.com/library/view/genomics-in-the/9781491975183/" rel="nofollow noreferrer">A. Van der Auwera, Brian D. O'Connor</a> published through O'Reilly Media. I just focus on small genomes.</p>
  <p><strong>O</strong> <strong>O</strong> and <strong>NOTE</strong> I cannot remember if <code>bwa-mem2</code> uses a reference genome e.g. hg38 type thing (Auwera and O'Connor will describe it). If it does the assembly of the integration site, in my opinion, would be compromised and alternative assembly strategies might (probably would) be required. I don't think this will be trivial - I think it would need a skilled eukaryotic bioinformatician who could be told about the biology of viral integration. Keep in mind, eukaryotic genome assembly is not my thing.</p>
  <p>However, riches await the investigator if this can be satisfactorily solved IMO.</p>
  <hr />
  <p>To answer @gringers point, <code>Kraken2</code> would be useful for viral integration <em>per se</em>  and yeah the results would be interesting because there's quite of them ... but in this case - I think - its an exact genetic sequence of a single species, thus I think direct blast-type blasting or <code>minimap2</code> mapping is a better strategy than <code>Kraken2</code>. Just my opinion.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
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Answers.LastActivityDate: 2023-02-13T02:28:14.697Z
Answers.LastEditDate: 2023-02-13T02:28:14.697Z
Answers.UserDisplayName: M__
Answers.Body: <p>You could start by trying out a metagenomics analysis program like kraken2. There are a few databases that combine bacteria, fungi, and viral searches.</p>
  <p>One benefit of using kraken2 is that it has options to output the most likely taxa of matches at the sub-read level, which can help with identifying integrated or chimeric sequences.</p>
  <p>I've written up the higher-level approach in more detail in this answer:</p>
  <p><a href="https://bioinformatics.stackexchange.com/questions/20542/how-do-i-measure-the-proportion-of-different-bacteria-in-a-sample-from-a-high-th">How do I measure the proportion of different bacteria in a sample from a high-throughput sequencer?</a></p>
  <p>The sequence-level hypotheses about kmer origin can be found in the direct output of kraken2 (last column):</p>
  <pre><code>$ kraken2 --threads 10 --db /media/minion/minion_temp/db/kraken/metagenome --memory-mapping fastq_pass/barcode0?/PAK83314_pass_barcode0?_*.fastq.gz --report kraken2.report.txt &gt; kraken2.out.txt
  $ head kraken2.out.txt
  C       a3e0b7c9-e6f3-40bf-900c-b1c59eb4977d    9606    208     0:70 623:3 0:53 9606:1 0:6 9606:32 131567:5 2759:4
  C       8030d33f-3838-4ed3-bab6-5f3b79fd059e    575614  164     0:68 623:1 0:46 575614:2 0:13
  C       5d87a648-799d-451f-b747-32a0fdf2517a    9606    190     0:108 9606:5 0:43
  C       8aba667d-4a5b-4f8b-9d18-1b5f761b87b2    9606    267     0:124 9606:9 0:99 1:1
  C       1b3501c2-6766-4ee8-b984-9e92470f37da    9606    335     0:70 28901:5 0:12 35885:1 0:23 9606:16 0:83 9606:17 0:6 9606:2 0:9 9606:5 0:11 9606:41
  U       75c2625d-ab87-4614-8d0d-c9d12c7d1e19    0       242     0:208
  U       902b0ec1-80d9-4e07-80c8-29a79191d931    0       171     0:137
  C       56b7c8a8-4cfd-44ec-8922-62871b29d31c    9606    338     0:103 9606:5 0:22 9606:5 0:1 9606:13 0:55 9606:27 0:38 9606:3 0:32
  C       68bf6718-2692-46e7-ba2d-f45952fd6138    9606    197     0:68 1:9 0:38 9606:19 0:29
  C       81f0f4ae-3bb5-4b94-89ff-152ff3b6f5d7    9606    238     0:146 9606:46 0:12
  </code></pre>
  <p>I'm aware that you're interested in looking for integration of a <em>single</em> viral genome. This can be specifically looked at with kraken2 because it can handle custom-built indexes as well. However, the benefit of using a comprehensive index (e.g. the <code>MinusB</code> database from <a href="https://benlangmead.github.io/aws-indexes/k2" rel="nofollow noreferrer">here</a>) is that it will make it much easier to exclude the other numerous endogenous viruses that are already integrated into the genome, whereas using a MCPyV-only index may lead to false-positive matches to sequences that are from another virus.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-02-12T19:23:52.190Z
Answers.LastActivityDate: 2023-02-13T00:30:15.037Z
Answers.LastEditDate: 2023-02-13T00:30:15.037Z
Answers.UserDisplayName: gringer
Body: <p>I have received two NGS files from an NGS company, both are FASTQ files that correspond to reads from a tumor sample. I heavily suspect that MCPyV is present, and I am hoping to identify it.</p>
  <p>What downloadable software can I use for viral-host integration detection? I have access to both Windows and Mac OS, but have no experience with Linux.</p>
  
