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Rendu ROMAIN DUBOIS #25

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216 changes: 188 additions & 28 deletions BINF2025_TP3.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,7 @@
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNSXnqaXAUgZK9rmJ1TWbGo"
"provenance": []
},
"kernelspec": {
"name": "python3",
Expand Down Expand Up @@ -74,7 +73,12 @@
"cell_type": "markdown",
"source": [
"```markdown\n",
"Votre réponse ici\n",
"1 + 1 + 1 - 1 - 1 - 1 + 1 + 1 + 1 + 1 = 4\n",
"y(2) = 0.5 * 2 + 0.5 = 1.5\n",
"y(3) = 0.5 * 3 + 0.5 = 2\n",
"\n",
"4 - (1,5 + 2) = 0,5\n",
"\n",
"```"
],
"metadata": {
Expand All @@ -93,8 +97,24 @@
{
"cell_type": "markdown",
"source": [
"\n",
"| | 0 | A | C | T | G | \n",
"| :---: | :---: | :---: | :---: | :---: | :---: |\n",
"| **0** | 0 | 1 | 2 | 3 | 4 |\n",
"| **A** | 1 | 0 | 1 | 2 | 3 |\n",
"| **T** | 2 | 1 | 1 | 1 | 2 |\n",
"| **G** | 3 | 2 | 2 | 2 | 1 |\n",
"\n",
"| | 0 | A | C | T | G |\n",
"| :---: | :---: | :---: | :---: | :---: | :---: |\n",
"| **0** | 0 | _ | _ | _ | _ |\n",
"| **A** | l | \\ | _ | _ | _ |\n",
"| **T** | l | l | \\ | \\ | _ |\n",
"| **G** | l | l | \\ | \\ | \\ |\n",
"\n",
"```markdown\n",
"Votre réponse ici\n",
"x = A_TG\n",
"y = ACTG\n",
"```"
],
"metadata": {
Expand Down Expand Up @@ -124,8 +144,23 @@
{
"cell_type": "markdown",
"source": [
"| |0 | A | T | G |A | C |\n",
"| :---: |:---:| :---: | :---: | :---: | :---: | :---: |\n",
"| **0** |0 | -0.5 | -1 | -1.5 | -2 | -2.5 |\n",
"| **T** |-0.5 | -0.5 | 0.5 | 0 | -0.5 | -1 |\n",
"| **A** |-1 | 0.5 | 0 | 0 | 1 | 0.5 |\n",
"| **T** |-1.5 | 0 | -1.5 | 1 | 0.5 | 0.5 |\n",
"\n",
"| |0 | A | T | G |A | C |\n",
"| :---: |:---:| :---: | :---: | :---: | :---: | :---: |\n",
"| **0** |0 | _ | _ | _ | _ | _ |\n",
"| **T** |l | \\ | \\ | _ | _ | _ |\n",
"| **A** |l | \\ | _ | \\ | \\ | _ |\n",
"| **T** |l | l | \\ | _ | _ | \\ |\n",
"\n",
"```markdown\n",
"Votre réponse ici\n",
"x = _T_AT\n",
"y = ATGAC\n",
"```"
],
"metadata": {
Expand Down Expand Up @@ -177,13 +212,39 @@
{
"cell_type": "code",
"source": [
"#Votre code ici"
"def levenshtein(x: str, y: str) -> int:\n",
" if not x:\n",
" return len(y)\n",
" if not y:\n",
" return len(x)\n",
"\n",
" cost = 0 if x[-1] == y[-1] else 1\n",
"\n",
" return min(\n",
" levenshtein(x[:-1], y) + 1,\n",
" levenshtein(x, y[:-1]) + 1,\n",
" levenshtein(x[:-1], y[:-1]) + cost\n",
" )\n",
"\n",
"print(levenshtein(\"CCGT\", \"CGTCA\"))\n"
],
"metadata": {
"id": "FJR69IEQ4aHv"
"id": "FJR69IEQ4aHv",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a7b912d1-5035-4f97-ed0f-a06975e619df"
},
"execution_count": null,
"outputs": []
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"3\n"
]
}
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -226,13 +287,78 @@
{
"cell_type": "code",
"source": [
"#Votre code ici"
"import numpy as np\n",
"\n",
"def sw_fwd(x: str, y: str, cmap: dict, sigma: np.array, gap_params: tuple) -> tuple:\n",
" DIAG, UP, LEFT = 1, 2, 3\n",
" m, n = len(x), len(y)\n",
" go, ge = gap_params\n",
"\n",
" S = np.zeros((m + 1, n + 1))\n",
" B = np.zeros((m + 1, n + 1), dtype=int)\n",
"\n",
" for i in range(1, m + 1):\n",
" for j in range(1, n + 1):\n",
" match_score = S[i-1, j-1] + sigma[cmap[x[i-1]], cmap[y[j-1]]]\n",
"\n",
" max_del, best_k_del = -np.inf, 0\n",
" for k in range(1, i+1):\n",
" current = S[i-k, j] - (go + ge*(k-1))\n",
" if current > max_del:\n",
" max_del = current\n",
" best_k_del = k\n",
"\n",
" max_ins, best_k_ins = -np.inf, 0\n",
" for k in range(1, j+1):\n",
" current = S[i, j-k] - (go + ge*(k-1))\n",
" if current > max_ins:\n",
" max_ins = current\n",
" best_k_ins = k\n",
"\n",
