Lactobacillales

DFAST accepts a FASTA-formatted file as a minimum required input, and users can customize parameters, tools and reference databases by providing command line options or defining an original configuration file (see Supplementary Notes for more details). The workflow is mainly composed of two annotation phases, i.e. structural annotation for predicting biological features such as CDSs, RNAs and CRISPRs, and functional annotation for inferring protein functions of predicted CDSs. Figure 1 shows a schematic depiction of the pipeline. Each annotation process is implemented as a module with common interfaces, allowing both flexible annotation workflows and extensions for new functions in the future.
In the default configuration, functional annotation will be processed in the following order:

Orthologous assignment (optional) All-against-all pairwise protein alignments are conducted between a query and each reference genome. Orthologous genes are identified based on a Reciprocal-Best-Hit approach. It also conducts self-to-self alignments within a query genome, in which genes scoring higher than their corresponding orthologs are considered in-paralogs and assigned with the same protein function. This process is effective in transferring annotations from closely related organisms and in reducing running time.

Homology search against the default reference database DFAST uses GHOSTX as a default aligner, which runs tens to hundred times faster than BLASTP with similar levels of sensitivity where E-values are less than 10⁻⁶ (Suzuki et al., 2014 (link)). Users can also choose BLASTP. For accurate annotation, we constructed a reference database from 124 well-curated prokaryotic genomes from public databases. See Supplementary Data for the breakdown of the database.

Pseudogene detection CDSs and their flanking regions are re-aligned to their subject protein sequences using LAST, which allows frameshift alignment (Kiełbasa et al., 2011 (link)). When stop codons or frameshifts are found in the flanking regions, the query is marked as a possible pseudogene. This also detects translation exceptions such as selenocysteine and pyrrolysine.

Profile HMM database search against TIGRFAM (Haft et al., 2013 (link)) It uses hmmscan of the HMMer software package.

Assignment of COG functional categories RPS-BLAST and the rpsbproc utility are used to search against the Clusters of Orthologous Groups (COG) database provided by the NCBI Conserved Domain Database (Marchler-Bauer et al., 2017 (link)).

DFAST output files include INSDC submission files as well as standard GFF3, GenBank and FASTA files. For GenBank submission, two input files for the tbl2asn program are generated, a feature table (.tbl) and a sequence file (.fsa). For DDBJ submission, DFAST generates submission files required for DDBJ Mass Submission System (MSS) (Mashima et al., 2017 (link)). In particular, if additional metadata such as contact and reference information are supplied, it can generate fully qualified files that are ready for submission to MSS.
While the workflow described above is fully customizable in the stand-alone version, only limited features are currently available in the web version, e.g. orthologous assignment is not available. As a merit of the web version, users can curate the assigned protein names by using an on-line annotation editor with an easy access to the NCBI BLAST web service. We also offer optional databases for specific organism groups (Escherichia coli, lactic acid bacteria, bifidobacteria and cyanobacteria). They are downloadable from our web site and can be used in the stand-alone version. We are updating reference databases to cover more diverse organisms.

