Radioallergosorbent Test

The input to our method, the Feature Abundance Matrix, can be easily constructed from both 16S rRNA and random shotgun data using available software packages. Specifically for 16S taxonomic analysis, tools such as the RDP Bayesian classifier [30] (link) and Greengenes SimRank [31] (link) output easily-parseable information regarding the abundance of each taxonomic unit present in a sample. As a complementary, unsupervised approach, 16S sequences can be clustered with DOTUR [9] (link) into operational taxonomic units (OTUs). Abundance data can be easily extracted from the “*.list” file detailing which sequences are members of the same OTU. Shotgun data can be functionally or taxonomically classified using MEGAN [13] (link), CARMA [32] (link), or MG-RAST [33] (link). MEGAN and CARMA are both capable of outputting lists of sequences assigned to a taxonomy or functional group. MG-RAST provides similar information for metabolic subsystems that can be downloaded as a tab-delimited file.
All data-types described above can be easily converted into a Feature Abundance Matrix suitable as input to our method. In the future we also plan to provide converters for data generated by commonly-used analysis tools.

Anvi’o uses this essential database to store contig (or scaffold) information that does not vary from sample to sample (i.e., k-mer frequencies, functional annotation of open reading frames (ORFs), or GC content). To ensure that longer contigs are given more statistical weight during automated binning and more visibility in interactive displays, anvi’o breaks up large contigs into multiple splits, which remain soft-linked throughout the workflow and are reconstructed in the correct order in result summaries. The user can override the default split size of 20,000 bases when creating the contigs database. Smaller split sizes increase the resolution of information stored in databases and displayed in the interactive interface during later steps of analysis at the expense of added computational complexity and decreased performance for applications that require robust k-mer frequency statistics per split. When the user creates a contigs database from a given FASTA file, anvi’o identifies splits and computes k-mer frequency tables for each contig and split separately. Optionally, anvi’o can identify ORFs, process functional and taxonomic annotations for ORFs, and search contigs for hidden Markov model (HMM) profiles to be stored in the contigs database for later use. Currently, anvi’o installs four previously published HMM profiles for bacterial single-copy gene collections (Alneberg et al., 2014 (link); Campbell et al., 2013 (link); Dupont et al., 2012 (link); Creevey et al., 2011 (link)). Presence or absence of these genes in contigs provides a metric for estimating the level of completeness of genome bins during the interactive human-guided binning (see ‘Binning’). The system also generates completion and redundancy (multiple occurrence of one or more single-copy genes in a bin) statistics in real-time to inform human-guided binning. Beyond single-copy genes, users can populate the contigs database with curated HMM profiles to identify the presence of protein families of interest. The contigs database also stores inferred functions and likely taxonomic origin of all recognized ORFs. Users can provide these data as a standard matrix file or use one of the pre-existing parsers. The initial version supports annotation files generated by the RAST annotation server (Aziz et al., 2008 (link)), but the design allows inclusion of annotations from other sources.

We revisited the pathway reconstruction for the 854 genomes in the KEGG database (as of December, 2008) that have at least 20 KEGG pathways annotated for each of these genomes. For these genomes, the function (or protein families) annotations were downloaded from the KEGG database (ftp://ftp.genome.jp/pub/kegg/release/current/).
We also applied MinPath to reanalyze the pathways for nine biome metagenomic datasets [17] (link). The FIG family annotations for the metagenomic sequences were downloaded from the MG-RAST server (http://metagenomics.theseed.org/). We conducted the KO family annotations of the sequences based on the best blast hits with E-value cutoff of 1e-5, a typical E-value cutoff used for KEGG pathway reconstruction in metagenomes [16] (link).

