In currocam/FascinRSCA: R and Python code for SCA with fascin protein
knitr::opts_chunk$set(echo = TRUE,
dpi=200)
The purpose of this notebook is to discriminate the homologous sequences to fascin that will be used for the SCA analysis and their reannotation.
# This is code to replicate the analysis and figures made to distinguish between
#FSCN1 and FSCN2 sequences. Code developed by currocam.
#load libraries
library(FascinRSCA)
library(tidyverse)
library(magrittr)
`%!in%` = Negate(`%in%`)
# Input file
outliers <- scan("../data-raw/df_Q16658/outliers_fasta_headers.txt",
what = "character")
outliers %<>% append("ENSPTRG00000041962.2|ENSPTRP00000061120.2")
df <- FascinRSCA::df_Q16658_WithOutOutliers %>%
filter(fasta_header%!in% outliers)%>%
mutate(charge_seq_length=charge/residue_n,
annotation_original = annotation,
annotation_cll = fct_collapse(annotation,
fascin1 = c("fascin1"),
fascin2 = c("fascin2", "fascin2a", "fascin2b"),
fascin = c("fascin"),
others = c("uncharacterized", "singed", "unknown")),
Status = replace_na(Status, 'unknown') %>% factor())
colour_values <- setNames(c('#999999','#E69F00', '#56B4E9', '#111111'),
c('Fascin','Fascin1', 'Fascin2', 'Others'))
status_shape <- set_names(c(3, 16, 17), c("NaN", "reviewed", "unreviewed"))
Taxonomic distribution
This is how our sequences are taxonomically distributed:
table_tax <- df %>%
mutate(phylum= fct_lump(phylum, 15, other_level= "Others")) %>%
mutate(class= fct_lump(class, 10, other_level= "Others")) %>%
drop_na(phylum, class) %>%
group_by(phylum, class,annotation) %>%
summarise(n = n()) %>%
arrange(phylum, class,annotation)
ggplot(table_tax, aes(fill=annotation, y=n, x=phylum)) +
geom_bar(position="stack", stat="identity")+
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
ggtitle("Taxonomically distributed sequences")+
ylab("Number of sequences")+
scale_x_discrete(guide = guide_axis(angle = 60))
ggsave(filename = "figures/FSCN1_FSCN2/taxonomic_distribution.pdf")
knitr::kable(table_tax,
caption = "Taxonomically distributed sequences")
base <- df %>%
filter(class %in% c('Mammalia' ))%>%
arrange(desc(phylum, class, order, family, genus, species)) %>%
mutate(order= fct_lump(order, 3, other_level= "Others")) %>%
ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) +
geom_point(alpha=1, aes(shape=Status), size=3)+
scale_shape_discrete(drop=TRUE,
limits = levels(df$Status))+
scale_colour_discrete(drop=TRUE,
limits = levels(df$annotation_cll))+
theme(axis.text.x = element_blank(),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
ggtitle("Differences between sequences in Mammalia's species")+
ylab("Charge divided by sequence length")+
xlab("Species")
base + facet_wrap(~order, ncol = 2, scales ="free_x")
Carnivora
There are no annotated sequences reviewed in Carnivora. The sequences are distributed according to the pattern discussed above. There are 2 FSCN1 sequences with an index higher than 0.01, belonging to Vulpes vulpes and Felis catus.
base <- df %>%
filter(order %in% c('Carnivora' ))%>%
arrange(desc(phylum, class, order, family, genus, species)) %>%
ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) +
geom_point(alpha=1, aes(shape=Status), size=5)+
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
scale_shape_discrete(drop=TRUE,
limits = levels(df$Status))+
scale_colour_discrete(drop=TRUE,
limits = levels(df$annotation_cll))+
ggtitle("Differences between sequences in Carnivora's species")+
ylab("Charge divided by sequence length")+
xlab("Species")+
geom_hline(yintercept=0.010, linetype="dashed", color = "blue")+
scale_x_discrete(guide = guide_axis(angle = 60))
base + facet_wrap(~suborder, ncol = 2, scales ="free_x")
- Destacar que inicialmente Red fox fue reanotado manualmente a fascina 2, aunque si se buscaba la anotación original, era fascina 1, y cayó en el cluster 1, por lo que se rectifico.
library(tidyverse)
library(magrittr)
df %<>% mutate(annotation = case_when(
fasta_header == "ENSNVIG00000005656.1|ENSNVIP00000007116.1" ~ "fascin1", #American mink
fasta_header == "A0A2Y9JHA1_ENHLU|A0A2Y9JHA1" ~ "fascin1", #Enhydra lutris kenyoni
fasta_header == "A0A2Y9L644_ENHLU|A0A2Y9L644" ~ "fascin2", #Enhydra lutris kenyoni
fasta_header == "ENSCAFG00020017175.1|ENSCAFP00020021724.1" ~ "fascin1", #Dingo
fasta_header == "ENSCAFG00020009227.1|ENSCAFP00020011452.1" ~ "fascin2", #Dingo
fasta_header == "ENSVVUG00000006354.1|ENSVVUP00000008455.1" ~ "fascin1", #Red fox
fasta_header == "ENSPPRG00000003969.1|ENSPPRP00000009547.1" ~ "fascin1", #Leopard
fasta_header == "ENSPPRG00000024836.1|ENSPPRP00000000416.1" ~ "fascin2", #Leopard
fasta_header == "A0A3Q7TUG0_URSAR|A0A3Q7TUG0" ~ "fascin2a", #Ursus arctos horribilis, anotado como fscn2b para mantener cohesión
TRUE ~ as.character(annotation)
))
Primates
In primates it works better to set a slightly higher limit, 0.0115, to include sequences from the Aotidae and Cebidae families. There is one automatically annotated FSCN2 sequence in Callithrix jacchus with an index lower than FSCN1, it is an exception. In Otolemur garnettii sequences the pattern is followed but shifted upwards. There are only reviewed annotated sequences in Homo sapiens.
base <- df %>%
filter(order %in% c('Primates'))%>%
arrange(desc(phylum, class, order, family, genus, species)) %>%
ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) +
geom_point(alpha=1, aes(shape=factor(Status)), size=4 )+
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
scale_shape_discrete(drop=TRUE,
limits = levels(df$Status))+
scale_colour_discrete(drop=TRUE,
limits = levels(df$annotation_cll))+
ggtitle("Differences between sequences in Primates")+
ylab("Charge divided by sequence length")+
xlab("Species")+
geom_hline(yintercept=0.0115, linetype="dashed", color = "red")+
scale_x_discrete(guide = guide_axis(angle = 60))
base + facet_wrap(~suborder, ncol = 2, scales ="free_x")
df %<>% mutate(annotation = case_when(
fasta_header == "ENSCANG00000013485.1|ENSCANP00000003877.1" ~ "fascin1", #Colobus angolensis palliatus
fasta_header == "ENSTGEG00000001910.1|ENSTGEP00000002162.1" ~ "fascin1", #Gelada
fasta_header == "ENSTGEG00000009852.1|ENSTGEP00000012087.1" ~ "fascin2", #Gelada
fasta_header == "ENSMFAG00000037869.1|ENSMFAP00000037244.1" ~ "fascin2", #Macaca fascicularis
fasta_header == "ENSMMUG00000001583.3|ENSMMUP00000002112.3" ~ "fascin2", #Macaca mulatta
fasta_header == "ENSRBIG00000007902.1|ENSRBIP00000001562.1" ~ "fascin2", #Rhinopithecus bieti
fasta_header == "ENSRROG00000034748.1|ENSRROP00000021684.1" ~ "fascin2", #Rhinopithecus roxellana
fasta_header == "ENSPTEG00000031612.1|ENSPTEP00000033004.1" ~ "fascin2", #Ugandan red Colobus
fasta_header == "ENSPSMG00000010879.1|ENSPSMP00000015242.1" ~ "fascin1", #Greater bamboo lemur
fasta_header == "ENSMICG00000010211.3|ENSMICP00000009297.2" ~ "fascin1", #Mouse lemur
fasta_header == "ENSMICG00000031762.2|ENSMICP00000018701.2" ~ "fascin2", #Mouse lemur
TRUE ~ as.character(annotation)
))
Rodentia
Most of the Rodentia sequences follow the pattern, however there are more sequences that deviate from it. All annotated sequences reviewed follow the pattern.
