Lecture 7: Python 3 - A more careful look at lists and dictionaries

--- tags: BMMB554-23 --- [![](https://imgs.xkcd.com/comics/seven.png)](https://xkcd.com/2483) # Lecture 7: Python 3 - A more careful look at lists and dictionaries ----- Preclass prep: Chapters [5](https://greenteapress.com/thinkpython2/html/thinkpython2009.html) and [7](https://greenteapress.com/thinkpython2/html/thinkpython2011.html) from "Think Python" :::warning This material uses examples from notebooks developed by [Ben Langmead](https://langmead-lab.org/teaching-materials/) and BioPython Cookbook. ::: ## Prep 1. Start [JupyterLab](https://mybinder.org/v2/gh/jupyterlab/jupyterlab-demo/try.jupyter.org?urlpath=lab) 2. Within JupyterLab start a new Python3 notebook 3. Open [this page](http://cs1110.cs.cornell.edu/tutor/#mode=edit) in a new browser tab ## Lists: Dynamic programming in sequence alignment An excellent way to illustrate the utility of lists is to implement dynamic programming algorithm for sequence alignment. Suppose we have two sequences that deliberately have different lengths: $\texttt{G C T A T A C}$ and $\texttt{G C G T A T G C}$ Let's represent them as the following matrix where the first character $\epsilon$ represents an empty string: $$ \begin{array}{ c | c | c | c | c | c | c} & \epsilon & G & C & T & A & T & A & C\\ \hline \epsilon \\ \hline G\\ \hline C\\ \hline G\\ \hline T\\ \hline A\\ \hline T\\ \hline G\\ \hline C \end{array} \\ \textbf{Note}: sequence\ \texttt{X}\ is\ vertical\ and\ sequence\ \texttt{Y}\ is\ horizontal. $$ In this matrix the cells are addressed as shown below. They filled using the following logic: $$ D[i,j] = min\begin{cases} \color{green}{D[i-1,j] + 1} & \\ \color{blue}{D[i,j-1] + 1} & \\ \color{red}{D[i-1,j-1] + \delta(x[i-1],y[j-1])} \end{cases} $$ where $\color{green}{green}$ is *upper* cell, $\color{blue}{blue}$ is *left* cell, and $\color{red}{red}$ is *upper-left* cell: $$ \begin{array}{ c | c | c | c | c | c | c} & \epsilon & G & C & T & A & T & A & C\\ \hline \epsilon & & & & & & & & \\ \hline G & \\ \hline C & & & \color{red}{D[2,2]} & \color{green}{D[2,3]}\\ \hline G & & & \color{blue}{D[3,2]} & D[3,3]\\ \hline T & \\ \hline A & \\ \hline T & \\ \hline G & \\ \hline C & \end{array} \\ \textbf{Note}: sequence\ \texttt{X}\ is\ vertical\ and\ sequence\ \texttt{Y}\ is\ horizontal. $$ Let's initialize the first column and first raw of the matrix. Because the distance between a string and an empty string is equal to the length of the string (e.g., a distance between, say, string $\texttt{TCG}$ and an empty string is 3) this resulting matrix will look like this: $$ \begin{array}{ c | c | c | c | c | c | c} & \epsilon & G & C & T & A & T & A & C\\ \hline \epsilon & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7\\ \hline G & 1\\ \hline C & 2\\ \hline G & 3\\ \hline T & 4\\ \hline A & 5\\ \hline T & 6\\ \hline G & 7\\ \hline C & 8 \end{array} \\ \textbf{Note}: sequence\ \texttt{X}\ is\ vertical\ and\ sequence\ \texttt{Y}\ is\ horizontal. $$ This can be achieved with the following code: ```python= D = np.zeros((len(x)+1, len(y)+1), dtype=int) D[0, 1:] = range(1, len(y)+1) D[1:, 0] = range(1, len(x)+1) ``` Now we can fill the entire