ORFograph: search for novel insecticidal protein genes in genomic and metagenomic assembly graphs
Microbiome volume 9, Article number: 149 (2021)
Since the prolonged use of insecticidal proteins has led to toxin resistance, it is important to search for novel insecticidal protein genes (IPGs) that are effective in controlling resistant insect populations. IPGs are usually encoded in the genomes of entomopathogenic bacteria, especially in large plasmids in strains of the ubiquitous soil bacteria, Bacillus thuringiensis (Bt). Since there are often multiple similar IPGs encoded by such plasmids, their assemblies are typically fragmented and many IPGs are scattered through multiple contigs. As a result, existing gene prediction tools (that analyze individual contigs) typically predict partial rather than complete IPGs, making it difficult to conduct downstream IPG engineering efforts in agricultural genomics.
Although it is difficult to assemble IPGs in a single contig, the structure of the genome assembly graph often provides clues on how to combine multiple contigs into segments encoding a single IPG.
We describe ORFograph, a pipeline for predicting IPGs in assembly graphs, benchmark it on (meta)genomic datasets, and discover nearly a hundred novel IPGs. This work shows that graph-aware gene prediction tools enable the discovery of greater diversity of IPGs from (meta)genomes.
We demonstrated that analysis of the assembly graphs reveals novel candidate IPGs. ORFograph identified both already known genes “hidden” in assembly graphs and potential novel IPGs that evaded existing tools for IPG identification. As ORFograph is fast, one could imagine a pipeline that processes many (meta)genomic assembly graphs to identify even more novel IPGs for phenotypic testing than would previously be inaccessible by traditional gene-finding methods. While here we demonstrated the results of ORFograph only for IPGs, the proposed approach can be generalized to any class of genes.
Biopesticides are important components of pest management programs that have been successful as an alternative to conventional chemical pesticides. These compounds, which are developed from the plant, animal, and bacterial proteins, do not leave harmful residues, are non-toxic to humans and the environment, and are more target-specific than conventional pesticides . These advantages led to a worldwide proliferation of biopesticides and resulted in a multi-billion-dollar biopesticide market.
Insecticidal proteins, representing an important class of biopesticides, have been widely used in agriculture. Entomopathogenic bacteria, especially strains of the species Bacillus thuringiensis (Bt), produce crystal (Cry) and cytolytic (Cyt) insecticidal proteins and secreted vegetative insecticidal proteins (VIPs) that specifically target various insects, including insects from the orders Lepidoptera, Coleoptera, Hemiptera, and Diptera . Insecticidal proteins are used to control pests of crop plants by mechanical methods, such as spraying to disperse microbial formulations containing various bacterial strains onto plant surfaces, and by using genetic transformation techniques to produce transgenic plants expressing insecticidal proteins. Indeed, the development of insecticidal transgenic crops has been transformative for agriculture. In 2017, 101 million hectares of cropland were devoted to their cultivation across the world and the adoption of specific transgenic crops has been associated with the reduction or elimination of broad-spectrum synthetic chemical insecticides in those environments .
Although insecticidal proteins from B. thuringiensis have become an important biopesticide against a wide range of insects, their prolonged use leads to rapidly developing toxin resistance . Thus, it is important to search for novel insecticidal proteins that are effective in controlling resistant insect populations. Although the number of known Cry-encoding genes grew from just 14 30 years ago  to over 700 today, there is a constant need to identify new insecticidal protein genes (IPGs) to overcome insecticide resistance. Since B. thuringiensis is indigenous to many environments (its strains have been isolated worldwide from soil, insects, and leaves), genomic and metagenomic samples containing B. thuringiensis or other entomopathogenic bacterial strains provide many opportunities for finding novel IPGs . However, the search for novel IPGs faces computational challenges that we describe below.
Initially, the Cry-encoding genes were searched for by PCR-based techniques using primers from their highly conserved regions [6, 10]. The basic PCR step was followed by variations such as E-PCR , PCR-RFLP , and PCR-SSCP . Historically, these methods, which are all limited by the success/failure of the primer selection had only been applied to the discovery of the three-domain Cry genes .
Next-generation sequencing opened new possibilities for IPG discovery as novel Cry and Cyt genes in a newly sequenced genome can be found by similarity search against a database of known genes . In particular, the similarity search based on Hidden Markov Models (HMMs) allows one to reveal more diverged Cry genes than those found using PCR-based methods. However, since Cry genes are rather variable, their HMMs typically represent only the main sequence domains rather than complete Cry genes. Ye et al.  and Zheng et al.  developed the BtToxin_scanner and BtToxin_Digger tools that use machine learning techniques to make the search for Cry genes more sensitive. BtToxin_scanner was applied for Cry gene identification in various studies [13, 19, 57].
However, all existing methods for IPG discovery are limited in their ability to reconstruct complete genes when their fragments scattered over multiple contigs. Since popular general-purpose gene prediction tools GeneMark , Prodigal , and Glimmer , as well as their metagenomic versions metaGeneMark , metaProdigal , and metaGlimmer , analyze individual contigs, they typically predict partial rather than complete IPGs, a bottleneck in the downstream IPG engineering efforts in agricultural genomics.
Development of a candidate IPG into a commercially viable toxin is a complex and time-consuming process that includes (i) prioritization of novel candidate IPGs for follow-up synthesis, (ii) synthesis and expression of selected IPGs for follow-up novel toxin production, and (iii) testing these novel toxins against various insects. ORFograph contributes to the first step of this pipeline by providing additional candidate IPGs whose parts are scattered over multiple contigs and thus were not available for a follow-up analysis in previous studies. This new stream of novel IPGs is important not only for agricultural genomics but also for biomedicine since some Cry proteins, such as parasporins, preferentially kill cancer cells .
ORFograph searches for novel IPGs in the assembly graphs (rather than individual contigs) that are generated by modern genome assemblers such as SPAdes  and metaSPAdes . Given a read-set, SPAdes and metaSPAdes first construct the de Bruijn graph that consists of nodes (k-mers that appear frequently in reads) and edges connecting these nodes that are labeled by substrings from reads . Since each error in a read creates a bubble in the de Bruijn graph (making this graph very complex), SPAdes and metaSPAdes error-correct reads and transform the de Bruijn graph into a simpler assembly graph. In the case of an “ideal” assembly graph, a genome is spelled by a path that visits all edges of the assembly graph.
Given a read-set, an assembly graph consists of nodes (k-mers that appear frequently in reads) and edges connecting these nodes that are labeled by substrings from the genome . In the case of an “ideal” assembly graph, a genome is spelled by a path that visits all edges of the assembly graph.