base <- df %>%
filter(order %in% c('Rodentia'))%>%
arrange(desc(phylum, class, order, family, genus, species)) %>%
ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) +
geom_point(alpha=1, aes(shape=factor(Status)), size=4 )+
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
scale_shape_discrete(drop=TRUE,
limits = levels(df$Status))+
scale_colour_discrete(drop=TRUE,
limits = levels(df$annotation_cll))+
ggtitle("Differences between sequences in Rodentia")+
ylab("Charge divided by sequence length")+
xlab("Species")+
geom_hline(yintercept=0.01, linetype="dashed", color = "blue")+
scale_x_discrete(guide = guide_axis(angle = 60))
base + facet_wrap(~suborder+family, ncol = 6, scales ="free_x")
- Bathyergidae sigue patrones distintos.
df %<>% mutate(annotation = case_when(
fasta_header == "ENSCCNG00000009421.1|ENSCCNP00000008921.1" ~ "fascin2", #American beaver
fasta_header == "ENSFDAG00000016895.1|ENSFDAP00000008686.1" ~ "fascin", #Damara mole rat
fasta_header == "ENSHGLG00000012271.1|ENSHGLP00000017138.1" ~ "fascin2", #Naked mole-rat male Como está anotado
fasta_header == "ENSHGLG00100005772.1|ENSHGLP00100007730.1" ~ "fascin2", #Naked mole-rat male Como está anotado
fasta_header == "ENSCLAG00000013884.1|ENSCLAP00000020263.1" ~ "fascin1", #Long-tailed chinchilla
fasta_header == "ENSCLAG00000014531.1|ENSCLAP00000021213.1" ~ "fascin2", #Long-tailed chinchilla
fasta_header == "ENSODEG00000012533.1|ENSODEP00000017010.1" ~ "fascin1", #Degu
fasta_header == "ENSODEG00000008403.1|ENSODEP00000011308.1" ~ "fascin2", #Degu
fasta_header == "ENSCGRG00015000357.1|ENSCGRP00015000341.1" ~ "fascin2", #Chinese hamster CHOK1GS
fasta_header == "ENSCGRG00000018194.1|ENSCGRP00000025123.1" ~ "fascin2", #Chinese hamster CriGri
fasta_header == "ENSCGRG00015013911.1|ENSCGRP00015018116.1" ~ "fascin1", #Cricetulus griseus
fasta_header == "A0A1A6HKX6_NEOLE|A0A1A6HKX6" ~ "fascin1", #Neotoma lepida
fasta_header == "A0A1A6HXR6_NEOLE|A0A1A6HXR6" ~ "fascin2", #Neotoma lepida
fasta_header == "ENSPEMG00000024060.2|ENSPEMP00000028558.1" ~ "fascin1", #Northern American deer mouse
fasta_header == "ENSPEMG00000028380.1|ENSPEMP00000031506.1" ~ "fascin2", #Northern American deer mouse
fasta_header == "ENSMOCG00000001115.1|ENSMOCP00000001465.1" ~ "fascin1", #Prairie vole
fasta_header == "ENSMOCG00000018078.1|ENSMOCP00000020377.1" ~ "fascin2", #Prairie vole
fasta_header == "ENSJJAG00000019494.1|ENSJJAP00000018145.1" ~ "fascin1", #Lesser Egyptian jerboa
fasta_header == "ENSJJAG00000015122.1|ENSJJAP00000011928.1" ~ "fascin2", #Lesser Egyptian jerboa
fasta_header == "MGP_SPRETEiJ_G0028973.1|MGP_SPRETEiJ_P0074768" ~ "fascin1", #Algerian mouse
fasta_header == "MGP_SPRETEiJ_G0018357.1|MGP_SPRETEiJ_P0032954" ~ "fascin2", #Algerian mouse
fasta_header == "ENSMUGG00000013467.1|ENSMUGP00000015650.1" ~ "fascin1", #Mongolian gerbil
fasta_header == "ENSMUGG00000001646.1|ENSMUGP00000001760.1" ~ "fascin2", #Mongolian gerbil
fasta_header == "MGP_CASTEiJ_G0029422.1|MGP_CASTEiJ_P0075825" ~ "fascin1", #Mouse CAST/EiJ
fasta_header == "MGP_CASTEiJ_G0018799.1|MGP_CASTEiJ_P0033632" ~ "fascin2", #Mouse CAST/EiJ
fasta_header == "MGP_DBA2J_G0030137.1|MGP_DBA2J_P0075380" ~ "fascin1", #Mouse DBA/2J
fasta_header == "MGP_DBA2J_G0019328.1|MGP_DBA2J_P0033800" ~ "fascin2", #Mouse DBA/2J
fasta_header == "MGP_FVBNJ_G0019320.1|MGP_FVBNJ_P0033798" ~ "fascin2", #Mouse FVB/NJ
fasta_header == "MGP_PWKPhJ_G0029137.1|MGP_PWKPhJ_P0075276" ~ "fascin1", #Mouse PWK/PhJ
fasta_header == "MGP_PWKPhJ_G0018565.1|MGP_PWKPhJ_P0033324" ~ "fascin2", #Mouse PWK/PhJ
fasta_header == "MGP_WSBEiJ_G0029496.1|MGP_WSBEiJ_P0074413" ~ "fascin1", #Mouse WSB/EiJ
fasta_header == "MGP_WSBEiJ_G0018851.1|MGP_WSBEiJ_P0033219" ~ "fascin2", #Mouse WSB/EiJ
fasta_header == "MGP_CAROLIEiJ_G0027969.1|MGP_CAROLIEiJ_P0071948" ~ "fascin1", #Ryukyu mouse
fasta_header == "MGP_CAROLIEiJ_G0017513.1|MGP_CAROLIEiJ_P0031506" ~ "fascin2", #Ryukyu mouse
fasta_header == "MGP_PahariEiJ_G0024541.1|MGP_PahariEiJ_P0060161" ~ "fascin1", #Shrew mouse
fasta_header == "MGP_PahariEiJ_G0018639.1|MGP_PahariEiJ_P0038222" ~ "fascin2", #Shrew mouse
fasta_header == "ENSMSIG00000029517.1|ENSMSIP00000035482.1" ~ "fascin1", #Steppe mouse
fasta_header == "ENSMSIG00000014526.1|ENSMSIP00000016977.1" ~ "fascin2", #Steppe mouse
fasta_header == "ENSNGAG00000003408.1|ENSNGAP00000003153.1" ~ "fascin1", #Upper Galilee mountains blind mole rat
fasta_header == "ENSMMMG00000006216.1|ENSMMMP00000006939.1" ~ "fascin2", #Alpine marmot
fasta_header == "ENSUPAG00010011357.1|ENSUPAP00010014127.1" ~ "fascin1", #Arctic ground squirrel
fasta_header == "ENSUPAG00010019602.1|ENSUPAP00010024748.1" ~ "fascin2", #Arctic ground squirrel
fasta_header == "ENSSTOG00000020923.2|ENSSTOP00000021848.2" ~ "fascin1", #Ictidomys tridecemlineatus
fasta_header == "ENSSTOG00000028223.2|ENSSTOP00000023251.1" ~ "fascin2", #Ictidomys tridecemlineatus
fasta_header == "ENSCAPG00000016296.1|ENSCAPP00000016394.1" ~ "fascin2", #Brazilian guinea pig
TRUE ~ as.character(annotation)
))
Artiodactyla
base <- df %>%
filter(order %in% c('Artiodactyla'))%>%
arrange(desc(phylum, class, order, family, genus, species)) %>%
ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) +
geom_point(alpha=1, aes(shape=factor(Status)), size=4 )+
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
scale_shape_discrete(drop=TRUE,
limits = levels(df$Status))+
scale_colour_discrete(drop=TRUE,
limits = levels(df$annotation_cll))+
ggtitle("Differences between sequences in Artiodactyla order")+
ylab("Charge divided by sequence length")+
xlab("Species")+
geom_hline(yintercept=0.009, linetype="dashed", color = "green")+
geom_hline(yintercept=0.01, linetype="dashed", color = "blue")+
scale_x_discrete(guide = guide_axis(angle = 60))
base + facet_wrap(~order+suborder, ncol = 2, scales ="free_x")
- Ha sucedido con Wild yak y American bison lo mismo que con Red fox.
