# Comparing Data Management Tools for Long-Read Sequencing: A Benchmark of File Formats and Compression Algorithms Long-read sequencing platforms generate data files that are substantially larger than those produced by short-read instruments, and the choice of file format and compression algorithm directly affects storage costs, transfer times, and downstream analysis speed. This article provides a practical benchmark of FASTQ compression tools, including gzip, bzip2, CRAM, and specialized formats such as FAST5 and POD5, with attention to compression ratio, speed, and compatibility for researchers and laboratory professionals managing nanopore or PacBio datasets. The core problem for bioinformaticians is straightforward: raw long-read data must be stored without information loss, retrieved quickly, and processed by alignment and variant-calling tools that expect specific input formats. A compression tool that achieves excellent size reduction but requires hours to decompress may be a poor choice for an active analysis pipeline. Conversely, a fast tool with modest compression may be ideal for temporary storage during active projects. This benchmark evaluates these tradeoffs using published evidence and practical workflow considerations. ## At a Glance The table below summarizes the key characteristics of common file formats and compression approaches for long-read sequencing data. These comparisons are drawn from published benchmark studies and official documentation from bioinformatics infrastructure projects. | Format or Tool | Compression Approach | Typical Use Case | Compatibility | Key Tradeoff | | --- | --- | --- | --- | --- | | FASTQ with gzip | General-purpose block compression | Standard raw read storage | Universal across all analysis tools | Fast and widely supported but moderate compression ratio | | FASTQ with bzip2 | General-purpose block compression with Burrows-Wheeler transform | Archival storage where size matters more than speed | Universal but slower decompression | Better compression than gzip but significantly slower | | CRAM | Reference-based compression | Aligned read storage | Supported by SAMtools and many downstream tools | Best compression for aligned data but requires reference genome | | FAST5 | Hierarchical data format for raw signal data | Native Oxford Nanopore output | Requires HDF5 libraries and platform-specific tools | Preserves raw signal but large file sizes | | POD5 | Columnar storage format for raw signal data | Newer Oxford Nanopore output | Growing support in nanopore analysis pipelines | Improved access speed compared to FAST5 but still large | | BAM | Compressed binary alignment format | Aligned read storage | Universal in bioinformatics | Good balance of compression and access speed | | FastqCLS | Read reordering with scoring model | Long-read FASTQ compression | Standalone tool with Docker image | Specialized for long reads with lossless compression | ## Understanding Long-Read Data Characteristics Long-read sequencing differs from short-read sequencing in ways that directly affect data management. Nanopore and PacBio platforms produce reads that range from thousands to hundreds of thousands of base pairs, compared to the 150 to 300 base pairs typical of Illumina instruments. This difference changes the information content of the files and the effectiveness of various compression strategies. ### Read Length Distribution and File Size The file size of a sequencing run depends on the number of reads, the average read length, and the quality score encoding. Long-read datasets often contain fewer reads than short-read datasets but each read occupies substantially more space. A single PromethION flow cell can produce tens of gigabytes of raw data, and a PacBio Sequel run can produce similar volumes. The [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/) provide access to public long-read datasets that demonstrate these file size characteristics, and researchers can use these repositories to estimate storage requirements before committing to a sequencing project. The read length distribution also affects compression effectiveness. Long reads contain longer stretches of sequence with less local redundancy than short reads, which can reduce the effectiveness of compression algorithms that rely on finding repeated patterns within a window. However, long reads also contain more internal structure, including homopolymer runs and repetitive elements, that specialized algorithms can exploit. ### Quality Score Encoding Quality scores in FASTQ files are typically encoded as ASCII characters, with each base position receiving a Phred score that represents the probability of an incorrect base call. Long-read platforms have historically produced lower average quality scores than short-read platforms, but the quality score distribution differs in important ways. Nanopore quality scores have improved substantially with newer chemistry and basecalling models, while PacBio HiFi reads achieve high accuracy through circular consensus sequencing. The quality score distribution affects how well compression algorithms can reduce file size, since repetitive or predictable quality values compress more effectively than random ones. The [EMBL-EBI Training](https://www.ebi.ac.uk/training) resources provide structured learning pathways for understanding sequencing data formats and quality metrics. Researchers who understand the quality score distribution of their data can make more informed decisions about compression tools and parameters. ### Raw Signal Data Versus Basecalled Data Oxford Nanopore instruments produce raw electrical signal data that must be basecalled before sequence analysis. The raw signal