Adaptive sequence alignment for metagenomic data analysis.

With advances in sequencing technologies, the use of high-throughput sequencing to characterize microbial communities is becoming increasingly feasible. However, metagenomic assembly poses computational challenges in reconstructing genes and organisms from complex samples. To address this issue, we introduce a new concept called Adaptive Sequence Alignment (ASA) for analyzing metagenomic DNA sequence data. By iteratively adapting a set of partial alignments of reference sequences to match the sample data, the approach can be applied in multiple scenarios, from taxonomic identification to assembly of target regions of interest. To demonstrate the benefits of ASA, we present two application scenarios and compare the results with state-of-the-art methods conventionally used for the same tasks. In the first, ASA accurately detected microorganisms from a sequenced metagenomic sample with a known composition. The second illustrated the utility of ASA in assembling target genetic regions of the microorganisms. An example implementation of the ASA concept is available at https://github.com/elolab/ASA.