+
p1 +
theme(legend.position = "bottom",
legend.direction = "vertical") +
@@ -260,9 +282,9 @@
y = 3,
x = 90,
label = "FAS domain")
+
The y axis is continuous - 1 on the plot corresponds to Q9C7T7, 2 corresponds to Q9ZPR7 and 3 to Q9ZQ23. The x axis represents amino acids in the sequence. This enables adding annotations anywhere on the plot.
-
+
p1 +
theme(legend.position = "bottom",
legend.direction = "vertical") +
@@ -270,30 +292,30 @@
y = 1.6,
x = 530,
label = "annotation 1")
+
-
change plot ratio: the ratio of the axes is determined automatically by
plot_prot() and scales with the longest sequence plotted. To change this use the ratio argument to coord_equal():
-
+
p1 +
theme(legend.position = "bottom",
legend.direction = "vertical") +
coord_equal(ratio = 80,
expand = FALSE)
+
-
change domain colors: use another fill palette:
-
+
-
+
or create a manual palette:
-
+
p1 +
theme(legend.position = "bottom",
legend.direction = "vertical") +
@@ -305,7 +327,7 @@
"blue",
"purple",
"grey30"))
+
diff --git a/docs/articles/pkgdown/PSV_files/header-attrs-2.11/header-attrs.js b/docs/articles/pkgdown/PSV_files/header-attrs-2.11/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/pkgdown/PSV_files/header-attrs-2.11/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/articles/pkgdown/analyse.html b/docs/articles/pkgdown/analyse.html
index 13859d4..a701378 100644
--- a/docs/articles/pkgdown/analyse.html
+++ b/docs/articles/pkgdown/analyse.html
@@ -31,7 +31,7 @@
ragp
- 0.3.2.0002
+ 0.3.5.9000
@@ -39,7 +39,7 @@
-
-
+
@@ -77,7 +77,7 @@
-
-
+
@@ -91,7 +91,7 @@
-
+
The column sequence contains the input sequence where only the amino acids in potential AG glycomodules are in uppercase while all other are in lower case. The column AG_aa contains the amino acid count found in potential AG glycomodules, while columns total_length and longest correspond to the matched sequence spans. From the output it can be observed that 100 amino acids were found in AG glycomodules, that the longest span of AG glycomodules is 144 amino acids long (AG glycomodule dipeptides in one span are separated by less then 10 amino acids, since div argument which determines this is set per default to 10) and that it is the only span in the sequence.
To obtain a more detailed data frame the function can be called with simplify = FALSE and tidy = TRUE:
@@ -160,10 +158,10 @@
scaned_at2 %>%
mutate(sequence = strtrim(sequence, 55)) %>% #take the first 55 amino acids from the sequences
filter(id == "AT2G14890.1")
-#> sequence id
-#> 1 marsfaiavicivliagvtgqAPTSPPTaTPAPPTPTTPpPAaTPpPVsAPpPVt AT2G14890.1
-#> location.start location.end P_pos length AG_aa
-#> 1 22 165 23, 26, .... 144 100
+
#> sequence id location.start location.end P_pos length
+
#> 1 marsfaiavicivliagvtgqAPTSPPTaTPAPPTPTTPpPAaTPpPVsAPpPVt AT2G14890.1 22 165 23, 26, .... 144
+
#> AG_aa
+
#> 1 100
Output obtained in this way contains the start and end location of each AG glycomodul span (in this example there is only one) as well as a vector of proline positions in AG glycomodules.
To incorporate hydroxyproline position knowledge when searching AG glycomodules the sequence output of predict_hyp() can be provided to scan_ag():
@@ -187,10 +185,10 @@
scaned_at3 %>%
mutate(sequence = strtrim(sequence, 55)) %>% #take the first 55 amino acids from the sequences
filter(id == "AT2G14890.1")
-#> sequence id
-#> 1 marsfaiavicivliagvtgqAOTSOOTaTOAOOTOTTOoOAaTOoOVsAOoOVt AT2G14890.1
-#> location.start location.end P_pos length AG_aa
-#> 1 22 165 23, 26, .... 144 98
+
#> sequence id location.start location.end P_pos length
+
#> 1 marsfaiavicivliagvtgqAOTSOOTaTOAOOTOTTOoOAaTOoOVsAOoOVt AT2G14890.1 22 165 23, 26, .... 144
+
#> AG_aa
+
#> 1 98It is clear almost all prolines (P) were predicted to be hydroxylated (O) so the start and end location as well as the length of the amino acid span containing AG glycomoduls are unchanged for this sequence.
Many of the found motifs in AT2G14890.1 (shown in uppercase) correspond to extensin motifs (PPP+ or OOO+). This might not be desired when searching for AG glycomodules, and scan_ag() offers the argument exclude_ext which enables the function to omit extensin motifs either of the common sequence SP{3,} or SO{3,} (exclude_ext = "yes") or of the form P[3,} or O{3,} (exclude_ext = "all"):
@@ -205,12 +203,12 @@
scaned_at4 %>%
mutate(sequence = strtrim(sequence, 55)) %>% #take the first 55 amino acids from the sequences
filter(id == "AT2G14890.1")
-#> sequence id
-#> 1 marsfaiavicivliagvtgqAOTSOOTaTOAOOTOTtoooaatooovsaooovt AT2G14890.1
-#> 2 marsfaiavicivliagvtgqAOTSOOTaTOAOOTOTtoooaatooovsaooovt AT2G14890.1
-#> location.start location.end P_pos length AG_aa
-#> 1 22 37 23, 26, .... 16 15
-#> 2 114 165 115, 119.... 52 34
When extensin motifs are masked two AG glycomodule spans are found in AT2G14890.1, from position 22 to 37 and from position 114 to 165.
Next we will compare how many sequences are identified as arabinogalactans with and without prediction of proline hydroxylation when extensin motifs are masked:
@@ -220,7 +218,7 @@
Transcript.id,
exclude_ext = "all")
sum(scaned_at5$total_length != 0)
-#> [1] 685
+#> [1] 627
#using the output of `predict_hyp()
scaned_at6 <- scan_ag(data = at_hyp$sequence,
@@ -229,7 +227,7 @@
exclude_ext = "all")
#> sequence vector contains O, O will be considered instead of P
sum(scaned_at6$total_length != 0)
-#> [1] 217
Incorporating hydroxyproline predictions in the search for AGPs reduced the pool of identified sequences by threefold. scan_ag() is capable of identifying potential chimeric and short AGPs without pronounced amino acid bias and with few characteristic motifs. In combination with predict_hyp() the specificity of the found sequences is greatly increased since the majority of sequences unlikely to contain hydroxyprolines are excluded.