ORNA: Algorithm for read normalization using set multi-cover optimization

The de bruijn graph (DBG) is one of the most commonly used data structures for assembly of sequencing data. Reads from the sequencer are chopped into small words of size k (k-mers) which form the nodes of the DBG. Two nodes are connected by an edge if they have a k-1 overlap. Each edge can be labelled with a k+1-mer formed by merging the kmers of the two nodes. For instance, if an edge connects two nodes of kmers ATCG and TCGT, the edge can be labelled as ATCGT. Assembly is generated by traversing paths in this graph. With the advances in deep sequencing technologies, assembling high coverage datasets has become a challenge in terms of memory and runtime requirements. Hence, read normalization, a lossy read filtering approach is gaining a lot of attention. Although current normalization algorithms are efficient, they provide no guarantee to preserve important k-mers that form connections between different genomic regions in the graph. There is a possibility that the resultant assembly is fragmented. In this work, normalization is phrased as a set multicover problem on reads and a linear time heuristic algorithm is proposed, named ORNA (Optimized Read Normalization Algorithm). ORNA normalizes to the minimum number of reads required to retain all labels (k+1-mers) and inturn all kmers and relative label abundances from the original dataset. Hence, no connections from the original graph are lost and coverage information is preserved.