01 9 / 2014

#hermans #hyperlapse #Stockholm #strömmen (在 Hermans Restaurang & Trädgårdscafé)

31 8 / 2014

Stieg Larsson #Sweden #Stockholm #Arlanda #airport #TheMillenniumTrilogy (在 Stockholm-Arlanda Airport: Terminal 5)

Stieg Larsson #Sweden #Stockholm #Arlanda #airport #TheMillenniumTrilogy (在 Stockholm-Arlanda Airport: Terminal 5)

29 8 / 2014

Gullfoss golden waterfall #gullfoss #waterfall #Iceland #reyex (在 Gullfoss)

29 8 / 2014

On my way! #Iceland #Keflavik (在 Keflavik, Iceland)

28 8 / 2014

白日夢冒險王 – at Keflavik International Airport / Keflavíkurflugvöllur (KEF) – See on Path.

25 8 / 2014

God morgen Geiranger! #Geiranger #Norway (在 Geiranger, Norge)

God morgen Geiranger! #Geiranger #Norway (在 Geiranger, Norge)

24 8 / 2014

Nobel peace center #Nobel #Oslo #Norway (在 Nobel Fredssenter - Nobel Peace Center)

Nobel peace center #Nobel #Oslo #Norway (在 Nobel Fredssenter - Nobel Peace Center)

23 8 / 2014

#DFDSSEAWAYS #Øresund (在 DFDS Seaways - Oslobåden)

#DFDSSEAWAYS #Øresund (在 DFDS Seaways - Oslobåden)

21 8 / 2014

Copenhagen, here we come! #HongKong #airport #britishairways #british (在 Hong Kong International Airport, Gate 18)

Copenhagen, here we come! #HongKong #airport #britishairways #british (在 Hong Kong International Airport, Gate 18)

09 7 / 2014

Polar bear #polarbear (在 宜得利家居)

Polar bear #polarbear (在 宜得利家居)

28 6 / 2014

by Studio English

by Studio English

24 6 / 2014

勇於嘗試 #化學芒果霜淇淋 🍦🍦 (在 全家便利商店興安店)

勇於嘗試 #化學芒果霜淇淋 🍦🍦 (在 全家便利商店興安店)

24 6 / 2014

4ub:

Show individ variation after ANNOWAR
After exome sequencing, variant calling and annotation there is a need to show variation. Here is an example - we can see stops, synonymous and non synonymous variants and their population frequencies if they are. Frequencies are getting higher from center to border.
Here is some R code:
## genomic ranges and circos
library("BSgenome.Hsapiens.UCSC.hg19")
library('ggbio')
seqlengths(Hsapiens)[1:25]
names(seqlengths(Hsapiens)[1:25])
ideo19<-GRanges(seqnames = names(seqlengths(Hsapiens)[1:24]), ranges = IRanges(start=1, width = seqlengths(Hsapiens)[1:24]))
seqlevels(ideo19)<-names(seqlengths(Hsapiens)[1:24])
seqlengths(ideo19)<-seqlengths(Hsapiens)[1:24]

nonsyn<-GRanges(seqnames=annovar$Chr[annovar$ExonicFunc =='nonsynonymous SNV'],ranges=IRanges(start=annovar$Start[annovar$ExonicFunc =='nonsynonymous SNV'],width=1),strand='+',freq=annovar$ESP5400_ALL[annovar$ExonicFunc =='nonsynonymous SNV'],seqlengths=seqlengths(Hsapiens)[1:24])

syn<-GRanges(seqnames=annovar$Chr[annovar$ExonicFunc =='synonymous SNV'],ranges=IRanges(start=annovar$Start[annovar$ExonicFunc =='synonymous SNV'],width=1),strand='+',freq=annovar$ESP5400_ALL[annovar$ExonicFunc =='synonymous SNV'],seqlengths=seqlengths(Hsapiens)[1:24])
                  
st<-GRanges(seqnames=annovar$Chr[annovar$ExonicFunc =='stopgain SNV'],ranges=IRanges(start=annovar$Start[annovar$ExonicFunc =='stopgain SNV'],width=1),strand='+',freq=annovar$ESP5400_ALL[annovar$ExonicFunc =='stopgain SNV'],seqlengths=seqlengths(Hsapiens)[1:24])

