Bioinformatics Pipeline for T-DNA Insertion Position Analysis: From Reads to Genotypes

Transgenic screens generate answers only when insertion sites are clear and trustworthy. This pipeline takes you from raw sequencing reads to precise coordinates and robust sample-level genotypes. It is built to handle high-throughput cohorts, mixed library types, and the quirks of plant genomes. The focus is practical: call real events, quantify their evidence, and produce files you can act on. You will see how t-dna insertion position analysis connects with downstream t dna insertion genotyping and how careful modelling avoids false positives in repetitive regions. By the end, you have clean maps for t-dna insertion site mapping, a defensible genotype matrix, and ready-to-use tracks for visual inspection and reporting.

1) What this pipeline delivers

The pipeline delivers a complete chain of custody from reads to decisions. It standardises input validation, alignment, junction discovery, breakpoint polishing, genotyping, and annotation. The outputs are designed for two audiences at once: bioinformatics teams who need transparent methods, and experimentalists who need clear, testable calls.

You can expect:

• High-confidence insertion coordinates with left and right breakpoints.
• Orientation, truncation, and microhomology notes for each junction.
• Per-sample presence/absence and zygosity status for every event.
• A genotype matrix suitable for segregation analysis and breeding decisions.
• Browser sessions and coverage tracks that make manual review fast.
• Full parameter manifests so runs are repeatable across clusters and studies.

2) Inputs and upfront QC

Strong calls begin with disciplined inputs. The pipeline accepts compressed FASTQ or UBAM, with optional read group tags to preserve lane and library context. Each submission includes:

• Reference genome build and annotation set used by your programme.
• T-DNA construct reference: borders, backbone, selectable markers, and any barcodes.
• Sample sheet with library type, indexing scheme, and pooling structure.
• Optional list of known control lines for baseline modelling.

Before any mapping, the pipeline performs pre-alignment QC to prevent noise from becoming "signal" later:

• Read quality and composition: base-quality decay, nucleotide bias, and k-mer spectra.
• Adapter and artefact detection: adapter prevalence, primer dimers, and any common chimeras.
• Library complexity checks: duplication levels, molecular barcode diversity if molecular barcodes are present, and predicted unique coverage.
• Contamination screens: low-stringency alignments against common contaminants and the construct backbone.
• Coverage sanity checks: expected insert-size distribution and read-through patterns for targeted enrichment libraries.

QC reports flag samples that are likely to generate ambiguous junctions or spurious clusters. When issues are detected, the pipeline quarantines those samples, generates a short diagnostic note, and proceeds with the remaining cohort. For high-throughput designs and pooled strategies, consult the design article (Article: Experimental Design for High-Throughput Detection of T-DNA Insertion Sites) for preventive tips that raise recall while keeping noise in check.

3) Read processing and mapping strategy

Read processing minimises downstream bias. The steps are conventional but tuned for junction discovery:

1. Adapter and quality trimming with conservative thresholds to avoid over-trimming border-spanning reads.
2. Molecular barcode handling (if available): grouping, error-aware collapsing, and consensus calling to reduce PCR bias.
3. Duplicate marking that respects molecular barcodes and paired-end geometry.

Mapping uses a two-track approach:

• Host-genome alignment with a gap-aware aligner that retains soft-clipped segments and records supplementary alignments. Soft clips and split reads carry junction evidence; discarding them is costly.
• Construct-reference alignment performed in parallel to catch reads that align strongly to the T-DNA but only partially to the host. This step recovers events with truncated borders and captures backbone fragments if present.

The pipeline preserves read groups, sample barcodes, and library notes in the BAM headers. This makes later metrics interpretable and supports lane-specific artefact detection. Mapping quality and alignment flags are tracked carefully; low-MAPQ pile-ups in repeats are isolated from high-confidence signals. For complex genomes with abundant transposable elements, optional masked alignments and k-mer-based filters reduce spurious clusters without sacrificing true borders.

