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cpwardell.com | Bioinformatics, code and computational biology

Bioinformatics, code and computational biology. August 28, 2015. Legoplots in R (3D barplots in R). I previously mentioned these. All 96 different mutation/context combinations, with added transparency. My code with transparency. A named vector of length 96 representing each of the 6 possible mutations in 16 different sequence contexts. Note that the names are hard-coded and can be found in the code below. Values represent the height of each column. It was essential to include the. Scaling z using the sc...

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cpwardell.com | Bioinformatics, code and computational biology | cpwardell.com Reviews

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Bioinformatics, code and computational biology. August 28, 2015. Legoplots in R (3D barplots in R). I previously mentioned these. All 96 different mutation/context combinations, with added transparency. My code with transparency. A named vector of length 96 representing each of the 6 possible mutations in 16 different sequence contexts. Note that the names are hard-coded and can be found in the code below. Values represent the height of each column. It was essential to include the. Scaling z using the sc...

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cpwardell.com | Bioinformatics, code and computational biology | cpwardell.com Reviews

https://cpwardell.com

Bioinformatics, code and computational biology. August 28, 2015. Legoplots in R (3D barplots in R). I previously mentioned these. All 96 different mutation/context combinations, with added transparency. My code with transparency. A named vector of length 96 representing each of the 6 possible mutations in 16 different sequence contexts. Note that the names are hard-coded and can be found in the code below. Values represent the height of each column. It was essential to include the. Scaling z using the sc...

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cpwardell.com

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R, GRanges objects and intersecting them | cpwardell.com

http://www.cpwardell.com/2015/04/20/r-granges-objects-and-intersecting-them

Bioinformatics, code and computational biology. April 20, 2015. R, GRanges objects and intersecting them. I discovered unpredicted behavior when intersecting GRanges objects; this is how to avoid the same problem I found. Maybe my intuition wasn’t good, but this represents the a bad type of bug; a silent one. If I had not spotted it, I would have lost several nonsynonymous KRAS mutations in my dataset. Fall within the exome. Stop the madness – no more pie charts. Leave a Reply Cancel reply. Understanding...

2

R | cpwardell.com

http://www.cpwardell.com/category/r

Bioinformatics, code and computational biology. August 28, 2015. Legoplots in R (3D barplots in R). I previously mentioned these. All 96 different mutation/context combinations, with added transparency. My code with transparency. A named vector of length 96 representing each of the 6 possible mutations in 16 different sequence contexts. Note that the names are hard-coded and can be found in the code below. Values represent the height of each column. It was essential to include the. Scaling z using the sc...

3

February | 2015 | cpwardell.com

http://www.cpwardell.com/2015/02

Bioinformatics, code and computational biology. February 24, 2015. February 24, 2015. Understanding BAM files; part 2. Now that you have the basics after part 1. Let’s look at something more complicated; the second column of the main body of BAM files, the bitwise flag. This isn’t simple. Both the samtools manual. And even the SAM specification. If you want a specific flag explained, you can use this handy calculator. But where are those values coming from? 000000000001 : 1 : read paired. 000010000000 : ...

4

Understanding BAM files; part 2 | cpwardell.com

http://www.cpwardell.com/2015/02/24/understanding-bam-files-part-2

Bioinformatics, code and computational biology. February 24, 2015. February 24, 2015. Understanding BAM files; part 2. Now that you have the basics after part 1. Let’s look at something more complicated; the second column of the main body of BAM files, the bitwise flag. This isn’t simple. Both the samtools manual. And even the SAM specification. If you want a specific flag explained, you can use this handy calculator. But where are those values coming from? 000000000001 : 1 : read paired. 000010000000 : ...

5

September | 2012 | cpwardell.com

http://www.cpwardell.com/2012/09

Bioinformatics, code and computational biology. September 3, 2012. September 3, 2012. GATK 2.X is here. If you’re in the business of variant discovery (and I am, amongst other things), you’ll know that things are changing and changing fast. A few years ago if you’d asked me what a SNV (Single Nucleotide Variant) was, I might have thought that you’d misspelled SNP ( Single Nucleotide Polymorphism. I was also fairly distrustful of SNP arrays owing to endless (and continuing) bad experiences with expression...

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cpwardell.com | Bioinformatics, code and computational biology

Bioinformatics, code and computational biology. August 28, 2015. Legoplots in R (3D barplots in R). I previously mentioned these. All 96 different mutation/context combinations, with added transparency. My code with transparency. A named vector of length 96 representing each of the 6 possible mutations in 16 different sequence contexts. Note that the names are hard-coded and can be found in the code below. Values represent the height of each column. It was essential to include the. Scaling z using the sc...

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