SprayNPray: user-friendly taxonomic profiling of genome and metagenome contigs

Abstract

Background

Shotgun sequencing of cultured microbial isolates/individual eukaryotes (whole-genome sequencing) and microbial communities (metagenomics) has become commonplace in biology. Very often, sequenced samples encompass organisms spanning multiple domains of life, necessitating increasingly elaborate software for accurate taxonomic classification of assembled sequences.

Results

While many software tools for taxonomic classification exist, SprayNPray offers a quick and user-friendly, semi-automated approach, allowing users to separate contigs by taxonomy (and other metrics) of interest. Easy installation, usage, and intuitive output, which is amenable to visual inspection and/or further computational parsing, will reduce barriers for biologists beginning to analyze genomes and metagenomes. This approach can be used for broad-level overviews, preliminary analyses, or as a supplement to other taxonomic classification or binning software. SprayNPray profiles contigs using multiple metrics, including closest homologs from a user-specified reference database, gene density, read coverage, GC content, tetranucleotide frequency, and codon-usage bias.

Conclusions

The output from this software is designed to allow users to spot-check metagenome-assembled genomes, identify, and remove contigs from putative contaminants in isolate assemblies, identify bacteria in eukaryotic assemblies (and vice-versa), and identify possible horizontal gene transfer events.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08382-2.

Background

There is particular demand among biologists for taxonomic classification and partitioning of genome and metagenome assemblies. In particular, there is a need for easy-to-use tools that allow novice users to more efficiently begin these analyses without sophisticated knowledge of programming languages like Python. Tools exist for taxonomic classification and tree-building, including those that use sets of specific gene markers on genomes or metagenome-assembled genomes (MAGs) binned from an original assembly (Table 1). The partitioning of contigs (i.e. binning) is often carried out independently of taxonomic-classification, and takes into account sequence compositional data (GC-content and tetranucleotide frequency) and read coverage; however, open-source software is available that can incorporate taxonomic/phylogenetic information to aid binning (Table 1). While many tools exist for classification of sequenced samples, several barriers exist for novice users, including the choice of which tools to use, how to use them, and the ability to efficiently iterate through these tools when analyzing multiple samples.

Table 1.

Summary of published software for taxonomic classification and binning

Overall purpose	Software	Citation
Taxonomic classification and tree-building (using specific markers)	CheckM	[1]
	PhyloSift	[2]
	GToTree	[3]
	phyloSkeleton	[4]
Taxonomic classification at the contig level	Kaiju	[5]
	Kraken	[6]
	CLARK	[7]
	FOCUS	[8]
	MEGAN	[9]
	BlobTools	[10]
Taxonomic classification at the contig level (using all predicted genes)	CAT	[11]
	Mmseqs2	[12]
Binning with sequence compositional data	MetaBAT	[13]
	MaxBin	[14]
	Concoct	[15]
	BinSanity	[16]
	VizBin	[17]
Binning with sequence compositional data and taxonomic information	Anvi’o	[18]
	BlobTools	[10]

Here, we present an open-source bioinformatics tool, SprayNPray, that combines taxonomic/phylogenetic information with a variety of other metrics for each contig (discussed in detail below), allowing users to manually or automatically group contigs based on these metrics. This software wraps together several steps, which would otherwise require more advanced knowledge of a coding language to efficiently iterate through, and processes the output in a way that allows for easy visual inspection, manual curation, and/or further computational parsing. It is also organized in a way to allow for identification of pathogens, symbionts, and horizontally acquired genes in eukaryotic assemblies. The case studies presented below demonstrate the software’s versatility and potential usefulness to biologists dealing with non-axenic samples.