data is stored in FAST5 or POD5 format, while the basecalled sequences are typically stored in FASTQ format. Researchers must decide whether to retain raw signal data, which enables re-basecalling with improved models but requires substantial storage, or to discard it after basecalling to save space. The decision to retain raw signal data has significant storage implications. Raw signal files are typically larger than basecalled FASTQ files because they contain the continuous electrical signal measurements instead of just the called bases. However, retaining raw data provides the option to re-basecall with improved models as they become available, which can improve read accuracy and downstream analysis quality. ## Core Principles of Sequence Data Compression Compression algorithms reduce file size by identifying and exploiting redundancy in the data. Sequence data contains several types of redundancy that different algorithms exploit in different ways. ### Entropy and Information Content The theoretical limit of lossless compression is determined by the entropy of the data, which measures the average amount of information per symbol. DNA sequences have low entropy because they use only four bases, and the base composition is often non-uniform. Quality scores have higher entropy because they can take many possible values, but they often follow predictable patterns along the length of a read. Compression algorithms that model these patterns can achieve better ratios than general-purpose tools that treat the data as a simple byte stream. For long-read data, the entropy characteristics differ from short-read data in several ways. The longer reads contain more sequence context, which can improve the effectiveness of context-based compression models. However, the quality score distributions for long-read platforms are often different from those of short-read platforms, and compression tools that were optimized for short-read quality score patterns may not perform as well on long-read data. ### Read Reordering Strategies One effective approach for FASTQ compression is to reorder reads before compression so that similar reads are adjacent in the file. This creates local redundancy that compression algorithms can exploit. The FastqCLS tool implements this strategy using a novel scoring model specifically designed for long-read data. According to the [published benchmark study](https://pubmed.ncbi.nlm.nih.gov/34623374), FastqCLS outperformed existing major FASTQ compression tools in terms of compression ratios for long-read sequencing data. The tool is available as a Docker image, which simplifies installation and execution for users who may not have experience compiling bioinformatics software. The read reordering approach is particularly effective for long-read data because long reads from the same genomic region share substantial sequence similarity. By placing similar reads adjacent to each other in the file, the compression algorithm can exploit this local redundancy more effectively. The scoring model used by FastqCLS is designed to identify reads that are likely to compress well when placed together, which is a different optimization target than the approaches used by general-purpose compressors. ### Reference-Based Compression CRAM format achieves high compression ratios by storing only the differences between sequenced reads and a reference genome, instead of storing the full read sequences. This approach is highly effective for aligned data but requires that the reference genome be available for decompression. The [rdeval evaluation tool](https://pubmed.ncbi.nlm.nih.gov/40694478) can convert FASTA and FASTQ files to and from BAM and CRAM formats, and its documentation notes that CRAM achieves the best compression while BAM offers the best compromise between compression and access speed. The effectiveness of reference-based compression depends on the similarity between the sequenced reads and the reference genome. For long-read data, the longer reads may span more reference sequence and contain more mismatches and indels, which can reduce compression effectiveness compared to short-read data. However, the reference-based approach still achieves better compression than storing full read sequences because it only stores the differences. ## File Format Options for Long-Read Data The choice of file format depends on the stage of the analysis pipeline and the intended use of the data. Raw signal data, basecalled reads, and aligned reads each have appropriate formats. ### FASTQ for Basecalled Reads FASTQ is the standard format for basecalled sequence data. Each read is represented by four lines: a header line with read identifier and metadata, the sequence line, a plus sign, and the quality score line. The format is simple, human-readable, and supported by virtually every bioinformatics tool. However, the text-based format is inefficient for storage, and compression is essential for managing large datasets. The [Galaxy Training Network](https://training.galaxyproject.org/) provides tutorials on working with FASTQ data, including quality control and preprocessing steps that are relevant before compression decisions are made. Understanding the data content before compression helps researchers choose appropriate tools and parameters. FASTQ files from long-read platforms may have different characteristics than those from short-read platforms. The read headers may contain additional metadata, such as channel information for nanopore data or polymerase read information for PacBio data. The quality score encoding may also differ, with some platforms using different Phred score ranges or encoding schemes. ### FAST5 and POD5 for Raw Signal