Figure 1 presents a small subgraph of the assembly graph of the SRR6238356 dataset constructed by SPAdes. The entire assembly graph consists of 1732 vertices and 1288 edges (654 of them are long edges with lengths exceeding 1000 bp). The green path in Fig. 1 represents one of three potential Cry1-like genes in this subgraph that has a length 3378 bp and traverses 21 edges. The existing gene prediction tools run on linear contigs and are not designed to predict genes on graphs. Therefore, if an IPG is scattered over multiple contigs, these tools can at best predict some fragments of this gene (losing information about the order of these fragments) rather than a complete gene, thus impairing any further IPG engineering efforts.
Figure 1 illustrates the importance of “threading” a known IPG (or an IPG domain represented by an HMM) through the assembly graph to discover novel IPGs. This sequence-to-graph alignment problem has been addressed for gene prediction in splicing graphs , for protein alignments in a graph describing all potential secondary structure predictions , and for gene prediction in metagenomic assembly graphs [, 31, 64, 66, 71]. However, since the existing sequence-to-graph alignment approaches lack the ability to align HMMs to metagenomic assembly graphs and do not take into account specific features of IPGs, they are not well suited for IPG discovery.
Here, we describe the ORFograph algorithm (the source code is publicly available at https://github.com/ablab/orf-search) and apply it to all publicly available read-sets representing the Bacillus genus as well as to an underexplored metagenomic datasets. ORFograph uses the SPAligner tool for graph-based sequence alignment  and the PathRacer tool for graph-based HMM alignment  to find novel IPGs (scattered over multiple contigs) that evaded detection by previous approaches. ORFograph identified nearly a hundred novel IPGs that evaded detection in all previous studies. Our work demonstrates that traditional “single contig” gene-finding approaches (both ab initio and similarity-based ) should be complemented by graph-based gene-prediction algorithms that use databases of proteins and protein domain models as additional evidence for finding genes in (meta)genomes. These graph-based algorithms can contribute to unmasking gene content and diversity found in (meta)genomes, especially for large and variable gene families. Although ORFograph is currently limited to insecticide toxins, our next goal is to extend it into a general tool for identifying arbitrary protein families in assembly graphs such as glycoside hydrolases and CAS proteins that are often scattered over multiple contigs.
After constructing the assembly graph, ORFograph searches for IPGs encoded in this graph. Below we describe the steps of the ORFograph pipeline (Fig. 2):
Aligning known insecticide proteins/HMMs to the assembly graph. ORFograph uses SPAligner  to align known insecticide proteins to the assembly graph and retains all alignments with a length exceeding 80% of the protein length. It also uses PathRacer  to align insecticide HMMs to the assembly graph and retains all alignments with e value below 10−9 and length exceeding 90% of the HMM length. These alignments reveal anchor-paths (partial ORFs) that have to be further extended into complete ORFs. An anchor-path either traverses a substring of an edge, or an entire edge, or multiple edges in the assembly graph.
Start and stop codon search. For each anchor-path, ORFograph finds all putative start and stop codons in the assembly graph by exploring all paths in this graph as described in the “Methods” section.
Generation of complete coding sequences (CDSs). Given a graph, we refer to the set of all paths between its vertices v and w (found by bounded exhaustive search limited to generating at most 1000 paths) as Paths(v,w). Given an anchor-path AnchorPath between vertices start(AnchorPath) and end(AnchorPath), a start codon ending at vertex start-codon and a stop-codon starting at vertex stop_codon, ORFograph generates path-sets Paths(start-codon,start(AnchorPath)) and Paths(end(AnchorPath),stop_codon). For each pair of paths PathFromStart and PathToStop from the constructed path-sets, it further constructs the concatenate formed by the start codon, PathFromStart, AnchorPath, PathToStop, and stop codon. ORFograph considers only the concatenated paths with length below max_restorable_length threshold (default value 3000). This operation is repeated for each anchor-path and each pair of start/stop codons, followed by filtering of the concatenated paths as described in “Methods” section. Finally, each resulting path (in nucleotides) is translated into the corresponding amino acid sequence and duplicates are filtered out.
IPG clustering and selecting representative IPGs. In the case of complex assembly graphs, ORFograph may output dozens (and even hundreds) of very similar IPGs, thus complicating further analysis. ORFograph thus clusters the resulting potential IPGs and selects a representative IPG in each cluster (see “Methods” section). Since this paper focuses on difficult-to-find IPGs scattered over multiple contigs, easy-to-identify sequences that are found in a single contig and sequences that represent known genes can be filtered out from the main output.
We benchmarked ORFograph using the following (meta)genomic datasets publicly available at https://figshare.com/s/f20604a5333bbe4514c9:
Bti1 and Bti2 datasets
These two Illumina read-sets (accession numbers SRR8474067 and SRR8467560) correspond to Bti strains ATCC35646 and AM65-52 that are further referred to as Bti1 and Bti2, respectively . ATCC35646 is a type strain of Bti often designated as ATCC35646. AM65-52 is the main component of the Vectobac®, a water-dispersible powder formulation of Bti for control of mosquito larvae. Both datasets were assembled with SPAdes (Table 1).
Statistics were generated with QUAST  and MetaQUAST  tools. The number of long edges (defined as edges longer than 1 kb) reflects the complexity of the assembly graph. The reads were trimmed and filtered using Trimmomatic-0.38 (with parameters: ILLUMINACLIP:adapters/TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:70) and assembled with SPAdes-3.12 (Bti1 and Bti2) and metaSPAdes-3.12 (NYCS) with k-mer size 55.
All isolate IPG-containing bacteria in the NCBI database (BALL dataset)
To extract all Bacillus datasets from the NCBI database, we used the search query (((Bacillus[Organism]) AND Illumina) AND WGS[Strategy]) AND Paired[Layout]) that resulted in 2749 datasets. We used the Diamond tool  to align reads from each of the extracted datasets to known IPGs and detect datasets that contain Cry, Cyt, or Vip toxin genes. We say that an IPG is covered by a read-set if at least 50% of the IPG length is covered by reads alignments with identity exceeding 80%. A read-set was considered for further analysis if its reads covered at least one known IPG, resulting in 342 datasets. Afterward, we assembled each selected dataset using SPAdes and analyzed the resulting assemblies with ORFograph. ORFograph identified 72 datasets (among all 342 BALL datasets) that have IPGs alignments scattered over multiple contigs (Supplementary Table S2). Supplementary Figure S1 presents information about assemblies of these 72 datasets.
New York City subway metagenome (NYCS)
Afshinenkoo et al.  explored the microbial diversity of New York City subways by analyzing read-sets from multiple metagenomic samples (that we refer to as the NYCS dataset). Although this study did not specifically pursue the goal of finding IPGs, Parks et al.  identified a metagenome-assembled genome B. thuringiensis UBA3967 in some of these samples (Illumina read-set with accession number SRR1748627). Gillis et al.  analyzed the B. thuringiensis UBA3967 strain and noticed that, although it is very similar to the Bti genomes, it cannot be classified as a Bti strain since it lacks the Bti plasmids. Since all known Cry-encoding genes reside on plasmids, Gillis et al.  came to a conclusion that the reported B. thuringiensis UBA3967 assembly does not encode the entire set of Bti Cry toxins. We used ORFograph to search for potentially missed Cry and Cyt toxins in the metagenomic dataset from which the B. thuringiensis UBA3967 sequence was inferred.