Genomic DNA was extracted from bacteria using a Nucleospin Microbial DNA kit (Thermo Fischer Scientific) as per manufacturer instructions. Further, the QIASeq FX DNA Library Kit (Qiagen) prepared genomic libraries for sequencing. L. amnigena PTJIIT1005 genome was sequenced on NGS (Next Generation Sequencing) Illumina NovaSeq6000 Platform by Redcliffe Lifetech, Noida. A total of 9,365,132 raw reads were obtained; 8,596,940 Illumina reads were de novo assembled using Unicycler (version 0.4.4). The assembled genome sequence was annotated by the tool Prokka 1.12. The complete genome sequence was submitted to NCBI.
Average Nucleotide Identity (ANI) [17 ] measures nucleotide-level genome similarity between the coding regions of two genomes. The complete genome sequence was submitted in FASTA format as an input file. This tool gives the similarity index percentage [18 (link)]. ANI is computed using the formula [19 ]: $$\underset{\mathbf{G}\mathbf{1}\to \mathbf{G}\mathbf{2}}{\mathbf{gANI}}=\frac{\underset{\text{bbh}}{\mathrm{\Sigma }}\left(\text{Percent},,\text{Identity},{}_{.},{}^{*},\text{A},\text{lignment},,\text{length}\right)}{\text{lengths}\text{of}\text{BBH}\text{genes}}$$
Genome annotation of Lelliottia amnigena was done by RAST (Rapid Annotation using Subsystems Technology), PATRIC (The Pathosystems Resource Integration Center), and PGAP (Prokaryotic Genome Annotation Pipeline). Assembled genome sequence was submitted in RAST in FASTA format as input files, assigned functions to the genes. It also predicted the subsystems which were represented in the genome. By using this information, it reconstructs the metabolic network and makes the output file easily downloadable. Similarly, contigs were submitted in PATRIC as input files which provided annotation, subsystem summary, phylogenetic tree, and pathways. NCBI PGAP was used to annotate the bacterial genome where the complete genomic sequence was submitted in FASTA format as an input file, and it predicted the protein-coding regions and functional genome units like tRNAs, rRNA, pseudogenes, transposons, and mobile elements.

Chromosomal DNA was extracted in accordance with Sambrook et al. [25 ] and was purified as described by Yoon et al. [26 (link)]. The cell biomass for DNA was cultured on TSA at 30°C for 2 days. The complete genome of the strain KUDC0405^T was sequenced using a MinION platform (Oxford Nanopore Technologies, UK). The reads were assembled de novo using Flye (version 2.9) [27 (link)]. The automatic NCBI Prokaryotic Genomes Annotation Pipeline (PGAP) [28 (link)] and the Rapid Annotation of microbial genomes using the Subsystems Technology (RAST) server [29 (link)] were used for combine the complete genome sequence. To identify biosynthetic gene clusters (BGCs) for secondary metabolites using The AntiSMASH server (version 6.0) [30 (link)]. The average nucleotide identity (ANI) values based on the blast+ algorithm (ANIb) and the MUMmer (ANIm) ultra-rapid alignment tool between strain KUDC0405^T and closely related strains were calculated using the JSpeciesWS website (https://jspecies.ribohost.com/jspeciesws/) [31 (link)]. The average amino acid identity (AAI) values were obtained using Kostas lab (http://enve-omics.ce.gatech.edu/) [32 (link)]. The dDDH values were calculated using server-based genome-to-genome distance calculator version 2.1 (http://ggdc.dsmz.de/distcalc2.php) [33 (link)]. The dDDH results were based on recommended formula 2 (identities/HSP length). Orthologous genes were identified between strain KUDC0405^T and its two closest relatives, M. bovistercoris NEAU-LLE^T and M. pseudoresistence CC-5209^T, using protein sequences annotated by Hyatt et al. [33 (link)] and the OrthoVenn diagram [34 (link)]. A multi-locus species tree based on the whole genome sequences of each reference strain was established using autoMLST (http://automlst.ziemertlab.com) [35 (link)].

Metagenomes derived
from DWDSs were downloaded from NCBI using SRA Toolkit v2.9.6^{43 (link)} or, if deposited on MG-RAST,⁴⁴ retrieved directly from corresponding researchers for a
total of 181 distinct samples. Only samples derived from finished
water at drinking water treatment plants (DWTPs) and DWDSs were considered,
excluding those either collected in raw water, within drinking water
treatment plants, and DWDS biofilms. Table S3 includes a list of the samples with the details regarding experimental
procedures used for each sample.^{35 (link),45 (link)−56}