Genopo: a nanopore sequencing analysis toolkit for portable Android devices

Portable DNA/RNA sequencing with Oxford Nanopore Technologies (ONT) devices like the MinION enables rapid, in situ analysis across diverse settings (e.g., Ebola surveillance in West Africa, Arctic microbiome profiling, and sequencing on the International Space Station). Despite portable sequencing and base-calling, downstream analysis typically relies on high-end servers or cloud services, which can be prohibitive in field settings due to bandwidth/connectivity constraints. This limits the full potential of ONT devices for real-time, on-site genomics. To address this gap, the study introduces Genopo, a smartphone-based toolkit that brings common ONT analysis workflows to Android devices, aiming to enable fully portable, offline computation for applications such as pathogen surveillance and epigenetic profiling.

Prior work has established ONT MinION utility for outbreak surveillance (e.g., Ebola in West Africa), environmental microbiome studies in extreme environments (Arctic), and extraterrestrial contexts (International Space Station). During the 2019–20 coronavirus pandemic, ONT sequencing has been widely used for viral transmission and evolution studies (e.g., ARTIC network). However, persistent challenges include reliance on server/cloud computing for analysis and field connectivity limitations (e.g., degraded 3G/2G networks causing slow uploads), motivating portable offline analysis solutions.

Genopo is an Android application providing a portable environment for executing nanopore sequencing analysis workflows on smartphones/tablets. Users can run individual tools or pre-built workflows via a GUI or by supplying command-line arguments; outputs are written to device storage with real-time logging. The toolkit integrates multiple bioinformatics tools ported to Android, including Minimap2, Samtools, Bfofools, Nanopolish, F5c, Bedtools, and Bioawk, with a catalogue that can grow per community needs. Demonstration workflows: 1) SARS-CoV-2 genome analysis (ARTIC pipeline): (i) align reads to SARS-CoV-2 reference with Minimap2; (ii) compress, sort, index, and preprocess alignments with Samtools; (iii) call variants and generate consensus with Nanopolish. This workflow was executed on four Android smartphones (Nokia, Huawei, LG, Sony) in parallel using ONT datasets from Sydney laboratories with ~200× genome coverage per isolate. 2) DNA methylation profiling in human sample NA12878: (i) align reads to GRCh38 using Minimap2 with eight index partitions; (ii) sort and index with Samtools; (iii) methylation calling using LiSeq. Two MinION flow-cell datasets were used; one analysis ran as a single batch of 16,688 reads (91.15 Mb). 3) Real-time feasibility test: Simulated batch processing by periodically sampling 4,000-read batches from a larger ONT dataset (451,359 reads, 3.89 Gbases) and processing in parallel across four smartphones using the methylation workflow. Base-calling/batch rate estimation followed R = T_h × B / N with T_h = 48 h, B = 4000, N = 51,359, yielding ~25.52 min per batch. Implementation details: Genopo GUI and framework were developed in Java using Android SDK and NDK. Popular bioinformatics tools were reconfigured and cross-compiled into ARM shared libraries (.so). JNI bridges invoke native functions, capturing outputs via Java pipes for display in the GUI. The framework supports integration of additional tools with instructions provided within the application. ONT datasets: Nine de-identified SARS-CoV-2 patient isolates underwent standard ONT library prep and sequencing; base-calling with Guppy 3.2.0; demultiplexing with Reeqon 0.3; resulting FASTQ and associated files were transferred to smartphones for independent runs of the SARS-CoV-2 workflow. Public NA12878 ONT datasets were used for methylation analyses.

- SARS-CoV-2 workflow performance: Genopo completed the ARTIC-based analysis in approximately 27 minutes per execution across smartphones, producing complete consensus genomes with ~5–10 mutations detected per sample. Approximately 98% of runtime was consumed by Nanopolish variant calling. The Nokia 6.1 Plus was the fastest model, attributed to higher RAM (4 GB). Results (mutations and consensus sequences) were equivalent across smartphones and matched a desktop high-performance computing pipeline, validating accuracy. - Flexibility for large genomes: Genopo handled the human reference genome (GRCh38) using index partitioning. - DNA methylation profiling (NA12878): For a single-run batch of 16,688 reads (91.15 Mb), the methylation workflow completed in ~21 minutes on average across smartphones. About ~96,500 CpG sites (30.5%) within analyzed reads were identified as methylated with high probability (log-likelihood ratio > 2.5). Concordance with a best-practice pipeline executed in parallel was 99.89%. The majority of execution time was attributed to read alignment (mean 18.0%). Peak RAM was highest during methylation calling on devices F5 (3.4 GB) and G5 (3.0 GB). - Real-time feasibility: Processing 4,000-read batches averaged ~1 minute per batch on smartphones. Given an estimated base-caller batch output every ~25 minutes (48 h run time, B=4000, N=51,359), a single smartphone could keep pace with real-time data generation for this dataset.

Genopo addresses a critical bottleneck in portable nanopore sequencing by enabling offline, on-device analysis on Android smartphones. The demonstrated SARS-CoV-2 workflow reproduces desktop-grade variant calling and consensus generation, supporting field-deployable pathogen genomics where network connectivity is limited. The methylation profiling results on human genomic data show compatibility with large genomes and high concordance with standard pipelines, suggesting that smartphones can support diverse ONT analyses. Together, these findings support the feasibility of real-time, in situ genomics using ubiquitous and inexpensive mobile hardware, advancing the democratization of genome science and supporting applications such as outbreak surveillance and point-of-care testing.

The study introduces Genopo, the first smartphone application enabling nanopore sequencing analysis on Android devices. Genopo integrates widely used bioinformatics tools to deliver portable, offline workflows for tasks such as SARS-CoV-2 genome analysis and human DNA methylation profiling. It achieves rapid runtimes comparable to desktop workflows and shows high concordance with best-practice pipelines, while accommodating both small viral genomes and large eukaryotic genomes via index partitioning. These results illustrate the practicality of leveraging smartphones for real-time, field-based genomics.