The Most Common Bioinformatics Mistakes and How to Avoid Them

const metadata = ; I once wiped an entire directory of FASTQ files because of a misplaced space. The cursor blinked, my heart sank, and I questioned every career decision instantly. If you’ve been in bioinformatics long enough, you’ve probably had a moment like that too. Bioinformatics is mostly tiny mistakes with massive consequences, followed by fixing those mistakes under deadline pressure. So let’s avoid the painful ones. This guide covers the small errors that turn into wasted compute, broken results, or conclusions that fall apart as soon as someone asks a basic question. If a mistake can change where a feature is or what gene it belongs to, it breaks the science. If a mistake can delete data, it breaks your soul. Let’s prevent both. The general mistakes that quietly break science Picture this: You align reads to a genome, load them into a browser, and the exons are completely empty. You question the experiment. You question your alignment settings. You even consider calling the sequencing provider to complain. Then you realize you are aligned to hg19 and viewed in hg38. You silently close your laptop and go for a long walk. That is a perfect example of a silent but deadly bioinformatics mistake. Here are the ones I see most often, especially when onboarding new colleagues or debugging messy pipelines. 1) Off by one coordinate errors (0 based vs 1 based) BED files start at zero. GTF files start at one. Python starts at zero. R starts at one. That mismatch will misplace every feature by one base. It looks small but it breaks everything from CRISPR target design to variant calling to feature counting. Region is bases 100 to 101 inclusive: 1-based: 100 to 101 | BED: 99 to 101 Quick sanity check: Before any coordinate conversion, ask: does length stay the same? If not, something is broken. 2) Wrong genome build or annotation If one step uses UCSC style names like chr1 and another uses Ensembl style names like 1, nothing will ever match up again. Same problem with old vs new builds. If your aligner used hg19 but your browser uses GRCh38, you will see mysterious deserts of coverage. How to prevent: Always label filenames with build: sample_hg38.bam. Convert naming conventions when needed: chr1 vs 1. Tools like CrossMap and liftOver can help, but never trust blindly. Whenever coverage disappears, question your build first. 3) Ignoring strand requirements and reverse complementing Interpreting reads on the wrong strand leads to frame shifts, fake variants, and incorrect reporter designs. It helps to ask before every analysis step: do I need the reverse complement here. The answer is surprisingly often yes. Example check: Reverse complement if and only if flag 0x10 (SAM) is set. Never assume a sequence string alone tells the story. Remember: If biology depends on direction, strand matters. 4) Bad parsing or reinventing format readers People reinvent FASTQ parsing because it’s “just text”. Then they discover quality strings can contain any ASCII between 33 and 74 and line wrapping rules vary. Same for SAM CIGAR strings. Same for BLAST outputs. Rule: Unless you are writing a new aligner, do not write your own parsers. Use BioPython, SeqKit, PySAM, HTSeq, Rust-Bio, BioJulia, etc. It’s faster anyway. 5) Letting Excel touch gene names SEPT9 becomes sept-9. MARCH1 becomes 1 March. DEC1 becomes December 1. These corrupted names can reach publications before anyone notices. Store identifiers as text or avoid Excel entirely for gene IDs. Always import gene names as text. Better yet, never use Excel for primary data. Use stable IDs (Ensembl ENSG) when possible. 6) Poor statistical practice and ignored batch effects Transferring huge BAMs fails silently a lot. FASTQ quality scores are not always the encoding you expect. Some aligners even ignore the second read if you pass them wrong. Always verify downloads, formats, and inputs before believing a single result. Pro tip: Check batch first PCA or UMAP color by batch Do not proceed until it’s fixed 7) Trusting data and software too much Large genomics files fail silently more often than you think. A BAM may truncate during download, FASTQ quality encodings can differ, and some aligners ignore the second read if inputs are mis-specified. These problems break results before analysis even begins. Always check integrity first: File size and checksum. Quality encoding. Both reads actually used. Trust the data only after you verify it. 8) Not documenting parameters What options did you use? Which genome version? Which tool to build? Results without traceability eventually become useless. Six months later, the pipeline works but nobody remembers the options used. Then you spend a week trying to reproduce your own results. It is faster to write it down the first time. - Tool version - Command with parameters - Input SHA - Git commit (if code) Automate logs whenever possible. 