**1. Introduction**

Next-generation sequencing technologies have transformed our perception of diversity and microbial distribution in natural ecosystems and have contributed substantially to the discovery of totally new microbial landscapes in such distinctive environments as the gut of mammals, the vegetal rhizosphere, vascular tissues of higher plants, and even in volcanic lakes [1–3]. There are two general approaches to profile microbial communities through next-generation sequencing techniques: shotgun sequencing of total DNA isolated directly from the environment and sequencing of variable regions coming from SSU-rRNA genes (we know these approaches as metagenomics methods since all involve the culture-independent genomic analysis of microbiomes on a particular environment [4, 5]). Both approaches have been widely used to trace microbial diversity at increasingly fine taxonomic levels, either by capturing a representative fraction of the total gene content or by amplicon sequencing techniques like the popular bacterial 16S rRNA. Each method has advantages and disadvantages, and the selection depends on several factors like taxonomic level resolution, cost, sensitivity, and primer bias, among others. One of the challenges associated with metagenomics methods is the analysis of massively generated data. Both the sequencing of amplicons and environmental DNA produces millions of short DNA sequences (reads), which must undergo preprocessing and quality control, before they can be used to extract

biologically useful information from them. One of the goals of massively sequencing data analysis is to obtain the patterns of phylogenetic diversity in ecological communities, an important trait in order to assess the classic ecological questions "Who is there?" or "What they are doing?" and provide better understandings into the phylogenetic relationships among microbial community taxa. Extracting phylogenetic information from massive sequencing reads is not a trivial task; however, it can be achieved with reasonable success by using several profiling tools adapted both to the analysis of amplicons of ribosomal genes and to the conserved genes between different domains [6, 7]. The microbial community structure has been approached mostly using the 16S SSU-rRNA gene as phylogenetic marker, mainly due to lower sequencing costs and an acceptable relation of specificity-resolution in taxonomic assignments [8], while methods that use single-copy markers obtained from shotgun sequencing reads or assembled samples are gaining relevance because they have demonstrated strain-level resolution [9, 10], a really hard issue when analyzing complex microbiomes.

To date, several computational tools have been developed to carry out community profiling and phylogenetic inferences from next-generation sequencing data with considerable success. In this chapter we present a compendium of open-source tools and easy-to-use with modest hardware requirements, with the aim that they can be applied by biology non-specialists to study microbial diversity in a phylogenetic context. We show several practical examples explained step by step, in order to provide to the reader, the replication using their own data.

We have selected tools for use on a local computer through the Unix command line, and tools are available from dedicated servers, with easy access and intuitive use. The examples described in the chapter were tested on a Dell Optiplex 7010 desktop, 6T6ZYV1 Series, Intel (R) Core (TM) i5-3550 CPU at 3.30 GHz, Memory 12 GiB.