Data Oxford Nanopore instruments store raw signal data in FAST5 format, which uses the Hierarchical Data Format 5 (HDF5) structure. Each read is stored as a separate group within the file, containing the raw signal samples, event data, and metadata. FAST5 files are large and can be slow to access because of the hierarchical structure. POD5 is a newer format developed by Oxford Nanopore that uses a columnar storage layout. This design improves access speed for random read retrieval and reduces file size compared to FAST5. The format is gaining support in nanopore analysis pipelines, and researchers should check whether their preferred tools support POD5 before adopting it. The transition from FAST5 to POD5 has implications for data management. Researchers with existing FAST5 data may need to convert to POD5 format to take advantage of the improved access speed, but the conversion process itself requires storage and processing resources. The [EMBL-EBI Training](https://www.ebi.ac.uk/training) resources provide guidance on data format conversions and the considerations involved. ### BAM and CRAM for Aligned Reads After reads are aligned to a reference genome, the aligned data is typically stored in BAM or CRAM format. BAM is a compressed binary format that has been the standard for aligned read storage for many years. CRAM is a newer format that uses reference-based compression to achieve smaller file sizes. The [benchmark study of compression software](https://pubmed.ncbi.nlm.nih.gov/40316539) for short-read data found that Genozip achieved approximately 16 percent higher compression for BAM files than SAMtools, which produces CRAM files. However, the study noted that CRAM files are compatible with many software packages, while some specialized compression tools are limited to specific input formats. This compatibility consideration is important for long-read analysis pipelines that may use tools with limited format support. For long-read data, the choice between BAM and CRAM depends on the specific analysis workflow. BAM files are more widely supported and provide faster access, while CRAM files achieve better compression. The [rdeval evaluation](https://pubmed.ncbi.nlm.nih.gov/40694478) found that the compression gain from CRAM compared to BAM is marginal for some datasets, which suggests that the compatibility and access speed advantages of BAM may outweigh the modest storage savings for many applications. ## Compression Algorithm Benchmarks Published benchmarks provide quantitative comparisons of compression tools, but researchers must interpret these results carefully because the performance of any tool depends on the specific characteristics of the data being compressed. ### General-Purpose Compressors Gzip and bzip2 are general-purpose compression tools that are widely available on Linux systems and are commonly used for FASTQ files. Gzip uses the DEFLATE algorithm and provides fast compression and decompression with moderate compression ratios. Bzip2 uses the Burrows-Wheeler transform and achieves better compression ratios but requires substantially more processing time. For long-read data, the effectiveness of these tools depends on the read length distribution and quality score patterns. Long reads contain longer stretches of sequence with less local redundancy than short reads, which can reduce compression effectiveness. However, the quality score patterns in long-read data may be more predictable, which can improve compression. The [The Carpentries Lessons](https://carpentries.org/lessons) provide foundational training on command-line tools and shell scripting that is relevant to understanding how general-purpose compressors work and how to use them effectively in bioinformatics workflows. ### Specialized FASTQ Compressors Several specialized tools have been developed for FASTQ compression, and some have been evaluated for long-read data. The FastqCLS tool uses read reordering with a scoring model to improve compression ratios for long-read data. According to the [published study](https://pubmed.ncbi.nlm.nih.gov/34623374), the tool outperformed existing major FASTQ compression tools on benchmark datasets that included long-read sequencing data. The [short-read benchmark study](https://pubmed.ncbi.nlm.nih.gov/40316539) evaluated four specialized compression tools: DRAGEN ORA, Genozip, repaq, and SPRING. While this study focused on short-read data, the findings are relevant to long-read data management because they demonstrate the range of compression ratios and speeds achievable with specialized tools. The study found that ORA and Genozip achieved compression ratios of approximately 1:6 when compressing FASTQ files, while repaq and SPRING achieved lower ratios of 1:2 and 1:4, respectively. The faster tools also achieved better compression, which is unusual and reflects differences in algorithmic approaches. The study also noted important limitations of specialized tools. ORA, repaq, and SPRING are limited to compressing FASTQ files, while Genozip supports various file formats. Genozip requires an annual license, although its source code is freely available. These licensing and format support considerations are important for researchers who need to integrate compression tools into existing analysis pipelines. ### Reference-Based Compression with CRAM CRAM compression requires an aligned dataset and a reference genome. The compression ratio depends on the similarity between the reads and the reference, with higher similarity producing better compression. For long-read data, the longer reads may span more reference sequence and contain more mismatches and indels, which can reduce compression effectiveness compared to