matrix by using two nested loops: one iterating over $i$ coordinate (sequence $x$) and the other iterating over $j$ coordinate (sequence $y$): ```python= for i in range(1, len(x)+1): for j in range(1, len(y)+1): delt = 1 if x[i-1] != y[j-1] else 0 D[i, j] = min(D[i-1, j-1]+delt, D[i-1, j]+1, D[i, j-1]+1) ``` Let's start with the cell between $\texttt{G}$ and $\texttt{G}$. Recall that: $$ D[i,j] = min\begin{cases} \color{green}{D[i-1,j] + 1} & \\ \color{blue}{D[i,j-1] + 1} & \\ \color{red}{D[i-1,j-1] + \delta(x[i-1],y[j-1])} \end{cases} $$ where $\delta(x[i-1],y[j-1]) = 0$ if $x[i-1] = y[j-1]$ and $1$ otherwise. And now let's color each of the cells corresponding to each part of the above expression: $$ \begin{array}{ c | c | c | c | c | c | c} & \epsilon & G & C & T & A & T & A & C\\ \hline \epsilon & \color{red}0 & \color{green}1 & 2 & 3 & 4 & 5 & 6 & 7\\ \hline G & \color{blue}1\\ \hline C & 2\\ \hline G & 3\\ \hline T & 4\\ \hline A & 5\\ \hline T & 6\\ \hline G & 7\\ \hline C & 8 \end{array} \\ \textbf{Note}: sequence\ \texttt{X}\ is\ vertical\ and\ sequence\ \texttt{Y}\ is\ horizontal. $$ In this specific case: $$ D[i,j] = min\begin{cases} \color{green}{D[i-1,j] + 1}\ or\ 0+0=0 & \\ \color{blue}{D[i,j-1] + 1}\ or\ 1+1=2 & \\ \color{red}{D[i-1,j-1] + \delta(x[i-1],y[j-1])}\ or\ 1+1=2 \end{cases} $$ The minimum of 0, 2, and 2 will be 0, so we are putting zero into that cell: $$ \begin{array}{ c | c | c | c | c | c | c} & \epsilon & G & C & T & A & T & A & C\\ \hline \epsilon & \color{red}0 & \color{green}1 & 2 & 3 & 4 & 5 & 6 & 7\\ \hline G & \color{blue}1 & \color{red}0\\ \hline C & 2\\ \hline G & 3\\ \hline T & 4\\ \hline A & 5\\ \hline T & 6\\ \hline G & 7\\ \hline C & 8 \end{array} \\ \textbf{Note}: sequence\ \texttt{X}\ is\ vertical\ and\ sequence\ \texttt{Y}\ is\ horizontal. $$ Using this logic we can fill the entire matrix like this: $$ \begin{array}{ c | c | c | c | c | c | c} & \epsilon & G & C & T & A & T & A & C\\ \hline \epsilon & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7\\ \hline G & 1 & 0 & 1 & 2 & 3 & 4 & 5 & 6\\ \hline C & 2 & 1 & 0 & 1 & 2 & 3 & 4 & 5\\ \hline G & 3 & 2 & 1 & 1 & 2 & 3 & 4 & 5\\ \hline T & 4 & 3 & 2 & 1 & 2 & 2 & 3 & 4\\ \hline A & 5 & 4 & 3 & 2 & 1 & 2 & 2 & 3\\ \hline T & 6 & 5 & 4 & 3 & 2 & 1 & 2 & 3\\ \hline G & 7 & 6 & 5 & 4 & 3 & 2 & 2 & 3\\ \hline C & 8 & 7 & 6 & 5 & 4 & 3 & 3 & \color{red}2 \end{array} \\ \textbf{Note}: sequence\ \texttt{X}\ is\ vertical\ and\ sequence\ \texttt{Y}\ is\ horizontal. $$ The lower rightmost cell highlighted in red is special. It contains the value for the edit distance between the two strings. The following Python script implements this idea. You can see that it is essentially instantaneous: ```python= import numpy as np def edDistDp(x, y): """ Calculate edit distance between sequences x and y using matrix dynamic programming. Return matrix and distance. """ D = np.zeros((len(x)+1, len(y)+1), dtype=int) D[0, 1:] = range(1, len(y)+1) D[1:, 0] = range(1, len(x)+1) for i in range(1, len(x)+1): for j in range(1, len(y)+1): delt = 1 if x[i-1] != y[j-1] else 0 D[i, j] = min(D[i-1, j-1]+delt, D[i-1, j]+1, D[i, j-1]+1) return D,D[len(x),len(y)] ``` A graphical representation of