Added in learning objectives and conclusion

Author: aolveraNIH

GoogleCloud/Submodule_3_basic_assembly.ipynb

@@ -68,7 +68,11 @@
    "metadata": {},
    "source": [
     "## Learning Objectives\n",
-    "\n"
+    "- **Understanding de novo Transcriptome Assembly**: Learn the process of assembling a transcriptome from paired-end short reads using a de novo approach.\n",
+    "- **Using denovotranscript for Assembly**: Gain practical experience using the denovotranscript Nextflow pipeline for a complete transcriptome assembly, including quality control, trimming, assembly, and quantification.\n",
+    "- **Working with Google Batch**: Learn how to utilize Google Batch for running Nextflow pipelines in a serverless environment, including setting up the necessary resources and configuring parameters.\n",
+    "- **Understanding and Using Bioinformatics Tools**: Become familiar with various bioinformatics tools used in the pipeline, such as FastQC, fastp, SortMeRNA, Trinity, rnaSPAdes, Evidential Gene, BUSCO, rnaQUAST, TransRate, and Salmon, and understand their roles in the transcriptome assembly process.\n",
+    "- **Interpreting Assembly and Quality Control Results**: Develop skills in interpreting the output files generated by the pipeline, including transcriptome assembly FASTA files, transcript abundance TSV files, and quality assessment reports from MultiQC, and understand their implications for the quality and completeness of the assembled transcriptome.\n"
    ]
   },
   {
@@ -391,7 +395,7 @@
    "metadata": {},
    "source": [
     "## Conclusion\n",
-    "\n"
+    "This notebook provided a comprehensive hands-on experience in performing a \"standard\" basic transcriptome assembly using the denovotranscript Nextflow pipeline, leveraging Google Batch for serverless execution. Through a guided workflow, users learned to set up the necessary environment, configure parameters, and execute the pipeline to assemble a transcriptome from paired-end short reads. The notebook covered essential steps, including quality control with FastQC, trimming with fastp, optional rRNA removal with SortMeRNA, assembly with Trinity and rnaSPAdes, redundancy reduction with Evidential Gene, and quantification with Salmon. Users also explored the outputs generated by these tools, including transcriptome assembly FASTA files, transcript abundance TSV files, and comprehensive quality assessment reports from MultiQC. The notebook emphasized the importance of understanding and interpreting the various quality control metrics and assembly results to ensure the reliability of the assembled transcriptome. By the end of the module, users gained practical experience in managing and exploring the extensive output files, solidifying their understanding of the de novo transcriptome assembly process and the tools involved.\n"
    ]
   },
   {