Tags: <fastq><virus>
Title: How can I detect MCPyV in a FASTQ file?
ThreadText: # How can I detect MCPyV in a FASTQ file?
  
  (Asked by: username_0 on 2023-02-11)
  
  I have received two NGS files from an NGS company, both are FASTQ files that correspond to reads from a tumor sample. I heavily suspect that MCPyV is present, and I am hoping to identify it.
  
  What downloadable software can I use for viral-host integration detection? I have access to both Windows and Mac OS, but have no experience with Linux.
  
  **Tags:** fastq, virus
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2023-02-13. Score: 0
  
  FYI (non-OPs) it is very likely this sample has already been diagnosed and proven positive via a PCR test. I don't know the OP, but I am pretty familiar with oncovirus studies. To be clear, my guess is that PCR positive - doesn't mean cancer.
  
  1. What I think is occurring (if I'm wrong hopefully the OP will say) - is the OP has a cancer patient(s) and has good evidence that the underlying cause is MCPyV: which is a very specific oncovirus.
  2. What they want to do is identify viral integration in the patient genome as evidence of oncogenesis. To put this *general* field in perspective I think this issue is literally the rise and fall of many investigators and companies in cancer (again not focusing on a singular virus *per se* but generically on the oncoviruses). Keep in mind I have an explanatory bias towards viruses.
  3. The molecular genetics of integration is likely to be complex.
  
  In summary, the OP will particularly want to do (I suspect) is map the viral integration site. What is difficult is not diagnosis of the virus because at any time post-any sort of approximate assembly the genome can be locally `blasted` against the reference virus and its likely to have already been PCR'd. It's getting an assembly good enough to understanding the integration site - to demonstrate integration has occurred and thats is likely difficult (but I hope I'm wrong). Inverse PCR was used for this sort of thing - but thats a wetlab approach and PCR-based strategies with traditional Sanger sequencing can still be very useful here, but its laborious but was/is a traditional approach.
  
  Thus, I suspect the OP is from a classical virological background venturing into NGS. I noted the OP used a human reference genome hg38 in a previous post.
  
  If thats correct then ... the big issue in my opinion is obtaining a correct assembly of the putative virus-human genome **O** Steps would be,
  
  * QC
  * Assembly e.g. via `bwa-mem2` ... **O** **O**
  * Construct a local `makeblastdb` database of the genome then ..
  * `blastn` between the human genome and a viral query to identify the integration site
  * Perform something like a `gatk` analysis to have a different perspective on the integration site via VCF
  * hone in on the contig via `IGV` for optical analysis and study how the virus has integrated
  
  **O** My eukaryotic genomics is outdated and the above approach is summarised in Genomics in the Cloud by Geraldine [A. Van der Auwera, Brian D. O'Connor](https://www.oreilly.com/library/view/genomics-in-the/9781491975183/) published through O'Reilly Media. I just focus on small genomes.
  