9) Assuming data is clean Biological data is messy. Reads can map outside contigs, sequences can contain masked N’s, tables contain duplicated rows. Some annotations make no biological sense. Public datasets are messy. Pipelines should validate input, not hope for perfection. Checklist: Drop duplicate IDs. Validate nucleotide alphabets. Check coordinates are within chromosome bounds. Confirm expected sample counts. 10) GO enrichment over-interpretation Statistical enrichment narrows where to look, it doesn’t answer the question. Lists of terms are not biological discoveries. They are suggestions and starting points. This is a classic case where statistics look like answers but are really hypotheses. What do these have in common? They do not fail loudly. They fail later. Those are the worst bugs. A good rule: Hypothesize from enrichment. Validate with mechanistic reasoning. Not everything significant is interesting. If a mistake changes where a feature is or what gene it is, assume catastrophe. The command line mistakes that ruin weekends I once wiped an entire directory of FASTQ files because of a misplaced space in a command. The cursor blinked, my heart sank, and I questioned every career decision instantly. These are the mistakes that don't just break your analysis—they break your weekend. Let's make sure they never happen to you. 1) Accidentally running rm -rf * with a space in the wrong place `bash rm -rf * .fastq ` This reads as: delete everything, and by the way also match .fastq. To make your shell protest before destroying data, add this to .bashrc: `bash alias rm='rm -I' ` This prompts if you target multiple files. It has saved me many times. 2) Using > instead of >> > overwrites files. >> appends. One wrong redirection and half a day’s results vanish. 3) grep > file instead of grep '>' file The shell thinks you want to redirect instead of search FASTA headers. The result is an empty file. Correct version: `bash grep ">" input.fasta ` 4) Wrong tar flag order You can overwrite the file you meant to archive: `bash tar cvfz file file.tgz ` Always double check before hitting enter. 5) Windows line endings Files from collaborators using Windows can produce invisible errors. dos2unix is still a lifesaver. 6) Ignoring pipeline failures Clusters happily output partial results if you forget to check exit codes and job statuses. Always inspect logs. 7) Running untested scripts on entire datasets A missing loop break can create terabytes of junk. Test on five records first, thank me later. 8) Dangerous scp and path assumptions Commands like: `bash scp -r badsyntax $home/ ` can create literal files named $home. Removing them deletes everything else too. 9) cat huge files There are better ways to peek at data: `bash head -n 20 file less file ` 10) Sorting chromosome labels alphabetically Alphabetical sorting puts chr10 before chr2. Correct: `bash sort -k1,1 -k2,2n input.bed > sorted.bed ` How I avoid these mistakes now Learning these things once is not enough. You need a routine that makes mistakes difficult to repeat. Here are the strategies that have kept my pipelines sane. Ask three questions before any analysis: - What is the coordinate system? - What genome build and annotation are we using everywhere? - Does strand matter in this step? Use known good tools for known hard problems: pysam for BAM parsing, bedtools for genomic math, seqkit for FASTA handling. Custom scripts should be the exception. Always test on tiny subsets first: Start with 10 lines of input. Check output. Then scale. Pipeline hygiene: Snakemake, Nextflow, or a simple driver script that stops on errors is better than clicking things manually. Run checksum validation on downloads. Version everything. Keep commands self-documenting. Tracer tie-in: If you work with large datasets or collaborative workflows, platforms like Tracer help enforce version consistency, catch silent input issues, and automate reproducibility. You still write code and stay in control, but many of these tiny disasters are caught early. The checklist you can download and tape next to your monitor Bioinformatics Mistake Prevention Checklist Before running anything: Confirm coordinate system Confirm genome build and annotation version everywhere Validate FASTQ quality encoding Check for Windows line endings Test commands on a small subset While running: Trust exit codes and logs Avoid untested file parsers Log all parameters and inputs Use numeric sorting for chromosomes After running: Spot check results visually Save environment and software versions Back up critical files immediately The first five items prevent 90 percent of disasters. The rest prevent the other 10 percent. Closing I hope my embarrassment has been educational. These mistakes used to cost me days of debugging and far too much coffee, but they no longer surprise me, and I want the same for you! We all start with chaos. The goal is to leave a little less chaos behind each time. If you have your own horror story or trick that saves you headaches, share it with the community and help the next person avoid disaster. Keep learning, The Tracer Bioinformatics Team