ggplot() + layout_circle(ideo19, geom = "ideo", fill = "gray70", radius = 39, trackWidth = 2) + layout_circle(ideo19, geom = "text", aes(label = seqnames), vjust = 0, radius = 40, trackWidth = 5,size = 5) + layout_circle(nonsyn, geom= 'point',size=1, aes(x=start,y=freq,color='Nonsynonimous'),radius = 29, trackWidth = 10,size = 15,grid=T,grid.background='white',grid.line='black',grid.n=3) + layout_circle(syn, geom= 'point',size=1, aes(x=start,y=freq,color='Synonymous'),radius = 19, trackWidth = 10,size = 15,grid=T,grid.background='white',grid.line='black',grid.n=3) + layout_circle(st, geom= 'point',size=3, aes(x=start,y=freq,color='Stopgain'),radius = 9, trackWidth = 10,size = 15,grid=T,grid.background='white',grid.line='black',grid.n=3) + ggtitle("Variation")

4ub:

Show individ variation after ANNOWAR

After exome sequencing, variant calling and annotation there is a need to show variation. Here is an example - we can see stops, synonymous and non synonymous variants and their population frequencies if they are. Frequencies are getting higher from center to border. Here is some R code:

## genomic ranges and circos
library("BSgenome.Hsapiens.UCSC.hg19")
library('ggbio')
seqlengths(Hsapiens)[1:25]
names(seqlengths(Hsapiens)[1:25])
ideo19<-GRanges(seqnames = names(seqlengths(Hsapiens)[1:24]), ranges = IRanges(start=1, width = seqlengths(Hsapiens)[1:24]))
seqlevels(ideo19)<-names(seqlengths(Hsapiens)[1:24])
seqlengths(ideo19)<-seqlengths(Hsapiens)[1:24]

nonsyn<-GRanges(seqnames=annovar$Chr[annovar$ExonicFunc =='nonsynonymous SNV'],ranges=IRanges(start=annovar$Start[annovar$ExonicFunc =='nonsynonymous SNV'],width=1),strand='+',freq=annovar$ESP5400_ALL[annovar$ExonicFunc =='nonsynonymous SNV'],seqlengths=seqlengths(Hsapiens)[1:24])

syn<-GRanges(seqnames=annovar$Chr[annovar$ExonicFunc =='synonymous SNV'],ranges=IRanges(start=annovar$Start[annovar$ExonicFunc =='synonymous SNV'],width=1),strand='+',freq=annovar$ESP5400_ALL[annovar$ExonicFunc =='synonymous SNV'],seqlengths=seqlengths(Hsapiens)[1:24])
                  
st<-GRanges(seqnames=annovar$Chr[annovar$ExonicFunc =='stopgain SNV'],ranges=IRanges(start=annovar$Start[annovar$ExonicFunc =='stopgain SNV'],width=1),strand='+',freq=annovar$ESP5400_ALL[annovar$ExonicFunc =='stopgain SNV'],seqlengths=seqlengths(Hsapiens)[1:24])

ggplot() + layout_circle(ideo19, geom = "ideo", fill = "gray70", radius = 39, trackWidth = 2) + layout_circle(ideo19, geom = "text", aes(label = seqnames), vjust = 0, radius = 40, trackWidth = 5,size = 5) + layout_circle(nonsyn, geom= 'point',size=1, aes(x=start,y=freq,color='Nonsynonimous'),radius = 29, trackWidth = 10,size = 15,grid=T,grid.background='white',grid.line='black',grid.n=3) + layout_circle(syn, geom= 'point',size=1, aes(x=start,y=freq,color='Synonymous'),radius = 19, trackWidth = 10,size = 15,grid=T,grid.background='white',grid.line='black',grid.n=3) + layout_circle(st, geom= 'point',size=3, aes(x=start,y=freq,color='Stopgain'),radius = 9, trackWidth = 10,size = 15,grid=T,grid.background='white',grid.line='black',grid.n=3) + ggtitle("Variation")

20 6 / 2014

Go Go @shawn1002 🍻🍻 (在 DNArails HQ 1.0)

Go Go @shawn1002 🍻🍻 (在 DNArails HQ 1.0)

20 6 / 2014

是有多好吃,來試試!#nagi #ramen (在 ラーメン凪ramen Nagi 台灣(西門店ximen))

是有多好吃,來試試!#nagi #ramen (在 ラーメン凪ramen Nagi 台灣(西門店ximen))