In addition to real metagenomic datasets, we analyzed the simulated dataset generated by the Critical Assessment of Metagenome Interpretation (CAMI) consortium , complemented by simulated reads from various Bt strains that are enriched by the Cry genes. Supplementary Note “Benchmarking ORFograph on simulated datasets” benchmarks ORFograph on these datasets.
Analyzing IPGs predicted by ORFograph
We used Bti1, Bti2, BALL, and NYCS datasets to analyze ORFograph predictions. For each dataset, we launched SPAdes (Bti1, Bti2, and BALL datasets) or metaSPAdes (NYCS dataset) to construct the assembly graph and further launched the ORFograph pipeline applying SPAligner (using all known Cry, Cyt, and Vip toxins) and PathRacer (using all known HMMs derived from these toxins). Supplementary Table S1 provides information about ORFograph runtime and memory footprint.
Shikov et al.  recently developed the CryProcessor pipeline for IPG discovery by applying the PathRacer tool  to an assembly graph. However, since CryProcessor only searches for the three-domain Cry toxins, it cannot be used as a general pipeline for IPGs discovery from assembly graphs. It cannot be benchmarked against ORFograph since it only takes into account the sequences found by PathRacer rather than the paths that contain these sequences.
ORFograph results for Bti1 and Bti2 genomes
Previous studies identified two genes encoding Cry proteins in Bti1 and seven genes encoding Cry proteins in Bti2 . Both Bti1 and Bti2 have the pBtic100 plasmid carrying cry60Aa and cry60Ba genes. In addition, Bti2 has the pBtoxis plasmid with five Cry genes (cry4Aa, two cry4Ba, cry10Aa, cry11Aa) and three Cyt genes (cyt1Aa, cyt2Ba, cyt1Ca).
ORFograph identified 17 (23) clusters of putative IPGs in Bti1 (Bti2). We selected cluster representatives and ran BLAST  against the non-redundant protein database. Although most of these representatives have > 99% identity with thioredoxins, metallophosphoesterases, and disulfide reductases, ORFograph also identified cry60Aa and cry60Ba toxins in Bti1 that each resided in a single contig (and thus can be found without using ORFograph). In the case of Bti2, it identified cry60Aa, cry60Ba, five other known toxins (cry11Aa, cry10Aa, cry4Aa, and cry4Ba), and three known Cyt proteins (cyt1Aa, cyt2Ba, and cyt1Ca).
Proteins cry11Aa, cry10Aa, cry60Aa, and cry60Ba and all three Cyt proteins are located within single contigs and thus can be found without using ORFograph. However, cry4Aa and cry4Ba are scattered across several contigs and thus would evade identification by existing gene prediction tools (Fig. 3). Identification of these genes by ORFograph is particularly important since they contribute to the most valuable toxin activity against mosquitoes .
Gene-paths for two copies of cry4Ba coincide since one of them is a substring of another. The cry4Aa toxin was found at the ORFs generation step but was filtered out as its path conflicts with one of the contigs shown in dark magenta in Fig. 3. This filtering is based on the concept of “unique edges” that are currently defined based on a fixed threshold of 300 nt (see the “Methods” section), pointing to the challenge of parameter selection in ORFograph. Since the Bti2 dataset resulted in a high-quality assembly (N50 = 157kb), increasing this threshold for assemblies with high N50 (e.g., from 300 nt to 500 nt) would lead to identifying cry4Aa. In the future, we will modify ORFosearch to make this threshold variable rather than fixed.
As most Cry genes reside on plasmids, we also checked if the specialized plasmid assembler plasmidSPAdes  can resolve the cry4 gene-paths in the Bti2 assembly graph. We launched plasmidSPAdes with default parameters on Bti2 reads and restored the same set of Cry genes as with the standard assembly approach.
Although plasmidSPAdes succeeded in this (relatively simple) case, our analysis revealed that it results in an only modest contribution to ORFograph identifications as compared to SPAdes. Supplementary Note “Benchmarking ORFograph against plasmidSPAdes and metaplasmidSPAdes” analyzes plasmidSPAdes and metaplasmidSPAdes  and demonstrates that these tools have limited benefits for IPG identification.
ORFograph results for BALL datasets
ORFograph identified 72 datasets (among all 342 BALL datasets) that have IPGs alignments scattered over multiple contigs and selected 419 novel cluster representatives among them (Supplementary Table S2). One of BALL datasets (SRR6238356) contained a very large number of potential IPGs, most of which arise from traversing a highly complex area of the assembly graph (Fig. 1). While this dataset greatly increased the number of potential IPGs (increasing it from 2488 to over 5600), it had only 40 representatives, that were included in the follow-up analysis.
Unclassified Cry1, Cry1A, and Cry1C are the most productive Cry families that served as seeds for identifying most IPGs (≅ 3522 ORFs and ≅ 122 cluster representatives in total). CW_binding_1 and Bacillus HBL are the most productive HMMs (Fig. 4).
Reliable and candidate IPGs
Since all gene prediction tools output some false positives, they face the challenge of assigning some measure of reliability to the predicted genes . For example, short predicted genes are typically less reliable than long predicted genes. We thus analyzed the similarities between cluster representatives and known IPGs to classify the predicted IPGs into reliable (with similarity above a threshold) and candidate (with similarity below a threshold). We emphasize that candidate IPGs are not necessarily incorrect since they may represent particularly interesting cases of novel IPGs that have limited similarity with known IPGs.
Since the Bacterial Pesticidal Protein Resource Center  uses a rather stringent criteria for identifying reliable novel IPGs (at least 95% amino acid identity (AAI) to an existing IPG), we decided to use a less stringent criteria (at least 80% AAI to an existing IPG) to identify IPGs that significantly diverged from known IPGs. We classify a reliable IPGs as novel if it has AAI exceeding 90% (but less 100%) to a known IPG from the BLAST database. Three hundred nine out of 419 cluster representatives were classified as reliable and 232 of them were classified as novel (Fig. 5 and Supplementary Table S3).
One hundred forty-five out of 232 novel IPGs have the length of 1000-1200 amino acids (Fig. 6). Sixty-seven candidate IPGs with length around 750-900 bp are similar to Vip1B, while other 20 candidate IPGs shorter than 1000 bp are similar to Cry2, Cry4, Cry5, Cry11, Cry13, Cry27, and Vip3 families. Most of 145 longer sequences have high identity with sequences from Cry1 family (Fig. 6, left) and only 8 of them are similar to Cry4, Cry5, and Cry9 genes.