Implementation

SprayNPray, implemented in Python (version 3), is an easy-to-use software that provides a broad overview of input contigs by comparing each predicted ORF against a user-set reference database (recommended: NCBI’s non-redundant [nr] protein database [ftp://ftp.ncbi.nih.gov/blast/db/FASTA]). SprayNPray requires two inputs, a FASTA file of contigs (files with extension “.fna”, according to NCBI’s naming standards for genome FASTA sequences, but also ".fa" and ".fasta" in some cases) and a user-defined reference database, preferably NCBI’s non-redundant (nr) database of proteins. Users also have the option of providing a BAM file containing read coverage information; in this case, a script from the MetaBAT package (jgi_summarize_bam_contig_depths) is used to calculate the average read depth per contig [13]. After predicting ORFs with Prodigal [19], SprayNPray runs DIAMOND v2.0.4.142 [20] to query each ORF against the reference database (Fig. 1). Results of this search are then grouped and written to a spreadsheet, where each row corresponds to a separate contig, followed by the taxonomic affiliation of the top DIAMOND hit to each ORF on that contig. SprayNPray ultimately writes three (optionally, four) output files:

1. In the main output file, SprayNPray provides the following metrics related to the user-supplied contigs:

   • Average amino acid identity (AAI) between the contig ORFs and closest matches in reference: this will provide users with an idea of how closely related their sequenced organisms are to what currently exists in public databases.
   • Number of genes normalized to the contig length: bacterial and archaeal genomes are typically gene-dense (~ 1 gene per kbp), compared to eukaryotic genomes (0.9 genes per kbp in some fungi to ~ 0.01 genes per kbp in plants and animals). Further, Prodigal is designed for prokaryotic ORF prediction, leading to suboptimal eukaryotic ORF prediction and subsequent lower gene density estimates. Thus, users can use coding density to deduce bacterial from eukaryotic contigs (Fig. 1).
   • GC-content: if the provided contigs have organisms of varying levels of GC content, this will allow users to separate sequences based on that metric.
   • Read coverage: (this metric outputs only if a BAM file is provided): read coverage is useful for separating sequences if organisms represented among the contigs have varying levels of abundance, resulting in different read coverages estimates.
   • Contig length: contig length is a useful metric for filtering out low-quality contigs, or those too short for binning.
   • Cluster affiliation: contigs are clustered into putative bins. Cluster/bin assignments are derived from hierarchical clustering of contigs based on tetranucleotide frequency and codon usage bias. The number of clusters is estimated from the average number of hmmsearch (v.3.1b2, [21]) hits per single-copy gene (using the ‘Universal_Hug_et_al’ set of 16 genes available in the GToTree package [3]).

2. SprayNPray provides an R-generated word cloud that is based on the distribution of the top taxonomic hits to genes predicted from the provided contigs (Supplemental Figure 1). When users provide a BAM file along with their contigs, the word sizes in the word cloud are corrected with read coverage information.

3. SprayNPray provides a file containing the top user-specified number of hits (default = 100) to each ORF, allowing users to assess the taxonomic and functional distribution of top homologs to each gene.

4. SprayNPray has the capacity to write FASTA files that represent subsets of the provided contigs. This capability allows users to easily extract contigs belonging to organism(s) of interest (e.g. contaminants, pathogens, symbionts, certain genera). Subsets are created based on user-specified parameters, including: GC-content, coding density, amino acid identity, contig length, read coverage, and cluster/bin affiliation. Additionally, users can directly specify a taxonomic group of interest, and SprayNPray will write a FASTA file containing only contigs where some user-specified percentage (e.g. > 50%) of DIAMOND hits are to that specified taxa. In the event that the parameters by which FASTA files need to be written are unknown prior to running SprayNPray, users can re-run the program, with newly specified parameters (inferred from visually inspecting the output file from a preceding run), with greatly reduced runtime by providing the DIAMOND BLAST output file (file with extension ".blast") from the previous run.