df %<>% mutate(annotation = case_when(
fasta_header == "ENSBBBG00000009842.1|ENSBBBP00000017441.1" ~ "fascin1", #American bison
fasta_header == "ENSBMUG00000021206.1|ENSBMUP00000028298.1" ~ "fascin1", #Wild yak
fasta_header == "ENSTTRG00000006435.1|ENSTTRP00000006091.1" ~ "fascin2", #Dolphin
fasta_header == "ENSMFAG00000037869.1|ENSMFAP00000037244.1" ~ "fascin2", #Macaca fascicularis
fasta_header == "ENSMMUG00000001583.3|ENSMMUP00000002112.3" ~ "fascin2", #Macaca mulatta
fasta_header == "ENSRBIG00000007902.1|ENSRBIP00000001562.1" ~ "fascin2", #Rhinopithecus bieti
fasta_header == "ENSRROG00000034748.1|ENSRROP00000021684.1" ~ "fascin2", #Rhinopithecus roxellana
fasta_header == "ENSPTEG00000031612.1|ENSPTEP00000033004.1" ~ "fascin2", #Ugandan red Colobus
fasta_header == "ENSPSMG00000010879.1|ENSPSMP00000015242.1" ~ "fascin1", #Greater bamboo lemur
fasta_header == "ENSMICG00000010211.3|ENSMICP00000009297.2" ~ "fascin1", #Mouse lemur
fasta_header == "ENSMICG00000031762.2|ENSMICP00000018701.2" ~ "fascin2", #Mouse lemur
TRUE ~ as.character(annotation)
))
Other orders in Mammalia
base <- df %>%
filter(class %in% c('Mammalia'))%>%
filter(order %!in% c('Primates', 'Rodentia', 'Carnivora', 'Artiodactyla'))%>%
arrange(desc(phylum, class, order, family, genus, species)) %>%
ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) +
geom_point(alpha=1, aes(shape=factor(Status)), size=4 )+
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
scale_shape_discrete(drop=TRUE,
limits = levels(df$Status))+
scale_colour_discrete(drop=TRUE,
limits = levels(df$annotation_cll))+
ggtitle("Differences between sequences in other orders in Mammalia")+
ylab("Charge divided by sequence length")+
xlab("Species")+
# geom_hline(yintercept=0.009, linetype="dashed", color = "green")+
geom_hline(yintercept=0.01, linetype="dashed", color = "blue")+
scale_x_discrete(guide = guide_axis(angle = 60))
base + facet_wrap(~order+suborder, ncol = 4, scales ="free_x")
df %<>% mutate(annotation = case_when(
fasta_header == "ENSPVAG00000017295.1|ENSPVAP00000016319.1" ~ "fascin1", #Megabat
fasta_header == "ENSPCIG00000015239.2|ENSPCIP00000019991.1" ~ "fascin1", #Koala
fasta_header == "ENSPCIG00000029609.2|ENSPCIP00000040449.1" ~ "fascin2", #Koala
fasta_header == "ENSMEUG00000013398.1|ENSMEUP00000012221.1" ~ "fascin2", #Wallaby
fasta_header == "ENSEEUG00000002694.1|ENSEEUP00000002460.1" ~ "fascin2", #Hedgehog
fasta_header == "ENSEASG00005000015.1|ENSEASP00005000018.1" ~ "fascin1", #Donkey
fasta_header == "ENSEASG00005005735.1|ENSEASP00005007879.1" ~ "fascin2", #Donkey
fasta_header == "A0A2Y9DGX1_TRIMA|A0A2Y9DGX1" ~ "fascin2", #Trichechus manatus latirostris
TRUE ~ as.character(annotation)))
Other classes in Chordata
Aves
In Aves the pattern just works in Galliformes.
base <- df %>%
filter(class %in% c('Aves'))%>%
arrange(desc(phylum, class, order, family, genus, species)) %>%
mutate(order= fct_lump(order, 2, other_level= "Other orders")) %>%
ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) +
geom_point(alpha=1, aes(shape=Status), size=4 )+
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
scale_shape_discrete(drop=TRUE,
limits = levels(df$Status))+
scale_colour_discrete(drop=TRUE,
limits = levels(df$annotation_cll))+
ggtitle("Differences between sequences in Aves")+
ylab("Charge divided by sequence length")+
xlab("Species")+
geom_hline(yintercept=0.007, linetype="dashed", color = "green")+
scale_x_discrete(guide = guide_axis(angle = 60))
base + facet_wrap(~order, ncol = 1, scales ="free_x")
En el caso de los Passeriformes nos encontramos que hay un patrón similar, pero la anotación está al revés. Entrando en la url, encontramos que "The sequence shown here is derived from an EMBL/GenBank/DDBJ whole genome shotgun (WGS) entry which is preliminary data". En ese sentido, comparando la interacción por string, nos parece razonable que el patrón sea al revés.
Amazona aestiva tiene muy poca diferencia, por lo que lo dejaremos como fascina.