When target-enrichment data are provided, the mapping logic honours bait orientation and expected insert geometry. That choice improves junction yield and speeds downstream clustering. If you are still comparing TES-NGS to WGS, see the method comparison article: Choosing the Right Method: TAIL-PCR, TES-NGS, or WGS for T-DNA Insertion Site Mapping

Workflow for the molecular characterization of genetically modified plants, using the MinION device of ONT. (Giraldo P.A. et al. (2021) Frontiers in Plant Science)

4) Insertion discovery from multiple evidence types

Insertion detection relies on converging evidence. The pipeline aggregates and scores signals that point to the same genomic breakpoint:

• Soft-clipped signatures: clips that map into the T-DNA construct are a primary cue. The clip orientation indicates which border is present.
• Split reads: hard evidence for chimeric junctions where one part maps to host and the other to the construct.
• Discordant pairs: mates with unexpected distance or orientation near a candidate site bolster support.
• Coverage steps and on-insert pile-ups: local changes reveal concatemers and copy number hints.

Schematic representation of the materials and overview of the method. (Edwards B. et al. (2022) BMC Genomics)

These signals feed a clustering stage that groups nearby alignments into candidate junctions. Clusters are scored using read support, strand balance, clip directionality, and the presence of border motifs. The model discounts clusters dominated by low-MAPQ hits or by a single library fragment seen many times. For repetitive regions, the algorithm requires stronger multi-signal support and penalises one-sided evidence.

Local re-assembly refines promising clusters. Short contigs spanning the junction are built from nearby reads, then aligned to both host and construct references. Re-assembly resolves micro-insertions, short deletions, and filler sequence that split-read alignment sometimes misses. It also disambiguates cases where two close insertions appear as one merge.

Special cases are handled explicitly:

• Truncated borders: events lacking canonical border sequences still surface if soft-clipped reads point into the construct.
• Backbone integration: reads mapping to non-T-DNA vector regions trigger a distinct event type for downstream review.
• Complex events: multiple borders at one locus suggest concatemers; the classifier records copy structure warnings.
• Pooled samples: barcode-aware counting ensures that signals do not bleed across pools, protecting specificity.

5) Breakpoint refinement and sample genotyping

After discovery, breakpoints are polished to single-base resolution. The polishing stage reconciles split reads, re-assembled contigs, and motif expectations to place left and right junctions precisely. Each event receives:

• Orientation: forward or reverse orientation with respect to the host locus.
• Border status: intact, partially truncated, or missing.
• Microhomology and filler notes: length and motif at both junctions.
• Confidence class: high, medium, or review, derived from a transparent scoring rubric.

Genotyping then assigns per-sample states while accounting for coverage, bias, and pooling:

1. Presence/absence calling uses a likelihood model that weighs junction support against local host coverage. The model is robust to uneven capture and low-coverage tails.
2. Zygosity estimation combines junction depth, on-insert coverage, and host allele balance when informative. In target-enrichment datasets, the method relies more heavily on junction-to-host depth ratios than on global coverage.
3. Copy number hints come from on-insert coverage relative to flanking host coverage and from the number of distinct junctions supporting the same locus.

Genotype calls are summarised in a cohort-wide matrix with explicit missing-data codes and quality flags. This structure makes segregation checks and plate-level QC straightforward. If your pipeline needs formal copy number and junction integrity checks, the validation article (Confirming Copy Number, Zygosity, and Junction Integrity for T-DNA Lines) provides deeper criteria and bench-level follow-ups.

Edge conditions deserve attention:

• Segmental duplications: events inside paralogous regions may appear multi-mapped; the genotyper labels them "review".
• Mosaic tissues: low-fraction signals are reported as "partial" with an explicit caution, rather than forced into heterozygous categories.
• Co-located events: two insertions within a small window are kept separate if distinct junction signatures exist; otherwise, the report notes the ambiguity.

Schematic of PCR method used to determine zygosity of T₂ generation plants Primer. (Edwards B. et al. (2022) BMC Genomics)

6) Functional annotation and impact assessment

Coordinates are only useful when tied to function. The annotation module overlays insertion sites on gene models, promoter windows, regulatory annotations, and transposable-element maps. For each event, the report summarises:

• Genic context: exon, intron, UTR, or intergenic.
• Promoter and enhancer proximity: distance to transcription start sites and regulatory marks where available.
• Coding impact cues: likely disruption of reading frames or splice sites when insertions fall in exons.
• Regulatory impact cues: potential promoter interference or enhancer disruption for nearby intergenic events.
• Repetitive context: notes when mapping ambiguity could affect interpretation.

