Terminal Repeat Sequences

PASTEC was developed in the REPET package [7] . In this context, we used PASTEC to classify the consensus TE sequences found de novo in a genome. PASTEC uses several features of TEs to classify TE consensus sequences. It searches for structural evidence and sequence similarities stored in a MySQL database obtained during a preprocessing step. The structural features considered are TE length, presence of a LTR (long terminal repeat) or TIR (terminal inverted repeat) detected with a custom-built tool (with a minimum length of 10 bp, a minimum identity of 80%, the taking into account of reciprocal orientations of terminal repeats and a maximal length of 7000 bp), the presence of SSRs (simple sequence repeats detected with the tandem repeat finder (TRF) tool [8] (link)), the polyA tail and an ORF (open reading frame). The blastx and tblastx routines are used to search for similarities to known TEs in Repbase Update, and the hmmer3 package [9] to search against a HMM profile databases (TE-specific or not), after translation in all six frames. Sequence similarities are also identified by blastn searches against known rDNA sequences, known host genes and known helitron ends. The databanks used are preprocessed and formatted. The Repbase Update for PASTEC can be downloaded from http://www.girinst.org/repbase/index.html, whereas the HMM profile databank formatted for PASTEC is available from the REPET download directory (http://urgi.versailles.inra.fr/download/repet/).
PASTEC classifies TEs by testing all classifications from Wicker's hierarchical TE classification system. Each possible classification is weighted according to the available evidence, with respect to the classification considered. TEs are currently classified to class and order level. PASTEC can also determine whether a TE is complete on the basis of four criteria: sequence coverage for known TEs, profile coverage, presence of terminal repeats for certain classes, presence of a polyA or SSR tail for LINEs and SINEs, and the length of the TEs with respect to expectations for the class concerned.
We designed PASTEC as a modular multi-agent classifier. The system is composed of four types of agents: retrievers, classifiers, filter agents, and a super-agent (Figure 1). The retriever agents retrieve the pre-computed analysis results stored in the MySQL database. They act on the requests of the classifier or filter agents, filtering, formatting and supplying the results. The classifier and filter agents are specialized to recognize a particular category. For example, the LTR agent can determine only whether the TE is a LTR or not. The classifier and filter agents act on the request of the super-agent, deciding whether they can classify the TE or not. For example, the LTR agent decides whether the consensus TE is a LTR on the basis of the following evidence: presence of the ENV (envelope protein) profile (a condition sufficient for classification), the presence of INT (integrase), RT (reverse transcriptase), GAG (capsid protein), AP (aspartate proteinase) and RH (RNase H) profiles together with the detection of a LTR (long terminal repeat), a blast match with the sequence of a known LTR retrotransposon. The super-agent resolves classification conflicts and formats the output file. It resolves conflicts by using a confidence index normalized to 100. For example, the LTR agent calculates a confidence index with the following rules: presence of ENV profiles (+2 because this condition is sufficient for classification), presence of a long terminal repeat and an INT, GAG, RT, RH or AP profile (+1 for each profile combined with the long terminal repeat), +1 for each profile (ENV, AP, RT, RH and GAG) found in the same frame in the same ORF. If the consensus matches at least one known LTR retrotransposon, the LTR agent adds +2 for each type of blast (blastx or tblastx) at the confidence index. Finally, the length of the TE is taken into account because we add +1 if the TE without the long terminal repeat is between 4000 and 15000 bp in length, and we decrease the confidence index by 1 if the TE without the long terminal repeat is less than 1000 bp or more than 15000 bp long. The super-agent uses the maximum confidence index defined for each classifier agent to normalize the confidence index for each classification to 100 and then compare the different classifications. Advanced users can edit all decisions rules and maximum confidence indices in the Decision_rules.yaml file.
The output can be read by humans and is biologist-friendly. A single line specifies the name of the TE, its length, status, class, order, completeness, confidence index and all the features characterizing it. A status of “potential chimeric” or “OK” is assigned to the TE. If the TE is not considered to be “OK” then users must apply their own expertise. A TE is declared “potential chimeric” when at least two classifications are possible. In this case, PASTEC chooses the best status based on the available evidence, or does not classify the TE if no decision is possible. In this last case, all possible classifications are given (separated by a pipe symbol “|”). We present an example of PASTEC output in table S1. PASTEC output is a tabular file, with the columns from left to right indicating the name of the TE, its length, the orientation of the sequence, chimeric/non-chimeric status (OK indicating that the element is not potentially chimeric), class (class I in this case), order. In the first line of the example provided, the TE is a LTR. We presume that the element is complete because we have no evidence to suggest that it is incomplete, and the confidence index is 71/100. The last column summarizes all the evidence found: coding sequence evidence, such as the results of tblastX queries against the Repbase database (TE_BLRtx evidence), blastX queries against the Repbase database (TE_BLRx evidence) and profiles. A blast match is taken account if coverage exceeds 5%, and a profile is taken into account if its coverage exceeds 20% (these parameters can be edited in the configuration file). For each item of coding sequence evidence, the coverage of the subject is specified. The structural evidence is also detailed: >4000 bp indicates that TE length without terminal repeats is between 4000 and 15000 bp, the next item of information presented in the comments columns is the presence of terminal repeats: we have a LTR in this case, with an LTR length of 433 bp; two long ORFs have been identified, the last of which contains four profiles in the same frame and is up to 3000 bp long. Other evidence provided for this example includes the partial match with a Drosophila melanogaster gene (coverage 16.55% and the TE contains 18% SSRs). The super-agent determines whether a TE is complete based on whether it is sufficiently long, whether the expected terminal repeats or polyA tail are present, whether blast match coverage exceeds 30% and profile coverage exceeds 75%. The second line of the example corresponds to a potentially chimeric TE, for which human expertise is required.