Figure 6, right illustrates that just three datasets (SRR6238356, SRR6238345, and SRR4996216) contain 77 out of 232 reliable IPGs. The first two of these datasets were generated in Méric et al.  where the authors analyzed the transfer of Cry-rich plasmids between various species. Méric et al.  analyzed assemblies of 190 B. cereus group isolates, identified genes encoding Bt toxins (Cry, Cyt, Vip, and Sip) using BtToxin_scanner, and revealed that most Cry gene variants belong to Cry1Ia2, Cry2Aa9, Cry2Ab3, and Vip3A families. Our analysis of these datasets is consistent with the results in Méric et al.  with respect to IPGs found in single contigs. However, neither of 59 potential IPGs identified by ORFograph was identified by BtToxin_scanner (Table S4 in ). In addition, two potential IPGs identified by BtToxin_scanner represent fragments (rather than complete genes) of the reliable IPGs identified by ORFograph.
SRR4996216 dataset (with 17 identified IPGs) contains Illumina reads from B. thuringiensis serovar aizawai strain HD-133 assembled via the A5-miseq pipeline  in the original study . Genome annotation was carried out using Prokka  and the NCBI’s Prokaryotic Genome Annotation Pipeline. The original study revealed Cry1Aa, Cry1Ab, Cry1Ca, Cry1Da, Cry1Ia, Cry2Ab, and Cry9Ea, which include many partial or apparently fragmented genes. In contrast, ORFograph found 16 full IPGs, including novel variants of Cry1Aa, Cry1b, Cry1Ca, and Cry1Da.
All Vip1B-like IPGs were obtained from datasets generated in ), where authors characterized the pathogen genes involved in coevolutionary adaptation in an animal host-pathogen interaction system of Caenorhabditis elegans and Bacillus thuringiensis.
Our analysis of the diverse BALL datasets demonstrates that ORFograph greatly extends the set of IPGs found in previous studies. Although our analysis is not exactly benchmarking (since we are identifying sequences not previously assembled), Supplementary Figure S2 present a comparison of putative novel IPGs to existing annotations in the IPG database (the labeled nodes on the tree show known IPGs and the others show putative novel IPGs), illustrating the value of ORFograph as a tool for novel IPG discovery.
ORFograph results for the NYCS dataset
ORFograph found 48 clusters of putative IPGs in the NYCS metagenome assembly graph. We compared all cluster representative with the non-redundant protein database using BLAST. Similar to the analysis of genomic datasets, most cluster representatives have > 99% identity with thioredoxins, metallophosphoesterases, and disulfide reductases. However, ORFograph also identified cry60Aa, cry60Ba, cry11Aa, cry10Aa, cry4Aa, and two cry4Ba genes as well as three Cyt toxins. Similar to the Bti2 dataset, since cry4 genes were scattered over several contigs (Fig. 7), they would evade identification without ORFograph. Since the NYCS dataset contains many Cry and Cyt genes that are similar to genes identified in the Bti2 dataset, we can conclude that UBA3967, also assembled from this dataset, represents mostly the chromosomal part of a viable Bti strain, but with only traces of plasmids that should also be there. We thus detected the corresponding toxins encoded by the plasmids, as it should be expected.
Previous analysis of B. thuringiensis UBA3967 strain
To analyze B. thuringiensis UBA3967 strain, Gillis et al.  compared all plasmids from the strain AM65-52 to all genome assemblies labeled as B. thuringiensis in NCBI (Table 2 in Gillis et al.  lists only those relevant to the Bti cluster). For the UBA3967 assembly, Gillis et al.  found only a rather short region of similarity with plasmids pBtic360, pBtoxis, and pBtic100. However, to conclude that a plasmid is present in the assembly, the similar region should typically cover almost the entire plasmid. Since it was not the case, Gillis et al.  concluded that no plasmid counterpart of those from AM65-52 exists in the UBA3967 strain. The found short similar regions may represent some insertions of transposable elements (IS-elements) or parts of Cry toxins existing in the assembly of the UBA3967 strain. Since all known environmental Bti isolates contain most of these plasmids, Gillis et al.  concluded that the UBA3967 assembly does not correspond to the entire genome (i.e., chromosome and plasmids) of a Bti isolate.
Constructing database of known IPGs and their Hidden Markov Models (HMMs)
Accession numbers of known Cry and Vip toxins were taken from the Bt nomenclature list (Crickmore et al. 2018), and sequences were downloaded from the NCBI database, forming the IPG database. This procedure resulted in extracting 941 protein sequences (811 Cry and Cyt toxins as well as 130 Vip toxins) with the average length of each protein approximately 800 amino acids (Fig. 8, left).
In addition to forming the IPG database, we selected 66 publicly available HMMs commonly found in IPGs and downloaded them from the Pfam database . The number of hidden states in the selected HMMs (referred to as HMM length) varies from 25 to 350 states (Fig. 8, middle).
Gene discovery in the assembly graphs
ORFograph first attempts to find open reading frames (ORFs) containing parts of Cry and Vip genes scattered over multiple contigs. Since Bt genomes typically contain multiple similar Cry and Vip genes, these genes are often fragmented in genomic assemblies. Since the vast majority of Cry and Vip genes are longer than typical bacterial genes (Fig. 8, right), they are more susceptible to fragmentation than other genes. This fragmentation further amplifies in assemblies of metagenomic samples that contain multiple Bt strains.
ORFograph attempts to find all paths in the assembly graph that represent Cry/Vip-encoding ORFs. It first constructs anchor-paths by performing the HMM-to-graph  or sequence-to-graph  alignments to detect partial ORFs and further extends each anchor-path into a complete ORF. Since HMMs for most Cry/Vip genes represent a short domain rather than a full gene sequence, ORFograph often finds a large number of short alignments, with each such alignment-path revealing only a part of the potential gene rather than a complete ORF. ORFograph removes such short alignment-paths from further consideration if they represent subpaths of longer (and thus more informative) alignment-paths resulting from longer gene segments. Specifically, if an anchor-path is a subpath of another anchor-path with the same frameshift, this anchor-path is filtered out. Afterward, ORFograph extends each remaining anchor-path to find the complete ORF containing it.
Since insecticide toxins share some protein domains with thioredoxins, metallophosphoesterases, and disulfide reductases (originating from alignments of thioredoxin, metallophos, and AhpC-TSA HMMs, respectively), many HMM alignments found by ORFograph arise from these three protein families rather than IPGs. To filter out these three protein families from the ORFograph output, we compare each putative IPG found by ORFograph against the database of known insecticidal toxins using BLAST and only retain IPGs that are similar to a known insecticide toxin (with percent identity exceeding a threshold Identity = 80%).
Search for start and stop codons in IPGs
ORFograph uses HMMs and known IPGs to find the highest-scoring anchor-paths that correspond to a partial Cry or Vip gene sequence. However, it is unclear how to extend these partial sequences into a complete coding region of a gene (referred to as CDS) since the choice of the start and stop codon for a given anchor-path is often ambiguous. In addition, the selected start and stop codons are often connected with the anchor-path by multiple paths. ORFograph finds all possible start/stop codons that can be reached from the leftmost/rightmost position of the anchor-path.