short-read data. The [rdeval tool documentation](https://pubmed.ncbi.nlm.nih.gov/40694478) notes that CRAM achieves the best compression but that the gain compared to BAM is marginal for some datasets. BAM achieves the best compromise between data compression and access speed, which is an important consideration for active analysis pipelines where files are accessed repeatedly. The [preprint version of the rdeval study](https://pubmed.ncbi.nlm.nih.gov/39975369) provides additional context on the evaluation of sequencing reads at scale, including the use of read sketches for dramatic compression gains when storing summary statistics. This approach is complementary to full data compression and can be useful for managing large collections of sequencing datasets. ## Practical Workflow for Choosing a Compression Strategy Selecting the right compression approach requires a systematic evaluation of the data characteristics, storage requirements, and analysis workflow. The following steps provide a practical framework for making this decision. ### Step 1: Characterize the Dataset Before choosing a compression tool, measure the size and composition of the dataset. Use tools like [rdeval](https://pubmed.ncbi.nlm.nih.gov/40694478) to generate summary statistics for the sequencing reads, including read length distribution, quality score distribution, and total file size. This information helps predict how different compression approaches will perform. Record the following measurements for each dataset: - Total number of reads - Mean and median read length - Read length N50 value - Mean quality score - Quality score distribution - Total file size in FASTQ format - File size after gzip compression These measurements provide a baseline for evaluating compression tools and for estimating storage requirements for future projects. The [rdeval tool](https://pubmed.ncbi.nlm.nih.gov/40694478) can generate these statistics efficiently and store them in small snapshot files that can be recalled for additional processing without re-analyzing the full dataset. ### Step 2: Test Compression Tools on Representative Samples Do not compress an entire dataset with an unfamiliar tool before testing on a representative sample. Select a subset of reads that reflects the diversity of the full dataset, including reads of varying lengths and quality scores. Test each candidate tool on this sample and record the compression ratio, compression time, and decompression time. The [nf-core documentation](https://nf-co.re/docs) provides guidance on reproducible workflow practices that are relevant to compression testing. Use the same computing environment for all tests to ensure fair comparisons, and record the hardware specifications and software versions used. When selecting a representative sample, consider the following factors: - Include reads from different parts of the flow cell or sequencing run - Include reads with different quality score distributions - Include reads of different lengths, including the longest and shortest reads - Include reads from different genomic regions if the data is from a mixed sample ### Step 3: Evaluate Compatibility with Downstream Tools A compression format is only useful if the downstream analysis tools can read it. Check the documentation for the alignment, variant calling, and visualization tools used in the analysis pipeline to confirm that they support the compressed format. The [Bioconductor project](https://bioconductor.org/) provides documentation for many genomic analysis packages, and researchers should verify format support before committing to a compression strategy. For example, if the analysis pipeline uses Minimap2 for read alignment, verify that the alignment output can be processed by the variant caller and that the compressed format is compatible with both tools. The [weighted minimizer sampling study](https://pubmed.ncbi.nlm.nih.gov/32657365) describes the development of Winnowmap, a long-read mapper built on the Minimap2 codebase, which demonstrates the importance of format compatibility in long-read analysis pipelines. The compatibility evaluation should include both the compression format and the decompression tool. Some formats may require specific software versions or libraries to decompress, and these dependencies should be documented as part of the workflow. ### Step 4: Consider Storage and Transfer Requirements Estimate the total storage required for the compressed data and compare this to available storage capacity. Consider both short-term storage for active projects and long-term archival storage. For data that will be transferred between institutions or uploaded to public repositories, consider the transfer time and bandwidth requirements. The [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/) provide guidance on data submission requirements, including accepted formats and compression approaches. Researchers planning to submit data to public repositories should check the specific requirements of the target repository before choosing a compression strategy. Storage planning should also consider the need for multiple copies of the data. Many institutions maintain primary storage for active analysis, backup storage for disaster recovery, and archival storage for long-term preservation. Each of these storage tiers may have different compression requirements. ### Step 5: Document the Compression Strategy Record the compression tools, versions, parameters, and hardware used for each dataset. This documentation is essential for reproducibility and for interpreting the results of downstream analyses. The [The Carpentries Lessons](https://carpentries.org/lessons) provide training on reproducible research