# Troubleshooting Low Methylation Call Rates in Nanopore Data: Common Causes and Fixes Low methylation call rates in nanopore sequencing data typically stem from basecalling errors, model mismatch, or data quality issues. When methylation detection fails or produces sparse calls, the problem usually originates before the methylation caller runs, not within the methylation analysis itself. This article systematically lists failure modes and provides actionable solutions, including quality filtering, model selection, and signal-level checks. Nanopore sequencing detects modified bases directly because the electrical current signal changes when a modified nucleotide passes through the pore. The basecaller interprets these signal changes and assigns methylation status at specific motifs. If the basecaller uses the wrong model, if the sequencing data has poor quality, or if downstream filtering removes too many reads, the methylation call rate drops. Understanding where the pipeline breaks helps you fix the right stage. ## At a Glance The table below summarizes the most common causes of low methylation call rates, their typical symptoms, and the first action to take. | Failure Mode | Typical Symptom | First Action | | --- | --- | --- | | Basecalling model mismatch | Methylation calls appear only at a few motifs or not at all | Confirm the basecaller model matches the chemistry and modification type | | Poor read quality after basecalling | Many reads fail quality filtering before methylation calling | Check read length and quality distributions in the basecalled output | | Incorrect reference or alignment | Methylation calls cluster at wrong genomic positions | Verify the reference genome and inspect alignment statistics | | Low sequencing depth at target regions | Methylation calls are sparse at specific loci | Assess coverage per site and consider deeper sequencing | | Modified base caller not enabled | Pipeline reports zero methylation calls | Confirm the basecaller configuration includes modified base calling | | Adapter or chimeric reads | Methylation calls appear at read ends or in repetitive regions | Apply adapter trimming and read filtering before methylation analysis | ## Understanding Methylation Calling in Nanopore Data Nanopore sequencing detects DNA and RNA modifications during the sequencing process itself. The Oxford Nanopore Technologies platform measures changes in ionic current as a DNA or RNA molecule passes through a protein nanopore. Modified bases such as 5-methylcytosine (5mC), N6-methyladenine (6mA), and others alter the current signature compared to unmodified bases. The basecaller uses neural network models trained on known modification patterns to identify these changes. Direct RNA sequencing (DRS) is a Nanopore-based technique for analyzing RNA in its native form. This technique promises breakthroughs in diagnostics and biomarker development. Coupled to RNA002 sequencing chemistry, its clinical implementation has been challenging due to low throughput, low accuracy, and lack of large-scale RNA-modification models. In a 2025 study, researchers evaluated the improvements achieved by pairing the latest RNA004 chemistry with novel modified-base-calling models for pseudouridine and N6-methyladenosine using diverse RNA samples from cell lines, synthetic oligos, and human blood. The combined use of RNA004 chemistry with the base-calling models significantly improved the throughput, accuracy, and site-specific detection of modifications. This study also presented the first clinical application of DRS by confirming the loss of RNA methylation in a patient carrying truncating mutations in the methyltransferase METTL5. The findings demonstrate that basecalling model selection directly affects methylation detection performance. For DNA methylation analysis, the basecaller must be configured to detect modified bases during the basecalling step. If the pipeline uses a basecaller model trained only for canonical bases, methylation information is lost before downstream analysis begins. The Dorado basecaller from Oxford Nanopore Technologies enables direct methylome profiling, but the comparative performance of ONT-only versus hybrid-assembly reference-based methylation calling remains understudied. A 2025 study in Microbial genomics had six operators independently prepare 15 sequencing libraries for nanopore and Illumina platforms for two Streptococcus dysgalactiae subsp. equisimilis strains. Methylation and motif profiles were identified with MicrobeMod using both Illumina-corrected hybrid reference assemblies and ONT-only reference assemblies. Motif identification concordance using hybrid and ONT-only references exceeded 99.9 percent. Reproducibility across replicates was high for G6mATC and 5mCCWGG motifs in both reference types, but lower for the degenerate G6mAGNNNNNTAA motif. This study shows that reference choice affects methylation call reproducibility for some motifs. ## Core Principles of Methylation Call Rate Methylation call rate refers to the proportion of genomic sites or reads for which the analysis pipeline produces a methylation status call. A low call rate means the pipeline cannot determine whether a specific base is methylated or unmethylated. This can happen at the read level, where individual reads lack methylation information, or at the site level, where a genomic position has insufficient coverage to make a confident call. The basecaller assigns a probability or quality score to each methylation call. Downstream tools use these scores to filter low-confidence calls. If the basecaller produces low-quality methylation predictions, the filtering step removes many calls, resulting in a low final call rate. The relationship between basecalling quality and methylation call rate is direct. Higher basecalling accuracy leads to more confident methylation calls that pass filtering thresholds. Latest nanopore chemistry has achieved a modal base calling accuracy of 99.55 percent, whereas previous kits achieved slightly lower accuracy around 99.1 percent. A 2023 study in Journal of animal science and