Mismatched Chromosome identifiers and how to avoid them

Reference data mismatches are similiar to bad reagents in a wet lab experiment: all sorts of odd problems can come up!

You inputs must be all based on an identical genome assembly build to achieve correct scientific results.

There are two areas to review for data to be considered identical.

1. The data are based on the same exact genome assembly (or “assembly release”).
   • The “assembly” refers to the nucleotide sequence of the genome.
   • If the base order and length of the chromosomes are not the same, then your coordinates will have scientific problems.
   • Converting coordinates between assemblies may be possible. Search tool panel with CrossMap.
2. The data are based on the same exact genome assembly build.
   • The “build” refers to the labels used inside the file. In this context, pay attention to the chromosome identifiers.
   • These all may mean the same thing to a person but not to a computer or tool: chr1, Chr1, 1, chr1.1
   • Converting identifiers between builds may be possible. Search tool panel with Replace.

The methods listed below help to identify and correct errors or unexpected results when the underlying genome assembly build for all inputs are not identical.

Method 1: Finding BAM dataset identifiers

Method 2: Directly obtaining UCSC sourced genome identifiers

Method 3: Adjusting identifiers for UCSC sourced data used with other sourced data

Method 4: Adjusting identifiers or input source for any mixed sourced data

tip Reference data is self referential. More help for your genome, transcriptome, and annotation

tip Genome not available as a native index? Use a custom genome fasta and create a custom build database instead.

tip More notes on Native Reference Genomes

• Native reference genomes (FASTA) are built as pre-computed indexes on the Galaxy server where you are working.
• Different servers host both common and different reference genome data.
• Most reference annotation (tabular, GTF, GFF3) is supplied from the history by the user, even when the genome is indexed.
• Public Galaxy servers source reference genomes preferentially from UCSC.
• A reference transcriptome (FASTA) is supplied from the history by the user.
• Many experiements use a combination of all three types of reference data. Consider pre-preparing your files at the start!
• The default variant for a native genome index is “Full”. Defined as: all primary chromosomes (or scaffolds/contigs) including mitochondrial plus associated unmapped, plasmid, and other segments.
• When only one version of a genome is available for a tool, it represents the default “Full” variant.
• Some genomes will have more than one variant available.
• The “Canonical Male” or sometimes simply “Canonical” variant contains the primary chromosomes for a genome. For example a human “Canonical” variant contains chr1-chr22, chrX, chrY, and chrM.
• The “Canonical Female” variant contains the primary chromosomes excluding chrY.

Still have questions?

Want to embed this snippet (FAQ) in your GTN Tutorial?

{% snippet  faqs/galaxy/datasets_chromosome_identifiers.md %}

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