Digital Normalization from C. Titus Brown

Digital Normalization from C. Titus Brown

For new readers, easiest way to follow us is through our twitter feed. The feed is updated, whenever we post a commentary here.

From time to time, we referenced commentaries from professor C. Titus Brown of Michigan State University, who writes an informative blog on next-gen sequencing algorithms. Today I received a ping from him in our comment section about ‘digital normalization’ for data reduction that their group is ready to publish. This approach is important for anyone trying to cope with large volume of next-gen data and likes to reduce the data without losing any useful information.

Here is the abstract of their paper -

Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified single-cell genomes, and metagenomes enable the sensitive investigation of a wide range of biological phenomena. However, it is increasingly difficult to deal with the volume of data emerging from deep short-read sequencers, in part because of random and systematic sampling variation as well as a high sequencing error rate. These challenges have led to the development of entire new classes of short-read mapping tools, as well as new de novo assemblers. Even newer assembly strategies for dealing with transcriptomes, single-cell genomes, and metagenomes have also emerged. Despite these advances, algorithms and compute capacity continue to be challenged by the continued improvements in sequencing technology throughput. We here describe an approach we term digital normalization, a single-pass computational algorithm that discards redundant data and both sampling variation and the number of errors present in deep sequencing data sets. Digital normalization substantially reduces the size of data sets and accordingly decreases the memory and time requirements for de novo sequence assembly, all without significantly impacting content of the generated contigs. In doing so, it converts high random coverage to low systematic coverage. Digital normalization is an effective and efficient approach to normalizing coverage, removing errors, and reducing data set size for shotgun sequencing data sets. It is particularly useful for reducing the compute requirements for de novo sequence assembly. We demonstrate this for the assembly of microbial genomes, amplified single-cell genomic data, and transcriptomic data. The software is freely available for use and modification.

The paper is actually published as far as I am concerned, because it is available from and his website. Titus has been writing about his work in bits and pieces in his blog over the last 18 months. So, regular readers of his blog are 12-18 months ahead of journal readers about his cutting-edge research.

Written by M. //