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exercise-sheet-3.Rmd
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exercise-sheet-3.Rmd
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```{r, include=FALSE}
source("custom_functions.R")
library(flextable)
library(officer)
```
---
title: "Exercise sheet 3: Sequence Alignment"
---
---------------------------------
# Exercise 1 - Alignments
For the given examples which ones can be called alignments:
### 1a)
```
AGTTTTTT
AGGTTTTT
```
#### {.tabset}
##### Hide
##### Solution
::: {.answer data-latex=""}
True
:::
#### {-}
### 1b)
```
CCGTTTTTT
-AGGTTTTT
```
#### {.tabset}
##### Hide
##### Solution
::: {.answer data-latex=""}
True
:::
####
### 1c)
```
CCCGTTTTTTGC
-CGGTTTTT
```
#### {.tabset}
##### Hide
##### Solution
::: {.answer data-latex=""}
False. In an alignment both strings must have the same length
:::
#### {-}
### 1d)
```
AG--TTTTTT
AG-GTTTTTT
```
#### {.tabset}
##### Hide
##### Solution
::: {.answer data-latex=""}
False. Gaps cannot be aligned with gaps
:::
#### {-}
# Exercise 2 - Hands on Needleman-Wunsch algorithm
The Needleman-Wunsch algorithm enables the calculation of the optimal pairwise sequence alignment with linear gap cost. Given the following two sequences S1, S2 and the given cost function complete the tasks *A-D*.
::: {.answer data-latex=""}
\begin{align}
S1 &= TACGCGC\\
S2 &= TCCGA
\end{align}
$$
w(x,y)=
\begin{cases}
+ 1 & if\ x = `-`\ \vee\ y=`-`\\
- 1 & if\ x = y \\
0 & else\\
\end{cases}
$$
:::
```{r, include=FALSE}
zm <- data.frame(a = c("-", "T", "A", "C", "G", "C", "G", "C"),
b = c(" ", " ", " ", " ", " ", " ", " ", " "),
c = c(" ", " ", " ", " ", " ", " ", " ", " "),
d = c(" ", " ", " ", " ", " ", " ", " ", " "),
e = c(" ", " ", " ", " ", " ", " ", " ", " "),
f = c(" ", " ", " ", " ", " ", " ", " ", " "),
g = c(" ", " ", " ", " ", " ", " ", " ", " "))
names(zm) <- c("D_{i,j}", "-", "T1", "C1", "C2", "G1", "A1")
```
```{r, results="asis", include=knitr::is_html_output(), echo=FALSE}
zm_ft <- flextable(zm)
zm_ft <- custom_theme(zm_ft)
index_replace(zm_ft)
```
### 2a)
Complete the provided table with the correct initialization step.
#### {.tabset}
##### Hide
##### Hint1: Formulae
\begin{align}
D_{0,0} &= 0\\
\forall i \leq |S1|: D_{i,0} &= \sum_{k=1}^{i} w(S1_i, -) \\
\forall j \leq |S2|: D_{0,j} &= \sum_{k=1}^{j} w(-, S2_j)
\end{align}
##### Solution
```{r, include=FALSE}
zm <- data.frame(a = c( "-", "T", "A", "C", "G", "C", "G", "C"),
b = c(0, 1, 2, 3, 4, 5, 6, 7),
c = c(1, " ", " ", " ", " ", " ", " ", " "),
d = c(2, " ", " ", " ", " ", " ", " ", " "),
e = c(3, " ", " ", " ", " ", " ", " ", " "),
f = c(4, " ", " ", " ", " ", " ", " ", " "),
g = c(5, " ", " ", " ", " ", " ", " ", " "))
names(zm) <- c("D_{i,j}", "-", "T1", "C1", "C2", "G1", "A1")
```
```{r, results="asis", include=knitr::is_html_output(), echo=FALSE}
zm_ft <- flextable(zm)
zm_ft <- custom_theme(zm_ft)
index_replace(zm_ft)
```
#### {-}
### 2b)
#### {.tabset}
##### Hide
Using dynamic programming technique fill in all values in the matrix.