practices, including documentation of computational workflows. Include the following information in the documentation: - Tool name and version - Compression parameters and settings - Hardware specifications - Compression time and decompression time - File sizes before and after compression - Checksums for verifying data integrity The documentation should be stored with the compressed data or in a location that is accessible to all members of the research team. This ensures that anyone who needs to decompress or analyze the data can do so without having to reconstruct the compression parameters. ## Records and Measurements for Compression Evaluation Systematic evaluation of compression tools requires consistent measurement and record-keeping. The following metrics provide a basis for comparing tools and for making informed decisions. ### Compression Ratio Compression ratio is the ratio of the original file size to the compressed file size. A compression ratio of 1:6 means that the compressed file is one-sixth the size of the original. Higher ratios indicate better compression but may come at the cost of slower processing or reduced compatibility. For long-read data, compression ratios vary widely depending on the tool and the data characteristics. The [short-read benchmark study](https://pubmed.ncbi.nlm.nih.gov/40316539) found compression ratios ranging from 1:2 to 1:6 for specialized tools, and general-purpose tools typically achieve lower ratios. Researchers should measure compression ratios for their specific data instead of relying on published benchmarks that may use different data. The compression ratio should be measured for both the full dataset and representative subsets. This helps identify whether the compression performance is consistent across different parts of the dataset or whether certain types of reads compress better or worse than others. ### Compression and Decompression Speed Compression speed is measured in megabytes per second or in total time required to compress a dataset. Decompression speed is equally important because compressed data must be decompressed before analysis. A tool that compresses slowly but decompresses quickly may be acceptable for archival storage, while a tool that is slow in both directions may be impractical for active analysis. The [short-read benchmark study](https://pubmed.ncbi.nlm.nih.gov/40316539) found that repaq and SPRING took longer for both compression and decompression than ORA and Genozip. This finding illustrates that compression tools vary substantially in speed, and researchers should measure performance on their own hardware and data. Speed measurements should be repeated multiple times to account for variability in system performance. The measurements should also be recorded with the hardware specifications, including CPU model, memory size, and storage type, to provide context for interpreting the results. ### Memory Usage Some compression tools require substantial memory, which can be a constraint on shared computing infrastructure. Measure peak memory usage during compression and decompression to ensure that the tool can run in the available computing environment. The [nf-core documentation](https://nf-co.re/docs) provides guidance on configuring workflows for different computing environments, including memory constraints. Memory usage is particularly important for long-read data because the larger read sizes and the read reordering strategies used by some tools may require more memory than short-read compression. Tools that use reference-based compression may also require memory for storing the reference genome index. ### Data Integrity Lossless compression must preserve the original data exactly. Verify data integrity by comparing checksums of the original and decompressed files. The [Galaxy Training Network](https://training.galaxyproject.org/) provides tutorials on data quality control that include checksum verification practices. Data integrity verification should be performed for every dataset and every compression tool. The verification process should be documented, including the checksum algorithm used and the expected checksum values. This documentation provides assurance that the compression process did not introduce errors. ## Common Failure Patterns in Compression Workflows Several common problems can compromise the effectiveness of a compression strategy. Recognizing these patterns helps researchers avoid costly mistakes. ### Incompatible Format Assumptions Some compression tools are designed for specific input formats and will fail or produce incorrect results when given unexpected input. The [short-read benchmark study](https://pubmed.ncbi.nlm.nih.gov/40316539) noted that ORA, repaq, and SPRING are limited to compressing FASTQ files, while Genozip supports various file formats. Researchers should verify that the compression tool supports the exact format of their data, including the specific FASTQ variant produced by their sequencing platform. Long-read platforms may produce FASTQ files with different header formats or quality score encodings than short-read platforms. These differences can cause compression tools to fail or produce suboptimal results. Researchers should test compression tools on their specific data format before committing to a compression strategy. ### Reference Mismatch in CRAM Compression CRAM compression requires the correct reference genome for decompression. If the reference used for compression is not available or does not match the reference used for alignment, decompression will fail. Store the reference genome version and checksum with the CRAM file to ensure that the correct reference is used. The reference mismatch