biotechnology evaluated genotype-by-low pass nanopore sequencing in dairy cattle. The direct genomic value accuracy from genotype-by-low pass sequencing ranged between 0.79 and 0.99 depending on the trait, with a sequencing depth as low as 2x and using the latest chemistry. More than one million high reliable methylated sites were obtained even at low sequencing depth, located mainly in distal intergenic and promoter regions. This study showed that the latest nanopore technology is useful in a low-pass sequencing framework to estimate direct genomic values with high reliability. Low pass sequencing provided nucleotide methylation status of more than one million nucleotides at 10x or higher coverage. The ability of nanopore sequencing to simultaneously detect modified nucleotides while producing long reads makes it ideal for detecting and phasing allele-specific methylation. A 2021 study in Genome biology presented NanoMethPhase, a software tool to phase 5-methylcytosine from nanopore sequencing. The study also presented SNVoter, which can post-process nanopore SNV calls to improve accuracy in low coverage regions. Together, these tools can accurately detect allele-specific methylation genome-wide using nanopore sequence data with low coverage of about ten-fold redundancy. This work demonstrates that low coverage nanopore data can still produce useful methylation calls when the analysis tools are designed for the data characteristics. ## Basecalling Model Selection The basecalling model is the most common source of low methylation call rates. Oxford Nanopore Technologies provides different models for different sequencing chemistries, flow cell types, and modification types. Using a model that does not match the sequencing chemistry or the expected modification type will produce poor methylation calls or no calls at all. ### Matching Model to Chemistry Each sequencing chemistry version has its own signal characteristics. The latest chemistry kits produce different current signatures compared to older kits. A basecalling model trained on data from one chemistry will not perform well on data from another chemistry. When you update your sequencing kit, you must also update your basecalling model. The 2023 dairy cattle study compared the latest nanopore chemistry with previous kits. The latest chemistry achieved a modal base calling accuracy of 99.55 percent, whereas the previous kit achieved slightly lower accuracy of 99.1 percent. The direct genomic value accuracy from genotype-by-low pass sequencing ranged between 0.79 and 0.99 depending on the trait, with a sequencing depth as low as 2x and using the latest chemistry. Lower sequencing depth led to biased estimates, yet with high rank correlations. The older chemistry achieved lower accuracies. This comparison shows that chemistry and basecalling model updates improve both accuracy and downstream analysis performance. ### Modification Type Configuration The basecaller must be configured to detect the specific modification type present in your sample. For example, bacterial samples may contain 6mA at specific motifs, while mammalian samples typically contain 5mC in CpG contexts. If you configure the basecaller for 5mC but your sample contains primarily 6mA, the methylation calls will be sparse or absent. The 2025 bacterial methylome study identified distinct methylation patterns in two Streptococcus dysgalactiae strains. One strain predominantly exhibited N6-methyladenine at G6mATC motifs, whereas the other strain displayed dual methylation patterns with C5-methylcytosine at 5mCCWGG motifs and 6mA at G6mAGNNNNNTAA motifs. The study found that reproducibility was high for the G6mATC and 5mCCWGG motifs but lower for the degenerate G6mAGNNNNNTAA motif. This finding shows that motif complexity affects methylation call consistency. Degenerate motifs with multiple possible sequence contexts are harder to call reliably than simple, well-defined motifs. ### Model Training and Updates Basecalling models are updated as Oxford Nanopore Technologies improves their algorithms. The 2025 direct RNA sequencing study evaluated the improvements achieved by pairing the latest RNA004 chemistry with novel modified-base-calling models for pseudouridine and N6-methyladenosine. The combined use of RNA004 chemistry with the base-calling models significantly improved the throughput, accuracy, and site-specific detection of modifications. This study demonstrates that model updates can substantially improve methylation detection performance. When troubleshooting low methylation call rates, check which version of the basecaller and which model you are using. Compare your model version to the latest recommended model for your chemistry. If you are using an outdated model, updating to the latest version may resolve the problem. ## Data Quality Assessment Poor data quality reduces methylation call rates because low-quality reads fail quality filters or produce low-confidence methylation calls. Assessing data quality before running methylation analysis helps identify problems early. ### Read Length and Quality Distributions After basecalling, examine the read length and quality distributions. Nanopore sequencing produces reads of varying lengths, and the quality scores vary along each read. Reads with low average quality or very short lengths may not contain enough information for reliable methylation calling. The 2021 PRINCESS study in Genome biology described a structured workflow that takes raw sequence reads and generates a fully phased SNV, SV, and methylation call set within a few hours. PRINCESS achieves high accuracy and long phasing even on low coverage datasets and can resolve repetitive, complex medical relevant genes that often escape detection. This workflow demonstrates that structured analysis pipelines can handle nanopore data characteristics effectively. ### Sequencing Depth Considerations Methylation calling requires sufficient coverage at each genomic site to make a confident call. Low sequencing depth leads to sparse methylation calls. The 2021 NanoMethPhase study showed that accurate