the matrix between `GCGTATGCACGC` and `GCTATGCCACGC` looks like this: ![](https://i.imgur.com/veMfPFt.png) This image is generated using [Seaborn](https://seaborn.pydata.org/index.html) package using matrix directly: ```python= sns.heatmap(D,annot=True,cmap="crest") ``` ------ ## Dictionaries: Translating sequences Perhaps the best way to demonstrate the utility of dictionaries is using the translation example. The universal genetic code can be represented as the following dictionary: # Using dictionaries to translare DNA ## Define and use translation table The following dictionary maps codons to corresponding amino acid translations. In this case codon is the *key* and amino acid is the *value*: ```python= table = { 'ATA':'I', 'ATC':'I', 'ATT':'I', 'ATG':'M', 'ACA':'T', 'ACC':'T', 'ACG':'T', 'ACT':'T', 'AAC':'N', 'AAT':'N', 'AAA':'K', 'AAG':'K', 'AGC':'S', 'AGT':'S', 'AGA':'R', 'AGG':'R', 'CTA':'L', 'CTC':'L', 'CTG':'L', 'CTT':'L', 'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCT':'P', 'CAC':'H', 'CAT':'H', 'CAA':'Q', 'CAG':'Q', 'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGT':'R', 'GTA':'V', 'GTC':'V', 'GTG':'V', 'GTT':'V', 'GCA':'A', 'GCC':'A', 'GCG':'A', 'GCT':'A', 'GAC':'D', 'GAT':'D', 'GAA':'E', 'GAG':'E', 'GGA':'G', 'GGC':'G', 'GGG':'G', 'GGT':'G', 'TCA':'S', 'TCC':'S', 'TCG':'S', 'TCT':'S', 'TTC':'F', 'TTT':'F', 'TTA':'L', 'TTG':'L', 'TAC':'Y', 'TAT':'Y', 'TAA':'_', 'TAG':'_', 'TGC':'C', 'TGT':'C', 'TGA':'_', 'TGG':'W', } ``` Let's generate random DNA sequence: ```python= import random seq = "".join([random.choice('atcg') for x in range(100)]) ``` ```python= seq ``` 'agaccgtagcccaagtgcgtttgaatgtggctacttgggaggatttcattgcggtctgtctccgtacttgttattggtcttctttctgcattatgacgca' To translate this sequence we write a code that uses a `for` loop that iterates over the DNA sequence in steps of 3, creating a codon at each iteration. If the codon is less than 3 letters long, the loop is broken. The resulting amino acid is then added to the `translation` string: ```python= translation = "" for i in range(0, len(seq), 3): codon = seq[i:i+3].upper() if len(codon) < 3: break if codon in table: translation += table[codon] else: translation += "X" print("Translation:", translation) ``` Translation: RP_PKCV_MWLLGRISLRSVSVLVIGLLSAL_R Note that the code uses the `upper()` method to ensure the codon is in uppercase, since the table dictionary is case sensitive. Additionally, the code checks if the codon is in the table dictionary, and if not, it adds the letter "X" to the translation. This is a common way to represent unknown or stop codons in a protein sequence. ```python= translation ``` 'RP_PKCV_MWLLGRISLRSVSVLVIGLLSAL_R' Now we define a function that would perform translation so that we can reuse it later: ```python= def translate(seq): translation = '' table = { 'ATA':'I', 'ATC':'I', 'ATT':'I', 'ATG':'M', 'ACA':'T', 'ACC':'T', 'ACG':'T', 'ACT':'T', 'AAC':'N', 'AAT':'N', 'AAA':'K', 'AAG':'K', 'AGC':'S', 'AGT':'S', 'AGA':'R', 'AGG':'R', 'CTA':'L', 'CTC':'L', 'CTG':'L', 'CTT':'L', 'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCT':'P', 'CAC':'H', 'CAT':'H', 'CAA':'Q', 'CAG':'Q', 'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGT':'R', 'GTA':'V', 