{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPbEAG/LQY+paWTtjxD746Y"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# 🧬 Visor Rápido y Control de Calidad de Archivos FASTQ Comprimidos / Fast FASTQ.GZ Viewer & QC\n","\n","### 🇪🇸 Instrucciones de Uso (Español)\n","Este cuaderno de Google Colab permite inspeccionar el formato y contenido de archivos de secuenciación `.fastq.gz` de forma ultraeficiente, leyendo directamente el archivo comprimido sin saturar la memoria RAM.\n","\n","**Pasos para ejecutar el análisis:**\n","1. **Instalar dependencias:** Ejecuta la primera celda de código (`!pip install biopython`) para preparar el entorno.\n","2. **Subir el archivo:** Abre la barra lateral izquierda (icono de la carpeta 📂) y arrastra tu archivo `.fastq.gz`. Espera a que la barra de carga circular de abajo a la izquierda se complete.\n","3. **Ejecutar las celdas:** Ejecuta las celdas de análisis de texto y control de calidad visual. El script detectará automáticamente el archivo.\n","\n","---\n","\n","### 🇬🇧 User Guide (English)\n","This Google Colab notebook allows you to inspect the format and content of `.fastq.gz` sequencing files ultra-efficiently. It reads directly from the compressed file without overloading the RAM memory.\n","\n","**Steps to run the analysis:**\n","1. **Install dependencies:** Run the first code cell (`!pip install biopython`) to set up the environment.\n","2. **Upload your file:** Open the left sidebar (folder icon 📂) and drag-and-drop your `.fastq.gz` file. Wait until the circular upload progress bar at the bottom-left is fully completed.\n","3. **Run the cells:** Execute both the text viewer and the visual quality control cells. The script will automatically detect your file."],"metadata":{"id":"8YpULvP7auUa"}},{"cell_type":"code","source":["# Instalar Biopython en el servidor de Google Colab\n","!pip install biopython"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"86j3alnGaxwj","executionInfo":{"status":"ok","timestamp":1782468867339,"user_tz":-120,"elapsed":4472,"user":{"displayName":"Madrid Macrogen","userId":"12903224377867150428"}},"outputId":"dc11279f-64f0-4415-f6fd-2e799bc48d0a"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: biopython in /usr/local/lib/python3.12/dist-packages (1.87)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from biopython) (2.0.2)\n"]}]},{"cell_type":"markdown","source":["### 🇪🇸 1. Visor de Estructura de Lecturas (Texto plano)\n","Esta celda abrirá el archivo comprimido y extraerá las primeras lecturas de forma limpia para revisar las cabeceras (IDs) de Illumina, las secuencias y la conversión automática a scores Phred numéricos.\n","\n","---\n","\n","### 🇬🇧 1. Read Structure Viewer (Plain Text)\n","This cell will open the compressed file and extract the first few reads cleanly to review Illumina headers (IDs), sequences, and the automatic conversion to numerical Phred scores."],"metadata":{"id":"RpAaoHysa0FA"}},{"cell_type":"code","source":["from Bio import SeqIO\n","import gzip\n","import os\n","\n","# Buscamos cualquier archivo que termine en .fastq.gz que hayas subido\n","archivos_fastq = [f for f in os.listdir(\"/content\") if f.endswith(\".fastq.gz\")]\n","\n","if not archivos_fastq:\n","    print(\"❌ ERROR: No he encontrado ningún archivo .fastq.gz en la barra lateral.\")\n","    print(\"Por favor, asegúrate de haberlo arrastrado correctamente.