Format to format, without breaking it

samtools view converts the bytes; sort and index are what make the BAM actually usable downstream.

CRAM cuts your disk usage roughly in half and quietly makes the file unreadable without the exact reference FASTA it was built against.

The two-step pattern that keeps paired reads paired, plus the default flag that quietly drops reads you didn't know you were missing.

The conversion tool never introduces a coordinate bug, the chrom.sizes file you feed it is where builds and chromosome naming quietly go wrong.

gtf2bed subtracts one from the start coordinate for you; a naive awk one-liner won't, and that gap costs you a silent off-by-one bug three steps downstream.

gffread reconstructs the Parent hierarchy GFF3 demands, but only if your GTF's gene_id and transcript_id were consistent to begin with.

gffread -T looks like a one-command fix until your NCBI GFF3 comes out the other side missing gene_id on half its transcripts.

Densifying a sparse 10x matrix for a spreadsheet is usually the wrong call, here's how to do it safely on the rare occasion you can't avoid it.

The default read call quietly swaps your Ensembl IDs for gene symbols, and the duplicate-name patch that follows hides the damage instead of fixing it.

Read10X reads a directory, not a file, and the column you pick for gene names quietly decides how many genes you actually have.

read_10x_h5 looks like a one-line import, but its two default arguments quietly decide which features and which gene identifiers survive into your AnnData object.

Read10X_h5 hands you a matrix with the wrong row names by default, and nobody notices until a marker gene lookup comes back empty.

readH5AD does the transpose and slot mapping for you, but obs metadata and uns objects can survive broken or vanish silently, so check before you trust the object.

anndataR and capseuratconverter handle the transpose and slot mapping for you; deciding which layer actually becomes your counts slot is still on you.

write.csv() on a full assay tries to densify a matrix that's sparse for a reason, export only what you need or you'll watch R run out of memory on a 40k-cell object.

Seurat v5's split layers and multiple assays don't collapse into a single AnnData X on their own; pick the assay and layer yourself or the converter will guess wrong.

SaveH5Seurat doesn't corrupt your data on Seurat v5 objects, it just refuses to write until you rejoin split layers, and knowing why saves you an hour of guessing.

SaveLoom writes one assay and can quietly hand you scaled data instead of raw counts, so check the matrix before you trust it in pySCENIC or velocyto.

Pick the wrong Seurat layer, or reach for Matrix::writeMM instead of write10xCounts, and your MTX directory looks complete right up until Scanpy or Cell Ranger tries to load it.

as.SingleCellExperiment() is one line, but that line decides which assay becomes primary, drops your scaled matrix, and can hand back an empty counts assay without ever throwing an error.

SeuratDisk's Connect-then-as.Seurat path works, but velocyto barcodes and Seurat v5 layers will quietly wreck your merge if you skip the cleanup step.

writeH5AD() decides which assay becomes X almost silently, get that wrong and every downstream scanpy step runs on the wrong matrix.

as.Seurat() copies your matrices and cell metadata fine; it quietly leaves your gene annotations and altExps behind.

The matrix loads without error every time; whether your gene IDs survive the trip is a separate question you have to check yourself.