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README.md
In qrqc: Quick Read Quality Control
qrqc - Quick Read Quality Control
qrqc and all supporting documentation
Copyright (c) Vince Buffalo, 2011-2012
Contact: Vince Buffalo vsbuffaloAAAAA@gmail.com (with the poly-A tail removed)
If you wish to report a bug, please open an issue on Github
(http://github.com/vsbuffalo/qrqc/issues) or post it on the
Bioconductor support site (https://support.bioconductor.org/).
You can contact me personally as well, but please open an issue first.
About
qrqc (short for "Quick Read Quality Control") is a fast and extensible
package that reports basic quality and summary statistics on FASTQ and
FASTA files, including base and quality distribution by position,
sequence length distribution, and common sequences.
License
GNU General Public License, version 2.
FAQ
Why ggplot2?
I've had some feature requests for qrqc since its release, mostly
related to customizing the graphics. Since data accessibility and
custom graphics were the reason I created qrqc, I initially rewrote
qrqc to provide more graphics options through lattice. However,
all the graphics parameters I added led to large numbers of arguments
to functions and high complexity. This rewrite uses ggplot2, which
is a very excellent way to create graphics as any graphics object can
be further manipulated.
Why do you use Monte Carlo simulations to generate the smooth curve?
qrqc is fast because it bins the quality scores of bases by
positions; there is data summarization done by readSeqFile. To
create a smooth curve, the function needs multiple data points (not
binned data), which I simulate via Monte Carlo draws from the quality
distribution by position. This is an approximation, but it leads to a
smooth curve which can create a useful visual tool in assessing
quality drops.
What do I do about bad quality regions?
Illumina reads often have poor 3'-end qualities. I've noticed that
HiSeq machines also produce poor quality 5'-ends. For increased
mapping rates and better assmeblies, it is generally advisable that
these poor quality regions be trimmed off. Nik Joshi's took sickle
tool can do this; you can get it here
http://github.com/najoshi/sickle.
3'-end adapter contamination can be difficult to recognize (and thus
remove) due to poor quality and likely incorrect bases. I've developed
a tool called scythe that removes
Try the qrqc package in your browser
Any scripts or data that you put into this service are public.
qrqc documentation built on Nov. 8, 2020, 7:03 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
qrqc - Quick Read Quality Control
qrqc and all supporting documentation Copyright (c) Vince Buffalo, 2011-2012
Contact: Vince Buffalo vsbuffaloAAAAA@gmail.com (with the poly-A tail removed)
If you wish to report a bug, please open an issue on Github (http://github.com/vsbuffalo/qrqc/issues) or post it on the Bioconductor support site (https://support.bioconductor.org/). You can contact me personally as well, but please open an issue first.
About
qrqc (short for "Quick Read Quality Control") is a fast and extensible package that reports basic quality and summary statistics on FASTQ and FASTA files, including base and quality distribution by position, sequence length distribution, and common sequences.
License
GNU General Public License, version 2.
FAQ
Why ggplot2?
I've had some feature requests for qrqc since its release, mostly
related to customizing the graphics. Since data accessibility and
custom graphics were the reason I created qrqc, I initially rewrote
qrqc to provide more graphics options through lattice. However,
all the graphics parameters I added led to large numbers of arguments
to functions and high complexity. This rewrite uses ggplot2, which
is a very excellent way to create graphics as any graphics object can
be further manipulated.
Why do you use Monte Carlo simulations to generate the smooth curve?
qrqc is fast because it bins the quality scores of bases by
positions; there is data summarization done by readSeqFile. To
create a smooth curve, the function needs multiple data points (not
binned data), which I simulate via Monte Carlo draws from the quality
distribution by position. This is an approximation, but it leads to a
smooth curve which can create a useful visual tool in assessing
quality drops.
What do I do about bad quality regions?
Illumina reads often have poor 3'-end qualities. I've noticed that
HiSeq machines also produce poor quality 5'-ends. For increased
mapping rates and better assmeblies, it is generally advisable that
these poor quality regions be trimmed off. Nik Joshi's took sickle
tool can do this; you can get it here
http://github.com/najoshi/sickle.
3'-end adapter contamination can be difficult to recognize (and thus
remove) due to poor quality and likely incorrect bases. I've developed
a tool called scythe that removes
Try the qrqc package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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