allele-specific methylation detection is possible genome-wide using nanopore sequence data with low coverage of about ten-fold redundancy. The 2023 dairy cattle study obtained more than one million high reliable methylated sites even at low sequencing depth, with methylation status of more than one million nucleotides at 10x or higher coverage. If your methylation call rate is low because of insufficient depth, you have two options. You can sequence more to increase coverage, or you can adjust your analysis to work with lower coverage data. The choice depends on your research question and the genomic regions of interest. ### Signal-Level Checks Sometimes the problem originates at the signal level, before basecalling. The raw electrical current signal from the nanopore must be of sufficient quality for the basecaller to interpret. Signal-level issues include pore blockages, adapter contamination, and sequencing run instability. If you suspect signal-level problems, examine the sequencing run metrics. Look at the number of active pores over time, the read length distribution, and the yield per flow cell. A run with many blocked pores or short reads may produce data that is difficult to basecall accurately, leading to low methylation call rates. ## Alignment and Reference Considerations Methylation calls are only useful when mapped to the correct genomic positions. Alignment errors or incorrect reference genomes produce methylation calls at wrong locations or cause calls to be discarded during filtering. ### Reference Genome Selection The choice of reference genome affects methylation call rates. A 2025 study compared ONT-only reference assemblies with Illumina-corrected hybrid reference assemblies for bacterial methylome profiling. Motif identification concordance using hybrid and ONT-only references exceeded 99.9 percent. ONT-only reference-based methylation site calls showed excellent precision and recall compared to hybrid reference-based calls with F1-score above 99.999 percent. This finding suggests that for bacterial genomes, ONT-only references can produce reliable methylation calls without the need for Illumina correction. However, the study also found that reproducibility was lower for the degenerate G6mAGNNNNNTAA motif in both reference types. This result indicates that some motifs are inherently more difficult to call consistently, regardless of reference quality. ### Alignment Parameters The alignment step maps reads to the reference genome. Nanopore reads are long and have higher error rates than short reads. Alignment tools designed for nanopore data account for these characteristics. Using alignment parameters optimized for short reads may produce poor alignments for nanopore reads, leading to reads being discarded or mapped to incorrect positions. Check the alignment statistics after mapping. Look at the percentage of reads mapped, the mapping quality distribution, and the coverage across the genome. If many reads fail to map or map with low quality, the methylation call rate will suffer. ### Phasing and Haplotype Context Methylation analysis can be performed at the haplotype level when the data supports phasing. The 2021 NanoMethPhase study presented software to phase 5-methylcytosine from nanopore sequencing. The study also presented SNVoter, which can post-process nanopore SNV calls to improve accuracy in low coverage regions. Together, these tools can accurately detect allele-specific methylation genome-wide using nanopore sequence data with low coverage of about ten-fold redundancy. The 2021 PRINCESS study described a workflow that generates a fully phased SNV, SV, and methylation call set. PRINCESS achieves high accuracy and long phasing even on low coverage datasets. If your analysis requires haplotype-resolved methylation calls, ensure your pipeline includes phasing steps and that the data has sufficient heterozygous variants for phasing. ## Practical Workflow for Diagnosing Low Methylation Call Rates Follow this systematic workflow to identify the cause of low methylation call rates in your nanopore data. ### Step 1: Verify Basecalling Configuration Check the basecaller version and model used for your data. Confirm that the model matches the sequencing chemistry and flow cell type. Verify that modified base calling was enabled and that the correct modification type was specified. If you are unsure which model was used, re-examine the basecalling logs or rerun basecalling with the correct model. ### Step 2: Assess Read Quality Examine the read length and quality distributions from the basecalled output. Calculate the mean read quality and the N50 read length. Compare these metrics to expected values for your sequencing chemistry. If the read quality is lower than expected, the sequencing run may have had problems, or the basecalling model may be incorrect. ### Step 3: Check Alignment Statistics Map the reads to the appropriate reference genome and examine the alignment statistics. Look at the percentage of reads mapped, the mean mapping quality, and the coverage distribution. Identify any genomic regions with zero or very low coverage. These regions will have no methylation calls regardless of the analysis pipeline. ### Step 4: Evaluate Methylation Call Distribution Run the methylation caller and examine the distribution of methylation calls across the genome. Check whether calls are present at expected motifs and whether the call density matches the expected methylation pattern for your sample type. If calls are absent at expected motifs, the basecalling model may not include the correct modification type. ### Step 5: Compare with Known Controls If you have a control sample with known methylation patterns, run the same pipeline on the control data. Compare the methylation call rates between the control and your experimental sample. If the control produces normal call rates but your sample does not, the problem may be sample-specific instead of pipeline-specific. ## Records and Measurements Maintain detailed records of your sequencing runs and analysis pipelines to facilitate troubleshooting. ### Sequencing