Manual curation of TEs in rice was started after the release of the map-based rice genome [22 (link)]. Repetitive sequences in the rice genome were compiled by RECON [44 (link)] with a copy number cutoff of 10. Details for manual curation of LTR sequences were previously described in the LTR_retriever paper [40 (link)]. In brief, for the curation of LTR retrotransposons, we first collected known LTR elements and used them to mask LTR candidates. Unmasked candidates were manually checked for terminal motifs, TSD sequences, and conserved coding sequences. Terminal repeats were aligned with extended sequences, from which candidates were discarded if alignments extended beyond their boundaries. For the curation of non-LTR retrotransposons, new candidates were required to have a poly-A tail and TSD. We also collected 13 curated SINE elements from [53 (link)] to complement our library.
For curation of DNA TEs with TIRs, flanking sequences (100 bp or longer, if necessary) were extracted and aligned using DIALIGN2 [72 (link)] to determine element boundaries. A boundary was defined as the position to which sequence homology is conserved over more than half of the aligned sequences. Then, sequences with defined boundaries were manually examined for the presence of TSD. To classify the TEs into families, features in the terminal and TSD sequences were used. Each transposon family is associated with distinct features in their terminal sequences and TSDs, which can be used to identify and classify elements into their respective families [14 (link)]. For Helitrons, each representative sequence requires at least two copies with intact terminal sequences, distinct flanking sequences, and inserts into “AT” target sites.
To make our non-redundant curated library, each new TE candidate was first masked by the current library. The unmasked candidates were further checked for structural integrity and conserved domains. For candidates that were partially masked and presented as true elements, the “80-80-80” rule (≥ 80% of the query aligned with ≥ 80% of identity and the alignment is ≥ 80 bp long) was applied to determine whether this element would be retained. For elements containing detectable known nested insertions, the nested portions were removed and the remaining regions were joined as a sequence. Finally, protein-coding sequences were removed using the ProtExcluder package [73 (link)]. The curated library version 6.9.5 was used in this study and is available as part of the EDTA toolkit.

To determine the termini of an unknown phage, a first step of de novo assembly was required to obtain a reference sequence. To validate that this assembly step did not alter the result of the software, we assembled the six reference phages (lambda, HK97, T7, P1, T4 and Mu) de novo using SPAdes^{21 (link)} with standard options. When using a reference assembled de novo one should consider two possible caveats: (i) assemblers frequently introduce mistakes at the edges of contigs due to the presence of low abundance reads that do not correspond to the actual phage sequence. This usually results in a drop of coverage at the contig ends. In such cases PhageTerm should still be able to call the termini correctly, but we recommend correcting the contig ends when possible. (ii) In the case of phages with terminal repeats, most assemblers will output a contig with a single copy of the repeat, which is the desired input for PhageTerm. Note, however that some assemblers might create a contig with a repeat at each end. In such a case PhageTerm will incorrectly call multiple termini. We tested this hypothesis using the T7 reference genome (NC_001604, Table S1) and the PhageTerm output was “Multiple” on both strands for the two analysis methods.

