text: user10296: I am new to low-pass whole genome sequencing and have the basic idea of phasing and imputation. I have .vcf file after calling haplotypecaller tools from GATK. After searching the phasing and imputation tools I've encountered some tools like Beagle, Minimac, Shapeit, Glimpse, Eagle. The problem is that I didn't find enough hands-on material on how to process a .vcf from unphased to phased and then to an imputed one.
  Can anyone suggests any hands-on tutorial regarding phasing and imputation?
  
  user3694: This is all relevant for data 0.5x-1x coverage.
  Assuming you have genotype likelihoods data, if you want to phase low-coverage data, the most suitable options are GLIMPSE, Beagle4 and QUILT. Of the three, GLIMSPE and QUILT are the most recent and similar in performance. shapeit, eagle and minimac aren't suitable for low-coverage data.
  I have found GLIMPSE to be fast and memory efficient, but it requires several different steps; breaking the genome into chunks, imputing, reforming the chunks and then sampling from the haplotypes to phase the data. It has a fairly good tutorial here.
  QUILT has fewer stages and faster, but is unlikely to make much of a difference unless you are working on very large datasets. It has a tutorial here.
  Which one you use probably doesn't matter a whole lot, but I would probably suggest using QUILT as it has fewer stages and is slightly more recent.
  
original_text: 10296: <p>I am new to low-pass whole genome sequencing and have the basic idea of phasing and imputation. I have .vcf file after calling <a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037225632-HaplotypeCaller" rel="nofollow noreferrer">haplotypecaller</a> tools from GATK. After searching the phasing and imputation tools I've encountered some tools like Beagle, Minimac, Shapeit, Glimpse, Eagle. The problem is that I didn't find enough hands-on material on how to process a .vcf from unphased to phased and then to an imputed one.</p>
  <p>Can anyone suggests any hands-on tutorial regarding phasing and imputation?</p>
   3694: <p>This is all relevant for data 0.5x-1x coverage.</p>
  <p>Assuming you have genotype likelihoods data, if you want to phase low-coverage data, the most suitable options are GLIMPSE, Beagle4 and QUILT. Of the three, GLIMSPE and QUILT are the most recent and similar in performance. shapeit, eagle and minimac aren't suitable for low-coverage data.</p>
  <p>I have found <a href="https://pubmed.ncbi.nlm.nih.gov/33414550/" rel="nofollow noreferrer">GLIMPSE</a> to be fast and memory efficient, but it requires several different steps; breaking the genome into chunks, imputing, reforming the chunks and then sampling from the haplotypes to phase the data. It has a fairly good tutorial <a href="https://odelaneau.github.io/GLIMPSE/tutorial_b38.html#run_preliminaries" rel="nofollow noreferrer">here</a>.</p>
  <p><a href="https://www.nature.com/articles/s41588-021-00877-0" rel="nofollow noreferrer">QUILT</a> has fewer stages and faster, but is unlikely to make much of a difference unless you are working on very large datasets. It has a tutorial <a href="https://github.com/rwdavies/QUILT" rel="nofollow noreferrer">here</a>.</p>
  <p>Which one you use probably doesn't matter a whole lot, but I would probably suggest using QUILT as it has fewer stages and is slightly more recent.</p>
  