df %<>% mutate(annotation = case_when(
fasta_header == "A0A226NJP1_CALSU|A0A226NJP1" ~ "fascin1", #Callipepla squamata
fasta_header == "A0A226MR24_CALSU|A0A226MR24" ~ "fascin2", #Callipepla squamata
fasta_header == "A0A226PLS9_COLVI|A0A226PLS9" ~ "fascin2", #Colinus virginianus
fasta_header == "ENSNMEG00000003221.1|ENSNMEP00000002566.1" ~ "fascin2", #Helmeted guineafowl
fasta_header == "ENSCJPG00005000395.1|ENSCJPP00005000313.1" ~ "fascin1", #Japanese quail
fasta_header == "ENSCJPG00005021245.1|ENSCJPP00005027399.1" ~ "fascin2", #Japanese quail
# Passeriformes
fasta_header == "ENSLCOG00000010881.1|ENSLCOP00000015232.1" ~ "fascin2", #Blue-crowned manakin
fasta_header == "ENSLCOG00000000385.1|ENSLCOP00000000419.1" ~ "fascin1", #Blue-crowned manakin
# fasta_header == "A0A3L8S262_CHLGU|A0A3L8S262" ~ "fascin2", #Chloebia gouldiae
fasta_header == "A0A3L8SG84_CHLGU|A0A3L8SG84" ~ "fascin1", #Chloebia gouldiae
fasta_header == "ENSJHYG00000006117.1|ENSJHYP00000007676.1" ~ "fascin1", #Dark-eyed junco
fasta_header == "ENSPMJG00000017038.1|ENSPMJP00000023043.1" ~ "fascin2", #Great Tit
fasta_header == "ENSPMJG00000017401.1|ENSPMJP00000023569.1" ~ "fascin1", #Great Tit
# fasta_header == "A0A3M0J8U2_HIRRU|A0A3M0J8U2" ~ "fascin2", #Hirundo rustica rustica
fasta_header == "A0A3M0KY54_HIRRU|A0A3M0KY54" ~ "fascin1", #Hirundo rustica rustica
##
fasta_header == "A0A0Q3XBK7_AMAAE|A0A0Q3XBK7" ~ "fascin1", #Amazona aestiva
fasta_header == "A0A0Q3XBK7_AMAAE|A0A0Q3XBK7" ~ "fascin2", #Amazona aestiva
fasta_header == "ENSNPEG00000000307.1|ENSNPEP00000000338.1" ~ "fascin1", #Chilean tinamou
fasta_header == "ENSNPEG00000010488.1|ENSNPEP00000014026.1" ~ "fascin2", #Chilean tinamou
fasta_header == "ENSDNVG00000009693.1|ENSDNVP00000013742.1" ~ "fascin2", #Emu
fasta_header == "ENSARWG00000006036.1|ENSARWP00000008623.1" ~ "fascin2", #Okarito brown kiwi
fasta_header == "ENSABRG00000010882.1|ENSABRP00000012102.1" ~ "fascin2", #Pink-footed goose
fasta_header == "ENSCPUG00000010544.1|ENSCPUP00000015119.1" ~ "fascin1", #Ruff
fasta_header == "ENSCPGG00000016204.1|ENSCPGP00000023396.1" ~ "fascin2", #Spoon-billed sandpiper
TRUE ~ as.character(annotation)))
Rest of Chordata
base <- df %>%
filter(phylum %in% c('Chordata'))%>%
filter(class %!in% c('Mammalia', 'Actinopteri', 'Aves'))%>%
arrange(desc(phylum, class, order, family, genus, species)) %>%
# mutate(family= fct_lump(family, 3, other_level= "Others families")) %>%
ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) +
geom_point(alpha=1, aes(shape=Status), size=4 )+
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
scale_shape_discrete(drop=TRUE,
limits = levels(df$Status))+
scale_colour_discrete(drop=TRUE,
limits = levels(df$annotation_cll))+
ggtitle("Differences between sequences in other Chordata's clasess")+
ylab("Charge divided by sequence length")+
xlab("Species")+
scale_x_discrete(guide = guide_axis(angle = 60))
base + facet_wrap(~class, ncol = 1, scales ="free_x")
df %<>% mutate(annotation = case_when(
#fasta_header == "A0A1L8ETI6_XENLA|A0A1L8ETI6" ~ "fascin2", #Xenopus laevis
#fasta_header == "FASC_XENLA|Q91837" ~ "fascin2", #Xenopus laevis
fasta_header == "A0A401P713_SCYTO|A0A401P713" ~ "fascin1", #Scyliorhinus torazame
fasta_header == "A0A401PG70_SCYTO|A0A401PG70" ~ "fascin2", #Scyliorhinus torazame
fasta_header == "ENSSMRG00000020250.1|ENSSMRP00000026211.1" ~ "fascin1", #Argentine black and white tegu
fasta_header == "ENSSMRG00000004642.1|ENSSMRP00000005740.1" ~ "fascin2", #Argentine black and white tegu
fasta_header == "ENSPVIG00000008238.1|ENSPVIP00000010398.1" ~ "fascin1", #Central bearded dragon
fasta_header == "ENSPVIG00000000772.1|ENSPVIP00000000848.1" ~ "fascin", #Central bearded dragon anotado como fascin-like
#Poca diferencia, lo pondría en duda, pero viene anotado así
fasta_header == "ENSNSUG00000014196.1|ENSNSUP00000019414.1" ~ "fascin", #Mainland tiger snake
fasta_header == "ENSNSUG00000009748.1|ENSNSUP00000013015.1" ~ "fascin", #Mainland tiger snake
fasta_header == "ENSSPUG00000002887.1|ENSSPUP00000003759.1" ~ "fascin1", #Tuatara
fasta_header == "ENSSPUG00000014373.1|ENSSPUP00000018627.1" ~ "fascin2", #Tuatara
fasta_header == "ENSCABG00000005872.1|ENSCABP00000007792.1" ~ "fascin1", #Abingdon island giant tortoise
fasta_header == "ENSCABG00000012992.1|ENSCABP00000017491.1" ~ "fascin2", #Abingdon island giant tortoise
fasta_header == "A0A1U7SFR2_ALLSI|A0A1U7SFR2" ~ "fascin2", #Alligator sinensis
species == "Australian saltwater crocodile" ~ "fascin", # Diferencia de dos aminoácidos
fasta_header == "ENSCPBG00000020810.1|ENSCPBP00000029528.1" ~ "fascin1", #Painted turtle
fasta_header == "ENSCPBG00000010404.1|ENSCPBP00000013973.1" ~ "fascin2", #Painted turtle
TRUE ~ as.character(annotation)))
Arthropoda, Mollusca and others
base <- df %>%
filter(phylum %!in% c('Chordata'))%>%
drop_na(class)%>%
arrange(desc(phylum, class, order, family, genus, species)) %>%
ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) +
geom_point(alpha=1, aes(shape=Status), size=4 )+
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
scale_shape_discrete(drop=TRUE,
limits = levels(df$Status))+
scale_colour_discrete(drop=TRUE,
limits = levels(df$annotation_cll))+
ggtitle("Differences between sequences in other phyla")+
ylab("Charge divided by sequence length")+
xlab("Species")+
scale_x_discrete(guide = guide_axis(angle = 60))
base + facet_wrap(~phylum, ncol = 2, scales ="free_x")
df %<>% mutate(annotation = case_when(
fasta_header == "D6WYE3_TRICA|D6WYE3" ~ "fascin", #Tribolium castaneum
fasta_header == "B4JLX1_DROGR|B4JLX1" ~ "fascin", #Drosophila grimshawi
fasta_header == "B4IL65_DROSE|B4IL65" ~ "fascin", #Drosophila grimshawi
TRUE ~ as.character(annotation)))
Final dataset
freezing <- df %>% filter(class %!in% c('Actinopteri', 'Chondrichthyes')) %>%
select(-c(B, Z, charge_seq_length, annotation_cll)) #Problem with B & Z in EMBOSS Pepstats
freezing$reannotated <- factor(ifelse(freezing$annotation != freezing$annotation_original, "Yes", "No"))
library(hrbrthemes)
library(viridis)
library(FascinRSCA)
freezing %>%
group_by(class, annotation)%>%
summarise(n = n(),
charge = mean (charge),
residue_n = mean(residue_n), .groups = "drop")%>%
ggplot(aes(x=charge, y=residue_n, size=n, fill=class)) +
geom_point(alpha=0.5, shape=21, color="black") +
scale_size(range = c(.1, 24), name="Number of sequences") +
#scale_fill_viridis(discrete=TRUE, guide="none", option="A") +
theme_ipsum() +
ylab("Charge") +
xlab("Number of residues")
ggsave(filename = "figures/FSCN1_FSCN2/bubble_plot.tiff", dpi = 400)
df %>%
mutate(class= fct_lump(class, 7, other_level= "Others")) %>%
filter(class != "Others")%>%
ggplot(aes(x=class, y=charge/residue_n)) +
geom_violin(show.legend = FALSE, adjust = 0.5)+
geom_dotplot(aes(fill = as.factor(annotation)), binaxis = "y",
binwidth = 0.0005,
stackdir = "center") +
theme(axis.text.x = element_text(face = "italic", size=13),
axis.ticks.x=element_blank(),legend.text = element_text(size = 15),
strip.text = element_text(face = "italic", size=13))+
scale_x_discrete(guide = guide_axis(angle = 60))
ggsave(filename = "figures/FSCN1_FSCN2/violin_plot.pdf", dpi = 300)
write.csv(freezing, paste("../data", 'freezing_fascin.csv', sep="/"), row.names = TRUE)
usethis::use_data(freezing, overwrite = TRUE)
freezing %>% count(reannotated)
human <- subset(freezing, species == 'Homo sapiens')
freezingNonRedundant <- rbind(human, freezing) %>% distinct(fasta_seq, .keep_all = TRUE)
write.csv(freezingNonRedundant, paste("../data", 'freezingNonRedundant_fascin.csv', sep="/"), row.names = TRUE)
usethis::use_data(freezingNonRedundant, overwrite = TRUE)
write.csv(df %>% filter(class %in% c('Actinopteri', 'Chondrichthyes')),
paste("../data", 'peces_fascin.csv', sep="/"), row.names = TRUE)
currocam/FascinRSCA documentation built on March 21, 2022, 6:29 a.m.