Run Records Record the flow cell type, sequencing chemistry, basecaller version, and basecalling model for each run. Note the run duration, yield, read length distribution, and quality metrics. These records help you identify patterns across runs and determine whether a specific configuration consistently produces low methylation call rates. ### Analysis Pipeline Records Document the exact commands and parameters used for each analysis step. Include the reference genome version, alignment tool and parameters, methylation caller and parameters, and any filtering thresholds. Version control your analysis scripts so you can reproduce the exact pipeline that produced a given result. ### Quality Control Metrics Track quality control metrics across runs. These include the percentage of reads passing quality filters, the mean read quality, the mapping rate, and the methylation call rate. Plot these metrics over time to identify trends. A gradual decline in methylation call rates may indicate a systematic issue with reagents, flow cells, or analysis software. ## Common Failure Patterns Several failure patterns recur when users encounter low methylation call rates. ### Pattern 1: Zero Methylation Calls If the pipeline reports zero methylation calls, the basecaller likely did not detect modified bases. This occurs when the basecalling model does not include modified base detection, when the wrong modification type is specified, or when the sequencing chemistry is incompatible with the model. Check the basecalling configuration first. ### Pattern 2: Calls Only at Specific Motifs If methylation calls appear only at a subset of expected motifs, the basecaller may have lower sensitivity for certain sequence contexts. The 2025 bacterial methylome study found lower reproducibility for the degenerate G6mAGNNNNNTAA motif compared to simpler motifs. Degenerate motifs with multiple possible sequence contexts are harder to call consistently. ### Pattern 3: Calls Concentrated in High-Coverage Regions If methylation calls appear only in regions with very high coverage, the analysis may require a coverage threshold that excludes most of the genome. The 2023 dairy cattle study obtained methylation status for more than one million nucleotides at 10x or higher coverage. If your data has lower coverage, you may need to lower the coverage threshold or sequence more deeply. ### Pattern 4: Calls Present but Low Confidence If methylation calls are present but fail quality filtering, the basecalling quality may be insufficient. This can occur with older sequencing chemistries or when the basecaller model does not match the data. Updating to the latest chemistry and basecalling model may improve call confidence. ### Pattern 5: Inconsistent Calls Between Replicates If replicate samples produce different methylation call rates, the variability may stem from library preparation differences or sequencing run variability. The 2025 bacterial methylome study had six operators independently prepare sequencing libraries. Reproducibility across replicates was high for simple motifs but lower for the degenerate motif. This finding suggests that some variability is inherent to the technology and the sequence context. ## Limitations of Methylation Calling Methylation calling from nanopore data has inherent limitations that affect call rates. ### Motif Complexity Simple motifs with well-defined sequence contexts produce more consistent methylation calls than degenerate motifs. The 2025 bacterial methylome study found that reproducibility was high for G6mATC and 5mCCWGG motifs but lower for the degenerate G6mAGNNNNNTAA motif. When designing methylation studies, consider the motif complexity of the modifications you expect to detect. ### Sequencing Depth Methylation calling requires sufficient coverage at each site. The 2021 NanoMethPhase study demonstrated accurate allele-specific methylation detection with about ten-fold redundancy. The 2023 dairy cattle study obtained methylation status for more than one million nucleotides at 10x or higher coverage. Lower coverage produces sparser methylation calls. ### Basecalling Accuracy Basecalling accuracy directly affects methylation call confidence. The 2023 dairy cattle study reported that the latest nanopore chemistry achieved a modal base calling accuracy of 99.55 percent, whereas the previous kit achieved 99.1 percent. Higher basecalling accuracy leads to more confident methylation calls. ### Reference Quality The reference genome quality affects methylation call accuracy. The 2025 bacterial methylome study found that ONT-only reference assemblies produced methylation calls with excellent precision and recall compared to hybrid reference assemblies. However, the study also noted that some motifs are harder to call consistently regardless of reference quality. ## Quality Control and Reproducibility Implement quality control measures to ensure reproducible methylation calls. ### Replicate Libraries Prepare replicate libraries to assess reproducibility. The 2025 bacterial methylome study had six operators independently prepare libraries for the same strains. This design allowed the researchers to quantify inter-operator variability. For your own studies, prepare at least two replicate libraries to assess variability. ### Control Samples Include control samples with known methylation patterns in each sequencing run. Compare methylation call rates between controls and experimental samples. If control call rates decline, the problem is likely in the sequencing or analysis pipeline instead of in the experimental samples. ### Pipeline Validation Validate your analysis pipeline using publicly available datasets with known methylation patterns. The Galaxy Training Network provides accessible workflow training and analysis tutorials for bioinformatics analysis. Bioconductor offers official package, workflow, installation, and reproducible genomic-analysis documentation. These resources can help you validate your pipeline and ensure it produces reliable results. ### Version Control Use version control for your analysis