Troubleshooting Common SeqMonk Errors and How to Fix Them

From FASTQ to Insight: Building Workflows with SeqMonk

Overview

A practical guide showing how to use SeqMonk to process high-throughput sequencing data end-to-end — from raw FASTQ files to biologically meaningful results and visualizations. Focuses on reproducible, modular workflows for common assays (RNA-seq, ChIP-seq, ATAC-seq).

Who it’s for

• Molecular biologists and bioinformaticians with basic command-line and sequencing concepts.
• Users who want GUI-driven analysis with powerful visualization and filtering.
• Labs seeking reproducible, shareable analysis pipelines without heavy scripting.

Key sections

1. Prepare and QC FASTQ

   • Recommended tools: FastQC, MultiQC.
   • Trimming and adapter removal with Trim Galore or cutadapt.
   • Typical checks: per-base quality, adapter content, duplication levels.

2. Alignment and read processing

   • Aligners: HISAT2/STAR for RNA-seq, BWA/Bowtie2 for DNA-based assays.
   • Mark/remove duplicates (Picard/samtools).
   • Convert to sorted BAM and index.

3. Importing into SeqMonk

   • Creating a new project and importing BAM files.
   • Choosing appropriate read counting mode (e.g., probe-based, binning).
   • Setting genome build and annotation sources (GTF/GFF).

4. Counting and normalization

   • Probe design: using features (exons/genes) or fixed-size bins.
   • Normalization strategies: CPM/TPM/RPKM and variance-stabilizing transforms.
   • Handling multi-mapping reads and strand specificity.

5. Filtering and quality control within SeqMonk

   • Visual checks: coverage plots, sample-level QC metrics.
   • Filtering low-count features and problematic samples.
   • PCA and clustering to detect batch effects or outliers.

6. Differential analysis

   • Exporting counts for DESeq2/edgeR OR using SeqMonk’s built-in statistical tests.
   • Designing contrasts and adjusting for covariates.
   • Interpreting volcano plots and MA plots.

7. Annotation and visualization

   • Adding gene annotations and external tracks.
   • Creating heatmaps, metaplots, and genome browser snapshots.
   • Annotating differentially enriched regions with gene ontology or pathway terms (export for clusterProfiler/GOseq).

8. Reproducibility and workflow sharing

   • Exporting project settings and probes.
   • Recording steps and using consistent parameter sets across projects.
   • Integrating SeqMonk steps into larger pipelines (Snakemake/Nextflow) via command-line tools where possible.

9. Troubleshooting & best practices

   • Common pitfalls (incorrect genome build, strandedness mismatches).
   • Tips for large datasets (downsampling, incremental imports).
   • Performance tuning (memory settings, probe resolution).

Deliverables readers can expect

• A ready-to-run example workflow (FASTQ → aligned BAM → SeqMonk project → differential hits).
• Example parameter values for popular assays.
• Checklist for QC and reproducibility.
• Recommendations for downstream analysis tools to complement SeqMonk.

Estimated time & prerequisites

• Time: 1–3 days to follow the full tutorial with a small dataset; longer for large experiments.
• Prerequisites: basic Linux/command-line, familiarity with sequencing concepts, installed alignment tools and SeqMonk.

If you want, I can:

• Provide a step-by-step command list for a specific assay (RNA-seq or ChIP-seq).
• Create example SeqMonk settings and probe definitions. Which assay should I use?