  Motivation: Identifying genomic variants is an essential step for connecting
genotype and phenotype. The usual approach consists of statistical inference of
variants from alignments of sequencing reads. State-of-the-art variant callers
can resolve a wide range of different variant types with high accuracy.
However, they require that all read alignments be available from the beginning
of variant calling and be sorted by coordinates. Sorting is computationally
expensive, both memory- and speed-wise, and the resulting pipelines suffer from
storing and retrieving large alignments files from external memory. Therefore,
there is interest in developing methods for resource-efficient variant calling.
  Results: We present Ococo, the first program capable of inferring variants in
a real-time, as read alignments are fed in. Ococo inputs unsorted alignments
from a stream and infers single-nucleotide variants, together with a genomic
consensus, using statistics stored in compact several-bit counters. Ococo
provides a fast and memory-efficient alternative to the usual variant calling.
It is particularly advantageous when reads are sequenced or mapped
progressively, or when available computational resources are at a premium.