Chromosomal map of single T-DNA insertions across 12 potato chromosomes, shown against gene-density backgrounds. (Magembe E.M. et al. (2023) Frontiers in Plant Science)

These annotations enable quick triage. You can filter for promoter insertions when screening for expression knockdowns or select intragenic events to validate loss-of-function candidates. At cohort scale, the module aggregates events by lines, families, or pooled groups and generates overviews:

• Counts of unique loci per line and per chromosome.
• Distribution across genic classes and regulatory categories.
• Shortlists that match user-defined hypotheses or target gene sets.

For integration with phenotype work, the pipeline exports gene-centric tables that link each affected gene to its nearest insertion and its sample genotypes.

7) Reporting, deliverables, and reproducibility

Deliverables are structured to be opened and understood without deciphering the pipeline code. You receive:

• Breakpoint VCF/BED: one line per junction with orientation, border status, microhomology, and confidence class.
• Event table: events collapsed to loci with left and right breakpoints, supporting evidence counts, and notes on complexity or backbone presence.
• Genotype matrix: samples x events with presence/absence/zygosity, quality flags, and missing-data markers.
• Coverage and signal tracks: bigWig/bedGraph files for host coverage; BED tracks for soft-clip pile-ups and split-read clusters.
• Genome browser sessions: pre-built sessions that load the reference, event tracks, and per-sample signals. Review becomes a few clicks rather than a hunt for files.
• QC dashboard: summaries of read quality, library complexity, mapping rates, and per-sample junction yields.
• Validation appendix: suggested primer coordinates for junction PCR and notes on assays suitable for complex or ambiguous calls.

Reproducibility is treated as a first-class requirement. Each run includes:

• A parameter manifest describing every threshold, reference path, and tool version.
• Pipeline hashes and container digests so environments can be rebuilt precisely.
• A change log that records deviations from the standard recipe for specific studies.
• A data dictionary describing each column and code used in the event and genotype tables.

This structure supports audits, cross-team collaboration, and incremental re-analysis when reference builds or construct definitions change. For a broader view of deliverable clarity and how we present results to mixed teams, see the pipeline transparency overview in the companion piece (Bioinformatics Pipeline for T-DNA Insertion Position Analysis: From Reads to Genotypes).

Practical notes for different study designs

While the core logic is constant, a few parameters shift with study design. These notes capture what changes and why, so your team can set expectations before sequencing begins.

Target-enrichment libraries

• Expect higher junction yields near baited regions and sparser signals elsewhere.
• Junction-to-host depth ratios carry more weight than absolute coverage.
• Off-bait detections are possible; the confidence class reflects the weaker support.

Whole-genome libraries

• Uniform coverage simplifies zygosity modelling and copy-number hints.
• Repetitive elements contribute more low-MAPQ clutter; the discovery model tightens filters accordingly.
• Disk and memory footprints scale quickly for large cohorts; batching by chromosome or scaffold can help.

Pooled cohorts and barcoded designs

• Barcode mis-assignment is monitored via control loci and expected barcode collision rates.
• Presence calls default to conservative thresholds to prevent cross-pool bleed.
• Event lists are exported both per pool and deconvolved, with explicit caveats where deconvolution is ambiguous.

Legacy datasets and mixed runs

• The pipeline can ingest a mix of read lengths, platforms, and library types.
• Normalisation steps prevent a high-quality subset from dominating cohort-level models.
• The dashboard highlights batch effects and suggests splits for fair genotyping thresholds.

For programmes planning sustained screening, the design article (Experimental Design for High-Throughput Detection of T-DNA Insertion Sites) offers layout templates that keep bioinformatics stable even as library strategies evolve.

Interpreting confidence and planning validation

Every call carries a confidence class derived from evidence patterns. High-confidence calls have convergent support: split reads, coherent soft-clip direction, and stable mapping in non-repetitive contexts. Medium-confidence calls lack one dimension or sit near repeats. "Review" calls often indicate complexity or partial borders. The dashboard includes counts by class per sample, so you can triage validation work pragmatically.

For confirmatory assays:

• Junction PCR is the first line of verification. Use the primer suggestions in the appendix as a starting point, then adapt to your polymerase and amplicon preferences.
• Copy structure checks for concatemerised events benefit from primer walking across multiple junctions or long-range PCR.
• Backbone notes trigger a separate review. Retain these labels so your team can decide case by case whether they matter for your project's goals.

