MOTIVATION: Trimmomatic is a widely adopted tool for preprocessing high-throughput sequencing data, particularly from Illumina platforms. Since its original publication in 2014, the volume and complexity of sequencing data have increased dramatically, necessitating continuous tool evolution. RESULTS: We present the substantial updates to Trimmomatic over the past decade. Key enhancements include a robust multithreading model for high-performance parallel processing, parallel GZIP/BZIP2 compression, and a suite of new trimming and filtering steps to provide users with more flexible quality control. Usability has been significantly improved through automatic PHRED encoding detection and simplified file handling. The codebase has also been modernized including Maven support, and continuous integration to ensure long-term sustainability and community contributions. These updates solidify Trimmomatic's role as an efficient, flexible, and essential tool in modern bioinformatics pipelines. AVAILABILITY: Trimmomatic remains open-source under the GPL V3 license, with the latest version available at https://github.com/usadellab/Trimmomatic and also on our website https://www.plabipd.de/trimmomatic_main.html (DOI: https://doi.org/10.5281/zenodo.18678155).

MOTIVATION: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. RESULTS: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. AVAILABILITY AND IMPLEMENTATION: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade. Different preprocessing techniques exist to convert the raw signal intensities measured by these chips into gene expression estimates. Although these techniques have been widely benchmarked in the context of differential gene expression analysis, there are only few examples where their performance has been assessed in respect to coexpression-based studies such as sample classification. RESULTS: In the present paper we benchmark the three most used normalization procedures (MAS5, RMA and GCRMA) in the context of inter-array correlation analysis, confirming and extending the finding that RMA and GCRMA consistently overestimate sample similarity upon normalization. We determine that median polish summarization is responsible for generating a large proportion of these over-similarity artifacts. Furthermore, we show that most affected probesets show also internal signal disagreement, and tend to be composed by individual probes hitting different gene transcripts. We finally provide a correction to the RMA/GCRMA summarization procedure that massively reduces inter-array correlation artifacts, without affecting the detection of differentially expressed genes. CONCLUSIONS: We propose tRMA as a modification of RMA to normalize microarray experiments for correlation-based analysis.