Motivation With a large number of metagenomic datasets becoming available, eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step towards a better understanding of eukaryotic diversity. Results We developed Tiara, a deep-learning-based approach for the identification of eukaryotic sequences in the metagenomic datasets. Its two-step classification process enables the classification of nuclear and organellar eukaryotic fractions and subsequently divides organellar sequences into plastidial and mitochondrial. Using the test dataset, we have shown that Tiara performed similarly to EukRep for prokaryotes classification and outperformed it for eukaryotes classification with lower calculation time. In the tests on the real data, Tiara performed better than EukRep in analysing the small dataset representing eukaryotic cell microbiome and large dataset from the pelagic zone of oceans. Tiara is also the only available tool correctly classifying organellar sequences, which was confirmed by the recovery of nearly complete plastid and mitochondrial genomes from the test data and real metagenomic data. Availability Tiara is implemented in python 3.8, available at https://github.com/ibe-uw/tiara and tested on Unix-based systems. It is released under an open-source MIT license and documentation is available at https://ibe-uw.github.io/tiara. Version 1.0.1 of Tiara has been used for all benchmarks. Supplementary information Supplementary data are available at Bioinformatics online.

Euglenophyceae are unicellular algae with the majority of their diversity known from small freshwater reservoirs. Only two dozen species have been described to occur in marine habitats, but their abundance and diversity remain unexplored. Phylogenetic studies revealed marine prasinophyte green alga, Pyramimonas parkeae, as the closest extant relative of the euglenophytes' plastid, but similarly to euglenophytes, our knowledge about the diversity of Pyramimonadales is limited. Here we explored Euglenophyceae and Pyramimonadales phylogenetic diversity in marine environmental samples. We yielded 18S rDNA and plastid 16S rDNA sequences deposited in public repositories and reconstructed Euglenophyceae reference trees. We searched high-throughput environmental sequences from the TARA Oceans expedition and Ocean Sampling Day initiative for 18S rDNA and 16S rDNA, placed them in the phylogenetic context and estimated their relative abundances. To avoid polymerase chain reaction (PCR) bias, we also exploited metagenomic data from the TARA Oceans expedition for the presence of rRNA sequences from these groups. Finally, we targeted these protists in coastal samples by specific PCR amplification of two parts of the plastid genome uniquely shared between euglenids and Pyramimonadales. All approaches revealed previously undetected, but relatively low-abundant lineages of marine Euglenophyceae. Surprisingly, some of those lineages are branching within the freshwater or brackish genera. ; Tel. (+48) 22 55 266 41. † These authors contributed equally to this work.

Motivation: With a large number of metagenomic datasets becoming available, the eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step towards the better understanding of eukaryotic diversity. Results: We developed Tiara, a deep-learning-based approach for identification of eukaryotic sequences in the metagenomic data sets. Its two-step classification process enables the classification of nuclear and organellar eukaryotic fractions and subsequently divides organellar sequences to plastidial and mitochondrial. Using test dataset, we have shown that Tiara performs similarly to EukRep for prokaryotes classification and outperformed it for eukaryotes classification with lower calculation time. Tiara is also the only available tool correctly classifying organellar sequences. Availability and implementation: Tiara is implemented in python 3.8, available at https://github.com/ibe-uw/tiara and tested on Unix-based systems. It is released under an open-source MIT license and documentation is available at https://ibe-uw.github.io/tiara. Version 1.0.1 of Tiara has been used for all benchmarks.

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The unique underground environment developed in the area of Fore-Sudetic Monocline as a result of mining activity. This environment is inhabited by bacteria, archaea and fungi. They are mainly lithobionts for which copper-bearing Kupferschiefer black shale is the source of carbon and energy as well as macro- and microelements. Among them, many interesting genera and species adapted to this unique environment were found. Particularly interesting are microbial communities that represent a variety of metabolic strategies: microorganisms using organic compounds (organoheterotrophs), utilizing only mineral compounds (chemolitoautotrophs), producing methane (methanogens) and degrading it (methanotrophs). Additionally, underground mines inhabit microorganisms resistant to heavy metals and high salinity. Microorganisms play an important role in the transformation of Kupferschiefer black shale, affecting its geochemical composition and physicochemical properties, and the groundwater chemistry. Microbial metabolic activity leads to biooxidation of fossil organic matter (including kerogen) and sulphide minerals. As a result of these processes a number of oxidized organic and inorganic compounds are formed. They are products of organic matter degradation, such as alcohols, organic acids, ketones and aldehydes as well as secondary inorganic compounds, including numerous biominerals (e.g. sulphates). Some of these compounds are mobilized to groundwater; some are immobilized in the form of sediments.