problem is particularly relevant for long-read data because long reads may span regions of the genome that are not well represented in the reference. If the reference genome is updated or replaced, the CRAM files may need to be recompressed or converted to a different format. ### Quality Score Truncation Some compression tools may truncate or round quality scores to achieve better compression, which can affect downstream variant calling. Verify that the compression tool is lossless and that quality scores are preserved exactly. The [FastqCLS study](https://pubmed.ncbi.nlm.nih.gov/34623374) emphasizes that the tool achieves compression with no information loss, which is an important property for clinical and research applications. Quality score truncation can be difficult to detect because the compressed file may decompress successfully but contain slightly different quality scores than the original. Comparing checksums of the original and decompressed files is the most reliable way to verify that the compression is truly lossless. ### Insufficient Testing on Representative Data Compression performance varies with data characteristics, and tools that perform well on one dataset may perform poorly on another. Test compression tools on data that is representative of the full dataset, including reads of varying lengths and quality scores. The [rdeval tool](https://pubmed.ncbi.nlm.nih.gov/40694478) provides summary statistics that help identify data characteristics that may affect compression performance. The [rdeval study](https://pubmed.ncbi.nlm.nih.gov/40694478) used long-read data from different sequencing platforms and species, including human, and found that read length and quality improvements varied by taxa. This finding highlights the importance of testing compression tools on data that is representative of the specific research application. ### Failure to Document Compression Parameters Compression parameters affect both compression ratio and speed, and undocumented parameters make it impossible to reproduce the compression or to compare results across datasets. Document all parameters and software versions as part of the analysis workflow. The documentation should include also the tool name and version but also the specific parameters used, such as compression level, thread count, and any format-specific options. This information is essential for reproducing the compression and for understanding the performance characteristics of the compressed data. ## Limitations of Published Benchmarks Published benchmarks provide useful guidance but have limitations that researchers should understand before applying the results to their own data. ### Data-Specific Performance Compression performance depends on the specific characteristics of the data being compressed, including read length, quality score distribution, and sequence composition. Benchmarks that use short-read data may not predict performance on long-read data, and benchmarks that use human data may not predict performance on other species. The [rdeval study](https://pubmed.ncbi.nlm.nih.gov/40694478) used long-read data from different sequencing platforms and species, including human, and found that read length and quality improvements varied by taxa. The [preprint version of the rdeval study](https://pubmed.ncbi.nlm.nih.gov/39975369) provides additional detail on the evaluation of sequencing reads at scale, including the use of read sketches for dramatic compression gains when storing summary statistics. This approach is complementary to full data compression and can be useful for managing large collections of sequencing datasets. ### Hardware Dependencies Compression speed depends on the hardware used, including CPU architecture, memory bandwidth, and storage speed. Benchmarks conducted on specific hardware may not reflect performance on other systems. Researchers should measure compression performance on their own hardware before making decisions. The hardware dependencies are particularly important for tools that use parallel processing or that require substantial memory. A tool that performs well on a high-performance computing cluster may not perform well on a desktop workstation with limited memory. ### Software Version Differences Compression tools are updated regularly, and new versions may have different performance characteristics. The [short-read benchmark study](https://pubmed.ncbi.nlm.nih.gov/40316539) evaluated specific versions of DRAGEN ORA, Genozip, repaq, and SPRING, and the results may not apply to other versions. Check the version of each tool and consider re-benchmarking when tools are updated. The version differences can be particularly significant for tools that are under active development. New versions may add features, improve compression ratios, or fix bugs that affect performance. Researchers should track the versions of compression tools used in their workflows and re-evaluate their compression strategy when tools are updated. ### Compatibility Considerations The best compression tool for a dataset is not necessarily the one that achieves the highest compression ratio. Compatibility with downstream analysis tools is essential, and some specialized compression tools produce formats that are not supported by common analysis software. The [short-read benchmark study](https://pubmed.ncbi.nlm.nih.gov/40316539) noted that CRAM files produced by SAMtools are compatible with many software packages, while some specialized tools are limited to specific input formats. The compatibility considerations are particularly important for long-read analysis pipelines, which may use a combination of tools from different developers. A compression format that