DNA was purified for sequencing from cells infected in six-well plates using a blood and cell culture DNA minikit (Qiagen). Samples were sequenced at the Northwestern University Genomics Core Facility using NextGen Illumina HiSeq SR500 sequencing. Unipro UGene (57 (link)) was used to create a trimmed reference sequence composed of the HSV-1 F strain genome (GenBank accession number GU734771.1) with the long- and short-terminal repeat regions deleted (58 (link)). The gB (UL27) gene in the reference sequence was replaced with either the SaHV-1 gB sequence or the KOS strain gB sequence (GenBank accession number KT899744.1) with the gB3A substitutions added. Using Geneious 8.1.9, sequencing reads were aligned to the reference genome and variants were identified. Variants present in both the revertant viruses and the original BACs were ignored, as were variants in the gJ gene due to the RFP insertion. Mutations identified in other glycoproteins are reported in Table 1. The SaHV-1 gB sequence of HSV-SaHVgB^pass viruses was confirmed using standard single pass DNA sequencing after PCR amplification.

For the genome termini analysis, raw >Q30 Nanopore sequencing reads above 78,000 bp were filtered with Filtlong v0.2.1. Reads were then manually inspected for the presence of direct terminal repeats (DTRs). DTR sequences in other Kuravirus-like group phages were identified using the “Annotate from…” function, as performed previously [22 (link)]. To annotate the YF01 phage genome, the assembly was imported into Geneious Prime v2022.2.1 and genes were predicted with Glimmer3 [39 (link)] and manually inspected for the presence of ribosome binding sites (RBS). ORFs were annotated using a combination of the NCBI Conserved Domain Database (CDD) [40 (link)], a profile hidden Markov model (HMM) similarity using Hhpred [41 (link)], and the Virfam webserver [42 (link)]. tRNAs were identified using tRNAscan-SE [43 ] and Aragorn v1.2.41 [44 (link)]. Figures were generated using CLC Genomics WorkBench v9.5.5 and Clinker v0.0.12 [45 ]. Amino acid similarity calculations were performed using Clustal Omega v1.2.3 [46 (link)] in Geneious Prime v2022.2.1 with the Blosum 62 similarity matrix. Average amino acid similarity values presented in the text refer to the average value of all similarity values in the matrix. Raw data and calculations are available in the Supplementary Materials.

Bacterial DNA was extracted by using the DNA Express set (LyTeh, Moscow, Russia). Multilocus sequence typing (MLST) of K. pneumoniae strains was performed by determining the nucleotide sequences of seven housekeeping genes as described previously [64 (link)]. The capsular type was determined by wzi gene sequencing [65 (link)]. Hypervirulence-associated genes rmpA, rmpA2, peg-344, fimH, iutA, and iroB were amplified using previously published sets of primers [28 (link),66 (link)] (Supplementary Table S2).
Extraction of the phage genomic DNA was performed using a standard phenol–chloroform extraction protocol [67 ]. Phage genome was sequenced using a high-throughput Illumina HiSeq system. SPAdes v3.14.0 software was used for genome assembly [68 (link)]. Phage terminal repeats were predicted with the PhageTerm tool v3.0.1 [69 (link)] and determined by direct Sanger sequencing using primers reported in Supplementary Table S2. GeneMarkS v4.32 was used for identification of open reading frames (ORFs) within the genome [70 (link)]. The tRNA genes were searched using tRNAScan-SE v2.0 [71 (link)] and ARAGORN v1.2.41 [72 (link)]. The Clinker tool was used in comparative genomic analysis [73 (link)].
Annotation of predicted genes was conducted manually using BLASTp v2.13.0, HHPred, PHROG v4, and InterPro v5.59-91.0. The absence of potentially toxic genes and antibiotic resistance determinants was confirmed by comparison with the databases Virulence Factors of Pathogenic Bacteria [74 (link)] and Antibiotic Resistance Genes Databases [75 (link)]. The annotated genome sequence of the vB_KpnP_Klyazma phage was deposited in the NCBI GenBank database under accession number OP125547.1.