Sequencing data · What you can do here
Quality-control and trim sequencing reads
Quality-control, filter, and trim raw FASTQ files using fastp or Trimmomatic. Inspect quality reports, assess base scores and adapter contamination, and prepare clean reads for downstream analysis.
What this involves
Raw high-throughput sequencing data contains sequencing artefacts, residual adapter sequences, and low-quality bases that can confound downstream mapping, variant calling, or de novo assembly. Systematic quality control and trimming ensure these defective cycles and contaminating sequences are identified and removed before reads enter primary analytical pipelines.
Doing this properly requires inspecting initial base qualities and sequence length distributions, selecting an appropriate tool for the library type, and evaluating trimming reports before accepting cleaned outputs. Default parameters or aggressive quality cut-offs may inadvertently discard usable sequences or create unpaired read fragments, so adapter trimming, sliding-window filtering, and read length thresholds must be matched to experimental protocols.
The research agent performs this processing directly on its dedicated research environment, reading inputs from uploaded FASTQ files and executing local command-line tools without requiring external installations. It runs trimming utilities like fastp or Trimmomatic, captures summary reports, and verifies post-trimming read metrics.
How it goes
- 1Inspect raw sequencing filesAssess raw FASTQ files using SeqKit or inspect file headers to evaluate read counts, length distributions, and file integrity before running trimming routines.
- 2Select and configure trimming softwareVerify tool availability on the PATH or review tool help screens, selecting either fastp for integrated quality reporting and adapter filtering or Trimmomatic for modular Illumina read trimming.
- 3Execute quality and adapter trimmingRun fastp or Trimmomatic via command execution on the machine, directing processed FASTQ files and output summaries into the working directory while allocating available CPU threads.
- 4Evaluate quality reports and metricsInspect fastp HTML/JSON summaries or Trimmomatic log outputs to review surviving read proportions, base quality improvements, adapter removal rates, and any discarded read pairs.
Start it here
Ask in plain language. The agent works on a Linux machine of its own — it installs and runs what the job needs, keeps every file, and reads the results back with the numbers explained. Nothing to set up, and a long run continues after you close the page.
What to watch for
- Guessing command-line flags wastes runs; read the tool's help output to confirm argument conventions before executing.
- Commands running longer than four minutes will exceed standard interactive run limits and require running as tracked background jobs.
- Unspecified parameters such as sliding-window cut-offs or minimum read lengths will use tool defaults that alter downstream sequence retention.
- Corrupted or disordered paired-end files will fail standard execution; pipelines like nf-core/fastqrepair may be needed to recover or reorder reads.
- Tools requiring GPU acceleration cannot run on this machine environment.
What it runs
Programs
- fastpPreinstalled on the research machineQuality control and preprocessing software that trims, filters and profiles short-read FASTQ sequencing data in one pass.
- TrimmomaticPreinstalled on the research machineA Java tool for trimming sequencing adapters and low-quality bases from single-end and paired-end Illumina NGS reads.
- SeqKitPreinstalled on the research machineA cross-platform toolkit for fast inspection, filtering, conversion, and analysis of FASTA and FASTQ files.
nf-core pipelines
Questions
Do I need to install anything to quality-control and trim sequencing reads?
No. The research agent does this work on a Linux machine of its own — it runs fastp, Trimmomatic, SeqKit there, keeps the files, and reads the results back to you. Long runs continue if you close the page.
Which tools are preinstalled on the research machine for FASTQ trimming?
fastp, Trimmomatic, and SeqKit are already installed and accessible on PATH on the research machine, alongside other core utilities.
Can corrupted or disordered FASTQ reads be repaired?
Yes. The nf-core/fastqrepair pipeline can be run to recover corrupted FASTQ.gz files, remove unpaired reads, drop or fix non-compliant entries, and reorder disordered reads.
How are threads managed during trimming?
Most tools support threading flags like -t or --threads. The agent assigns the allocated cores on the machine (defaulting to 2 cores unless otherwise requested).
Written from the method the research agent follows for this work and the documentation of the tools listed above. Statements citing a figure that does not appear there are removed before publication. Corollary is a non-commercial research demonstration; results are not for clinical use.