5. When running SprayNPray on an assembly containing eukaryotic contigs, users can also direct the program to specifically look for potential horizontal gene transfers (HGTs) from Bacteria or Archaea to Eukaryota. In this case, SprayNPray will write a separate output file containing putative HGTs. To identify ORFs of possible bacterial or archaeal origin, SprayNPray evaluates the taxonomic distribution of the top user-specified (default = 100) DIAMOND matches for each ORF on each eukaryotic contig, and if more than a user-specified percentage (default 50%) of the hits are to bacterial proteins, that ORF is flagged as a potential HGT of bacterial or archaeal origin. In order for this part of the software to function properly, users need to be sure to include a reference database that encompasses protein sequences from all domains of life (e.g. nr).

Fig. 1.

Overall workflow of the SprayNPray pipeline, with the four different uses (contaminant identification, bin validation, symbiont identification, and HGT prediction) shown. Horizontal lines in each gray box represent contigs, while the smaller vertical lines perpendicular to the contigs represent ORFs

Results

Case study: simulated metagenome

To demonstrate SprayNPray’s capacity to efficiently and accurately summarize contigs, and extract contigs relevant to species/genera/domains of interest, we ran the software on a fully controlled dataset consisting of a concatenated set of 15 bacterial isolate genomes, 1 archaeal isolate genome, 2 phage genomes, and the genome of unicellular eukaryote Sphaeroforma arctica (Supplemental Table 1). Bacterial isolate genomes included the genomes of two closely related species of Shewanella, as well as two closely related species of Geobacter. To simulate the contig fragmentation that is commonly observed in metagenomic assemblies, we manually broke up contigs longer than 1 Mb into 100 kb fragments. Contigs from each assembly were then concatenated into a single file. This combined file was then used as input to SprayNPray. For 11 of the bacterial genomes and 1 archaeal genome, we selected for each individual isolate by setting the ‘-genus’ flag, requiring that more than 50% of hits to each contig consist of the selected genus. For the two Shewanella and two Geobacter species, we additionally set the ‘-species’ flags, with the same > 50% threshold, to specify the exact species of interest. The Bacillus and Caulobacter phage contigs were extracted by setting the ‘-genus’ flags to either ‘Bacillus’ and ‘Caulobacter’, along with the ‘--phage’ flag. To extract contigs corresponding to S. arctica, we set the ‘-domain’ flag to ‘Eukaryota’, maximum GC-content to 50%, and maximum coding density to 0.5 genes per kb; the SprayNPray output file from this run (Supplemental File 1) was inspected prior to extracting the S. arctica contigs, to confirm that none had a GC-content higher than 50%, or coding density higher than 0.5 genes per kb.

Results demonstrate that the top taxonomic hits to each contig match closely the genome from each contig is derived (Supplemental File 1); although, it is worth noting that since many of these isolate genomes represent model organisms, they are well-represented in the reference database used (nr). Analysis of unpublished data that represents underrepresented organisms is likely to yield results that are more ambiguous (e.g. see Case study: bin validation). Using SprayNPray’s ‘--fa’ flag, we were able to generate 19 FASTA files, each of which contained 100% of the contigs belonging to a single isolate genome that was part of the mock community (Supplemental Files 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 and 20). The exact flags and thresholds that were used to extract each genome is listed in Supplemental Table 1.

We used MetaBAT to generate bins from the simulated assembly using all five of MetaBAT’s specificity/sensitivity settings. Overall, MetaBAT performed well in binning genomes from the simulated metagenome, although certain bins appeared contaminated with contigs from a closely related (e.g. Class-level) genome (Supplemental File 21). Moreover, there was no resolution observed between the closely-related genomes of Geobacter sulfurreducens and G. metallireducens, as well as between Shewanella oneidensis and S. denitrificans. In other words, a single bin was generated for the two Geobacter species, and another single bin for the two Shewanella species.

