Answers.Body: <blockquote>
  <p>for illumina reads (only)</p>
  <p><a href="https://docs.google.com/document/d/1ZJpGTMl319v-YRrXvNbM5OGSy0jBTVfUbpawVAPqaMU/edit?usp=sharing" rel="nofollow noreferrer">prerequisite: download n prepare</a></p>
  </blockquote>
  <blockquote>
  <p><a href="https://docs.google.com/document/d/1p9ZsfkqykNgcX7F5fpOl64vDi46tMEL7yY3VfiK8mZE/edit?usp=sharing" rel="nofollow noreferrer">the fna n gff file of yeast reference</a></p>
  </blockquote>
  <blockquote>
  <p><a href="https://drive.google.com/drive/folders/1rRfMXvK3KtrppFQm-TChRDIOYR0UphKI?usp=sharing" rel="nofollow noreferrer">mapping_in_02.directory</a></p>
  </blockquote>
  <p>1st step is mapping the downloaded reads to the reference genome:</p>
  <ol>
  <li>build index for the reference using the following command</li>
  </ol>
  <p>bwa index ../01./GCF_000146045.2_R64_genomic.fna</p>
  <blockquote>
  <p>index the reference genome for rapid searching and aligning</p>
  </blockquote>
  <ol start="2">
  <li><p>do the mapping using bwa mem (better with bash shell script, then there will be no need for each individual one by one)</p>
  </li>
  <li><p>After mapping, you could use GATK4 for the variation calling based on the mapping result.</p>
  </li>
  </ol>
  <p>The steps of
  variation calling by GATK4 include dealing with the mapping result ( mark duplicates and add read group ), raw variation
  identification ( joint call followed by single individual variation calling ), quality recalibration and realign, second round of variation
  calling and final variation filtering ( filtering is
  applied to haplotyping to remove uninformative mutations. An obvious filter is to remove SNPs with identical calls in all the samples ).</p>
  <p>The final variation calling result with SNPs and InDels could be obtained and the final vcf file could be sent to SnpEff for further annotation ( an easier way is using SnpEff in Galaxy with the right reference genome version,  eg  R64-1-1.75 ).</p>
  <p>The isolates from NCBI would indicate that there are 16 chromosomes and one mitochondrial chromosome after SnpEff analysis.  In the analysis, the numbers of insertions and deletions will be obtained.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-09-03T09:57:05.670Z
Answers.Text: domesticated populations of Far East Asia-->  https://www.nature.com/articles/s41467-018-05106-7
Answers.UserDisplayName: envs_h_gang_5
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-09-03T10:02:12.003Z
Answers.Text: method within this 2021 manuscript--> https://www.frontiersin.org/articles/10.3389/fmicb.2021.631250/full
Answers.UserDisplayName: envs_h_gang_5
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-09-03T10:35:45.050Z
Answers.Text: Chinese explanation:  https://www.jianshu.com/p/9b5aac4b43d9  (if your company could find someone to explain this recent web site.)
Answers.UserDisplayName: envs_h_gang_5
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-09-03T09:31:36.507Z
Answers.LastActivityDate: 2022-09-04T06:22:26.047Z
Answers.LastEditDate: 2022-09-04T06:22:26.047Z
Answers.UserDisplayName: envs_h_gang_5
Body: <p>I'm a new Bioinformatic scientist working for a yeast genetics company.</p>
  <p><strong>Objective</strong> To create a database of yeast genomes from NCBI and identify SNP variants.</p>
  <p>In my pipeline</p>
  <ol>
  <li>FastQC,</li>
  <li>Trimmomatic,</li>
  <li>BWA</li>
  <li>GATK</li>
  </ol>
  <p>The method being to check the quality of the downloaded reads, trim off any unnecessary information, and to align them prior to VCF.</p>
  <p>NCBI sequence data comes from both <em>Illumina</em> sequencing and <em>PacBio</em>.</p>
  <p><strong>Questions</strong></p>
  <ol>
  <li>Is the pipeline reasonable, for example, is BWA ok over more recent developments?</li>
  <li>Could the same pipeline be used both for Illumina and PacBio sequences?</li>
  </ol>
  <p><strong>Note</strong>
  I don't plan on assembling any of the reads I have as I'm just looking to comb through to find variations.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-31T00:15:02.610Z
Comments.Text: I attempted to edit the question with a view to it being more focused, albeit remains 'broad'. The OP can decide whether this is okay. In case the OP is unaware the preferred format is a focused, specific question to invite an equally focused answer. Further focusing the question to provide a single question would be great. The other components can be asked in separate question(s).
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-31T20:18:02.840Z
Comments.Text: I'd suggest using minimap2 for long reads, BWA is not well suited to higher error reads. See the settings under its `-x` flag for the specific technology.
Comments.UserDisplayName: Maximilian Press
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-09-01T15:37:31.183Z
Comments.Text: Thank you @M__ for cleaning up my question it reads a lot better now.
Comments.UserDisplayName: rimo
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-09-01T15:38:21.277Z
Comments.Text: Would you reccomend BWA for Illumina reads and then minimap2 for PacBio reads then? @MaximilianPress
Comments.UserDisplayName: rimo
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-09-01T16:16:08.973Z
Comments.Text: I'd recommend using minimap2 for both. I believe that [Heng Li has written](https://lh3.github.io/2018/04/02/minimap2-and-the-future-of-bwa) that minimap2 is at least as good as bwa for Illumina if you set the `-x sr` flag.
Comments.UserDisplayName: Maximilian Press
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-09-01T16:17:57.140Z
Comments.Text: For pacbio variant calling, there are a few resources online: https://www.biostars.org/p/393823/, https://gatk.broadinstitute.org/hc/en-us/community/posts/360072716972-Variant-calling-with-PacBio-HiFi-reads
Comments.UserDisplayName: Maximilian Press
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-09-12T12:18:26.307Z
Comments.Text: Hi @rimo, please look at the answer and Maximillian's response and consider upvoting and/or accepting.
Comments.UserDisplayName: M__
Tags: <snp><variant-calling><genetics>
Title: Find SNPs in yeast genomes
ThreadText: # Find SNPs in yeast genomes
  