R Package Documentation
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knitr::opts_chunk$set(echo = TRUE, dpi=200)
The purpose of this notebook is to discriminate the homologous sequences to fascin that will be used for the SCA analysis and their reannotation.
# This is code to replicate the analysis and figures made to distinguish between #FSCN1 and FSCN2 sequences. Code developed by currocam. #load libraries library(FascinRSCA) library(tidyverse) library(magrittr) `%!in%` = Negate(`%in%`) # Input file outliers <- scan("../data-raw/df_Q16658/outliers_fasta_headers.txt", what = "character") outliers %<>% append("ENSPTRG00000041962.2|ENSPTRP00000061120.2") df <- FascinRSCA::df_Q16658_WithOutOutliers %>% filter(fasta_header%!in% outliers)%>% mutate(charge_seq_length=charge/residue_n, annotation_original = annotation, annotation_cll = fct_collapse(annotation, fascin1 = c("fascin1"), fascin2 = c("fascin2", "fascin2a", "fascin2b"), fascin = c("fascin"), others = c("uncharacterized", "singed", "unknown")), Status = replace_na(Status, 'unknown') %>% factor()) colour_values <- setNames(c('#999999','#E69F00', '#56B4E9', '#111111'), c('Fascin','Fascin1', 'Fascin2', 'Others')) status_shape <- set_names(c(3, 16, 17), c("NaN", "reviewed", "unreviewed"))
Taxonomic distribution
This is how our sequences are taxonomically distributed:
table_tax <- df %>% mutate(phylum= fct_lump(phylum, 15, other_level= "Others")) %>% mutate(class= fct_lump(class, 10, other_level= "Others")) %>% drop_na(phylum, class) %>% group_by(phylum, class,annotation) %>% summarise(n = n()) %>% arrange(phylum, class,annotation) ggplot(table_tax, aes(fill=annotation, y=n, x=phylum)) + geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ ggtitle("Taxonomically distributed sequences")+ ylab("Number of sequences")+ scale_x_discrete(guide = guide_axis(angle = 60)) ggsave(filename = "figures/FSCN1_FSCN2/taxonomic_distribution.pdf")
knitr::kable(table_tax, caption = "Taxonomically distributed sequences")
base <- df %>% filter(class %in% c('Mammalia' ))%>% arrange(desc(phylum, class, order, family, genus, species)) %>% mutate(order= fct_lump(order, 3, other_level= "Others")) %>% ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) + geom_point(alpha=1, aes(shape=Status), size=3)+ scale_shape_discrete(drop=TRUE, limits = levels(df$Status))+ scale_colour_discrete(drop=TRUE, limits = levels(df$annotation_cll))+ theme(axis.text.x = element_blank(), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ ggtitle("Differences between sequences in Mammalia's species")+ ylab("Charge divided by sequence length")+ xlab("Species") base + facet_wrap(~order, ncol = 2, scales ="free_x")
Carnivora
There are no annotated sequences reviewed in Carnivora. The sequences are distributed according to the pattern discussed above. There are 2 FSCN1 sequences with an index higher than 0.01, belonging to Vulpes vulpes and Felis catus.
base <- df %>% filter(order %in% c('Carnivora' ))%>% arrange(desc(phylum, class, order, family, genus, species)) %>% ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) + geom_point(alpha=1, aes(shape=Status), size=5)+ theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ scale_shape_discrete(drop=TRUE, limits = levels(df$Status))+ scale_colour_discrete(drop=TRUE, limits = levels(df$annotation_cll))+ ggtitle("Differences between sequences in Carnivora's species")+ ylab("Charge divided by sequence length")+ xlab("Species")+ geom_hline(yintercept=0.010, linetype="dashed", color = "blue")+ scale_x_discrete(guide = guide_axis(angle = 60)) base + facet_wrap(~suborder, ncol = 2, scales ="free_x")
- Destacar que inicialmente Red fox fue reanotado manualmente a fascina 2, aunque si se buscaba la anotación original, era fascina 1, y cayó en el cluster 1, por lo que se rectifico.
library(tidyverse) library(magrittr) df %<>% mutate(annotation = case_when( fasta_header == "ENSNVIG00000005656.1|ENSNVIP00000007116.1" ~ "fascin1", #American mink fasta_header == "A0A2Y9JHA1_ENHLU|A0A2Y9JHA1" ~ "fascin1", #Enhydra lutris kenyoni fasta_header == "A0A2Y9L644_ENHLU|A0A2Y9L644" ~ "fascin2", #Enhydra lutris kenyoni fasta_header == "ENSCAFG00020017175.1|ENSCAFP00020021724.1" ~ "fascin1", #Dingo fasta_header == "ENSCAFG00020009227.1|ENSCAFP00020011452.1" ~ "fascin2", #Dingo fasta_header == "ENSVVUG00000006354.1|ENSVVUP00000008455.1" ~ "fascin1", #Red fox fasta_header == "ENSPPRG00000003969.1|ENSPPRP00000009547.1" ~ "fascin1", #Leopard fasta_header == "ENSPPRG00000024836.1|ENSPPRP00000000416.1" ~ "fascin2", #Leopard fasta_header == "A0A3Q7TUG0_URSAR|A0A3Q7TUG0" ~ "fascin2a", #Ursus arctos horribilis, anotado como fscn2b para mantener cohesión TRUE ~ as.character(annotation) ))
Primates
In primates it works better to set a slightly higher limit, 0.0115, to include sequences from the Aotidae and Cebidae families. There is one automatically annotated FSCN2 sequence in Callithrix jacchus with an index lower than FSCN1, it is an exception. In Otolemur garnettii sequences the pattern is followed but shifted upwards. There are only reviewed annotated sequences in Homo sapiens.
base <- df %>% filter(order %in% c('Primates'))%>% arrange(desc(phylum, class, order, family, genus, species)) %>% ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) + geom_point(alpha=1, aes(shape=factor(Status)), size=4 )+ theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ scale_shape_discrete(drop=TRUE, limits = levels(df$Status))+ scale_colour_discrete(drop=TRUE, limits = levels(df$annotation_cll))+ ggtitle("Differences between sequences in Primates")+ ylab("Charge divided by sequence length")+ xlab("Species")+ geom_hline(yintercept=0.0115, linetype="dashed", color = "red")+ scale_x_discrete(guide = guide_axis(angle = 60)) base + facet_wrap(~suborder, ncol = 2, scales ="free_x")
df %<>% mutate(annotation = case_when( fasta_header == "ENSCANG00000013485.1|ENSCANP00000003877.1" ~ "fascin1", #Colobus angolensis palliatus fasta_header == "ENSTGEG00000001910.1|ENSTGEP00000002162.1" ~ "fascin1", #Gelada fasta_header == "ENSTGEG00000009852.1|ENSTGEP00000012087.1" ~ "fascin2", #Gelada fasta_header == "ENSMFAG00000037869.1|ENSMFAP00000037244.1" ~ "fascin2", #Macaca fascicularis fasta_header == "ENSMMUG00000001583.3|ENSMMUP00000002112.3" ~ "fascin2", #Macaca mulatta fasta_header == "ENSRBIG00000007902.1|ENSRBIP00000001562.1" ~ "fascin2", #Rhinopithecus bieti fasta_header == "ENSRROG00000034748.1|ENSRROP00000021684.1" ~ "fascin2", #Rhinopithecus roxellana fasta_header == "ENSPTEG00000031612.1|ENSPTEP00000033004.1" ~ "fascin2", #Ugandan red Colobus fasta_header == "ENSPSMG00000010879.1|ENSPSMP00000015242.1" ~ "fascin1", #Greater bamboo lemur fasta_header == "ENSMICG00000010211.3|ENSMICP00000009297.2" ~ "fascin1", #Mouse lemur fasta_header == "ENSMICG00000031762.2|ENSMICP00000018701.2" ~ "fascin2", #Mouse lemur TRUE ~ as.character(annotation) ))
Rodentia
Most of the Rodentia sequences follow the pattern, however there are more sequences that deviate from it. All annotated sequences reviewed follow the pattern.