##### Hint1: Formulae
$$
\forall\ i,j >0\ : D_{i,j}=min
\begin{cases}
D_{i-1,j-1} + w(S1_i, S2_j)\\
D_{i,j-1} + w(-, S2_j) \\
D_{i-1,j} + w(S1_i, -)
\end{cases}
$$
##### Solution
```{r, include=FALSE}
zm <- data.frame(a = c( "-", "T", "A", "C", "G", "C", "G", "C"),
b = c(0, 1, 2, 3, 4, 5, 6, 7),
c = c(1, -1, 0, 1, 2, 3, 4, 5),
d = c(2, 0, -1, -1, 0, 1, 2, 3),
e = c(3, 1, 0, -2, -1, -1, 0, 1),
f = c(4, 2, 1, -1, -3, -2, -2, -1),
g = c(5, 3, 1, 0, -2, -3, -2, -2))
names(zm) <- c("D_{i,j}", "-", "T1", "C1", "C2", "G1", "A1")
```
```{r, results="asis", include=knitr::is_html_output(), echo=FALSE}
zm_ft <- flextable(zm)
zm_ft <- custom_theme(zm_ft)
index_replace(zm_ft)
```
#### {-}
### 2c)
Using the matrix from 1B) find the optimal alignment of the given sequences.
#### {.tabset}
##### Hide
##### Solution
```
TACGCGC
| | ||:
T-C-CGA
```
#### {-}
### 2d)
Find an optimal alignment of the given sequences, while assuming that the first G character in each sequence has to be matched/aligned.
#### {.tabset}
##### Hide
##### Hint1:
You can split the alignment at the G and treat it as two separate alignments
##### Solution
```
TACGCGC TACGCGC TACGCGC
|:||| |:|| : |:|| :
TCCGA-- TCCG-A- TCCG--A
```
#### {-}
# Exercise 3 - Hirschberg recursion
Which statements about Needleman-Wunsch and the Hirschberg recursion are True
and which are False.
### 3a)
#### {.tabset}
##### Statements
- [ ] Hirschberg computes global alignment in O(n²) space.
- [ ] Needleman-Wunsch computes global alignment in O(n²) time.
- [ ] The Hirschberg recursion is a space optimized version of the Needleman-Wunsch algorithm.
- [ ] Hirschberg computes global alignment in O(n) time.
##### Solution
- [ ] Hirschberg computes global alignment in O(n²) space.
- [x] Needleman-Wunsch computes global alignment in O(n²) time.
- [x] The Hirschberg recursion is a space optimized version of the Needleman-Wunsch algorithm.
- [ ] Hirschberg computes global alignment in O(n) time.
#### {-}
---------------------------------
# Exercise 4 - Programming assignment
Programming assignments are available via Github Classroom and contain automatic tests.
We recommend doing these assignments since they will help you to further understand this topic.
Access the Github Classroom link: [Programming Assignment: Sheet 03](https://classroom.github.com/a/oacOHCaV).
-------------------------------------------
# Preparing for the Exam
Below you find an interactive tool that allows you to create dynamic programming matrices to practice for the exam.
You can fill in the table and check which values are correct via the Check Matrix button. Correctly filled cells will turn green.
For now this tool only supports the maximization approach.
:::: {#explaining .warning-box }
::: {#note-exp .warning-header}
```{r, include=knitr::is_html_output(), echo=FALSE,}
knitr::include_graphics("figures/warningicon.svg")
```
**Warning**
:::
::: {#note-exp .note-body}
This tool is in beta and might contain bugs.
:::
::::
```{r, echo=FALSE}
htmltools::includeHTML("html/needlemanwunsch.html")
```
```{r, echo=FALSE}
htmltools::tags$script(src = "assets/js/needlemanwunsch.js", type= "text/javascript")
```