For each anchor-path, ORFograph finds all putative start and stop codons in the assembly graph by exploring all paths in this graph using the Breadth-First-Search . During this search, it assigns a frameshift string (of length 1, 2, or 3) that specifies the part of the last codon traversed on the way to this node. A vertex is classified as terminal if its frameshift string represents a stop codon. The Breadth-First-Search identifies all terminal vertices and stops further graph exploration in these vertices. Information about sequences with start codons that are positioned after a stop codon or the Shine-Dalgarno sequence in the graph is reflected in the CDS file.
After identifying the set of start and stop codons for each partial alignment, ORFograph explores CDSs corresponding to these alignments. A CDS given by the partial alignment corresponds to a path between a pair of start and stop codons that passes through the partial alignment and represents a putative IPG. For each pair of start and stop codons, the path is divided into prefix (an unknown path from the start codon to the leftmost position of the alignment), middle (the known partial alignment), and suffix (an unknown path from the rightmost position of the alignment to the stop codon).
ORFograph performs an exhaustive search to generate all potential prefixes and suffixes separately and concatenates them with the middle part to construct a set of full paths that represent potential IPGs. To speed up CDS generation, ORFograph pre-calculates a set of edges that can be traversed on the way from potential start codons to the beginning of the anchor-path (and from the end of the anchor-path to the potential stop codons) using the Depth-First Search . ORFograph only uses these edges (rather than all edges of the graph) to generate prefixes and suffixes and further concatenates each prefix, the anchor-path, and each suffix to generate the putative IPG. Since the number of putative full-length genes is typically large, ORFograph filters and ranks the constructed putative IPGs as described below.
Analyzing HMM positions within IPGs
We aligned all HMMs to the IPG database using the HMMer tool  and constructed the distribution of their starting positions inside the IPGs. ORFograph uses this distribution to find the most likely distance from the start codon to the position of the HMMs within an IPG. We aligned each HMM to all known IPGs and generated a histogram of all distances from the gene start to the start of each full HMM alignment. As Supplementary Figure S3 illustrates, this distance is very conserved for some HMMs (e.g., PA14) and less conserved for other HMMs (e.g., Aegerolysin). We thus defined the likelihood of a given distance d as the fraction of distances in the interval [d-binSize, d+binSize] in the histogram (the default value binSize=150). Using the computed likelihoods, ORFograph derives a set of the most likely distances from the start codon for each HMM alignment in the assembly graph and uses these distances to find the most likely prefixes as the prefixes with likelihoods exceeding the default threshold (a similar procedure is used to find the most likely suffixes).
Filtering putative IPGs that conflict with contigs
ORFograph compares each putative IPGs with the set of contigs output by SPAdes/metaSPAdes and filters out IPGs that “contradict” to the contig-set as described below.
While any genome segment is expected to be represented as a path (correct path) in the assembly graph, many paths do not correspond to genome fragments (incorrect paths). Figure 9 shows a fragment of an assembly graph that can be traversed in four different ways: AB1CD1E, AB1CD2E, AB2CD1E, and AB2CD2E. Each of these paths may be either correct or incorrect. Since alternative sequences in bulges (B1/B2 and D1/D2) are typically similar, it is likely that if one of them is chosen as an anchor-path, then another will be chosen as well. This effect can exponentially amplify the number of reported paths. Finding the correct path among many incorrect paths is not unlike the repeat resolution problem in genome assembly . State-of-the-art genome assemblers solve this problem using a variety of additional information (e.g., paired-end reads) for finding correct paths (contig) in the assembly graph . ORFograph takes advantage of contigs output by SPAdes/metaSPAdes and uses them to filter out incorrect IPGs as described below.
Two paths in the assembly graph are called compatible if they overlap (suffix of one of them coincides with a prefix of another) or if one of them is contained within another. ORFograph relies on unique edges in the assembly graph, i.e., the edges that are only passed once by the path representing the genome (genome path). If two correct paths both contain the same unique edge then these paths are compatible. Thus, any putative IPG that contains the same unique edge as a contig path and is not compatible with it can be filtered out as incorrect. In this case, we say that a putative IPG and a contig contradict each other.
Since the identification of unique edges in the assembly graph is a non-trivial problem, we use a simple (albeit error-prone) heuristic for finding them. An edge is considered unique if its length exceeds a threshold minLength (the default value 300 nucleotides), its start node has an outdegree larger than 1, and its end node has an indegree larger than 1. Figure 9 illustrates an example with a putative (red) IPG B1CD2 that contradicts the green contig AB1C and thus is filtered out. Although the putative (blue) IPG AB2CD2 shares an edge with the green contig, it does not contradict this contig because the shared edge is non-unique.
Even after applying the described filters, many putative IPGs may still remain, making it difficult to determine which of them are correct. ORFograph organizes putative IPGs into clusters using the single linkage clustering. Two sequences are clustered together if one of them is a substring of another or their percent identity exceeds a threshold PI (the default value PI = 90%). We further consider all edges of the assembly graph that contribute to IPGs in a given cluster and classify long (exceeding 1 kb) edges in the IPGs from a given cluster as long cluster edges.
Selecting representative IPGs
In some cases, the set of putative IPGs remains large even after the filtering step. Since many of them may represent erroneous variants of the correct IPGs and since reporting all of them may be counter-productive for the downstream experimental analysis, ORFograph selects the most reliable putative IPGs as the best candidates for a further experimental analysis. Since long cluster edges (in difference from the potentially spurious short cluster edges) likely belong to some IPGs, we require that each long cluster edge belongs to at least one representative IPGs.
ORFograph selects a small set of the reliable potential IPGs in each cluster (representative IPGs) in such a way that paths spelled out by representative IPGs include each long cluster edge. For each path, we consider a triplet (start codon distance likelihood, coverage by reads, path length) and classify a path P as more reliable than a path P’ from the same cluster if its triplet is lexicographically larger.
To select representative IPGs, we consider all potential IPGs in each cluster in a lexicographically non-decreasing order of their triplets. ORFograph scans the resulting ordered list and classifies an IPG in this list as representative if it contains a long edge that was not present in the previously considered IPGs from this list.
Although the recently developed SPAligner  and PathRacer  tools offer a possibility to search for any protein family in assembly graphs, we have decided to limit the scope of this project to insecticide toxins as each protein family has specifics that have to be taken into account during the tool development. Our next goal is to extend ORFograph into a general tool for identifying arbitrary protein families in assembly graphs such as glycoside hydrolases  and CAS proteins . Existing tools for identifying protein families, such as GeneHunt , CasFinder , and HMMCAS  are limited to single contigs, thus missing proteins whose parts are scattered over multiple contigs. biosyntheticSPAdes  is the first tool from the SPAdes toolkit aimed at gene finding in assembly graphs. However, since biosyntheticSPAdes has a rather narrow focus on non-ribosomal peptide synthetases (NRPSs), it is not clear how to extend it to an arbitrary protein family.