scripts and document the software versions used. The Carpentries Lessons provide foundational computing, data, shell, Git, and programming training context. Version control ensures you can reproduce your analysis exactly and identify when software updates change results. ## Professional Escalation Criteria Some problems require escalation to experienced bioinformaticians or the sequencing platform provider. ### When to Seek Help Escalate to a bioinformatics specialist when you have verified the basecalling configuration, assessed data quality, checked alignment statistics, and confirmed that the problem persists. If your pipeline produces zero methylation calls across multiple runs with different samples, the issue may require specialized expertise. ### What to Provide When Escalating When escalating a problem, provide the following information: the sequencing chemistry and flow cell type, the basecaller version and model, the basecalled read quality metrics, the alignment statistics, the methylation caller and parameters, and the specific error or symptom observed. Include representative examples of the problematic data so the specialist can reproduce the issue. ### Platform-Specific Support Oxford Nanopore Technologies provides support for their sequencing platforms and basecalling software. The NCBI Data Resources provide official descriptions of NCBI databases, search systems, sequence resources, and analysis services that may be useful for data management and analysis. The EMBL-EBI Training provides bioinformatics learning pathways, data-resource training, and practical analysis education that can help you build the skills needed to troubleshoot complex problems. ## Safety and Regulatory Context Methylation analysis from nanopore data has applications in clinical diagnostics and therapeutic development. The 2025 direct RNA sequencing study presented the first clinical application of DRS by confirming the loss of RNA methylation in a patient carrying truncating mutations in the methyltransferase METTL5. The study also demonstrated potential roadblocks for routine quality assessments of RNA therapeutics based on a stop-codon readthrough enhancing agent. When working with clinical samples or developing diagnostic applications, follow applicable regulations and guidelines for data handling, privacy, and reporting. The nf-core Documentation provides community pipeline standards, usage, configuration, and reproducible workflow context that can help you implement robust analysis pipelines for regulated environments. ## Building a Methylation Call Rate Decision Framework for Routine Runs A systematic decision framework helps you move from symptom to fix without re-running the entire pipeline each time. The workflow below organizes troubleshooting into a tiered structure that matches the most common failure points in order of likelihood and cost of correction. This framework assumes you have already confirmed that modified base calling was enabled during basecalling, since that is the single most common cause of zero methylation calls. ### Tier 1: Configuration Verification Before Data Re-Processing The first tier costs nothing but time and prevents wasted compute. Before examining reads or alignments, verify that every configuration choice in your pipeline matches the data you actually generated. This tier addresses the most common cause of low methylation call rates: model mismatch. Start with the basecaller model. Confirm that the model name includes the correct chemistry identifier for your flow cell and kit. The 2023 dairy cattle study in Journal of animal science and biotechnology compared the latest nanopore chemistry with previous kits and found that the latest chemistry achieved a modal base calling accuracy of 99.55 percent, whereas the previous kit achieved 99.1 percent. This accuracy difference directly affects methylation call confidence because the basecaller assigns modification probabilities based on the signal patterns it learned during training. A model trained on older chemistry data will misinterpret the current signatures from newer chemistry, producing lower modification probabilities that fail downstream filtering thresholds. Next verify the modification type configuration. The 2025 bacterial methylome study in Microbial genomics identified distinct methylation patterns in two Streptococcus dysgalactiae strains, with one strain predominantly exhibiting N6-methyladenine at G6mATC motifs and the other displaying dual methylation patterns with C5-methylcytosine at 5mCCWGG motifs and 6mA at G6mAGNNNNNTAA motifs. If your basecaller was configured for 5mC detection but your sample contains primarily 6mA, the pipeline will report sparse or absent methylation calls even though the data contains abundant modification signals. Check the basecaller output logs for the modification types that were actually detected during basecalling, beyond the ones you intended to detect. Finally verify the reference genome version and the alignment tool parameters. The 2025 bacterial methylome study found that ONT-only reference assemblies produced methylation site calls with excellent precision and recall compared to hybrid reference-based calls, with F1-score above 99.999 percent. This finding means reference choice is rarely the primary cause of low call rates for bacterial genomes. However, if you changed reference versions between runs, confirm that the chromosome names and coordinate systems match what your methylation caller expects. Mismatched chromosome naming conventions cause reads to fail mapping or map to incorrect positions, which reduces the number of usable methylation calls. ### Tier 2: Read-Level Quality Triage If configuration checks pass, move to read-level quality assessment. This tier identifies whether the problem lies in the sequencing data itself or in the downstream analysis steps. The goal is to determine whether you have enough high-quality reads with sufficient length to support methylation calling. Calculate three metrics from your basecalled output: the read N50, the mean read quality score, and the