Characterizing Sequencing Samples with k-mer Spectra for Good and Evil.

With many diverse applications for multiplexed, high-throughput sequencing, sequence data always has a common format, although for different applications it can have very different content. K-mer spectra afford diagnostics that characterize sequence-level diversity and have proven useful in revealing coverage bias, sequencing adapter contamination, and even inadvertently mixed and contaminated samples. We present how summarizing datasets using spectrum of long (15+ base pair) k-mers can help recognize commonalities in and manage sequencing datasets.