base <- df %>% filter(order %in% c('Rodentia'))%>% arrange(desc(phylum, class, order, family, genus, species)) %>% ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) + geom_point(alpha=1, aes(shape=factor(Status)), size=4 )+ theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ scale_shape_discrete(drop=TRUE, limits = levels(df$Status))+ scale_colour_discrete(drop=TRUE, limits = levels(df$annotation_cll))+ ggtitle("Differences between sequences in Rodentia")+ ylab("Charge divided by sequence length")+ xlab("Species")+ geom_hline(yintercept=0.01, linetype="dashed", color = "blue")+ scale_x_discrete(guide = guide_axis(angle = 60)) base + facet_wrap(~suborder+family, ncol = 6, scales ="free_x")
- Bathyergidae sigue patrones distintos.
df %<>% mutate(annotation = case_when( fasta_header == "ENSCCNG00000009421.1|ENSCCNP00000008921.1" ~ "fascin2", #American beaver fasta_header == "ENSFDAG00000016895.1|ENSFDAP00000008686.1" ~ "fascin", #Damara mole rat fasta_header == "ENSHGLG00000012271.1|ENSHGLP00000017138.1" ~ "fascin2", #Naked mole-rat male Como está anotado fasta_header == "ENSHGLG00100005772.1|ENSHGLP00100007730.1" ~ "fascin2", #Naked mole-rat male Como está anotado fasta_header == "ENSCLAG00000013884.1|ENSCLAP00000020263.1" ~ "fascin1", #Long-tailed chinchilla fasta_header == "ENSCLAG00000014531.1|ENSCLAP00000021213.1" ~ "fascin2", #Long-tailed chinchilla fasta_header == "ENSODEG00000012533.1|ENSODEP00000017010.1" ~ "fascin1", #Degu fasta_header == "ENSODEG00000008403.1|ENSODEP00000011308.1" ~ "fascin2", #Degu fasta_header == "ENSCGRG00015000357.1|ENSCGRP00015000341.1" ~ "fascin2", #Chinese hamster CHOK1GS fasta_header == "ENSCGRG00000018194.1|ENSCGRP00000025123.1" ~ "fascin2", #Chinese hamster CriGri fasta_header == "ENSCGRG00015013911.1|ENSCGRP00015018116.1" ~ "fascin1", #Cricetulus griseus fasta_header == "A0A1A6HKX6_NEOLE|A0A1A6HKX6" ~ "fascin1", #Neotoma lepida fasta_header == "A0A1A6HXR6_NEOLE|A0A1A6HXR6" ~ "fascin2", #Neotoma lepida fasta_header == "ENSPEMG00000024060.2|ENSPEMP00000028558.1" ~ "fascin1", #Northern American deer mouse fasta_header == "ENSPEMG00000028380.1|ENSPEMP00000031506.1" ~ "fascin2", #Northern American deer mouse fasta_header == "ENSMOCG00000001115.1|ENSMOCP00000001465.1" ~ "fascin1", #Prairie vole fasta_header == "ENSMOCG00000018078.1|ENSMOCP00000020377.1" ~ "fascin2", #Prairie vole fasta_header == "ENSJJAG00000019494.1|ENSJJAP00000018145.1" ~ "fascin1", #Lesser Egyptian jerboa fasta_header == "ENSJJAG00000015122.1|ENSJJAP00000011928.1" ~ "fascin2", #Lesser Egyptian jerboa fasta_header == "MGP_SPRETEiJ_G0028973.1|MGP_SPRETEiJ_P0074768" ~ "fascin1", #Algerian mouse fasta_header == "MGP_SPRETEiJ_G0018357.1|MGP_SPRETEiJ_P0032954" ~ "fascin2", #Algerian mouse fasta_header == "ENSMUGG00000013467.1|ENSMUGP00000015650.1" ~ "fascin1", #Mongolian gerbil fasta_header == "ENSMUGG00000001646.1|ENSMUGP00000001760.1" ~ "fascin2", #Mongolian gerbil fasta_header == "MGP_CASTEiJ_G0029422.1|MGP_CASTEiJ_P0075825" ~ "fascin1", #Mouse CAST/EiJ fasta_header == "MGP_CASTEiJ_G0018799.1|MGP_CASTEiJ_P0033632" ~ "fascin2", #Mouse CAST/EiJ fasta_header == "MGP_DBA2J_G0030137.1|MGP_DBA2J_P0075380" ~ "fascin1", #Mouse DBA/2J fasta_header == "MGP_DBA2J_G0019328.1|MGP_DBA2J_P0033800" ~ "fascin2", #Mouse DBA/2J fasta_header == "MGP_FVBNJ_G0019320.1|MGP_FVBNJ_P0033798" ~ "fascin2", #Mouse FVB/NJ fasta_header == "MGP_PWKPhJ_G0029137.1|MGP_PWKPhJ_P0075276" ~ "fascin1", #Mouse PWK/PhJ fasta_header == "MGP_PWKPhJ_G0018565.1|MGP_PWKPhJ_P0033324" ~ "fascin2", #Mouse PWK/PhJ fasta_header == "MGP_WSBEiJ_G0029496.1|MGP_WSBEiJ_P0074413" ~ "fascin1", #Mouse WSB/EiJ fasta_header == "MGP_WSBEiJ_G0018851.1|MGP_WSBEiJ_P0033219" ~ "fascin2", #Mouse WSB/EiJ fasta_header == "MGP_CAROLIEiJ_G0027969.1|MGP_CAROLIEiJ_P0071948" ~ "fascin1", #Ryukyu mouse fasta_header == "MGP_CAROLIEiJ_G0017513.1|MGP_CAROLIEiJ_P0031506" ~ "fascin2", #Ryukyu mouse fasta_header == "MGP_PahariEiJ_G0024541.1|MGP_PahariEiJ_P0060161" ~ "fascin1", #Shrew mouse fasta_header == "MGP_PahariEiJ_G0018639.1|MGP_PahariEiJ_P0038222" ~ "fascin2", #Shrew mouse fasta_header == "ENSMSIG00000029517.1|ENSMSIP00000035482.1" ~ "fascin1", #Steppe mouse fasta_header == "ENSMSIG00000014526.1|ENSMSIP00000016977.1" ~ "fascin2", #Steppe mouse fasta_header == "ENSNGAG00000003408.1|ENSNGAP00000003153.1" ~ "fascin1", #Upper Galilee mountains blind mole rat fasta_header == "ENSMMMG00000006216.1|ENSMMMP00000006939.1" ~ "fascin2", #Alpine marmot fasta_header == "ENSUPAG00010011357.1|ENSUPAP00010014127.1" ~ "fascin1", #Arctic ground squirrel fasta_header == "ENSUPAG00010019602.1|ENSUPAP00010024748.1" ~ "fascin2", #Arctic ground squirrel fasta_header == "ENSSTOG00000020923.2|ENSSTOP00000021848.2" ~ "fascin1", #Ictidomys tridecemlineatus fasta_header == "ENSSTOG00000028223.2|ENSSTOP00000023251.1" ~ "fascin2", #Ictidomys tridecemlineatus fasta_header == "ENSCAPG00000016296.1|ENSCAPP00000016394.1" ~ "fascin2", #Brazilian guinea pig TRUE ~ as.character(annotation) ))
Artiodactyla
base <- df %>% filter(order %in% c('Artiodactyla'))%>% arrange(desc(phylum, class, order, family, genus, species)) %>% ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) + geom_point(alpha=1, aes(shape=factor(Status)), size=4 )+ theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ scale_shape_discrete(drop=TRUE, limits = levels(df$Status))+ scale_colour_discrete(drop=TRUE, limits = levels(df$annotation_cll))+ ggtitle("Differences between sequences in Artiodactyla order")+ ylab("Charge divided by sequence length")+ xlab("Species")+ geom_hline(yintercept=0.009, linetype="dashed", color = "green")+ geom_hline(yintercept=0.01, linetype="dashed", color = "blue")+ scale_x_discrete(guide = guide_axis(angle = 60)) base + facet_wrap(~order+suborder, ncol = 2, scales ="free_x")
- Ha sucedido con Wild yak y American bison lo mismo que con Red fox.
