Next-generation sequencing technologies such as pyrosequencing have revolutionized the field of microbial ecology by enabling a hitherto unprecedented sampling depth. Since their introduction, these technologies have been rapidly refined and improved, and they are currently becoming the method of choice for microbial diversity estimates. The gene encoding the 16S ribosomal RNA is commonly used as phylogenetic marker in environmental studies. A variety of protocols and pipelines have been developed to generate and in particular, to analyze the massive datasets retrieved from 16S rRNA gene amplicon sequencing or meta-genome studies;
however, protein encoding genes can also be valuable markers for microbial diversity. If such genes can be directly linked to a defined biogeochemical function, they are often referred to as “functional genes” or “functional marker genes”. Ideally, their evolution is tightly correlated to the 16S rRNA gene evolution and thus, they unite information on the phylogeny and the physiological role of a microorganism. Amplicon pyrosequencing of protein encoding genes has the great advantage that sequencing errors such as insertions or deletions of nucleotides (indels) can be easily detected. They result in reading frame shifts that are directly evident in the amino acid alignment. Indels are the most frequent error modality in pyrosequencing. In 16S rRNA gene sequence analysis, such errors cannot be defined; they are only a statistical estimate.
Thus, next generation sequencing approaches targeting “functional marker genes” have great advantages and potential for diversity studies of microbial guilds catalyzing well-defined biogeochemical processes. Nevertheless, only few studies have applied these new techniques to protein encoding genes so far. With this Research Topic, we would like to help filling this gap. We aim to collect ongoing original work targeting genes encoding key enzymes of nitrogen cycling (e.g. nifH, amoA, nirK, nirS, norB, ureA), carbon cycling (e.g. mcrA, pmoA, rbcI, fhs, cooS), sulfur cycling (e.g. dsrAB, apsA), but also many other genes. In addition to diversity studies, we aim to focus on bioinformatic approaches and the development of pipelines for data analysis. We wish to summarize and review studies that already have addressed this topic and encourage the discussion of future perspectives. Manuscripts (original research papers, reviews, perspective and opinion papers) are very welcome.

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