is not supported by one tool in the pipeline can create a bottleneck that requires format conversion and additional storage. ## Quality and Safety Considerations for Data Management Data management decisions affect data quality and the reliability of downstream analyses. The following considerations are important for maintaining data integrity. ### Lossless Compression Requirements For research and clinical applications, lossless compression is essential to ensure that no information is lost. Lossy compression may achieve better compression ratios but can introduce errors that affect variant calling and other analyses. Verify that compression tools are lossless by comparing checksums of original and decompressed files. The [FastqCLS study](https://pubmed.ncbi.nlm.nih.gov/34623374) emphasizes that the tool achieves compression with no information loss, which is an important property for clinical and research applications. Researchers should verify the lossless property of any compression tool before using it on important data. ### Data Retention Policies Decisions about raw data retention affect the ability to reanalyze data with improved tools and algorithms. Raw signal data in FAST5 or POD5 format enables re-basecalling with improved models, which can improve read accuracy. However, retaining raw data requires substantial storage. Develop a data retention policy that balances the value of raw data against storage costs. The [EMBL-EBI Training](https://www.ebi.ac.uk/training) resources provide guidance on data management planning for sequencing projects, including recommendations for data retention and sharing. The data retention policy should consider the following factors: - The expected value of future reanalysis with improved tools - The storage costs for raw and processed data - The requirements of funding agencies and journals - The requirements of data repositories and archives ### Reproducibility Requirements Reproducible analysis requires that the data and analysis steps are documented and preserved. This includes the compression tools and parameters used, as well as the original and compressed data files. The [nf-core documentation](https://nf-co.re/docs) provides guidance on reproducible workflow practices that are relevant to data management. The [Bioconductor project](https://bioconductor.org/) provides documentation for many genomic analysis packages, including guidance on reproducible analysis workflows. Researchers should integrate compression documentation into their overall reproducibility practices. ### Data Security and Access Control Sequencing data may contain sensitive information about individuals, and access must be controlled according to applicable regulations and institutional policies. Compressed data files should be stored in secure locations with appropriate access controls. The [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/) provide guidance on data submission and access for sensitive data. The security considerations are particularly important for compressed data because the compressed format may obscure the content of the data, making it more difficult to detect unauthorized access. Researchers should implement appropriate access controls and monitoring for compressed data files. ## Professional Escalation Criteria Certain situations warrant consultation with bioinformatics specialists or institutional data management experts. The following criteria indicate when professional escalation is appropriate. ### Unusual Compression Performance If a compression tool produces unexpected results, such as a compression ratio that is substantially different from published benchmarks, investigate the cause before proceeding. The issue may indicate a problem with the data, the tool, or the compression parameters. The investigation should include the following steps: - Verify that the data is in the expected format - Check the compression tool version and parameters - Test the tool on a known dataset to verify that it is working correctly - Compare the results with published benchmarks for similar data ### Data Integrity Concerns If checksum verification fails after compression or decompression, stop the analysis and investigate the cause. Data corruption can compromise downstream analyses and may require re-sequencing or re-downloading data. The investigation should determine whether the corruption occurred during compression, during storage, or during decompression. The compressed file should be compared with the original file to identify the source of the corruption. ### Regulatory or Compliance Requirements If the data is subject to regulatory requirements, such as clinical trial data or human subjects data, consult with institutional compliance experts before choosing a compression strategy. Some regulations may require specific data formats or retention periods. The compliance consultation should occur before the compression strategy is implemented, not after the data has been compressed. This ensures that the compression approach is compatible with all applicable requirements. ### Large-Scale Storage Planning If the storage requirements for a sequencing project exceed available capacity, consult with institutional IT or bioinformatics support to develop a storage plan. The plan should consider short-term storage for active analysis, long-term archival storage, and data transfer requirements. The storage plan should also consider the compression tools and formats that will be used, as well as the expected growth of the data over time. The [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/) provide guidance on data submission and storage requirements that can inform the planning