df %<>% mutate(annotation = case_when( fasta_header == "ENSBBBG00000009842.1|ENSBBBP00000017441.1" ~ "fascin1", #American bison fasta_header == "ENSBMUG00000021206.1|ENSBMUP00000028298.1" ~ "fascin1", #Wild yak fasta_header == "ENSTTRG00000006435.1|ENSTTRP00000006091.1" ~ "fascin2", #Dolphin fasta_header == "ENSMFAG00000037869.1|ENSMFAP00000037244.1" ~ "fascin2", #Macaca fascicularis fasta_header == "ENSMMUG00000001583.3|ENSMMUP00000002112.3" ~ "fascin2", #Macaca mulatta fasta_header == "ENSRBIG00000007902.1|ENSRBIP00000001562.1" ~ "fascin2", #Rhinopithecus bieti fasta_header == "ENSRROG00000034748.1|ENSRROP00000021684.1" ~ "fascin2", #Rhinopithecus roxellana fasta_header == "ENSPTEG00000031612.1|ENSPTEP00000033004.1" ~ "fascin2", #Ugandan red Colobus fasta_header == "ENSPSMG00000010879.1|ENSPSMP00000015242.1" ~ "fascin1", #Greater bamboo lemur fasta_header == "ENSMICG00000010211.3|ENSMICP00000009297.2" ~ "fascin1", #Mouse lemur fasta_header == "ENSMICG00000031762.2|ENSMICP00000018701.2" ~ "fascin2", #Mouse lemur TRUE ~ as.character(annotation) ))
Other orders in Mammalia
base <- df %>% filter(class %in% c('Mammalia'))%>% filter(order %!in% c('Primates', 'Rodentia', 'Carnivora', 'Artiodactyla'))%>% arrange(desc(phylum, class, order, family, genus, species)) %>% ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) + geom_point(alpha=1, aes(shape=factor(Status)), size=4 )+ theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ scale_shape_discrete(drop=TRUE, limits = levels(df$Status))+ scale_colour_discrete(drop=TRUE, limits = levels(df$annotation_cll))+ ggtitle("Differences between sequences in other orders in Mammalia")+ ylab("Charge divided by sequence length")+ xlab("Species")+ # geom_hline(yintercept=0.009, linetype="dashed", color = "green")+ geom_hline(yintercept=0.01, linetype="dashed", color = "blue")+ scale_x_discrete(guide = guide_axis(angle = 60)) base + facet_wrap(~order+suborder, ncol = 4, scales ="free_x")
df %<>% mutate(annotation = case_when( fasta_header == "ENSPVAG00000017295.1|ENSPVAP00000016319.1" ~ "fascin1", #Megabat fasta_header == "ENSPCIG00000015239.2|ENSPCIP00000019991.1" ~ "fascin1", #Koala fasta_header == "ENSPCIG00000029609.2|ENSPCIP00000040449.1" ~ "fascin2", #Koala fasta_header == "ENSMEUG00000013398.1|ENSMEUP00000012221.1" ~ "fascin2", #Wallaby fasta_header == "ENSEEUG00000002694.1|ENSEEUP00000002460.1" ~ "fascin2", #Hedgehog fasta_header == "ENSEASG00005000015.1|ENSEASP00005000018.1" ~ "fascin1", #Donkey fasta_header == "ENSEASG00005005735.1|ENSEASP00005007879.1" ~ "fascin2", #Donkey fasta_header == "A0A2Y9DGX1_TRIMA|A0A2Y9DGX1" ~ "fascin2", #Trichechus manatus latirostris TRUE ~ as.character(annotation)))
Other classes in Chordata
Aves
In Aves the pattern just works in Galliformes.
base <- df %>% filter(class %in% c('Aves'))%>% arrange(desc(phylum, class, order, family, genus, species)) %>% mutate(order= fct_lump(order, 2, other_level= "Other orders")) %>% ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) + geom_point(alpha=1, aes(shape=Status), size=4 )+ theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ scale_shape_discrete(drop=TRUE, limits = levels(df$Status))+ scale_colour_discrete(drop=TRUE, limits = levels(df$annotation_cll))+ ggtitle("Differences between sequences in Aves")+ ylab("Charge divided by sequence length")+ xlab("Species")+ geom_hline(yintercept=0.007, linetype="dashed", color = "green")+ scale_x_discrete(guide = guide_axis(angle = 60)) base + facet_wrap(~order, ncol = 1, scales ="free_x")
En el caso de los Passeriformes nos encontramos que hay un patrón similar, pero la anotación está al revés. Entrando en la url, encontramos que "The sequence shown here is derived from an EMBL/GenBank/DDBJ whole genome shotgun (WGS) entry which is preliminary data". En ese sentido, comparando la interacción por string, nos parece razonable que el patrón sea al revés.
Amazona aestiva tiene muy poca diferencia, por lo que lo dejaremos como fascina.