ORFograph, currently limited to IPG finding, represents the first software from the SPAdes toolkit that can be easily extended into a more general tool for identifying an arbitrary protein family based on a set of family-specific HMMs and a set of approaches generalizing various metrics described in this paper. Development of such a general tool will enable mining breadth of genes from meta(genomes) for a variety of biotechnological uses, such as the discovery of novel antibiotic biosynthesis clusters, gene editing enzymes, or metabolic pathways for industrial biosynthetic use.
Although this paper analyzes IPGs predicted by ORFograph in the SPAdes assemblies, ORFograph can be applied to assembly graphs constructed by any genomic (e.g., Velvet ) or metagenomic (e.g., Megahit ) assembler.
It is important to note that B. thuringiensis is a member of the Bacillus cereus group and is closely related to the pathogenic B. anthracis and B. cereus strains. Since ORFograph facilitates high-throughput discovery of novel IPGs, it is important to verify that the newly discovered insecticide toxins do not harm humans.
We demonstrated that analysis of the assembly graphs reveals novel candidate IPGs. ORFograph identified both already known genes “hidden” in assembly graphs and potential novel IPGs that evaded existing tools for IPG identification. As ORFograph is fast, one could imagine a pipeline that processes many (meta)genomic assembly graphs to identify even more novel IPGs for phenotypic testing than would previously be inaccessible by traditional gene-finding methods.
Availability of data and materials
The datasets generated and/or analyzed during the current study are available at the figshare repository https://figshare.com/s/f20604a5333bbe4514c9
Afshinnekoo E, Meydan C, Chowdhury S, Jaroudi D, Boyer C, Bernstein N, et al. Geospatial resolution of human and bacterial diversity with city-scale metagenomics. Cell Syst. 2015;1(1):72–87. https://doi.org/10.1016/j.cels.2015.01.001.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10. https://doi.org/10.1016/S0022-2836(05)80360-2.
Antipov D, Hartwick N, Shen M, Raiko M, Lapidus A, Pevzner PA. plasmidSPAdes: assembling plasmids from whole genome sequencing data. Bioinformatics. 2016;32(22):3380–7. https://doi.org/10.1093/bioinformatics/btw493.
Antipov D, Raiko M, Lapidus A, Pevzner PA. Plasmid detection and assembly in genomic and metagenomic data sets. Genome Res. 2019;29(6):961–8. https://doi.org/10.1101/gr.241299.118.
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single cell sequencing. J Comput Biol. 2012;19(5):455–77. https://doi.org/10.1089/cmb.2012.0021.
Beron CM, Curatti L, Salerno GL. New strategy for identification of novel cry-type genes from Bacillus thuringiensis strains. Appl Environ Microbiol. 2005;71(2):761–5. https://doi.org/10.1128/AEM.71.2.761-765.2005.
Besemer J, Borodovsky M. GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res. 2005;33(suppl. 2):W451–4. https://doi.org/10.1093/nar/gki487.
Bolotin A, Gillis A, Sanchis V, Nielsen-LeRoux C, Mahillon J, Lereclus D, et al. Comparative genomics of extrachromosomal elements in Bacillus thuringiensis subsp. israelensis. Res Microbiol. 2017;168(4):331–44. https://doi.org/10.1016/j.resmic.2016.10.008.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60. https://doi.org/10.1038/nmeth.3176.
Carozzi NB, Kramer VC, Warren GW, Evola S, Koziel MG. Prediction of insecticidal activity of Bacillus thuringiensis strains by polymerase chain reaction product profiles. Appl Environ Microbiol. 1991;57(11):3057–61. https://doi.org/10.1128/AEM.57.11.3057-3061.1991.
Chai G, Yu M, Jiang L, Duan Y, Huang J. HMMCAS: a web tool for the identification and domain annotations of Cas proteins. IEEE/ACM Trans Comput Biol Bioinformatics. 2018;16:1313–5.
Chari R, Mali P, Moosburner M, Church GM. Unraveling CRISPR-Cas9 genome engineering parameters via a library-on-library approach. Nat Methods. 2015;12:823–6.
Chelliah R, Wei S, Park B-J, Park J-H, Park Y-S, Kim S-H, et al. New perspectives on Mega plasmid sequence (poh1) in Bacillus thuringiensis ATCC 10792 harbouring antimicrobial, insecticidal and antibiotic resistance genes. Microb Pathog. 2019;126:14–8. https://doi.org/10.1016/j.micpath.2018.10.013.
Coil D, Jospin G, Darling AE. A5-miseq: an updated pipeline to assemble microbial genomes from Illumina MiSeq data. Bioinformatics. 2015;31(4):587–9. https://doi.org/10.1093/bioinformatics/btu661.
Crickmore N, Berry C, Panneerselvam S, Mishra R, Connor TR, Bonning BC. Bacterial Pesticidal Protein Res Cent. 2020. https://www.bpprc.org.
Crickmore N, Berry C, Panneerselvam S, Mishra R, Connor TR, Bonning BC. A structure-based nomenclature for Bacillus thuringiensis and other bacteria-derived pesticidal proteins. J Invertebr Pathol. 2020;107438.
Compeau PEC, Pevzner PA, Tesler G. How to apply de Bruijn graphs to genome assembly. Nat Biotechnol. 2011;29(11):987–91. https://doi.org/10.1038/nbt.2023.
Cormen TH, Leiserson CE, Rivest RL, Stein C. Introduction to Algorithms, Third Edition (3rd. ed.). Cambridge: The MIT Press; 2009.
Daas MS, Rosana ARR, Acedo JZ, Douzane M, Nateche F, Kebbouche-Gana S, et al. Insights into the draft genome sequence of bioactives-producing Bacillus thuringiensis DNG9 isolated from Algerian soil-oil slough. Stand Genomic Sci. 2018;13(1):25. https://doi.org/10.1186/s40793-018-0331-1.
Delcher AL, Bratke KA, Powers EC, Salzberg SL. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics. 2007;23(6):673–9. https://doi.org/10.1093/bioinformatics/btm009.
Dvorkina T, Antipov D, Korobeynikov A, Nurk S. SPAligner: alignment of long diverged molecular sequences to assembly graphs. BMC Bioinformatics. 2020;21(S12):306 https://doi.org/10.1186/s12859-020-03590-7.
Eddy SR. Profile Hidden Markov Models. Bioinformatics. 1998;14(9):755–63. https://doi.org/10.1093/bioinformatics/14.9.755.
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2014;32 (5):1792–7. https://doi.org/10.1093/nar/gkh340.
Gassmann AJ, Petzold-Maxwell JL, Clifton EH, Dunbar MW, Hoffmann AM, Ingber DA, et al. Field-evolved resistance by western corn rootworm to multiple Bacillus thuringiensis toxins in transgenic maize. Proc Natl Acad Sci. 2014;111(14):5141–6. https://doi.org/10.1073/pnas.1317179111.