'GTC':'V', 'GTG':'V', 'GTT':'V', 'GCA':'A', 'GCC':'A', 'GCG':'A', 'GCT':'A', 'GAC':'D', 'GAT':'D', 'GAA':'E', 'GAG':'E', 'GGA':'G', 'GGC':'G', 'GGG':'G', 'GGT':'G', 'TCA':'S', 'TCC':'S', 'TCG':'S', 'TCT':'S', 'TTC':'F', 'TTT':'F', 'TTA':'L', 'TTG':'L', 'TAC':'Y', 'TAT':'Y', 'TAA':'_', 'TAG':'_', 'TGC':'C', 'TGT':'C', 'TGA':'_', 'TGG':'W', } for i in range(0, len(seq), 3): codon = seq[i:i+3].upper() if len(codon) < 3: break if codon in table: translation += table[codon] else: translation += "X" return(translation) ``` ```python= translate(seq) ``` 'RP_PKCV_MWLLGRISLRSVSVLVIGLLSAL_R' We can further modify the function by adding a `phase` parameter that would allow translating in any of the three phases: ```python= def translate_phase(seq,phase): translation = '' table = { 'ATA':'I', 'ATC':'I', 'ATT':'I', 'ATG':'M', 'ACA':'T', 'ACC':'T', 'ACG':'T', 'ACT':'T', 'AAC':'N', 'AAT':'N', 'AAA':'K', 'AAG':'K', 'AGC':'S', 'AGT':'S', 'AGA':'R', 'AGG':'R', 'CTA':'L', 'CTC':'L', 'CTG':'L', 'CTT':'L', 'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCT':'P', 'CAC':'H', 'CAT':'H', 'CAA':'Q', 'CAG':'Q', 'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGT':'R', 'GTA':'V', 'GTC':'V', 'GTG':'V', 'GTT':'V', 'GCA':'A', 'GCC':'A', 'GCG':'A', 'GCT':'A', 'GAC':'D', 'GAT':'D', 'GAA':'E', 'GAG':'E', 'GGA':'G', 'GGC':'G', 'GGG':'G', 'GGT':'G', 'TCA':'S', 'TCC':'S', 'TCG':'S', 'TCT':'S', 'TTC':'F', 'TTT':'F', 'TTA':'L', 'TTG':'L', 'TAC':'Y', 'TAT':'Y', 'TAA':'_', 'TAG':'_', 'TGC':'C', 'TGT':'C', 'TGA':'_', 'TGG':'W', } assert phase >= 0 and phase <= 3, "Phase parameter can only be set to 0, 1, or 2! You specified {}".format(phase) for i in range(phase, len(seq), 3): codon = seq[i:i+3].upper() if len(codon) < 3: break if codon in table: translation += table[codon] else: translation += "X" return(translation) ``` ```python= translate_phase(seq,2) ``` 'TVAQVRLNVATWEDFIAVCLRTCYWSSFCIMT' To translate in all six reading franes (three of the "+" strand and three of the "-" strand) we need to be able to create a reverse complement of the sequence. Let's wrire a simple function for that. The cell below implements a function `revcomp` that takes a DNA sequence as input and returns its reverse complement. It works by first reversing the sequence using the slice notation `seq[::-1]`, which returns the sequence in reverse order. Then, the `translate` method is used with the `str.maketrans` function to replace each occurrence of 'a', 't', 'g', 'c', 'A', 'T', 'G', and 'C' in the reversed sequence with 't', 'a', 'c', 'g', 'T', 'A', 'C', and 'G', respectively: ```python= import string def revcomp(seq): return seq[::-1].translate(str.maketrans('atgcATCG','tagcTACG')) ``` Now let's use this function to create translation in all six reading frames. The cell below uses a `for` loop that iterates over the range `(0, 3)`, representing the different phases (or starting positions) of the translation. At each iteration, the `translate_phase` function is called with the DNA sequence and the current phase, and the resulting protein sequence is appended to the `translations` list along with the `phase` and the `strand` orientation (+ or -): ```python= translations = [] for i in