Case study: bin validation

SprayNPray was used to profile a subset of metagenome bins from the North Pond aquifer [22] using NCBI’s nr database (release 200) as reference. Metagenome-assembled genomes (MAGs) from the North Pond aquifer were obtained from the FigShare link provided by Tully et al. [22]: (https://figshare.com/s/939160bb2d4156022558). To simulate the SprayNPray run as if it were conducted on an unpublished metagenome, we removed top hits corresponding to proteins generated from the original publication [22]. The results (Supplemental Files 22, 23, 24 and 25) demonstrate SprayNPray’s capacity to 1) provide a rough taxonomic prediction of a genome bin and 2) demonstrate that the apparent taxonomic heterogeneity of a bin is due not to contaminating contigs, but to the low representation of a genome in NCBI’s nr database. This deduction is supported by the low average AAI between the ORFs predicted in each contig and their closest hits in NCBI’s RefSeq database (Supplemental Files 22 and 23). Nonetheless, a subset of genome bins with higher similarities (> 85%) to reference proteins recruited a more taxonomically homogenous set of homologs from the reference database (Supplemental Files 24 and 25), and appear less “contaminated”.

Case study: contaminant identification in cultured isolates

SprayNPray can be used to identify putative contaminants in isolate assemblies. This software was used to remove contaminating Serratia marcescens contigs from a genome assembly of Pseudomonas aeruginosa, isolated from clinical specimens following an IRB-approved protocol (STUDY19100149) at the University of Pittsburgh [23]. Specimens were streaked onto Pseudomonas Isolation agar (PIA), a Pseudomonas-selective media on which Serratia marcescens and some other species can also grow, and incubated at 37˚C for 48 h. Single colonies were stored as a 30% glycerol stock at -80˚C. Genomic DNA was extracted using a QIAgen DNeasy kit (Qiagen, Hilden, Germany) and sequenced on an Illumina NextSeq 500. Reads were assembled using SPAdes v3.11.0 [24] and contigs smaller than 1kbp were removed.

Visual inspection of SprayNPray’s output revealed the presence of contigs from multiple species (Supplemental Files 26, 27 and 28). Running SprayNPray on these assemblies with the ‘-genus’ flag set to ‘Pseudomonas’ created new FASTA files with only sequences corresponding to the Pseudomonas aeruginosa (Supplemental Files 29, 30 and 31). In this run, we set the ‘-perc’ flag to 50%, meaning that if more than 50% of the genes on each contig recruited a Pseudomonas-related gene as a top hit, then they would be classified as Pseudomonas, and written to a separate FASTA file, while contigs that did not meet this criteria were automatically written to a second FASTA file that contained contaminating contigs, which were affiliated with Serratia (Supplemental Files 32, 33 and 34). Subsequent analysis with CheckM v1.1.1 [1] confirmed the lack of contamination in the FASTA files written by SprayNPray (Supplemental File 35), while the completeness score for the newly written, clean Pseudomonas assemblies remained > 99%, indicating that none of the Pseudomonas-sequences were likely removed. It is worth noting, however, that CheckM uses a set of marker genes for completeness estimation. Thus, this completion metric is based solely on those contigs that encode the marker genes. Nonetheless, the file containing Pseudomonas classified sequences is similar in size to the expected size of Pseudomonas aeruginosa, so we expect that all or most of the contigs were correctly identified. In any case, assessment of genome quality and completeness is not part of SprayNPray’s pipeline and it is up to the user to assess their genome assemblies and metagenome bins.

We also used MetaBAT to generate bins from the contaminated Pseudomonas assemblies. MetaBAT generally performed well in creating bins containing Pseudomonas contigs. While the MetaBAT-generated Pseudomonas bins contained the majority of the contigs that are affiliated with Pseudomonas taxonomically (in terms of the number of contigs, as well as the total combined length of contigs), within these bins, we also identified contigs that are taxonomically affiliated with Serratia, the contaminant in these assemblies. Moreover, on all five sensitivity/specificity settings, one of the Pseudomonas assemblies was consistently split into two different bins.

