Introduction

The study of viral sequences often leads to analysis of proviral genomes. This guide is designed to help people without programming expertise analyze these proviral sequences efficiently using our dedicated bioinformatics pipeline. By leveraging a Docker container, we simplify the technical setup, allowing them to focus on the biological insights from their data.

Background

Proviral sequences are fragments of viral DNA that become integrated into the host’s genome during an infection. In the case of HIV, once the virus infects a cell, its RNA is reverse-transcribed into DNA and then inserted into the host’s chromatin, creating a provirus. This integrated viral DNA may remain latent for long periods, and its genomic integrity (or lack thereof) can have profound implications for viral persistence, reactivation potential, and the overall effectiveness of antiretroviral therapies.

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By studying proviral sequences, researchers can identify both intact and defective viral genomes. Intact proviruses may be capable of reactivation and replication, contributing to the viral reservoir that is a major barrier to achieving a cure. Defective proviruses, on the other hand, can accumulate mutations, deletions, or other structural defects that prevent productive infection but may still influence immune activation or misdirect therapeutic interventions.

The pipeline is designed to evaluate the quality of the input data. This includes identifying potential procedural issues such as low read coverage, missing primers, internal sequence gaps, or the presence of ambiguous bases. Such quality control is essential because accurate primer detection and gene splicing depend on having reliable sequence data. Errors such as “primer not found” or “non-HIV sequence” serve as flags to the researcher that sample preparation or sequencing might have been suboptimal.

While the pipeline automates many of the bioinformatics tasks — ranging from quality filtering and error annotation to the integration of secondary analysis tools like HIVSeqinR or CFEIntact — it ultimately provides insights into the biology of HIV integration. The generated output files not only serve as quality checkpoints but also help researchers gauge the relative proportions of intact versus defective proviruses, detect unusual patterns of mutation or recombination, and plan further experimental validations.

In summary, this pipeline was created to help researchers — especially those who may not have deep programming expertise—translate raw sequencing data into meaningful biological insights. Every stage of the pipeline, from primer detection and sequence alignment to downstream defect classification, is tailored to elucidate the complex biology of proviral genomes. This enables a clearer understanding of viral reservoirs and offers potential pathways for therapeutic intervention.

Pipeline Features

The pipeline offers the following core features:

  • Primer Analysis: Checks for primer sequences used in polymerase chain reactions.
  • Sequence Alignment and Filtering: Uses tools like minimap2 to align sequences and applies filters to ensure data quality.
  • Error Detection: Categorizes sequence errors, such as non-proviral sequences or low coverage.
  • Result Summarization: Generates comprehensive reports to aid in data interpretation and decision-making.

Setup and Environment

To make our pipeline accessible and easy to use, we have encapsulated it within a Docker container. This ensures all dependencies and configurations are pre-installed, minimizing compatibility issues across different systems. Docker is a platform to develop, ship, and run applications in isolated environments.

By following the rest of this guide, you will be up and running with proviral sequence analysis quickly, even with minimal technical background.

The proviral pipeline is a command-line program — it is designed to be interacted with from within a terminal. If you are not experienced with that, see the following guides:

Next: installation.