range(0,3): translations.append((translate_phase(seq,i),str(i),'+')) translations.append((translate_phase(revcomp(seq),i),str(i),'-')) ``` ```python= translations ``` [('RP_PKCV_MWLLGRISLRSVSVLVIGLLSAL_R', '0', '+'), ('SLIIDKQQE_ELATDRRIKWWELRRLKASSRSL', '0', '-'), ('DRSPSAFECGYLGGFHCGLSPYLLLVFFLHYDA', '1', '+'), ('R__STNNRNKN_PQTGESNGGNYGD_KRVPVAC', '1', '-'), ('TVAQVRLNVATWEDFIAVCLRTCYWSSFCIMT', '2', '+'), ('ADNRQTTGIRTSHRQANQMVGTTEIESEFP_P', '2', '-')] ## Finding coordinates of continuous translations The translation we've generated above contain stops (e.g., `_` symbols). The actual biologically relevant protein sequences are between stops. We now need to split translation strings into meaningful peptide sequences and combite their coordinates. Let's begin by splitting a string on `_` and computing start and end positions of each peptide: ```python= string = "aadsds_dsds_dsds" split_indices = [] for i,char in enumerate(string): if char == "_": split_indices.append(i) print(split_indices) ``` [6, 11] The code above generates a list of split indices for a string. The list contains the indices of the characters in the string that match a specified character (in this case, the underscore `_` character). The `enumerate` function is used to loop over the characters in the string, and at each iteration, the current index and character are stored in the variables `i` and `char`, respectively. If the current character matches the specified character, the index `i` is appended to the `split_indices` list. After the loop, the `split_indices` list is printed to the console. For the input string `"aadsds_dsds_dsds"`, the output would be `[6, 11]`, indicating that the dashes are located at indices 6 and 11. But we actually need coordinates of peptides bound by `_` characters. To get to this let's first modify `split_indices` by adding beginning and end: ```python= string = "aadsds_dsds_dsds" split_indices = [] for i,char in enumerate(string): if char == "_": split_indices.append(i) split_indices.insert(0,-1) split_indices.append(len(string)) print(split_indices) ``` [-1, 6, 11, 16] Now, let's convert these to ranges and also stick the peptide sequence in: ```python= string = "aadsds_dsds_dsds" split_indices = [] for i,char in enumerate(string): if char == "_": split_indices.append(i) split_indices.insert(0,-1) split_indices.append(len(string)) orfs = string.split('_') parts = [] for i in range(len(split_indices)-1): parts.append((orfs[i],split_indices[i]+1, split_indices[i+1])) print(parts) ``` [('aadsds', 0, 6), ('dsds', 7, 11), ('dsds', 12, 16)] Now let's convert this to function: ```python= def extract_coords(translation): split_indices = [] for i,char in enumerate(translation): if char == "_": split_indices.append(i) split_indices.insert(0,-1) split_indices.append(len(translation)) parts = [] for i in range(len(split_indices)-1): parts.append((translation.split('_')[i], split_indices[i]+1, split_indices[i+1])) return(parts) ``` ```python= extract_coords(string) ``` [('aadsds', 0, 6), ('dsds', 7, 11), ('dsds', 12, 16)] And specify right parameters to make it truly generic: ```python= def extract_coords_with_annotation(separator,translation,phase,strand): split_indices = [] for i,char in