The event table flags candidates that can anchor segregation analyses. When you promote an event to "validated," the genotype matrix updates with a confirmed tag, supporting downstream analyses and breeding decisions.

Troubleshooting common pitfalls

Most pipeline issues trace back to three themes: library artifacts, reference mismatches, and overly aggressive filtering. The framework anticipates these patterns and provides remedies.

• Library artifacts: Very high duplication or biased fragment sizes can inflate single-fragment clusters. The molecular barcode logic and duplicate-aware counts protect against this, but severe cases still warrant resequencing.
• Reference mismatches: If your construct reference omits a local feature (e.g., a barcode or minor cassette variant), junction placement can wobble. Keep the construct FASTA aligned with the actual build sheet.
• Repetitive contexts: Insertions inside repeats are not impossible, just harder to resolve. Expect more "review" labels and plan validation accordingly.
• Over-filtering: When prior runs used strict MAPQ cut-offs, border-spanning reads were sometimes dropped. This pipeline keeps informative soft clips; resist the urge to pre-filter them away.

How this pipeline supports downstream science

The aim is not only to report coordinates but to accelerate discovery. The deliverables weave smoothly into downstream tasks:

• Candidate selection: promoter-proximal events shortlisted for regulatory studies.
• Functional screens: gene-centric tables ready for pathway or network analysis.
• Breeding workflows: genotype matrices formatted for tracking lines across generations.
• Data sharing: VCF/BED, bigWig, and IGV sessions that collaborators can open without extra tooling.

Teams using multiple transformation strategies can keep one bioinformatics standard and vary only the library plan. That stability cuts re-training overhead and eases cross-project comparisons.

Summary

A reliable t-dna insertion position analysis pipeline rests on behaviour you can see and defend: preserve junction-bearing evidence, integrate signals, polish with local context, and annotate with function in mind. Build genotypes from likelihoods rather than single thresholds, and treat reproducibility as part of the product, not an afterthought. With that foundation, t dna insertion genotyping becomes a stable, repeatable step in your research, not an exploratory exercise each time. The outputs are actionable, the caveats are explicit, and the path from reads to insight is clear.

References

1. Giraldo, P.A., Shinozuka, H., Spangenberg, G.C., Smith, K.F., Cogan, N.O.I. Rapid and detailed characterization of transgene insertion sites in genetically modified plants via nanopore sequencing. Frontiers in Plant Science 11, 602313 (2021).
2. Edwards, B., Hornstein, E.D., Wilson, N.J. et al. High-throughput detection of T-DNA insertion sites for multiple transgenes in complex genomes. BMC Genomics 23, 685 (2022).
3. Magembe, E.M., Li, H., Taheri, A., Zhou, S., Ghislain, M. Identification of T-DNA structure and insertion site in transgenic crops using targeted capture sequencing. Frontiers in Plant Science 14, 1156665 (2023).
4. Sun, L., Ge, Y., Sparks, J.A., Robinson, Z.T., Cheng, X., Wen, J., Blancaflor, E.B. TDNAscan: a software to identify complete and truncated T-DNA insertions. Frontiers in Genetics 10, 685 (2019).
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6. Zastrow-Hayes, G.M., Lin, H., Sigmund, A.L. et al. Southern-by-Sequencing: a robust screening approach for molecular characterization of genetically modified crops. The Plant Genome 8(1), eplantgenome2014.08.0037 (2015).
7. Kovalic, D., Garlick, A., et al. The use of next generation sequencing and junction sequence analysis bioinformatics to achieve molecular characterization of a genetically modified event. The Plant Genome 5, 149–163 (2012).
8. Börjesson, V., Martinez-Monleon, A., Fransson, S. et al. TC-hunter: identification of the insertion site of a transgenic gene within the host genome. BMC Genomics 23, 149 (2022).
9. Zarka, K.A., Jagd, L.M., Douches, D.S. T-DNA characterization of genetically modified 3-R-gene late blight-resistant potato events with a novel procedure utilizing the Samplix Xdrop® enrichment technology. Frontiers in Plant Science 15, 1330429 (2024).
10. Grassi, L., Harris, C.L., Zhu, J., Hardman, C., Hatton, D. DetectIS: a pipeline to rapidly detect exogenous DNA integration sites using DNA or RNA paired-end sequencing data. Bioinformatics 37(22), 4230–4232 (2021).