A New Pipeline for Removing Paralogs in Target Enrichment Data
Bibliographic record
Abstract
Target enrichment (such as Hyb-Seq) is a well-established high throughput sequencing method that has been increasingly used for phylogenomic studies. Unfortunately, current widely used pipelines for analysis of target enrichment data do not have a vigorous procedure to remove paralogs in target enrichment data. In this study, we develop a pipeline we call Putative Paralogs Detection (PPD) to better address putative paralogs from enrichment data. The new pipeline is an add-on to the existing HybPiper pipeline, and the entire pipeline applies criteria in both sequence similarity and heterozygous sites at each locus in the identification of paralogs. Users may adjust the thresholds of sequence identity and heterozygous sites to identify and remove paralogs according to the level of phylogenetic divergence of their group of interest. The new pipeline also removes highly polymorphic sites attributed to errors in sequence assembly and gappy regions in the alignment. We demonstrated the value of the new pipeline using empirical data generated from Hyb-Seq and the Angiosperms353 kit for two woody genera Castanea (Fagaceae, Fagales) and Hamamelis (Hamamelidaceae, Saxifragales). Comparisons of data sets showed that the PPD identified many more putative paralogs than the popular method HybPiper. Comparisons of tree topologies and divergence times showed evident differences between data from HybPiper and data from our new PPD pipeline. We further evaluated the accuracy and error rates of PPD by BLAST mapping of putative paralogous and orthologous sequences to a reference genome sequence of Castanea mollissima. Compared to HybPiper alone, PPD identified substantially more paralogous gene sequences that mapped to multiple regions of the reference genome (31 genes for PPD compared with 4 genes for HybPiper alone). In conjunction with HybPiper, paralogous genes identified by both pipelines can be removed resulting in the construction of more robust orthologous gene data sets for phylogenomic and divergence time analyses. Our study demonstrates the value of Hyb-Seq with data derived from the Angiosperms353 probe set for elucidating species relationships within a genus, and argues for the importance of additional steps to filter paralogous genes and poorly aligned regions (e.g., as occur through assembly errors), such as our new PPD pipeline described in this study. [Angiosperms353; Castanea; divergence time; Hamamelis; Hyb-Seq, paralogs, phylogenomics.].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".