Option B is the correct answer. Defining a custom schema for the genomic sequences allows for a structured approach to reading, transforming, and processing the data using Apache Spark. Apache Spark's machine learning and graph processing libraries can be leveraged to handle the complex transformations and analysis required for genomic data. Using built-in functions without a schema or a traditional database system may not be suitable for the given use case. Ignoring the unstructured nature of the data would not be appropriate for processing genomic sequences.