\")\n","else:\n","    archivo_detectado = os.path.join(\"/content\", archivos_fastq[0])\n","    print(f\"✅ Archivo detectado con éxito: {archivo_detectado}\\n\")\n","\n","    # Configuración: Cuántas lecturas espiar\n","    NUM_LECTURAS_A_MOSTRAR = 3\n","\n","    with gzip.open(archivo_detectado, \"rt\") as archivo_descomprimido:\n","        lector_fastq = SeqIO.parse(archivo_descomprimido, \"fastq\")\n","\n","        for contador, lectura in enumerate(lector_fastq):\n","            if contador >= NUM_LECTURAS_A_MOSTRAR:\n","                break\n","\n","            print(f\"=== LECTURA NÚMERO {contador + 1} ===\")\n","            print(f\"ID (Cabecera): @{lectura.description}\")\n","            print(f\"Secuencia: {lectura.seq}\")\n","            print(f\"Longitud: {len(lectura.seq)} bp\")\n","\n","            calidades_phred = lectura.letter_annotations[\"phred_quality\"]\n","            print(f\"Calidad (Primeros 15 Phred scores): {calidades_phred[:15]}\")\n","            print(\"-\" * 50 + \"\\n\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nn6irLrSa2y3","executionInfo":{"status":"ok","timestamp":1782468867493,"user_tz":-120,"elapsed":132,"user":{"displayName":"Madrid Macrogen","userId":"12903224377867150428"}},"outputId":"81b19403-5ec9-49a7-8855-747fd12112ef"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Archivo detectado con éxito: /content/TW2_NA12878_1.fastq.gz\n","\n","=== LECTURA NÚMERO 1 ===\n","ID (Cabecera): @LH00129:23:223NNNLT3:5:1143:13186:24392 1:N:0:ACACTGGCTA+TCCAAGCAGG\n","Secuencia: TTACATATTCAGAGAATACAGCCCAGTAGAGTTTATTGCCACCAAAGGTACGCCGCATGAAAAAGGCACCCGACATTCGTAGCAGCTCACCAACCATTTTCATTCCCAGGAAGTCTAAAAAAGAGGGAGATCGGAAGAGCACACGTCTGAA\n","Longitud: 151 bp\n","Calidad (Primeros 15 Phred scores): [37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37]\n","--------------------------------------------------\n","\n","=== LECTURA NÚMERO 2 ===\n","ID (Cabecera): @LH00129:23:223NNNLT3:4:1101:32557:1048 1:N:0:ACACTGGCTA+TCCAAGCAGG\n","Secuencia: ANATTATTGTGCATTGAAGGGTTGTGGTCTCCTGAACAGAAGTCTGTCTCCAAACACAAATCAAGCAGCAAGATCATCTGCTGCAGGTAAGTGCAGCTCTGACGATAAGATTTATGTTCCTTCATCTTCCACACAGACTCTCAAAATCCAA\n","Longitud: 151 bp\n","Calidad (Primeros 15 Phred scores): [37, 2, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37]\n","--------------------------------------------------\n","\n","=== LECTURA NÚMERO 3 ===\n","ID (Cabecera): @LH00129:23:223NNNLT3:5:2243:39658:22454 1:N:0:ACACTGGCTA+TCCAAGCAGG\n","Secuencia: AAATGCTCTGAGGTGCTGTCGGGTTGGGTGTGGGGGCTTTCCCGCAGGCAGAAGGCTGTCCAGAACAACCCGTACCCGTCTGCCTTCCTGCAGGGAGAACAGGACACCGTCGGCGTTTTGGACCAGGTCTCTCCCCCGCCGCCTCGTCCTT\n","Longitud: 151 bp\n","Calidad (Primeros 15 Phred scores): [37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37]\n","--------------------------------------------------\n","\n"]}]},{"cell_type":"markdown","source":["### 🇪🇸 2. Control de Calidad Visual (Submuestreo de 10k lecturas)\n","Esta sección analiza las primeras 10,000 lecturas del archivo para generar métricas de control de calidad visual instantáneas:\n","* **Gráfico de Calidad Phred:** Distribución de calidad media. Lo ideal es que la mayoría esté a la derecha de la línea roja (Q30), lo que indica un 99.9% de exactitud por base.\n","* **Gráfico de Contenido GC:** Porcentaje de Guaninas y Citosinas. Suele mostrar una campana de Gauss centrada entre el 40% y 50% según la especie.\n","\n","---\n","\n","### 🇬🇧 2. Visual Quality Control (10k Reads Subsampling)\n","This section analyzes the first 10,000 reads of the file to generate instant visual quality control metrics:\n","* **Phred Score Plot:** Average quality distribution. Ideally, most reads should be to the right of the red dashed line (Q30), indicating 99.9% base call accuracy.\n","* **GC Content Plot:** Percentage of Guanines and Cytosines. It typically displays a Gaussian bell curve centered between 40% and 50% depending on the species."],"metadata":{"id":"JDa5C2xSa5EB"}},{"cell_type":"code","source":["from Bio import SeqIO\n","from Bio.SeqUtils import gc_fraction\n","import gzip\n","import os\n","import matplotlib.pyplot as plt\n","\n","archivos_fastq = [f for f in os.listdir(\"/content\") if f.endswith(\".fastq.gz\")]\n","\n","if not archivos_fastq:\n","    print(\"❌ No se encontró el archivo .fastq.gz para el análisis visual.\")\n","else:\n","    archivo_detectado = os.path.join(\"/content\", archivos_fastq[0])\n","    print(\"📊 Analizando el archivo para generar gráficos... Por favor, espera.