CoNet offers a series of features that distinguish it from other network inference tools, such as its support for object groups. This feature allows a user to assign objects to different groups (
e.g. metabolites and enzymes). Relationships can then be inferred only between different object types (resulting in a bipartite network) or only within the same object type. CoNet's treatment of two input matrices is built upon this feature.
Furthermore, CoNet can handle row metadata, which allows for instance to infer links between objects at different hierarchical levels (
e.g. between order Lactobacillales and genus Ureaplasma) while preventing links between different levels of the same hierarchy (e.g. Lactobacillales and Lactobacillaceae). CoNet can also read in sample metadata such as temperature or oxygen concentration. When sample metadata are provided, associations among metadata items and between taxa and metadata items are inferred in addition to the taxon associations. Metadata are then represented as additional nodes in the resulting network. In addition, CoNet recognizes abundance tables generated from biom files (
McDonald
et al., 2012) and, in its Cytoscape 3.× version, reads biom files in HDF5 format directly, using the BiomIO Java library (
Ladau ). Taxonomic lineages in biom files or biom-derived tables are automatically parsed and displayed as node attributes of the resulting network. For instance, the lineage "k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactobacillus; s_Lactobacillus acidophilus" of an operating taxonomic unit with identifier 12 would create a kingdom, phylum, class, order, family, genus and species attribute in the node property table for node OTU-12, filled with the corresponding values from the lineage. CoNet also computes a node's total edge number as well as the number of positive and negative edges, the total row sum and the number of samples in which the object was observed (e.g. was different from zero or a missing value).
To ease the selection of suitable preprocessing steps, CoNet can display input matrix properties and recommendations based on them. Importantly, CoNet can also handle missing values, by omitting sample pairs with missing values from the association strength calculation. Finally, CoNet supports a few input and output network formats absent in Cytoscape, including adjacency matrices (import), dot (the format of GraphViz (
http://www.graphviz.org/)) and VisML (VisANT's format (
Hu
et al., 2013)) (both for export).

Notice on the previous plot that
Lactobacillales appears to be a taxonomic Order with bimodal abundance profile in the data. We can check for a taxonomic explanation of this pattern by plotting just that taxonomic subset of the data. For this, we subset with the
subset_taxa() function, and then specify a more precise taxonomic rank to the
Facet argument of the
plot_abundance function that we defined above.
psOrd= subset_taxa(ps3ra, Order=="Lactobacillales")plot_abundance(psOrd,Facet="Genus",Color=NULL)At this stage in the workflow, after converting raw reads to interpretable species abundances, and after filtering and transforming these abundances to focus attention on scientifically meaningful quantities, we are in a position to consider more careful statistical analysis. R is an ideal environment for performing these analyses, as it has an active community of package developers building simple interfaces to sophisticated techniques. As a variety of methods are available, there is no need to commit to any rigid analysis strategy a priori. Further, the ability to easily call packages without reimplementing methods frees researchers to iterate rapidly through alternative analysis ideas. The advantage of performing this full workflow in R is that this transition from bioinformatics to statistics is effortless.
We back these claims by illustrating several analyses on the mouse data prepared above. We experiment with several flavors of exploratory ordination before shifting to more formal testing and modeling, explaining the settings in which the different points of view are most appropriate. Finally, we provide example analyses of multitable data, using a study in which both metabolomic and microbial abundance measurements were collected on the same samples, to demonstrate that the general workflow presented here can be adapted to the multitable setting.
.cran_packages<- c("knitr","phyloseqGraphTest","phyloseq","shiny", "miniUI","caret","pls","e1071","ggplot2","randomForest", "vegan","plyr","dplyr","ggrepel","nlme", "reshape2",
"devtools","PMA","structSSI","ade4", "igraph","ggnetwork","intergraph","scales").github_packages<- c("jfukuyama/phyloseqGraphTest").bioc_packages<- c("phyloseq","genefilter","impute")# Install CRAN packages (if not already installed).inst<- .cran_packages%in%installed.packages()if(any(!.
inst)){ install.packages(.cran_packages[!.inst],repos="http://cran.rstudio.com/")}.inst<- .github_packages%in% installed.packages()if(any(!.inst)){ devtools::install_github(.github_packages[!.inst])}.inst<- .bioc_packages%in% installed.packages()if(any(!.inst)){ source("http://bioconductor.org/biocLite.R") biocLite(.bioc_packages[!.inst])}