Case study: symbiont identification

SprayNPray can be used to extract bacterial symbiont contigs from an assembly that contains DNA from a variety of sources and domains of life. As an example, SprayNPray was run on an assembly of Maconellicoccus hirsutus (GCA_003261595; [25], the hibiscus mealybug, which contains two bacterial endosymbionts [26]. In this assembly, the majority of DNA is from the host insect. Visual inspection of the initial output of this assembly (Supplemental File 37) allowed for the identification of metrics (e.g. GC-content, top taxonomic hits, gene density) with which the software was re-run to generate two additional FASTA files, each corresponding to an individual endosymbiont (Supplemental Files 38 and 39).

Case study: HGT identification

SprayNPray can be used to search eukaryotic contigs for genes that may have been horizontally/laterally obtained from bacteria via HGT. To showcase this functionality, we ran SprayNPray on an assembly of the citrus mealybug, Planococcus citri (de la Filia et al., [27], obtained from the MealyBugBase download server (https://download.mealybug.org/v1/Planococcus_citri_Pcitri.v1/fasta/dna/), and available for download here: 10.6084/m9.figshare.19184357 (Supplemental Files 40 and 41). This organism is known to encode multiple HGTs from bacteria on its nuclear genome [28]. A total of 519 putative HGTs were identified (Supplemental File 44), including those previously confirmed by Husnik et al., [28] (e.g. murACDEF, bioABD, dapF, ddl), as well as those that were reported but not confirmed (e.g. numerous AAA [ATPases Associated with diverse cellular Activities]-family ATPases of diverse origins, ankyrin repeat proteins with close homology to Wolbachia spp., and type III effectors) [28]. We note, however, that extreme caution should be taken in interpreting candidate HGTs,which should be validated by exploring the genomic context of each putative gene that is thought to have been horizontally acquired.

Conclusions

Here, we present SprayNPray, a bioinformatics software designed to aid in the taxonomic analysis of diverse (meta)genomic datasets. The appeal of this software is its ease-of-use and straightforward output that is amenable to visual inspection and/or computational parsing. We designed this versatile software to lower barriers for those with limited experience in bioinformatics and programming.

Availability and requirements

Project Name: SprayNPray.

Project home page: https://github.com/Arkadiy-Garber/SprayNPray

Operating system(s): Linux/MacOS.

Programming language: Python.

Other requirements: Python3, DIAMOND, Prodigal, MetaBAT.

License: GNU General Public License v3.0

Any restrictions to use by non-academics: No further restrictions to use beyond license.

Abbreviations

GC content

Guanine cytosine content, represented as a percentage of total base pairs

MAGs

Metagenome-assembled genomes

ORF

Open reading frame

NCBI

National Center for Biotechnology Information

nr

NCBI’s non-redundant protein database

BAM

Binary alignment map (file type)

AAI

Average amino acid identity

kb/kbp

Kilobase-pair

BLAST

Basic local alignment search tool

HGT

Horizontal gene transfer

Authors’ contributions

AIG and SMM designed and implemented SprayNPray. CRA, VSC, SEL, and JMB generated data for case studies. AIG, CRA, and SMM wrote the early version of the manuscript. AIG, CRA, VSC, SEL, JMB, and SMM read, commented, edited, and approved the final manuscript.

Funding

This publication was supported in part by a Cystic Fibrosis Foundation (CFF) Carol Basbaum Memorial Research Fellowship (ARMBRU19F0) and National Institutes of Health (NIH) T32HL129949 to CRA, University of Pittsburgh CTSI Pilot Program and NIH NCATS UL1 TR0000005 to SEL and JMB, NIH R33HL137077 and GILEAD Investigator Sponsored Research Award to SEL, VSC, and JMB, NIH U01AI124302 to VSC, and NIH R01HL123771, CFF BOMBER18G0, and CFF RDP BOMBER19R0 to JMB. This publication is partially funded by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement NA15OAR4320063.

Availability of data and materials

Supplemental files, corresponding to the genome data used and SprayNPray output, are provided with this article and at https://doi.org/10.6084/m9.figshare.19127084. These data are also freely available in the GitHub repository of this software: https://github.com/Arkadiy-Garber/SprayNPray/tree/master/Supplement-Case_Studies.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.