Tag: alignment

A practical guide to the three things people mean by genome mapping, the aligners and flags we would use on a personal whole-genome dataset, and what the compute costs.

A working guide to the TopHat/TopHat2 spliced alignment pipeline: when to use it, the exact commands, and the HISAT2 and salmon pipelines we would use for a personal transcriptome today.

A step-by-step guide to indexing a human reference, running bwa mem on 30x whole-genome FASTQs, and producing a sorted CRAM for variant calling.

A working guide to going from FASTQ files to a gene-level count matrix you trust: reference choice, STAR two-pass alignment, Salmon selective alignment, strandedness, and the QC numbers that tell you whether the run is usable.

A working guide to building a STAR index, running a two-pass spliced alignment of human RNA-seq, and reading the output files that tell you whether the run is any good.

A working guide to aligning and quantifying your own RNA-seq data: reference choice, STAR and Salmon parameters, strandedness, quality thresholds, and the failure modes that silently distort expression estimates.

Genome mapping means at least four different things: historical linkage maps, aligning your reads to a reference, optical genome mapping for structural variants, and the colloquial sense of getting your genome sequenced. Here is how to tell them apart, what each costs, and the pipeline we would run on your own data.

hg19 is UCSC's packaging of the GRCh37 human reference assembly. They are nearly the same sequence with different chromosome names and a different mitochondrion, and the coordinates do not match GRCh38. Here is how to tell which build your files use and how to move between them without corrupting your data.

A working guide to turning personal RNA sequencing FASTQs into gene- and transcript-level expression estimates: reference choice, selective alignment with Salmon, spliced alignment with STAR, QC thresholds, and the checks that catch a swapped or degraded sample.