In this study, the database was built by collecting the datasets from previously published articles. In detail, literature was obtained through a number of steps, i.e., identification and screening, and then valid articles were inserted into an excel spreadsheet. During the identification process, the search engines, namely Google, Scopus, and Google Scholar, were used for searching the datasets of the previously published articles. The keywords used were lactic acid bacteria, silage quality, bacterial diversity, and fermentation.
The identification process was carried out based on the titles of the collected articles. In this stage, we put general criteria in the article that would be involved in the database. These criteria are as follows: (1) Article must be written in the English language; (2) Only published articles; (3) Collected article must contain a control treatment with at least one experiment of LAB addition among their treatments; and (4) The collected articles must contain at least one parameter on silage microbiome or at least one parameter on silage quality. Here, in this stage, we obtained 181 articles.
The process was continued by scanning the entire abstract of each of the collected articles to ensure that the article is valid to be used in this stage. At this stage, 54 articles were obtained. The screening process was done by reading carefully the entire content of each collected article to determine which one of the collected articles is valid to be inserted into the database. The literature obtained at this stage was 37 articles with 185 studies and 485 datasets. All valid articles were inserted into an excel spreadsheet. Information about articles used in the database is presented in Table 1.
While creating the excel spreadsheet, datasets were divided into main categories which are main and branched cells. In the main cells, we included information on authors, year of publication, treatments, studies, doses, and substrates used as raw materials of the experience. Information on observed parameters was inserted in the branched cells of the created excel spreadsheet. The observed parameters include the chemical composition of silage material (DM, dry matter content; OM, organic matter; CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber; WSC, water-soluble carbohydrates; EE, ether extract; ASH, the ash content in the fresh material; DM recovery, cellulose and hemicellulose; and ADL, acid detergent lignin), silage quality (pH value; LA, lactic acid; AA, acetic acid; PA, propionic acid; BA, butyric acid; AS, aerobic stability; LAB, lactic acid bacteria; AB, aerobic bacteria; yeasts, yeasts and molds, molds, and the starch content), silage microbiome, and information on sequencing. All the data of the targeted parameters were inserted into the created excel sheet to be ready for evaluation.

For lactic acid bacteria enumeration, serial dilutions were prepared and plated on man rogosa sharpe (MRS) agar (VWR, Milano, Italy) containing 0.01% l-Cysteine-HCl (Merck, Darmstadt, Germany), 0.1% fructose (Sigma-Aldrich, Milano, Italy) and 0.1% cycloheximide (Sigma-Aldrich, Milano, Italy). Analyses were performed in triplicate. Plates were incubated anaerobically at 35°C for 72–120 h, the number of colony forming units (CFU) were recorded and counts were made. Around 100 isolated colonies were re-streaked and purified. For long term storage, purified isolates were stored at −80°C with their respective liquid medium containing 20% glycerol. DNA extraction from pure cultures was performed with the Wizard^® Genomic DNA Purification Kit (Promega, Madison, WI, USA). Fingerprinting was then obtained using BOX-PCR, as in Gaggìa et al. (2015) . Cluster analysis and grouping BOX profiles was carried out with Bionumerics 7.1 (Applied Maths, Sint-Martens-Latem, Belgium) using Dice’s Coefficient of similarity and the un-weighted pair group method arithmetic averages clustering algorithm (UPGMA). Based on the genotypic grouping, representative isolates were selected, the 16S rRNA gene amplified with primers 8-fw and 1520-rev and sequenced according to Gaggìa et al. (2015) . Sequences were then deposited to GenBank^®1 with the following accession number: MT381710-MT381736 and MG649988-MG650060. The obtained 16S rRNA gene sequences were used to generate a phylogenetic tree together with sequences of A. kunkeei retrieved from the National Center for Biotechnology Information (NCBI) Genomes RefSeq database (Supplementary Table 2) especially from Germany, Sweden (Tamarit et al., 2015 (link)), and Switzerland (Crovadore et al., 2021 (link)). The phylogenetic tree was generated with MEGA11 (Tamura et al., 2021 (link)) inferred by using the Maximum Likelihood method (K2 + G substitution model) with rate variation among sites. Lactobacillus melliventris MT53, Lactobacillus apis MT61, and Gilliamella apicola MT1 and MT6 were used as outgroups.