df %<>% mutate(annotation = case_when( fasta_header == "A0A226NJP1_CALSU|A0A226NJP1" ~ "fascin1", #Callipepla squamata fasta_header == "A0A226MR24_CALSU|A0A226MR24" ~ "fascin2", #Callipepla squamata fasta_header == "A0A226PLS9_COLVI|A0A226PLS9" ~ "fascin2", #Colinus virginianus fasta_header == "ENSNMEG00000003221.1|ENSNMEP00000002566.1" ~ "fascin2", #Helmeted guineafowl fasta_header == "ENSCJPG00005000395.1|ENSCJPP00005000313.1" ~ "fascin1", #Japanese quail fasta_header == "ENSCJPG00005021245.1|ENSCJPP00005027399.1" ~ "fascin2", #Japanese quail # Passeriformes fasta_header == "ENSLCOG00000010881.1|ENSLCOP00000015232.1" ~ "fascin2", #Blue-crowned manakin fasta_header == "ENSLCOG00000000385.1|ENSLCOP00000000419.1" ~ "fascin1", #Blue-crowned manakin # fasta_header == "A0A3L8S262_CHLGU|A0A3L8S262" ~ "fascin2", #Chloebia gouldiae fasta_header == "A0A3L8SG84_CHLGU|A0A3L8SG84" ~ "fascin1", #Chloebia gouldiae fasta_header == "ENSJHYG00000006117.1|ENSJHYP00000007676.1" ~ "fascin1", #Dark-eyed junco fasta_header == "ENSPMJG00000017038.1|ENSPMJP00000023043.1" ~ "fascin2", #Great Tit fasta_header == "ENSPMJG00000017401.1|ENSPMJP00000023569.1" ~ "fascin1", #Great Tit # fasta_header == "A0A3M0J8U2_HIRRU|A0A3M0J8U2" ~ "fascin2", #Hirundo rustica rustica fasta_header == "A0A3M0KY54_HIRRU|A0A3M0KY54" ~ "fascin1", #Hirundo rustica rustica ## fasta_header == "A0A0Q3XBK7_AMAAE|A0A0Q3XBK7" ~ "fascin1", #Amazona aestiva fasta_header == "A0A0Q3XBK7_AMAAE|A0A0Q3XBK7" ~ "fascin2", #Amazona aestiva fasta_header == "ENSNPEG00000000307.1|ENSNPEP00000000338.1" ~ "fascin1", #Chilean tinamou fasta_header == "ENSNPEG00000010488.1|ENSNPEP00000014026.1" ~ "fascin2", #Chilean tinamou fasta_header == "ENSDNVG00000009693.1|ENSDNVP00000013742.1" ~ "fascin2", #Emu fasta_header == "ENSARWG00000006036.1|ENSARWP00000008623.1" ~ "fascin2", #Okarito brown kiwi fasta_header == "ENSABRG00000010882.1|ENSABRP00000012102.1" ~ "fascin2", #Pink-footed goose fasta_header == "ENSCPUG00000010544.1|ENSCPUP00000015119.1" ~ "fascin1", #Ruff fasta_header == "ENSCPGG00000016204.1|ENSCPGP00000023396.1" ~ "fascin2", #Spoon-billed sandpiper TRUE ~ as.character(annotation)))
Rest of Chordata
base <- df %>% filter(phylum %in% c('Chordata'))%>% filter(class %!in% c('Mammalia', 'Actinopteri', 'Aves'))%>% arrange(desc(phylum, class, order, family, genus, species)) %>% # mutate(family= fct_lump(family, 3, other_level= "Others families")) %>% ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) + geom_point(alpha=1, aes(shape=Status), size=4 )+ theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ scale_shape_discrete(drop=TRUE, limits = levels(df$Status))+ scale_colour_discrete(drop=TRUE, limits = levels(df$annotation_cll))+ ggtitle("Differences between sequences in other Chordata's clasess")+ ylab("Charge divided by sequence length")+ xlab("Species")+ scale_x_discrete(guide = guide_axis(angle = 60)) base + facet_wrap(~class, ncol = 1, scales ="free_x")
df %<>% mutate(annotation = case_when( #fasta_header == "A0A1L8ETI6_XENLA|A0A1L8ETI6" ~ "fascin2", #Xenopus laevis #fasta_header == "FASC_XENLA|Q91837" ~ "fascin2", #Xenopus laevis fasta_header == "A0A401P713_SCYTO|A0A401P713" ~ "fascin1", #Scyliorhinus torazame fasta_header == "A0A401PG70_SCYTO|A0A401PG70" ~ "fascin2", #Scyliorhinus torazame fasta_header == "ENSSMRG00000020250.1|ENSSMRP00000026211.1" ~ "fascin1", #Argentine black and white tegu fasta_header == "ENSSMRG00000004642.1|ENSSMRP00000005740.1" ~ "fascin2", #Argentine black and white tegu fasta_header == "ENSPVIG00000008238.1|ENSPVIP00000010398.1" ~ "fascin1", #Central bearded dragon fasta_header == "ENSPVIG00000000772.1|ENSPVIP00000000848.1" ~ "fascin", #Central bearded dragon anotado como fascin-like #Poca diferencia, lo pondría en duda, pero viene anotado así fasta_header == "ENSNSUG00000014196.1|ENSNSUP00000019414.1" ~ "fascin", #Mainland tiger snake fasta_header == "ENSNSUG00000009748.1|ENSNSUP00000013015.1" ~ "fascin", #Mainland tiger snake fasta_header == "ENSSPUG00000002887.1|ENSSPUP00000003759.1" ~ "fascin1", #Tuatara fasta_header == "ENSSPUG00000014373.1|ENSSPUP00000018627.1" ~ "fascin2", #Tuatara fasta_header == "ENSCABG00000005872.1|ENSCABP00000007792.1" ~ "fascin1", #Abingdon island giant tortoise fasta_header == "ENSCABG00000012992.1|ENSCABP00000017491.1" ~ "fascin2", #Abingdon island giant tortoise fasta_header == "A0A1U7SFR2_ALLSI|A0A1U7SFR2" ~ "fascin2", #Alligator sinensis species == "Australian saltwater crocodile" ~ "fascin", # Diferencia de dos aminoácidos fasta_header == "ENSCPBG00000020810.1|ENSCPBP00000029528.1" ~ "fascin1", #Painted turtle fasta_header == "ENSCPBG00000010404.1|ENSCPBP00000013973.1" ~ "fascin2", #Painted turtle TRUE ~ as.character(annotation)))
Arthropoda, Mollusca and others
base <- df %>% filter(phylum %!in% c('Chordata'))%>% drop_na(class)%>% arrange(desc(phylum, class, order, family, genus, species)) %>% ggplot(aes(y=charge_seq_length, x=species, color=annotation_cll)) + geom_point(alpha=1, aes(shape=Status), size=4 )+ theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ scale_shape_discrete(drop=TRUE, limits = levels(df$Status))+ scale_colour_discrete(drop=TRUE, limits = levels(df$annotation_cll))+ ggtitle("Differences between sequences in other phyla")+ ylab("Charge divided by sequence length")+ xlab("Species")+ scale_x_discrete(guide = guide_axis(angle = 60)) base + facet_wrap(~phylum, ncol = 2, scales ="free_x")
df %<>% mutate(annotation = case_when( fasta_header == "D6WYE3_TRICA|D6WYE3" ~ "fascin", #Tribolium castaneum fasta_header == "B4JLX1_DROGR|B4JLX1" ~ "fascin", #Drosophila grimshawi fasta_header == "B4IL65_DROSE|B4IL65" ~ "fascin", #Drosophila grimshawi TRUE ~ as.character(annotation)))
Final dataset
freezing <- df %>% filter(class %!in% c('Actinopteri', 'Chondrichthyes')) %>% select(-c(B, Z, charge_seq_length, annotation_cll)) #Problem with B & Z in EMBOSS Pepstats freezing$reannotated <- factor(ifelse(freezing$annotation != freezing$annotation_original, "Yes", "No"))
library(hrbrthemes) library(viridis) library(FascinRSCA) freezing %>% group_by(class, annotation)%>% summarise(n = n(), charge = mean (charge), residue_n = mean(residue_n), .groups = "drop")%>% ggplot(aes(x=charge, y=residue_n, size=n, fill=class)) + geom_point(alpha=0.5, shape=21, color="black") + scale_size(range = c(.1, 24), name="Number of sequences") + #scale_fill_viridis(discrete=TRUE, guide="none", option="A") + theme_ipsum() + ylab("Charge") + xlab("Number of residues") ggsave(filename = "figures/FSCN1_FSCN2/bubble_plot.tiff", dpi = 400)
df %>% mutate(class= fct_lump(class, 7, other_level= "Others")) %>% filter(class != "Others")%>% ggplot(aes(x=class, y=charge/residue_n)) + geom_violin(show.legend = FALSE, adjust = 0.5)+ geom_dotplot(aes(fill = as.factor(annotation)), binaxis = "y", binwidth = 0.0005, stackdir = "center") + theme(axis.text.x = element_text(face = "italic", size=13), axis.ticks.x=element_blank(),legend.text = element_text(size = 15), strip.text = element_text(face = "italic", size=13))+ scale_x_discrete(guide = guide_axis(angle = 60)) ggsave(filename = "figures/FSCN1_FSCN2/violin_plot.pdf", dpi = 300)
write.csv(freezing, paste("../data", 'freezing_fascin.csv', sep="/"), row.names = TRUE) usethis::use_data(freezing, overwrite = TRUE) freezing %>% count(reannotated)
human <- subset(freezing, species == 'Homo sapiens') freezingNonRedundant <- rbind(human, freezing) %>% distinct(fasta_seq, .keep_all = TRUE) write.csv(freezingNonRedundant, paste("../data", 'freezingNonRedundant_fascin.csv', sep="/"), row.names = TRUE) usethis::use_data(freezingNonRedundant, overwrite = TRUE) write.csv(df %>% filter(class %in% c('Actinopteri', 'Chondrichthyes')), paste("../data", 'peces_fascin.csv', sep="/"), row.names = TRUE)
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