PGR-TK Maps the Pan-genome of Angiosperm Plastomes

Methodology

Despite this promise, the comprehensive analysis of entire plastid genomes remains computationally demanding, a challenge further amplified by the increasing volume of data generated by next-and third-generation sequencing platforms. A parallel analysis was conducted at the family level, including all flowering plant families represented by at least three plastid genomes.

Study Design

Additionally PGR-TK analysis across combined genera often accounted for the observed length differences in the outlier species.

Subsequent BLAST analysis revealed that this sequence belongs to Hosta (Fig. 6a ) a genus in a different family within the same order, suggesting a potential misclassification or sample mix-up.

Observations at the Family levels

Observations at the Family levels The comparative analysis using PGR-TK was extended to the family level by grouping sequences according to their respective families, each comprising one or more genera. As observed at the genus level, there was notable variation in plot complexity among families. In some orders, family-level plots revealed intricate and highly variable patterns, while in others, families exhibited consistently simple quadripartite structures. For example, all families within Liliales showed clear and well-resolved quadripartite plastome structures, whereas Caryophyllales members displayed a heterogeneous array of patterns. Several families within.

Errors in Plant Identification

Errors in Plant Identification Although the majority of the plots displayed the characteristic quadripartite structure of the plastid genome, a small subset exhibited more complex and atypical patterns. It came as a surprise that some of these complex patterns were the results of plant misclassification. The application of PGR-TK proved instrumental in addressing this issue. When the PGR-TK analysis was conducted using representatives from both genera simultaneously, a notable correction occurred: the plastid genomes that had been erroneously annotated were clearly clustered with the genus to which they actually belonged.

Genome Evolution

Genome Evolution PGR-TK is a powerful tool for exploring chloroplast genome evolution, capturing features such as plastid length variation, gene rearrangements, and changes in intergenic regions. While this study did not utilize the gene sequences directly, the tool supports their inclusion in the generated visualizations. Moreover, to support researchers who require access to sequence-level data, PGR-TK provides text files at every stage of the analysis. These outputs can be further processed or integrated into other workflows, promoting flexibility and reproducibility in genomic studies. Several steps in our analysis relied on.

A. Raw Data

A. Raw Data Plastid genomes from the RefSeq database were downloaded from NCBI ( https:\/\/ftp.ncbi.nlm.nih.gov\/refseq\/release\/plastid\/ ). Using custom Biopython scripts, the files were split into FASTA format, with sequences organized by genus. Only files containing three or more sequences were retained for further analysis. Additionally, NCBI files were segregated into FASTA files for angiosperm families with three or more complete plastid genomes.

B. PGR-TK Analysis

B. PGR-TK Analysis Once collected, PGR-TK was used to generate the alignments and compare different species in a genus or family. First, ‘pgr-pbundle-decomp’ was used with the following parameters to generate the conserved segments (principal bundles) in the sequences. pgr-pbundle-decomp -w 20 -k 32 -r 1 –min-branch-size 10 –min-span 0 –min-cov 0 –bundle-length-cutoff 10 extract2.fa out Next ‘pgr-pbundle-bed2dist’ was applied on the bundles to generate the phylogeny.

C. Rotation

C. Rotation A custom script was developed to align plastid sequences to a common origin. PGR-TK identified conserved blocks across sequences, and all plastids were rotated to align with this common block. The steps followed in the analysis were:.

E. Density Analysis

E. Density Analysis Density analysis was conducted on the bed file generated by ‘pgr-pbundle-decomp’. This analysis counted the number of principal bundles for each plastid. Since plastid lengths within a genus typically do not vary significantly, no normalization was required.

F. BLAST Analysis of Incorrectly Annotated Sequences

F. BLAST Analysis of Incorrectly Annotated Sequences A subset of plastid genomes was identified as being incorrectly annotated. BLAST was first used to search a subsequence from the outlier plastid against NCBI. The top hits, excluding the original plastid, were examined to suggest potential alternate genera. If a match was found, a combined PGR-TK analysis of both genera was performed to visually assess the accuracy of the BLAST annotation. (a) (a) (b) Figure 3. PGR-TK plots for (a) Berchemia and (b) Berchemia and Phyllantha combined. In (a), Berchemia lineata shows.

Angelica ternata

Angelica ternata Both BLAST and PGR-TK show that Tongoloa zhongdianensis should be assigned to the Angelica genus. Viscum articulatum Korthalsella sp. PGR-TK analysis suggests that Viscum articulatum should be reassigned to the Korthalsella genus. This is likely to be a taxonomic reassignment, and not an error in sample annotation.

Zephyranthes bifida Dioscorea futschauensis Both BLAST and PGR-TK show that

Zephyranthes bifida Dioscorea futschauensis Both BLAST and PGR-TK show that Zephyranthes bifida should be assigned to the Dioscorea genus. Given the evolutionary distance between the current and revised annotation, the current assignment is likely an error. Table 2 . Angiosperm genera with one sequence diverging from the rest. Among the 33 angiosperm genera listed in the table above, one species in each genus (two in the case of Ctenium) diverges from the others. Further analysis using BLAST and PGR-TK revealed that, in most cases, the divergent species had been incorrectly.

Limitations and Cautions

A useful limitation and caution is that this article summarizes the available paper text and extracted evidence; readers should consult the source paper before treating any interpretation as definitive.

The paper’s conclusions may depend on its source selection, definitions, assumptions, and the scope of its analysis, so follow-up reading is important.