\")\n","\n","    lista_calidades_medias = []\n","    lista_porcentajes_gc = []\n","    MAX_LECTURAS_GRAFICO = 10000\n","\n","    with gzip.open(archivo_detectado, \"rt\") as handle:\n","        for contador, lectura in enumerate(SeqIO.parse(handle, \"fastq\")):\n","            if contador >= MAX_LECTURAS_GRAFICO:\n","                break\n","\n","            calidades = lectura.letter_annotations[\"phred_quality\"]\n","            lista_calidades_medias.append(sum(calidades) / len(calidades))\n","            lista_porcentajes_gc.append(gc_fraction(lectura.seq) * 100)\n","\n","    # Generar los gráficos con Matplotlib\n","    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n","\n","    # Histograma Phred\n","    ax1.hist(lista_calidades_medias, bins=30, color='#2ca02c', alpha=0.7, edgecolor='black')\n","    ax1.axvline(30, color='red', linestyle='dashed', linewidth=2, label='Umbral Q30 (Excelente)')\n","    ax1.set_title('Distribución de la Calidad Media (Phred Score)', fontsize=12, fontweight='bold')\n","    ax1.set_xlabel('Score Phred Medio por Lectura')\n","    ax1.set_ylabel('Número de Lecturas')\n","    ax1.grid(axis='y', linestyle='--', alpha=0.7)\n","    ax1.legend()\n","\n","    # Histograma GC\n","    ax2.hist(lista_porcentajes_gc, bins=30, color='#1f77b4', alpha=0.7, edgecolor='black')\n","    ax2.set_title('Distribución del Contenido GC (%)', fontsize=12, fontweight='bold')\n","    ax2.set_xlabel('% GC')\n","    ax2.set_ylabel('Número de Lecturas')\n","    ax2.grid(axis='y', linestyle='--', alpha=0.7)\n","\n","    plt.tight_layout()\n","    plt.show()\n","    print(f\"\\n📈 Gráficos generados con éxito basados en las primeras {contador} lecturas.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":530},"id":"0y2yRiFha7jI","executionInfo":{"status":"ok","timestamp":1782468868357,"user_tz":-120,"elapsed":861,"user":{"displayName":"Madrid Macrogen","userId":"12903224377867150428"}},"outputId":"4bcc1f9e-9912-4924-d44c-7925640c8f81"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["📊 Analizando el archivo para generar gráficos... Por favor, espera.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x500 with 2 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Gráficos generados con éxito basados en las primeras 10000 lecturas.\n"]}]},{"cell_type":"markdown","source":["* **Gráfico de Calidad por Posición (Estilo FastQC):** Muestra cómo varía la calidad a medida que el secuenciador avanza a lo largo de la lectura (de la base 1 a la final). La línea roja representa la mediana. Si la línea se mantiene en la zona verde, la carrera es excelente. Si cae a la zona amarilla o roja al final, indica que esas bases finales tienen menor fiabilidad.\n","\n","---\n","\n","* **Quality per Base Position Plot (FastQC Style):** Shows how quality changes as the sequencer moves along the read (from base 1 to the end). The red line represents the median score. If the line stays in the green zone, the run is excellent. If it drops into the yellow or red zones toward the end, it indicates that those final bases are less reliable."],"metadata":{"id":"4qLWIr5HbBkW"}},{"cell_type":"code","source":["from Bio import SeqIO\n","from Bio.SeqUtils import gc_fraction\n","import gzip\n","import os\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","archivos_fastq = [f for f in os.listdir(\"/content\") if f.endswith(\".fastq.gz\")]\n","\n","if not archivos_fastq:\n","    print(\"❌ No se encontró el archivo .fastq.gz para el análisis visual.\")\n","else:\n","    archivo_detected = os.path.join(\"/content\", archivos_fastq[0])\n","    print(\"📊 Analizando el archivo para generar el triple panel de gráficos... Por favor, espera.