BACKGROUND: The emergence of HIV drug resistance is a challenge in controlling the acquired immunodeficiency syndrome (AIDS) pandemic caused by human immunodeficiency virus-1 (HIV-1) infection. Detection of drug resistance variants at minor frequencies can help to formulate successful antiretroviral therapy (ART) regimens for people living with HIV (PLWH) and reduce the emergence of drug resistance. Therefore, a pipeline which can accurately produce consensus nucleotide sequences and identify drug resistance mutations (DRMs) at defined frequency thresholds will be helpful in the treatment of PLWH, analysis of virus evolution, and the control of the pandemic. RESULTS: We have developed a pipeline, HIVGenoPipe, to determine HIV drug resistance variants within the gag-pol region above user-defined frequencies for HIV-1 samples sequenced using Illumina technology. The pipeline has been validated by comparing its results with the results generated by a widely used pipeline, HyDRA, which is limited to the pol region, and with the results generated by Sanger sequencing technology using the same set of 30 samples. The variant frequency used to generate ambiguous consensus sequences in HIVGenoPipe is more accurate than other pipelines because a sample-specific reference, which is generated in real-time with a novel hybrid strategy of de novo and reference-based assembly, is used for the frequency calculation, leading to more accurate drug resistance calls for use by clinicians. In addition, since Nextflow is used as the pipeline platform, HIVGenoPipe inherently has great portability, scalability and reproducibility; and the components can be updated or replaced independently if required. CONCLUSIONS: We developed HIVGenoPipe for the detection of HIV-1 drug resistance. It constructs more accurate gag-pol consensus sequences, leading to improved detection of DRMs. HIVGenoPipe is open source and freely available under the MIT license at https://github.com/LHRI-Bioinformatics/HIVGenoPipe . The current release (v1.0.1) is archived and available at https://doi.org/ https://doi.org/10.5281/zenodo.15528502 .

MOTIVATION: The Database for Annotation, Visualization, and Integrated Discovery (DAVID) is a web-based bioinformatics system for the functional interpretation of large lists of genes/proteins generated from high-throughput assays. It has been cited in 72 287 papers since its debut in 2003 as of 23 July 2024. The analysis is usually limited to the species of study. However, the knowledge of genes may be incomplete or unavailable for some species. Model organisms have been studied more extensively and analyzing gene lists in the context of these species can offer valuable insights, helping users better understand the genes and biological themes in their species of interest. RESULTS: We developed DAVID Ortholog for the conversion of gene lists between species. We utilized the ortholog data downloaded from Orthologous MAtrix (OMA) and Ensembl Compara as the base for the conversion. The OMA ortholog IDs and Ensembl gene IDs were converted to DAVID gene IDs and the pairing information of these IDs from these two sources was integrated into the DAVID Knowledgebase. DAVID Ortholog can convert the user's source gene list to an ortholog list of a desired species and the downstream DAVID analysis, in the context of that species, can be continued seamlessly, allowing users to further understand the biological meaning of their gene list based on the functional annotation found for the orthologs. AVAILABILITY AND IMPLEMENTATION: https://davidbioinformatics.nih.gov/ortholog.jsp.

MOTIVATION: The existence of quasispecies in the viral population causes difficulties for disease prevention and treatment. High-throughput sequencing provides opportunity to determine rare quasispecies and long sequencing reads covering full genomes reduce quasispecies determination to a clustering problem. The challenge is high similarity of quasispecies and high error rate of long sequencing reads. RESULTS: We developed QuasiSeq using a novel signature-based self-tuning clustering method, SigClust, to profile viral mixtures with high accuracy and sensitivity. QuasiSeq can correctly identify quasispecies even using low-quality sequencing reads (accuracy <80%) and produce quasispecies sequences with high accuracy (≥99.55%). Using high-quality circular consensus sequencing reads, QuasiSeq can produce quasispecies sequences with 100% accuracy. QuasiSeq has higher sensitivity and specificity than similar published software. Moreover, the requirement of the computational resource can be controlled by the size of the signature, which makes it possible to handle big sequencing data for rare quasispecies discovery. Furthermore, parallel computation is implemented to process the clusters and further reduce the runtime. Finally, we developed a web interface for the QuasiSeq workflow with simple parameter settings based on the quality of sequencing data, making it easy to use for users without advanced data science skills. AVAILABILITY AND IMPLEMENTATION: QuasiSeq is open source and freely available at https://github.com/LHRI-Bioinformatics/QuasiSeq. The current release (v1.0.0) is archived and available at https://zenodo.org/badge/latestdoi/340494542. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.