fraction of reads passing your quality threshold. Compare these values to the expected performance for your chemistry. The 2023 dairy cattle study demonstrated that low-pass nanopore sequencing at 2x depth could still produce more than one million high reliable methylated sites, with methylation status obtained for more than one million nucleotides at 10x or higher coverage. This result shows that even modest sequencing depth can support methylation analysis when read quality is adequate. If your read N50 is substantially below expected values for your chemistry, the sequencing run may have experienced pore blockages or adapter contamination that truncated reads before they could traverse enough of the template. Examine the quality score distribution along reads. Nanopore quality scores typically decline toward the end of long reads. If you observe a steep quality drop in the first few hundred bases, the sequencing run may have had issues with pore occupancy or salt concentration. Reads with poor quality in the regions where methylation calls would be made will produce low-confidence modification probabilities that fail filtering. The 2021 PRINCESS study in Genome biology described a structured workflow that takes raw sequence reads and generates a fully phased SNV, SV, and methylation call set within a few hours. PRINCESS achieves high accuracy and long phasing even on low coverage datasets. This workflow demonstrates that structured analysis pipelines can handle nanopore data characteristics effectively, but the input read quality still determines the upper bound of what any downstream tool can extract. If your read quality metrics fall below expected ranges, the problem is in the sequencing run, not the analysis pipeline. ### Tier 3: Alignment and Coverage Diagnostics After confirming read quality, examine the alignment output. This tier distinguishes between problems caused by poor mapping and problems caused by insufficient coverage at target sites. Check the percentage of reads mapped and the mapping quality distribution. Low mapping rates indicate either a reference mismatch or alignment parameter problems. The 2021 NanoMethPhase study in Genome biology presented software to phase 5-methylcytosine from nanopore sequencing and demonstrated accurate allele-specific methylation detection genome-wide using nanopore sequence data with low coverage of about ten-fold redundancy. The study also presented SNVoter, which can post-process nanopore SNV calls to improve accuracy in low coverage regions. These tools were designed specifically for nanopore data characteristics, and using alignment parameters optimized for short reads will produce poor results with long nanopore reads. Generate a coverage histogram across the genome. Identify regions with zero coverage and regions with coverage below your methylation caller threshold. The 2023 dairy cattle study obtained methylation status for more than one million nucleotides at 10x or higher coverage, which suggests that 10x is a practical target for reliable site-level methylation calls. If large genomic regions fall below this threshold, you have two options: sequence more to increase coverage, or adjust your analysis to work with lower coverage data. The choice depends on your research question. For genome-wide methylation profiling, deeper sequencing is usually necessary. For targeted analysis of specific loci, you may be able to proceed with lower coverage if your methylation caller supports it. Check for systematic coverage biases. Regions with extreme GC content, repetitive sequences, or complex structural variation often have lower coverage in nanopore data. The 2021 PRINCESS study noted that the workflow can resolve repetitive, complex medical relevant genes that often escape detection. If your low methylation call rate concentrates in such regions, the problem is inherent to the genomic context instead of a pipeline error. ### Tier 4: Methylation Call Distribution Analysis If alignment and coverage look acceptable, examine the methylation calls themselves. This tier identifies whether the problem is in the methylation caller configuration or in the biological signal present in the data. Plot the distribution of methylation calls across the genome. Check whether calls appear at expected motifs and whether the call density matches the expected methylation pattern for your sample type. The 2025 bacterial methylome study found that reproducibility across replicates was high for G6mATC and 5mCCWGG motifs in both hybrid and ONT-only reference assemblies, with Pearson correlation coefficients above 0.989, but lower for the degenerate G6mAGNNNNNTAA motif with Pearson correlation coefficients of 0.80 for hybrid references and 0.78 for ONT-only references. This finding shows that some motifs are inherently harder to call consistently. If your methylation calls are absent at simple, well-defined motifs, the problem is likely in the basecalling or methylation calling configuration. If calls are present but inconsistent at degenerate motifs, the problem may be inherent to the sequence context. Examine the methylation call quality scores. Most methylation callers output a probability or quality score for each call. If the distribution of these scores is shifted toward low values, the basecaller may have produced low-confidence modification predictions. The 2025 direct RNA sequencing study in Nucleic acids research evaluated the improvements achieved by pairing the latest RNA004 chemistry with novel modified-base-calling models for pseudouridine and N6-methyladenosine. The combined use of RNA004 chemistry with the base-calling models significantly improved the throughput, accuracy, and site-specific detection of modifications. This study demonstrates that model updates can substantially improve methylation detection performance, and the same principle applies to DNA methylation calling. Compare your methylation call distribution to known patterns for your sample type. If you are analyzing a bacterial genome, check whether the expected restriction-modification system motifs appear