  (Asked by: username_0 on 2022-08-30)
  
  I'm a new Bioinformatic scientist working for a yeast genetics company.
  
  **Objective** To create a database of yeast genomes from NCBI and identify SNP variants.
  
  In my pipeline
  
  1. FastQC,
  2. Trimmomatic,
  3. BWA
  4. GATK
  
  The method being to check the quality of the downloaded reads, trim off any unnecessary information, and to align them prior to VCF.
  
  NCBI sequence data comes from both *Illumina* sequencing and *PacBio*.
  
  **Questions**
  
  1. Is the pipeline reasonable, for example, is BWA ok over more recent developments?
  2. Could the same pipeline be used both for Illumina and PacBio sequences?
  
  **Note**
  I don't plan on assembling any of the reads I have as I'm just looking to comb through to find variations.
  
  **Tags:** snp, variant-calling, genetics
  
  > **Comments:**
  > **username_1**: I attempted to edit the question with a view to it being more focused, albeit remains 'broad'. The OP can decide whether this is okay. In case the OP is unaware the preferred format is a focused, specific question to invite an equally focused answer. Further focusing the question to provide a single question would be great. The other components can be asked in separate question(s).  
  > **username_2**: I'd suggest using minimap2 for long reads, BWA is not well suited to higher error reads. See the settings under its `-x` flag for the specific technology.  
  > **username_0**: Thank you @username_1 for cleaning up my question it reads a lot better now.  
  > **username_0**: Would you reccomend BWA for Illumina reads and then minimap2 for PacBio reads then? @username_2  
  > **username_2**: I'd recommend using minimap2 for both. I believe that [Heng Li has written](https://lh3.github.io/2018/04/02/minimap2-and-the-future-of-bwa) that minimap2 is at least as good as bwa for Illumina if you set the `-x sr` flag.  
  > **username_2**: For pacbio variant calling, there are a few resources online: https://www.biostars.org/p/393823/, https://gatk.broadinstitute.org/hc/en-us/community/posts/360072716972-Variant-calling-with-PacBio-HiFi-reads  
  > **username_1**: Hi @username_0, please look at the answer and Maximillian's response and consider upvoting and/or accepting.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2022-09-03. Score: 4 | Accepted answer
  
  > for illumina reads (only)
  >
  > [prerequisite: download n prepare](https://docs.google.com/document/d/1ZJpGTMl319v-YRrXvNbM5OGSy0jBTVfUbpawVAPqaMU/edit?usp=sharing)
  
  > [the fna n gff file of yeast reference](https://docs.google.com/document/d/1p9ZsfkqykNgcX7F5fpOl64vDi46tMEL7yY3VfiK8mZE/edit?usp=sharing)
  
  > [mapping\_in\_02.directory](https://drive.google.com/drive/folders/1rRfMXvK3KtrppFQm-TChRDIOYR0UphKI?usp=sharing)
  
  1st step is mapping the downloaded reads to the reference genome:
  
  1. build index for the reference using the following command
  
  bwa index ../01./GCF\_000146045.2\_R64\_genomic.fna
  
  > index the reference genome for rapid searching and aligning
  
  2. do the mapping using bwa mem (better with bash shell script, then there will be no need for each individual one by one)
  3. After mapping, you could use GATK4 for the variation calling based on the mapping result.
  
  The steps of
  variation calling by GATK4 include dealing with the mapping result ( mark duplicates and add read group ), raw variation
  identification ( joint call followed by single individual variation calling ), quality recalibration and realign, second round of variation
  calling and final variation filtering ( filtering is
  applied to haplotyping to remove uninformative mutations. An obvious filter is to remove SNPs with identical calls in all the samples ).
  
  The final variation calling result with SNPs and InDels could be obtained and the final vcf file could be sent to SnpEff for further annotation ( an easier way is using SnpEff in Galaxy with the right reference genome version, eg R64-1-1.75 ).
  
  The isolates from NCBI would indicate that there are 16 chromosomes and one mitochondrial chromosome after SnpEff analysis. In the analysis, the numbers of insertions and deletions will be obtained.
  
  > **Comments:**
  > **username_3**: domesticated populations of Far East Asia-->  https://www.nature.com/articles/s41467-018-05106-7  
  > **username_3**: method within this 2021 manuscript--> https://www.frontiersin.org/articles/10.3389/fmicb.2021.631250/full  
  > **username_3**: Chinese explanation:  https://www.jianshu.com/p/9b5aac4b43d9  (if your company could find someone to explain this recent web site.)  
  
  ---
  
  