Gelfand MS, Mironov AA, Pevzner PA. Gene recognition via spliced sequence alignment. Proc Natl Acad Sci. 1996;93(17):9061–6. https://doi.org/10.1073/pnas.93.17.9061.
Gillis A, Fayad N, Makart L, Bolotin A, Sorokin A, Kallassy M, et al. Role of plasmid plasticity and mobile genetic elements in the entomopathogen Bacillus thuringiensis serovar israelensis. FEMS Microbiol Rev. 2018;42(6):829–56. https://doi.org/10.1093/femsre/fuy034.
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29(8):1072–5. https://doi.org/10.1093/bioinformatics/btt086.
Guigó R, Agarwal P, Abril JF, Burset M, Fickett JW. An assessment of gene prediction accuracy in large DNA sequences. Genome Res. 2000;10(10):1631–42. https://doi.org/10.1101/gr.122800.
Hernández-Rodríguez CS, Boets A, Van Rie J, Ferré J. Screening and identification of vip genes in Bacillus thuringiensis strains. J Appl Microbiol. 2009;107(1):219–25. https://doi.org/10.1111/j.1365-2672.2009.04199.x.
Höfte H, Whiteley HR. Insecticidal crystal proteins of Bacillus thuringiensis. Microbiol Rev. 1989;53(2):242–55. https://doi.org/10.1128/mr.53.2.242-255.1989.
Huson DH, Tappu R, Bazinet AL, Xie C, Cummings MP, Nieselt K, et al. Fast and simple protein-alignment-guided assembly of orthologous gene families from microbiome sequencing reads. Microbiome. 2017). https://doi.org/10.1186/s40168-017-0233-2;5(1):11.
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11(1):119. https://doi.org/10.1186/1471-2105-11-119.
Hyatt D, LoCascio PF, Hauser LJ, m Edward C Uberbacher, E.C. Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics. 2012;28(17):2223–30. https://doi.org/10.1093/bioinformatics/bts429.
Jeong H, Choi SK, Park SH. Genome sequences of Bacillus thuringiensis Serovar kurstaki strain BP865 and B. thuringiensis Serovar aizawai Strain HD-133. Genome Announcements. 2017;5:e01544–16.
Juárez-Pérez VM, Ferrandis MD, Frutos R. PCR-based approach for detection of novel Bacillus thuringiensis Cry genes. ApplEnviron Microbiol. 1997;63(8):2997–3002. https://doi.org/10.1128/aem.63.8.2997-3002.1997.
Kaoutari AE, Armougom F, Gordon JI, Raoult D, Henrissat B. The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nate Rev Microbiol. 2013;11(7):497–504. https://doi.org/10.1038/nrmicro3050.
Kelley DR, Liu B, Delcher AL, Pop M, Salzberg SL. Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering. Nucleic Acids Res. 2012;40(1):e9. https://doi.org/10.1093/nar/gkr1067.
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-nodesolution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31(10):1674–6. https://doi.org/10.1093/bioinformatics/btv033.
Lin Y, Fang G, Peng K. Characterization of the highly variable cry gene regions of Bacillus thuringiensis strain ly4a3 by PCR-SSCP profiling and sequencing. Biotechnol Lett. 2007;29(2):247–51. https://doi.org/10.1007/s10529-006-9224-2.
Makarova KS, Wolf YI, Alkhnbashi OS, Costa F, Shah SA, Saunders SJ, et al. An updated evolutionary classification of CRISPR–Cas systems. Nat Rev Microbiol. 2015;13(11):722–36. https://doi.org/10.1038/nrmicro3569.
Masri L, Branca A, Sheppard AE, Papkou A, Laehnemann D, Guenther PS, et al. Host–pathogen coevolution: the selective advantage of Bacillus thuringiensis virulence and its Cry toxin genes. PLoS Biol. 2015;13(6):e1002169. https://doi.org/10.1371/journal.pbio.1002169.
Meleshko D, Mohimani H, Tracanna V, Hajirasouliha I, Medema MH, Korobeynikov A, et al. BiosyntheticSPAdes: reconstructing biosynthetic gene clusters from assembly graphs. Genome Res. 2019;29(8):1352–62. https://doi.org/10.1101/gr.243477.118.
Méric G, Mageiros L, Pascoe B, Woodcock DJ, Mourkas E, Lamble S, et al. Lineage-specific plasmid acquisition and the evolution of specialized pathogens in Bacillus thuringiensis and the Bacillus cereus group. Mol Ecol. 2018;27(7):1524–40. https://doi.org/10.1111/mec.14546.
Mikheenko A, Saveliev V, Gurevich A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics. 2016;32(7):1088–90. https://doi.org/10.1093/bioinformatics/btv697.
Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, Tosatto SCE, Paladin L, Raj S, Richardson LJ, Finn RD, Bateman A. Pfam: The protein families database in 2021. Nucleic Acids Res. 2021;49(D1):D412–9.
Nielsen P, Krogh A. Large-scale prokaryotic gene prediction and comparison to genome annotation. Bioinformatics. 2005;21(24):4322–9. https://doi.org/10.1093/bioinformatics/bti701.
Nguyen SN, Flores A, Talamantes D, Dar F, Valdez A, Schwans J, Berlemont R. GeneHunt for rapid domain-specific annotation of glycoside hydrolases. Sci Rep. 2019;9(1). https://doi.org/10.1038/s41598-019-46290-w.
Noguera PA, Ibarra JE. Detection of new cry genes of Bacillus thuringiensis by use of a novel PCR primer system. Appl Environ Microbiol. 2010;76(18):6150–5. https://doi.org/10.1128/AEM.00797-10.
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27(5):824–34. https://doi.org/10.1101/gr.213959.116.
Ohba M, Mizuki E, Uemori A. Parasporin, a new anticancer protein group from Bacillus thuringiensis. Anticancer Res. 2009;29(1):427–33.
Palma L, Muñoz D, Berry C, Murillo J, de Escudero I, Caballero P. Molecular and insecticidal characterization of a novel Cry-related protein from Bacillus Thuringiensis toxic against Myzus persicae. Toxins. 2014;6(11):3144–56. https://doi.org/10.3390/toxins6113144.
Palma L, Muñoz D, Berry C, Murillo J, Caballero P. Bacillus thuringiensis toxins: an overview of their biocidal activity. Toxins. 2014;6(12):3296–325. https://doi.org/10.3390/toxins6123296.
Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2(11):1533–42. https://doi.org/10.1038/s41564-017-0012-7.
Prjibelski AD, Vasilinetc I, Bankevich A, Gurevich A, Krivosheeva T, Nurk S, et al. ExSPAnder: a universal repeat resolver for DNA fragment assembly. Bioinformatics. 2014;30(12):i293–301. https://doi.org/10.1093/bioinformatics/btu266.