in your calls. If you are analyzing a mammalian genome, check whether CpG methylation follows the expected bimodal distribution with high methylation at intergenic regions and low methylation at CpG islands. Deviations from expected patterns indicate a systematic problem in the detection pipeline. ### Tier 5: Control-Based Comparison The final tier uses control samples to isolate the cause of low methylation call rates. This tier is the most informative because it distinguishes between pipeline problems and sample-specific problems. If you have a control sample with known methylation patterns, run the same pipeline on the control data. Compare the methylation call rates between the control and your experimental sample. If the control produces normal call rates but your sample does not, the problem is sample-specific. Sample-specific causes include degraded DNA, incomplete bisulfite conversion if you are using a hybrid approach, or biological variation in methylation patterns. If both control and experimental samples produce low call rates, the problem is in the pipeline or the sequencing run. The 2025 bacterial methylome study had six operators independently prepare 15 sequencing libraries for nanopore and Illumina platforms for two Streptococcus dysgalactiae strains. This design allowed the researchers to quantify inter-operator variability. Reproducibility across replicates was high for simple motifs but lower for the degenerate motif. If your replicates show high variability, the problem may be in library preparation consistency instead of in the analysis pipeline. If you do not have a control sample, consider using publicly available datasets with known methylation patterns. The Galaxy Training Network provides accessible workflow training and analysis tutorials for bioinformatics analysis. Bioconductor offers official package, workflow, installation, and reproducible genomic-analysis documentation. These resources can help you validate your pipeline and ensure it produces reliable results. ### Decision Matrix for Common Scenarios The table below summarizes the decision path for common scenarios encountered during methylation call rate troubleshooting. | Scenario | Tier 1 Result | Tier 2 Result | Tier 3 Result | Likely Cause | Recommended Action | | --- | --- | --- | --- | --- | --- | | Zero methylation calls | Model mismatch confirmed | Reads pass quality checks | Alignment looks normal | Basecaller model lacks modification detection | Re-run basecalling with correct model | | Calls only at high coverage regions | Configuration correct | Reads pass quality checks | Coverage below 10x at most sites | Insufficient sequencing depth | Sequence more or lower coverage threshold | | Calls present but low confidence | Configuration correct | Read quality below expected | Alignment normal | Sequencing run quality issues | Check run metrics and consider re-sequencing | | Calls inconsistent between replicates | Configuration correct | Read quality varies between runs | Coverage varies between runs | Library preparation variability | Standardize library preparation protocol | | Calls absent at specific motifs | Configuration correct | Reads pass quality checks | Coverage adequate at target sites | Motif complexity or modification type mismatch | Verify modification type and consider degenerate motif limitations | ### Record Keeping for the Decision Framework Maintain a structured record for each sequencing run and analysis to make the decision framework actionable. Create a run log that captures the flow cell type, sequencing chemistry, basecaller version, basecalling model, modification types configured, and the date of the run. Record the read N50, mean read quality, total yield, and the fraction of reads passing quality filters. After alignment, record the mapping rate, mean mapping quality, and the fraction of the genome covered at 10x or higher. After methylation calling, record the total number of methylation calls, the number of sites with methylation calls, and the distribution of call quality scores. Store these records in a consistent format that allows comparison across runs. The Carpentries Lessons provide foundational computing, data, shell, Git, and programming training context that can help you build reproducible record-keeping practices. Version control your analysis scripts and document the software versions used for each step. The nf-core Documentation provides community pipeline standards, usage, configuration, and reproducible workflow context that can help you implement robust analysis pipelines. When you encounter a low methylation call rate, work through the tiers in order. Tier 1 checks take minutes and cost nothing. Tier 2 and Tier 3 checks require examining existing output files and take less than an hour. Tier 4 requires running the methylation caller and examining its output. Tier 5 requires access to control data or public datasets. Most problems are identified in Tier 1 or Tier 2, and the decision matrix helps you determine the appropriate corrective action without re-running the entire pipeline unnecessarily. ### Escalation Criteria Within the Framework Escalate to a bioinformatics specialist when you have completed all five tiers and the problem persists. Provide the specialist with your run log, quality metrics, alignment statistics, methylation call distribution, and the results of your control comparisons. Include representative examples of the problematic data so the specialist can reproduce the issue. The NCBI Data Resources provide official descriptions of NCBI databases, search systems, sequence resources, and analysis services that may be useful for data management and analysis. The EMBL-EBI Training provides bioinformatics learning pathways, data-resource training, and practical analysis education that can help you build the skills needed to troubleshoot complex problems. Escalate to the sequencing platform provider when you suspect a hardware or chemistry issue. This includes situations where read quality metrics fall well below expected values for your chemistry, where pore occupancy