text: user247: What are good means for performing quality control (QC) or NGS reads?
  I'm aware of:
  
  FastQC
  NGS Screen
  Kraken (e.g., for screening against contaminants)
  
  What are other popular means for such QC?
  
  user110: The kind of QC you do routinely depends on what your lab's focus is. We do a lot of low-quality, multiplexed DNA and RNA. If you routinely do fresh frozen whole genomes, your QC will be different.
  Weighing in from the resequencing side of things (i.e. sequencing an organism that has a good reference), there are several things we are testing with quality control:
  
  Did the library preparation succeed? Was the capture successful? Is our input material of high enough quality? 
  Did the sequencing work? For Illumina, did enough clusters pass filter? What's our Q30? Did we sequence into adapters?
  Can we proceed with analysis? Are the indices on multiplexed samples correct?
  Do we have the correct reference sequence? Do we have the right targets?
  
  You may use a combination of tools:
  
  bcl2fastq : if you multiplexed, check the undetermined indices file after converting to fastq to make sure you caught all of the indexed reads
  FastQC: General sequencing quality, sequencing into adapters, handy flagging for obvious problems
  Illumina interop/SAV: Cluster pass filter, Q30
  Alignment (with BWA, bowtie, etc), bedtools, samtools, Picard: check for contamination, make sure we're using the right reference, overlap with target regions, depth of coverage, soft-clipping for low-quality sequence
  
  
  user57: The quality control of ngs reads is heavily dependent on type of the project. 
  For genome assembly projects based on short reads, beside already covered checking quality of sequencing, you would like to look at the kmer spectra to find out, if your reads are going to make sense when they will be translated to De Brujin graph. You will get also a clues about genome coverage, genome size, repetitive content and small clue about heterozygocity. A lot of useful info about interpretation you can find in the README of GenomeScope.
  A list tools I used:
  
  jellyfish - to count k-mer frequencies
  GenomeScope - a package for analysis and visualisation of k-mer frequencies (they recommend to use jellyfish for counting k-mer frequencies)
  kmergenie - for prediction of optimal kmer for assembly
  
  Using these tools can save you a lot of frustration if you accidentally sequence a contaminated sample or if your colleagues / a sequencing company have sent you a wrong file!
  
  user224: We routinely run both FastQC and FastQ Screen on all of our raw sequencing reads. FastQ Screen is a tool for detecting cross-species contamination. MGA is another similar tool.
  There are then lots of QC tools specific to different types of data, most of which run after alignment. For example RSeQC (RNA data), Qualimap and many many others. Without specifying what kind of data you have this is a bit difficult to make recommendations for though.
  Phil
  
  user485: MultiQC can merge all your different reports into a single one.
  Which could be useful once you manage to know which QC tools to use.
  
original_text: 247: <p>What are good means for performing quality control (QC) or NGS reads?</p>
  
  <p>I'm aware of:</p>
  
  <ul>
  <li>FastQC</li>
  <li>NGS Screen</li>
  <li>Kraken (e.g., for screening against contaminants)</li>
  </ul>
  
  <p>What are other popular means for such QC?</p>
   110: <p>The kind of QC you do routinely depends on what your lab's focus is. We do a lot of low-quality, multiplexed DNA and RNA. If you routinely do fresh frozen whole genomes, your QC will be different.</p>
  
  <p>Weighing in from the resequencing side of things (i.e. sequencing an organism that has a good reference), there are several things we are testing with quality control:</p>
  
  <ul>
  <li>Did the library preparation succeed? Was the capture successful? Is our input material of high enough quality? </li>
  <li>Did the sequencing work? For Illumina, did enough clusters pass filter? What's our Q30? Did we sequence into adapters?</li>
  <li>Can we proceed with analysis? Are the indices on multiplexed samples correct?
  Do we have the correct reference sequence? Do we have the right targets?</li>
  </ul>
  
  <p>You may use a combination of tools:</p>
  
  <ul>
  <li><a href="https://support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html" rel="nofollow noreferrer">bcl2fastq</a> : if you multiplexed, check the undetermined indices file after converting to fastq to make sure you caught all of the indexed reads</li>
  <li><a href="https://www.bioinformatics.babraham.ac.uk/projects/fastqc/" rel="nofollow noreferrer">FastQC</a>: General sequencing quality, sequencing into adapters, handy flagging for obvious problems</li>
  <li><a href="https://github.com/Illumina/interop" rel="nofollow noreferrer">Illumina interop</a>/SAV: Cluster pass filter, Q30</li>
  <li>Alignment (with <a href="http://bio-bwa.sourceforge.net/" rel="nofollow noreferrer">BWA</a>, bowtie, etc), <a href="http://bedtools.readthedocs.io/en/latest/" rel="nofollow noreferrer">bedtools</a>, <a href="http://www.htslib.org/" rel="nofollow noreferrer">samtools</a>, <a href="https://broadinstitute.github.io/picard/" rel="nofollow noreferrer">Picard</a>: check for contamination, make sure we're using the right reference, overlap with target regions, depth of coverage, soft-clipping for low-quality sequence</li>
  </ul>
   57: <p>The quality control of ngs reads is heavily dependent on type of the project. </p>
  
  <p><strong>For genome assembly</strong> projects based on short reads, beside already covered checking quality of sequencing, you would like to look at the kmer spectra to find out, if your reads are going to make sense when they will be translated to De Brujin graph. You will get also a clues about genome coverage, genome size, repetitive content and small clue about heterozygocity. A lot of useful info about interpretation you can find in the README of GenomeScope.</p>
  
  <p>A list tools I used:</p>
  
  <ul>
  <li><a href="http://www.genome.umd.edu/jellyfish.html" rel="noreferrer">jellyfish</a> - to count k-mer frequencies</li>
  <li><a href="https://github.com/schatzlab/genomescope" rel="noreferrer">GenomeScope</a> - a package for analysis and visualisation of k-mer frequencies (they recommend to use jellyfish for counting k-mer frequencies)</li>
  <li><a href="http://kmergenie.bx.psu.edu/" rel="noreferrer">kmergenie</a> - for prediction of optimal kmer for assembly</li>
  </ul>
  
  <p>Using these tools can save you a lot of frustration if you accidentally sequence a contaminated sample or if your colleagues / a sequencing company have sent you a wrong file!</p>
   224: <p>We routinely run both <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/" rel="noreferrer">FastQC</a> and <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/" rel="noreferrer">FastQ Screen</a> on all of our raw sequencing reads. FastQ Screen is a tool for detecting cross-species contamination. <a href="https://github.com/crukci-bioinformatics/MGA" rel="noreferrer">MGA</a> is another similar tool.</p>
  
  <p>There are then lots of QC tools specific to different types of data, most of which run after alignment. For example <a href="http://rseqc.sourceforge.net/" rel="noreferrer">RSeQC</a> (RNA data), <a href="http://qualimap.bioinfo.cipf.es/" rel="noreferrer">Qualimap</a> and many many others. Without specifying what kind of data you have this is a bit difficult to make recommendations for though.</p>
  
  <p>Phil</p>
   485: <p><a href="http://multiqc.info/" rel="noreferrer">MultiQC</a> can merge all your different reports into a single one.
  Which could be useful once you manage to know which QC tools to use.</p>
  