enumerate(translation): if char == separator: split_indices.append(i) split_indices.insert(0,-1) split_indices.append(len(translation)) parts = [] for i in range(len(split_indices)-1): parts.append((translation.split(separator)[i], phase, strand, split_indices[i]+1, split_indices[i+1])) return(parts) ``` ```python= extract_coords_with_annotation('_',string,'0','+') ``` [('aadsds', '0', '+', 0, 6), ('dsds', '0', '+', 7, 11), ('dsds', '0', '+', 12, 16)] ## Analyzing all translations of a given sequence We begin by parsing the `translations` list we defined above: ```python= all_translations = [] for item in translations: all_translations.append(extract_coords_with_annotation('_',item[0],item[1],item[2])) ``` ```python= all_translations ``` [[('RP', '0', '+', 0, 2), ('PKCV', '0', '+', 3, 7), ('MWLLGRISLRSVSVLVIGLLSAL', '0', '+', 8, 31), ('R', '0', '+', 32, 33)], [('SLIIDKQQE', '0', '-', 0, 9), ('ELATDRRIKWWELRRLKASSRSL', '0', '-', 10, 33)], [('DRSPSAFECGYLGGFHCGLSPYLLLVFFLHYDA', '1', '+', 0, 33)], [('R', '1', '-', 0, 1), ('', '1', '-', 2, 2), ('STNNRNKN', '1', '-', 3, 11), ('PQTGESNGGNYGD', '1', '-', 12, 25), ('KRVPVAC', '1', '-', 26, 33)], [('TVAQVRLNVATWEDFIAVCLRTCYWSSFCIMT', '2', '+', 0, 32)], [('ADNRQTTGIRTSHRQANQMVGTTEIESEFP', '2', '-', 0, 30), ('P', '2', '-', 31, 32)]] Now the problem with this list is that it is nested. However, we need to make it flat: ```python= flat_list = [] for sublist in all_translations: for item in sublist: flat_list.append(item) ``` ```python= flat_list ``` [('RP', '0', '+', 0, 2), ('PKCV', '0', '+', 3, 7), ('MWLLGRISLRSVSVLVIGLLSAL', '0', '+', 8, 31), ('R', '0', '+', 32, 33), ('SLIIDKQQE', '0', '-', 0, 9), ('ELATDRRIKWWELRRLKASSRSL', '0', '-', 10, 33), ('DRSPSAFECGYLGGFHCGLSPYLLLVFFLHYDA', '1', '+', 0, 33), ('R', '1', '-', 0, 1), ('', '1', '-', 2, 2), ('STNNRNKN', '1', '-', 3, 11), ('PQTGESNGGNYGD', '1', '-', 12, 25), ('KRVPVAC', '1', '-', 26, 33), ('TVAQVRLNVATWEDFIAVCLRTCYWSSFCIMT', '2', '+', 0, 32), ('ADNRQTTGIRTSHRQANQMVGTTEIESEFP', '2', '-', 0, 30), ('P', '2', '-', 31, 32)] Now we can load the list into `Pandas` and plot away: ```python= import pandas as pd df = pd.DataFrame(flat_list,columns=['aa','frame','phase','start','end']) ``` ```python= df ``` <div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>aa</th> <th>frame</th> <th>phase</th> <th>start</th> <th>end</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>RP</td> <td>0</td> <td>+</td> <td>0</td> <td>2</td> </tr> <tr> <th>1</th> <td>PKCV</td> <td>0</td> <td>+</td> <td>3</td> <td>7</td> </tr> <tr> <th>2</th> <td>MWLLGRISLRSVSVLVIGLLSAL</td> <td>0</td> <td>+</td> <td>8</td> <td>31</td> </tr> <tr> <th>3</th> <td>R</td> <td>0</td> <td>+</td> <td>32</td> <td>33</td> </tr> <tr> <th>4</th> <td>SLIIDKQQE</td> <td>0</td> <td>-</td> <td>0</td> <td>9</td> </tr> <tr> <th>5</th> <td>ELATDRRIKWWELRRLKASSRSL</td> <td>0</td> <td>-</td> <td>10</td> <td>33</td> </tr> <tr> <th>6</th> <td>DRSPSAFECGYLGGFHCGLSPYLLLVFFLHYDA</td> <td>1</td> <td>+</td> <td>0</td> <td>33</td> </tr> <tr> <th>7</th> <td>R</td> <td>1</td> <td>-</td> <td>0</td> <td>1</td> </tr> <tr> <th>8</th> <td></td> <td>1</td> <td>-</td> <td>2</td> <td>2</td> </tr> <tr> <th>9</th> <td>STNNRNKN</td> <td>1</td> <td>-</