declines rapidly during the run, or where multiple runs with different samples produce consistently poor results. Provide the provider with your run metrics and the specific symptoms observed. ## Frequently Asked Questions ### Why does my nanopore methylation analysis report zero methylation calls? Zero methylation calls usually mean the basecaller did not detect modified bases. Check that the basecalling model includes modified base detection, that the correct modification type was specified, and that the model matches the sequencing chemistry. If the basecaller was run with a canonical-only model, methylation information is lost before downstream analysis begins. ### How does sequencing chemistry affect methylation call rates? Different sequencing chemistries produce different signal characteristics. The 2023 dairy cattle study found that the latest nanopore chemistry achieved a modal base calling accuracy of 99.55 percent, whereas the previous kit achieved 99.1 percent. The newer chemistry also produced higher direct genomic value accuracies. Using a basecalling model that matches your chemistry is essential for reliable methylation detection. ### What coverage is needed for reliable methylation calls? The 2021 NanoMethPhase study demonstrated accurate allele-specific methylation detection with about ten-fold redundancy. The 2023 dairy cattle study obtained methylation status for more than one million nucleotides at 10x or higher coverage. Lower coverage produces sparser calls, but the 2023 study also showed that high reliable methylated sites can be obtained even at low sequencing depth. ### Why are methylation calls inconsistent for some motifs? Motif complexity affects call consistency. The 2025 bacterial methylome study found high reproducibility for simple motifs like G6mATC and 5mCCWGG but lower reproducibility for the degenerate G6mAGNNNNNTAA motif. Degenerate motifs with multiple possible sequence contexts are inherently harder to call consistently. ### Can I use ONT-only reference assemblies for methylation calling? Yes. The 2025 bacterial methylome study found that ONT-only reference-based methylation site calls showed excellent precision and recall compared to hybrid reference-based calls with F1-score above 99.999 percent. Motif identification concordance using hybrid and ONT-only references exceeded 99.9 percent. ### How do I know if my basecalling model is correct? Verify that the basecalling model matches your sequencing chemistry and flow cell type. Check the basecaller documentation for the recommended model for your specific configuration. If you updated your sequencing kit, update your basecalling model accordingly. The 2025 direct RNA sequencing study showed that pairing the latest chemistry with novel modified-base-calling models significantly improved throughput, accuracy, and site-specific detection of modifications. ### What should I do if my methylation call rate is low but not zero? Assess your data quality and coverage. Check the read length and quality distributions, the alignment statistics, and the coverage across the genome. If coverage is low at specific regions, those regions will have sparse methylation calls. Consider sequencing more deeply or adjusting your analysis to work with lower coverage data. ### When should I escalate a methylation calling problem to a specialist? Escalate when you have verified the basecalling configuration, assessed data quality, checked alignment statistics, and confirmed that the problem persists across multiple runs. Provide the specialist with your sequencing chemistry, basecaller version and model, quality metrics, alignment statistics, and the specific symptom observed. ## Related Bioinformatics Guides - [Long-Read Metagenome Assembly: Overcoming Challenges with Nanopore and PacBio Data](/knowledge/bioinformatics/long-read-metagenome-assembly-overcoming-challenges-with-nanopore-and-pacbio-data) - [FAIR Data Maturity Model: A Practical Assessment Framework for Bioinformatics Workflows](/knowledge/bioinformatics/fair-data-maturity-model-a-practical-assessment-framework-for-bioinformatics-workflows) - [Data Stewardship vs Data Governance: What's the Difference?](/knowledge/bioinformatics/data-stewardship-vs-data-governance-what-s-the-difference) - [Metabolomics Data Analysis in R: A Practical Workflow](/knowledge/bioinformatics/metabolomics-data-analysis-in-r-a-practical-workflow) - [Microbiome Data Analysis in R: A Practical Guide for Compositional Data](/knowledge/bioinformatics/microbiome-data-analysis-in-r-a-practical-guide-for-compositional-data) ## References and Further Reading - [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/). National Center for Biotechnology Information. - [EMBL-EBI Training](https://www.ebi.ac.uk/training). European Bioinformatics Institute. - [Bioconductor](https://bioconductor.org/). Bioconductor Project. - [Galaxy Training Network](https://training.galaxyproject.org/). Galaxy Project. - [nf-core Documentation](https://nf-co.re/docs). nf-core. - [The Carpentries Lessons](https://carpentries.org/lessons). The Carpentries. - [Direct RNA sequencing enables improved transcriptome assessment and tracking of RNA modifications for medical applications.](https://pubmed.ncbi.nlm.nih.gov/41325774). Nucleic acids research, 2025. - [Evaluating the potential of (epi)genotype-by-low pass nanopore sequencing in dairy cattle: a study on direct genomic value and methylation analysis.](https://pubmed.ncbi.nlm.nih.gov/37434255). Journal of animal science and biotechnology, 2023. - [Megabase-scale methylation phasing using nanopore long reads and NanoMethPhase.](https://pubmed.ncbi.nlm.nih.gov/33618748). Genome biology, 2021. - [Reproducibility and accuracy of bacterial methylome profiling using Oxford Nanopore Technologies nanopore sequencing platform.](https://pubmed.ncbi.nlm.nih.gov/41259098). Microbial genomics, 2025. - [PRINCESS: comprehensive detection of haplotype resolved SNVs, SVs, and methylation.](https://pubmed.ncbi.nlm.nih.gov/34521442). Genome biology, 2021. > This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.