Long-read sequencing technologies are advancing, offering higher accuracy and longer reads, expanding their use in genomics. Dedicated tools are needed for analyzing long-read data, but the rapid development of these tools can be overwhelming. This review summarizes current tools and presents an online database, long-read-tools.org, to help users navigate them. It discusses key challenges in long-read data analysis, including error correction, base modification detection, and transcriptomics. Long-read sequencing technologies, such as PacBio's SMRT and Oxford Nanopore's nanopore, produce reads far longer than short-read sequencing. These technologies differ in their principles: SMRT uses fluorescence to detect nucleotide addition, while nanopore measures ionic current changes. Both have improved accuracy, with SMRT achieving <1% error rate and nanopore <5%. However, challenges remain, particularly in error correction and base modification detection. Long-read data analysis involves basecalling, error correction, and polishing. Basecalling converts raw data into nucleic acid sequences, with nanopore being more complex. Error correction improves accuracy by aligning reads and using hybrid methods with short-read data. Polishing further refines assemblies using tools like Nanopolish and Pilon. Structural variant detection is more accurate with long reads, as they can span repetitive regions. However, benchmarking is challenging due to incomplete datasets. Base modification detection is also more accurate with long reads, allowing for phasing of modifications and genetic variants. However, high coverage is needed for some modifications, which is not feasible for large genomes. Long-read transcriptomics is still in early stages, with tools like Iso-Seq3 and FLAIR used for isoform detection. Challenges include high error rates and coverage biases. Hybrid approaches combining long and short reads are promising but require further development. Long-read sequencing is becoming more accessible, with cost-effective solutions for large-scale projects. However, challenges remain in scalability, data processing, and integration. The field is rapidly evolving, with new tools and methods being developed to improve accuracy and efficiency. Continued efforts in benchmarking and tool development are essential for advancing long-read sequencing applications.Long-read sequencing technologies are advancing, offering higher accuracy and longer reads, expanding their use in genomics. Dedicated tools are needed for analyzing long-read data, but the rapid development of these tools can be overwhelming. This review summarizes current tools and presents an online database, long-read-tools.org, to help users navigate them. It discusses key challenges in long-read data analysis, including error correction, base modification detection, and transcriptomics. Long-read sequencing technologies, such as PacBio's SMRT and Oxford Nanopore's nanopore, produce reads far longer than short-read sequencing. These technologies differ in their principles: SMRT uses fluorescence to detect nucleotide addition, while nanopore measures ionic current changes. Both have improved accuracy, with SMRT achieving <1% error rate and nanopore <5%. However, challenges remain, particularly in error correction and base modification detection. Long-read data analysis involves basecalling, error correction, and polishing. Basecalling converts raw data into nucleic acid sequences, with nanopore being more complex. Error correction improves accuracy by aligning reads and using hybrid methods with short-read data. Polishing further refines assemblies using tools like Nanopolish and Pilon. Structural variant detection is more accurate with long reads, as they can span repetitive regions. However, benchmarking is challenging due to incomplete datasets. Base modification detection is also more accurate with long reads, allowing for phasing of modifications and genetic variants. However, high coverage is needed for some modifications, which is not feasible for large genomes. Long-read transcriptomics is still in early stages, with tools like Iso-Seq3 and FLAIR used for isoform detection. Challenges include high error rates and coverage biases. Hybrid approaches combining long and short reads are promising but require further development. Long-read sequencing is becoming more accessible, with cost-effective solutions for large-scale projects. However, challenges remain in scalability, data processing, and integration. The field is rapidly evolving, with new tools and methods being developed to improve accuracy and efficiency. Continued efforts in benchmarking and tool development are essential for advancing long-read sequencing applications.