\")\n","\n","    lista_calidades_medias = []\n","    lista_porcentajes_gc = []\n","\n","    # Para el gráfico base por base, necesitamos registrar las calidades en cada posición de la lectura\n","    # Analizamos primero la longitud máxima leyendo una única secuencia de prueba\n","    with gzip.open(archivo_detected, \"rt\") as test_handle:\n","        primera_lectura = next(SeqIO.parse(test_handle, \"fastq\"))\n","        longitud_lectura = len(primera_lectura.seq)\n","\n","    # Creamos una matriz vacía para acumular las calidades por posición (Lecturas x Posición)\n","    MAX_LECTURAS_GRAFICO = 10000\n","    matriz_calidades = np.zeros((MAX_LECTURAS_GRAFICO, longitud_lectura))\n","\n","    with gzip.open(archivo_detected, \"rt\") as handle:\n","        for contador, lectura in enumerate(SeqIO.parse(handle, \"fastq\")):\n","            if contador >= MAX_LECTURAS_GRAFICO:\n","                break\n","\n","            calidades = lectura.letter_annotations[\"phred_quality\"]\n","            lista_calidades_medias.append(sum(calidades) / len(calidades))\n","            lista_porcentajes_gc.append(gc_fraction(lectura.seq) * 100)\n","\n","            # Guardamos las calidades de esta lectura en la fila correspondiente de la matriz\n","            # (Ajustamos por si alguna lectura fuera más corta por control de calidad previo)\n","            matriz_calidades[contador, :len(calidades)] = calidades\n","\n","    # Recortamos la matriz al número real de lecturas procesadas si el archivo tuviera menos de 10k\n","    matriz_calidades = matriz_calidades[:contador, :]\n","\n","    # Calculamos la mediana de calidad para cada una de las posiciones (columnas)\n","    calidad_por_posicion = np.median(matriz_calidades, axis=0)\n","\n","    # =======================================================\n","    # 🎨 PARTE VISUAL: GENERAR EL TRIPLE PANEL\n","    # =======================================================\n","    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\n","\n","    # Gráfico 1: Histograma Phred Medio\n","    ax1.hist(lista_calidades_medias, bins=30, color='#2ca02c', alpha=0.7, edgecolor='black')\n","    ax1.axvline(30, color='red', linestyle='dashed', linewidth=2, label='Umbral Q30')\n","    ax1.set_title('Calidad Media por Lectura', fontsize=12, fontweight='bold')\n","    ax1.set_xlabel('Score Phred Medio')\n","    ax1.set_ylabel('Número de Lecturas')\n","    ax1.grid(axis='y', linestyle='--', alpha=0.5)\n","    ax1.legend()\n","\n","    # Gráfico 2: Histograma Contenido GC\n","    ax2.hist(lista_porcentajes_gc, bins=30, color='#1f77b4', alpha=0.7, edgecolor='black')\n","    ax2.set_title('Contenido GC (%)', fontsize=12, fontweight='bold')\n","    ax2.set_xlabel('% GC')\n","    ax2.set_ylabel('Número de Lecturas')\n","    ax2.grid(axis='y', linestyle='--', alpha=0.5)\n","\n","    # Gráfico 3: Calidad Base por Base (Estilo FastQC)\n","    ax3.plot(range(1, longitud_lectura + 1), calidad_por_posicion, color='#d62728', linewidth=2.5, label='Mediana de Calidad')\n","    # Pintamos zonas de calidad (Verde = Buena, Amarillo = Alerta, Rojo = Mala)\n","    ax3.fill_between(range(1, longitud_lectura + 1), 28, 40, color='green', alpha=0.1)\n","    ax3.fill_between(range(1, longitud_lectura + 1), 20, 28, color='yellow', alpha=0.1)\n","    ax3.fill_between(range(1, longitud_lectura + 1), 0, 20, color='red', alpha=0.1)\n","\n","    ax3.set_title('Calidad por Posición de Base', fontsize=12, fontweight='bold')\n","    ax3.set_xlabel('Posición en la Lectura (bp)')\n","    ax3.set_ylabel('Score Phred')\n","    ax3.set_ylim(0, 42)\n","    ax3.grid(linestyle='--', alpha=0.5)\n","    ax3.legend()\n","\n","    plt.tight_layout()\n","    plt.show()\n","    print(f\"\\n📈 ¡Triple panel de Control de Calidad generado con éxito!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":414},"id":"z0bQNvZ_bCuB","executionInfo":{"status":"ok","timestamp":1782468869480,"user_tz":-120,"elapsed":1125,"user":{"displayName":"Madrid Macrogen","userId":"12903224377867150428"}},"outputId":"308d1d11-dcc9-49a3-a720-3da673a4d75d"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["📊 Analizando el archivo para generar el triple panel de gráficos... Por favor, espera.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 2000x500 with 3 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","📈 ¡Triple panel de Control de Calidad generado con éxito!\n"]}]}]}