td> <td>3</td> <td>11</td> </tr> <tr> <th>10</th> <td>PQTGESNGGNYGD</td> <td>1</td> <td>-</td> <td>12</td> <td>25</td> </tr> <tr> <th>11</th> <td>KRVPVAC</td> <td>1</td> <td>-</td> <td>26</td> <td>33</td> </tr> <tr> <th>12</th> <td>TVAQVRLNVATWEDFIAVCLRTCYWSSFCIMT</td> <td>2</td> <td>+</td> <td>0</td> <td>32</td> </tr> <tr> <th>13</th> <td>ADNRQTTGIRTSHRQANQMVGTTEIESEFP</td> <td>2</td> <td>-</td> <td>0</td> <td>30</td> </tr> <tr> <th>14</th> <td>P</td> <td>2</td> <td>-</td> <td>31</td> <td>32</td> </tr> </tbody> </table> </div> ```python= import altair as alt plus = alt.Chart(df[df['phase']=='+']).mark_rect().encode( x = alt.X('start:Q'), x2 = alt.X2('end:Q'), y = alt.Y('frame:N'), color='frame', tooltip='aa:N' ).properties( width=600, height=100) minus = alt.Chart(df[df['phase']=='-']).mark_rect().encode( x = alt.X('start:Q',sort=alt.EncodingSortField('start:Q', order='descending')), x2 = alt.X2('end:Q'), y = alt.Y('frame:N'), color='frame', tooltip='aa:N' ).properties( width=600, height=100) plus & minus ``` ```vega { "config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "vconcat": [ { "data": {"name": "data-469e9fe221acf63f14944504f41743eb"}, "mark": "rect", "encoding": { "color": {"field": "frame", "type": "nominal"}, "tooltip": {"field": "aa", "type": "nominal"}, "x": {"field": "start", "type": "quantitative"}, "x2": {"field": "end"}, "y": {"field": "frame", "type": "nominal"} }, "height": 100, "width": 600 }, { "data": {"name": "data-80bd80655a31c6aeabbc25549c6683a8"}, "mark": "rect", "encoding": { "color": {"field": "frame", "type": "nominal"}, "tooltip": {"field": "aa", "type": "nominal"}, "x": { "field": "start", "sort": {"field": "start:Q", "order": "descending"}, "type": "quantitative" }, "x2": {"field": "end"}, "y": {"field": "frame", "type": "nominal"} }, "height": 100, "width": 600 } ], "$schema": "https://vega.github.io/schema/vega-lite/v4.17.0.json", "datasets": { "data-469e9fe221acf63f14944504f41743eb": [ {"aa": "RP", "frame": "0", "phase": "+", "start": 0, "end": 2}, {"aa": "PKCV", "frame": "0", "phase": "+", "start": 3, "end": 7}, { "aa": "MWLLGRISLRSVSVLVIGLLSAL", "frame": "0", "phase": "+", "start": 8, "end": 31 }, {"aa": "R", "frame": "0", "phase": "+", "start": 32, "end": 33}, { "aa": "DRSPSAFECGYLGGFHCGLSPYLLLVFFLHYDA", "frame": "1", "phase": "+", "start": 0, "end": 33 }, { "aa": "TVAQVRLNVATWEDFIAVCLRTCYWSSFCIMT", "frame": "2", "phase": "+", "start": 0, "end": 32 } ], "data-80bd80655a31c6aeabbc25549c6683a8": [ {"aa": "SLIIDKQQE", "frame": "0", "phase": "-", "start": 0, "end": 9}, { "aa": "ELATDRRIKWWELRRLKASSRSL", "frame": "0", "phase": "-", "start": 10, "end": 33 }, {"aa": "R", "frame": "1", "phase": "-", "start": 0, "end": 1}, {"aa": "", "frame": "1", "phase": "-", "start": 2, "end": 2}, {"aa": "STNNRNKN", "frame": "1", "phase": "-", "start": 3, "end": 11}, { "aa": "PQTGESNGGNYGD", "frame": "1", "phase": "-", "start": 12, "end": 25 }, {"aa": "KRVPVAC", "frame": "1", "phase": "-", "start": 26, "end": 33}, { "aa": "ADNRQTTGIRTSHRQANQMVGTTEIESEFP", "frame": "2", "phase": "-", "start": 0, "end": 30 }, {"aa": "P", "frame": "2", "phase": "-", "start": 31, "end": 32} ] } } ```

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