Price MN, Dehal PS, Arkin AP, Poon AFY. FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE. 2010;5(3):e9490. https://doi.org/10.1371/journal.pone.0009490.
Romeis J, Naranjo SE, Meissle M, Shelton AM. Genetically engineered crops help support conservation biological control. Biol Control. 2019;130:136–54. https://doi.org/10.1016/j.biocontrol.2018.10.001.
Sajid M, Geng C, Li M, Wang Y, Liu H, Zheng J, et al. Whole-genome analysis of Bacillus thuringiensis revealing partial genes as a source of novel Cry toxins. Appl Environ Microbiol. 2018;84:e00277–18.
Sanahuja G, Banakar R, Twyman RM, Capell T, Christou P. Bacillus thuringiensis: a century of research, development and commercial applications: a century of Bacillus thuringiensis. Plant Biotechnol J. 2011;9(3):283–300. https://doi.org/10.1111/j.1467-7652.2011.00595.x.
Schnepf E, Crickmore N, Van Rie J, Lereclus D, Baum J, Feitelson J, et al. Bacillus thuringiensis and its pesticidal crystal proteins. Microbiol Mol Biol Rev. 1998;62(3):775–806. https://doi.org/10.1128/MMBR.62.3.775-806.1998.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–9. https://doi.org/10.1093/bioinformatics/btu153.
Shikov AE, Malovichko YV, Skitchenko RK, Nizhnikov AA, Antonets KS. No more tears: mining sequencing data for novel Bt Cry toxins with CryProcessor. Toxins. 2020;12(3):204. https://doi.org/10.3390/toxins12030204.
Shlemov A, Korobeynikov A. PathRacer: racing profile HMM paths on assembly graph. Lecture Notes Comput Sci. 2019;11488:80–94. https://doi.org/10.1007/978-3-030-18174-1_6.
Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Dröge J, et al. Critical assessment of metagenome interpretation-a benchmark of metagenomics software. Nat Methods. 2017;14(11):1063–71. https://doi.org/10.1038/nmeth.4458.
Wang Q, Fish JA, Gilman M, et al. Xander: employing a novel method for efficient gene-targeted metagenomic assembly. Microbiome. 2015;3:32. https://doi.org/10.1186/s40168-015-0093-6.
Wick RR, Schultz MB, Zobel J, Holt KE. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics. 2015;31(20):3350–2. https://doi.org/10.1093/bioinformatics/btv383.
Wu YW, Rho M, Doak TG, Ye Y. Stitching gene fragments with a network matching algorithm improves gene assembly for metagenomics. Bioinformatics. 2012;28(18):i363–9.
Ye W, Zhu L, Liu Y, Crickmore N, Peng D, Ruan L, et al. Mining new crystal protein genes from Bacillus thuringiensis on the basis of mixed plasmid-enriched genome sequencing and a computational pipeline. Appl Environ Microbiol. 2012;78(14):4795–801. https://doi.org/10.1128/AEM.00340-12.
Ye Y, Jaroszewski L, Li W, Godzik A. A segment alignment approach to protein comparison. Bioinformatics. 2003;19(6):742–9. https://doi.org/10.1093/bioinformatics/btg073.
Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18(5):821–9. https://doi.org/10.1101/gr.074492.107.
Zheng J, Yu Y, Ye W, Peng D, Sun M. BtToxin_Digger: a comprehensive and high-throughput pipeline for mining toxin protein genes from Bacillus thuringiensis. bioRxiv. 2020; https://doi.org/10.1101/2020.05.26.114520.
Zhong C, Yang Y, Yooseph S. GRASP2: fast and memory-efficient gene-centric assembly and homolog search for metagenomic sequencing data. BMC Bioinformatics. 2019). https://doi.org/10.1186/s12859-019-2818-1;20(S11):276.
Zhu W, Lomsadze A, Borodovsky M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010;8(12):e132.
This work represents a research collaboration funded by Bayer U.S. - Crop Science, Data Science & Analytics. This work of T.D. was supported by the Russian Science Foundation (grant 19-14-00172).
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Information about ORFograph runtime and memory usage. ORFograph was run in 16 threads. Supplementary Table S2. ORFograph results for all 72 BALLdatasets with novel candidate IPGs. The “#novel candidate IPGs” column shows the number of ORFs that differ from known IPGs from the Bt nomenclature database. Datasets that produced reliable novel IPGs are marked as bold. Supplementary Figure S1. Assembly statistics for 72 datasets with potential novel IPGs. Each bar represents a measurement for one of 72 datasets. (Upper left) Number of reads per dataset (in millions); (Upper right) Number of long edges (exceeding 1 kb in length) in the assembly graph (in thousands). Number of ultralong edges (exceeding 5 kb in length) is shown in blue; (Bottom left) N50 of all contigs (in kb); (Bottom right) Total assembly length of long contigs (in megabases). Total assembly length of ultralong contigs is shown in blue. Supplementary Figure S2. Diversity of the reliable Cry1 sequences identified from the BALL dataset. Putative Cry1 novel IPGs identified by ORFograph were aligned with sequences from the Bt nomenclature list (Crickmore et al., 2018) using Muscle v3.8.31 , and a maximum likelihood phylogeny was constructed with FastTree v2.1.10 . Supplementary Figure S3. Histograms of locations of matches of HMMs along the IPG sequences (only 14 out of 66 HMM have reliable matches). Orange (blue) columns show the distribution of distance from the gene starts (ends) to the location of the matches of HMMs. Supplementary Note. Benchmarking ORFograph on simulated datasets. Supplementary Table S3. Information about seven simulated datasets enriched with Cry genes. Supplementary Table S4. Information about the percent identity between six analyzed Cry proteins. Each cell shows the percent identity for between cry1aa1, cry1ab1, cry1ac1, cry1ca1, cry2aa1, and cry2ab1. Supplementary Table S5. Benchmarking ORFograph on five simulated datasets of varying complexity. Supplementary Figure S4. Most reliable IPGs (paths) identified by ORFograph in the Cry1Aa1+Cry1Ab1 dataset. Grey edges represent the subgraph containing all paths, while blue edges represent paths of four potential IPGs identified by ORFograph. In this subgraph, each edge represents a separate contig, illustrating that ORFograph faced the challenge of filtering spurious paths in the absence of the contig-related information from SPAdes. The shortest edge in the shown subgraph has length 348 bp. Supplementary Figure S5. The subgraph (grey edges) and the path (blue edges) of the Cry1-like IPG generated by ORFograph for the AllCry dataset. Supplementary Note. Benchmarking ORFograph against plasmidSPAdes and metaplasmidSPAdes.
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Dvorkina, T., Bankevich, A., Sorokin, A. et al. ORFograph: search for novel insecticidal protein genes in genomic and metagenomic assembly graphs. Microbiome 9, 149 (2021). https://doi.org/10.1186/s40168-021-01092-z