  **O** **O** and **NOTE** I cannot remember if `bwa-mem2` uses a reference genome e.g. hg38 type thing (Auwera and O'Connor will describe it). If it does the assembly of the integration site, in my opinion, would be compromised and alternative assembly strategies might (probably would) be required. I don't think this will be trivial - I think it would need a skilled eukaryotic bioinformatician who could be told about the biology of viral integration. Keep in mind, eukaryotic genome assembly is not my thing.
  
  However, riches await the investigator if this can be satisfactorily solved IMO.
  
  ---
  
  To answer @gringers point, `Kraken2` would be useful for viral integration *per se* and yeah the results would be interesting because there's quite of them ... but in this case - I think - its an exact genetic sequence of a single species, thus I think direct blast-type blasting or `minimap2` mapping is a better strategy than `Kraken2`. Just my opinion.
  
  
  ---
  
  ### Answer by username_2 on 2023-02-12. Score: 0
  
  You could start by trying out a metagenomics analysis program like kraken2. There are a few databases that combine bacteria, fungi, and viral searches.
  
  One benefit of using kraken2 is that it has options to output the most likely taxa of matches at the sub-read level, which can help with identifying integrated or chimeric sequences.
  
  I've written up the higher-level approach in more detail in this answer:
  
  [How do I measure the proportion of different bacteria in a sample from a high-throughput sequencer?](https://bioinformatics.stackexchange.com/questions/20542/how-do-i-measure-the-proportion-of-different-bacteria-in-a-sample-from-a-high-th)
  
  The sequence-level hypotheses about kmer origin can be found in the direct output of kraken2 (last column):
  
  ```
  $ kraken2 --threads 10 --db /media/minion/minion_temp/db/kraken/metagenome --memory-mapping fastq_pass/barcode0?/PAK83314_pass_barcode0?_*.fastq.gz --report kraken2.report.txt > kraken2.out.txt
  $ head kraken2.out.txt
  C       a3e0b7c9-e6f3-40bf-900c-b1c59eb4977d    9606    208     0:70 623:3 0:53 9606:1 0:6 9606:32 131567:5 2759:4
  C       8030d33f-3838-4ed3-bab6-5f3b79fd059e    575614  164     0:68 623:1 0:46 575614:2 0:13
  C       5d87a648-799d-451f-b747-32a0fdf2517a    9606    190     0:108 9606:5 0:43
  C       8aba667d-4a5b-4f8b-9d18-1b5f761b87b2    9606    267     0:124 9606:9 0:99 1:1
  C       1b3501c2-6766-4ee8-b984-9e92470f37da    9606    335     0:70 28901:5 0:12 35885:1 0:23 9606:16 0:83 9606:17 0:6 9606:2 0:9 9606:5 0:11 9606:41
  U       75c2625d-ab87-4614-8d0d-c9d12c7d1e19    0       242     0:208
  U       902b0ec1-80d9-4e07-80c8-29a79191d931    0       171     0:137
  C       56b7c8a8-4cfd-44ec-8922-62871b29d31c    9606    338     0:103 9606:5 0:22 9606:5 0:1 9606:13 0:55 9606:27 0:38 9606:3 0:32
  C       68bf6718-2692-46e7-ba2d-f45952fd6138    9606    197     0:68 1:9 0:38 9606:19 0:29
  C       81f0f4ae-3bb5-4b94-89ff-152ff3b6f5d7    9606    238     0:146 9606:46 0:12
  
  ```
  
  I'm aware that you're interested in looking for integration of a *single* viral genome. This can be specifically looked at with kraken2 because it can handle custom-built indexes as well. However, the benefit of using a comprehensive index (e.g. the `MinusB` database from [here](https://benlangmead.github.io/aws-indexes/k2)) is that it will make it much easier to exclude the other numerous endogenous viruses that are already integrated into the genome, whereas using a MCPyV-only index may lead to false-positive matches to sequences that are from another virus.
  
  
  ---
  
  