2006), or on hands of humans (Fierer et al. Besides rapid full-genome assembly, another important application is the sampling of microbial communities from, for example, permafrost-affected soils (Ganzert et al.
Such sequencing runs can be carried out within about an hour. We can no longer expect that the steady increase in computing power, according to Moore's law, is fast enough to handle this flood of sequence data.ĭepending on the DNA sequencing method used, a single sequencing run can already generate more than 100,000 short-read sequences, which comprise sequence fragments with a length of ∼30–450 nucleotides (base pairs). This rapid increase in the amount of sequence data available poses new challenges for short-read sequence identification tools. Recently, the advent of new DNA sequencing techniques (e.g., pyrosequencing Ronaghi 2001) has increased the amount of sequence data available for identification and analysis of microbial communities by several orders of magnitude. Identification of organisms from, for example, microbial communities increasingly relies on analysis of DNA extracted from soil or water samples containing many, often unknown, organisms rather than from the individual organisms. Maximum likelihood, metagenomics, phylogenetic placement, RAxML, short sequence reads We are also actively developing a Web server that offers a freely available service for computing read placements on trees using the EPA. Our algorithm, which has been integrated into RAxML, therefore provides an equally fast but more accurate alternative to BLAST for tree-based inference of the evolutionary origin and composition of short sequence reads. Moreover, the accuracy of the EPA is significantly higher, in particular when the sample of taxa in the reference topology is sparse or inadequate. When those additional heuristics are employed, the run time of the more accurate algorithm is comparable with that of a simple BLAST search for data sets with a high number of short query sequences. For the slow algorithm, we develop additional heuristic techniques that yield almost the same run times as the fast version with only a small loss of accuracy. We introduce a slow and accurate as well as a fast and less accurate placement algorithm. The accuracy of the algorithm is evaluated on several real-world data sets and compared with placement by pair-wise sequence comparison, using edit distances and BLAST.
We present an evolutionary placement algorithm (EPA) and a Web server for the rapid assignment of sequence fragments (short reads) to edges of a given phylogenetic tree under the maximum-likelihood model.
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