" options = [(0, 0), (match_score, DIAG), (max_del, UP), (max_ins, LEFT)]\n",
" S[i,j], B[i,j] = max(options, key=lambda x: x[0])\n",
"\n",
" return S, B\n",
"\n",
"\n",
"dna_cmap = {'A': 0, 'C': 1, 'G': 2, 'T': 3}\n",
"sigma_matrix = np.array([[2, -1, -1, -1],\n",
" [-1, 2, -1, -1],\n",
" [-1, -1, 2, -1],\n",
" [-1, -1, -1, 2]])\n",
"\n",
"go, ge = -2, -1\n",
"x_seq = \"AGT\"\n",
"y_seq = \"GTT\"\n",
"\n",
"S, B = sw_fwd(x_seq, y_seq, dna_cmap, sigma_matrix, (go, ge))\n",
"print(S)\n",
"print(B)\n"
],
"metadata": {
"id": "njn3JB0b-WHj"
"id": "njn3JB0b-WHj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e8c2609b-8e44-43ff-de17-8bd19dc20a5f"
},
"execution_count": null,
"outputs": []
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[ 0. 0. 0. 0.]\n",
" [ 0. 2. 4. 6.]\n",
" [ 0. 4. 6. 8.]\n",
" [ 0. 6. 8. 10.]]\n",
"[[0 0 0 0]\n",
" [0 2 3 3]\n",
" [0 2 2 2]\n",
" [0 2 2 2]]\n"
]
}
]
},
{
"cell_type": "markdown",
Expand All @@ -250,12 +376,47 @@
{
"cell_type": "code",
"source": [
"#Votre code ici"
"import numpy as np\n",
"\n",
"def sw_bwd(x: str, y: str, S: np.ndarray, B: np.ndarray) -> (str, str, float):\n",
" # Initialiser les alignements et le score\n",
" align_x = []\n",
" align_y = []\n",
"\n",
" # Trouver la position du score maximum dans la matrice S (c'est ici que l'alignement commence)\n",
" i, j = np.unravel_index(np.argmax(S), S.shape)\n",
" score = S[i, j]\n",
"\n",
" # Effectuer le traceback\n",
" while S[i, j] > 0:\n",
" if B[i, j] == 1: # Diagonale\n",
" align_x.append(x[i-1])\n",
" align_y.append(y[j-1])\n",
" i -= 1\n",
" j -= 1\n",
" elif B[i, j] == 2: # Haut\n",
" align_x.append(x[i-1])\n",
" align_y.append('-')\n",
" i -= 1\n",
" elif B[i, j] == 3: # Gauche\n",
" align_x.append('-')\n",
" align_y.append(y[j-1])\n",
" j -= 1\n",
"\n",
" # Inverser les alignements car on a commencé à partir de la fin\n",
" align_x.reverse()\n",
" align_y.reverse()\n",
"\n",
" # Convertir les listes en chaînes\n",
" align_x_str = ''.join(align_x)\n",
" align_y_str = ''.join(align_y)\n",
"\n",
" return align_x_str, align_y_str, score\n"
],
"metadata": {
"id": "ij9JDpBm_UZ7"
},
"execution_count": null,
"execution_count": 7,
"outputs": []
},
{
Expand All @@ -281,7 +442,7 @@
"metadata": {
"id": "JUtYRFTBAwwZ"
},
"execution_count": null,
"execution_count": 2,
"outputs": []
},
{
Expand All @@ -296,17 +457,18 @@
{
"cell_type": "code",
"source": [
"from IPython.display import HTML\n",
"HTML(\"<table align='left' style='font-family:Courier New'><tr><th>x:</th><th>TCG</th></tr><tr><th>y:</th><th>TAG</th></tr></table>\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 60
"height": 62
},
"id": "joHNwJ9AIf6F",
"outputId": "a9206810-a083-4d86-8b14-38183f1dd80c"
"outputId": "d5d9b15e-5e70-4d64-958a-3587ddcd8510"
},
"execution_count": null,
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
Expand All @@ -319,7 +481,7 @@
]
},
"metadata": {},
"execution_count": 18
"execution_count": 5
}
]
},
Expand All @@ -341,12 +503,12 @@
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 80
"height": 82
},
"id": "HUELvWKMFtIO",
"outputId": "976bab6f-f1fc-4c5a-c69c-8de02fc838d0"
"outputId": "eab87ab3-9db2-46b3-8b8f-7c17f3466ec7"
},
"execution_count": null,
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
Expand All @@ -359,7 +521,7 @@
]
},
"metadata": {},
"execution_count": 15
"execution_count": 4
}
]
},
Expand Down Expand Up @@ -387,7 +549,7 @@
"cell_type": "markdown",
"source": [
"```markdown\n",
"Votre réponse ici\n",
"1/4\n",
"```"
],
"metadata": {
Expand Down Expand Up @@ -428,13 +590,11 @@
},
{
"cell_type": "code",
"source": [
"#Votre code ici"
],
"source": [],
"metadata": {
"id": "UX0afNaqOVZ2"
},
"execution_count": null,
"execution_count": 8,
"outputs": []
},
{
Expand Down