process. ## Frequently Asked Questions ### What is the best file format for storing long-read sequencing data? The best format depends on the stage of the analysis and the intended use of the data. For raw basecalled reads, FASTQ with gzip compression is the most widely compatible option. For aligned reads, BAM offers the best balance of compression and access speed, while CRAM achieves better compression but requires a reference genome for decompression. For raw signal data from Oxford Nanopore instruments, POD5 is the newer format with improved access speed compared to FAST5. ### How does CRAM compression compare to BAM for long-read data? CRAM achieves better compression than BAM because it stores only the differences between reads and a reference genome. However, the [rdeval evaluation](https://pubmed.ncbi.nlm.nih.gov/40694478) found that the compression gain is marginal for some datasets, and BAM achieves the best compromise between data compression and access speed. CRAM requires the reference genome for decompression, which adds complexity to data management. ### Are specialized FASTQ compression tools worth using for long-read data? Specialized tools can achieve substantially better compression ratios than general-purpose tools. The [FastqCLS study](https://pubmed.ncbi.nlm.nih.gov/34623374) demonstrated that a tool designed for long-read data outperformed existing major FASTQ compression tools. However, specialized tools may have limited compatibility with downstream analysis software, and researchers should verify that the compressed format can be read by their analysis pipeline. ### What compression ratio can I expect for long-read FASTQ data? Compression ratios vary widely depending on the data characteristics and the compression tool. The [short-read benchmark study](https://pubmed.ncbi.nlm.nih.gov/40316539) found compression ratios ranging from 1:2 to 1:6 for specialized tools, and general-purpose tools typically achieve lower ratios. Long-read data may compress differently than short-read data because of differences in read length and quality score patterns. ### Should I retain raw signal data from Oxford Nanopore sequencing? Retaining raw signal data in FAST5 or POD5 format enables re-basecalling with improved models, which can improve read accuracy. However, raw signal data requires substantial storage. The decision to retain raw data should balance the value of future reanalysis against storage costs and should be documented in a data management plan. ### How do I verify that compression is lossless? Verify lossless compression by comparing checksums of the original and decompressed files. Compute a checksum for the original file before compression, then decompress the compressed file and compute a checksum for the decompressed file. The checksums must match exactly. The [Galaxy Training Network](https://training.galaxyproject.org/) provides tutorials on data quality control that include checksum verification. ### What information should I document about my compression workflow? Document the compression tool name and version, compression parameters, hardware specifications, compression and decompression times, file sizes before and after compression, and checksums. This documentation is essential for reproducibility and for interpreting the results of downstream analyses. The [The Carpentries Lessons](https://carpentries.org/lessons) provide training on reproducible research practices. ### How do I choose between compression ratio and speed? The choice depends on the intended use of the data. For archival storage where files are rarely accessed, a tool with a high compression ratio and slow speed may be appropriate. For active analysis where files are accessed repeatedly, a tool with faster compression and decompression may be more practical even if the compression ratio is lower. Measure both compression ratio and speed on representative data to inform the decision. ## Related Bioinformatics Guides - [How to Choose a Long-Read Sequencing Platform: PacBio vs Oxford Nanopore](/knowledge/bioinformatics/how-to-choose-a-long-read-sequencing-platform-pacbio-vs-oxford-nanopore) - [Evaluating Metagenomic Assembly Tools: A Benchmarking Framework for Short-Read and Long-Read Data](/knowledge/bioinformatics/evaluating-metagenomic-assembly-tools-a-benchmarking-framework-for-short-read-and-long-read-data) - [Genomic Data Analysis Tools: A Comparative Guide for Researchers](/knowledge/bioinformatics/genomic-data-analysis-tools-a-comparative-guide-for-researchers) - [Metagenomic Binning Tools Benchmark: How to Evaluate and Choose](/knowledge/bioinformatics/metagenomic-binning-tools-benchmark-how-to-evaluate-and-choose) - [Long-Read Sequencing Cost and Market: What to Expect](/knowledge/bioinformatics/long-read-sequencing-cost-and-market-what-to-expect) ## 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. - [A benchmark study of compression software for human short-read sequence data.](https://pubmed.ncbi.nlm.nih.gov/40316539). Scientific reports, 2025. - [FastqCLS: a FASTQ compressor for long-read sequencing via read reordering using a novel scoring model.](https://pubmed.ncbi.nlm.nih.gov/34623374). Bioinformatics (Oxford, England), 2022. - [Weighted minimizer sampling improves long read mapping.](https://pubmed.ncbi.nlm.nih.gov/32657365). Bioinformatics (Oxford, England), 2020. - [Evaluation of sequencing reads at scale using rdeval.](https://pubmed.ncbi.nlm.nih.gov/40694478). Bioinformatics (Oxford, England), 2025. - [Evaluation of sequencing reads at scale using rdeval.](https://pubmed.ncbi.nlm.nih.gov/39975369). bioRxiv : the preprint server for biology, 2025. > This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.