tandem-genotypes

tandem-genotypes finds changes in length of tandem repeats, from “long” DNA reads aligned to a genome.

Requirements & installation

Python (>= 2.6 or 3) needs to be installed on your computer. To draw histograms, R needs to be installed on your computer. You can download tandem-genotypes to your computer, put the programs in any convenient directory, and use them as-is. You can also get tandem-genotypes from bioconda.

Usage

First, align your sequences as described here. Then, do:

tandem-genotypes -g refGene.txt microsat.txt alignments.maf > tg.txt

This will check the tandem repeats in microsat.txt, annotate them with the genes in refGene.txt, and create an output file tg.txt.

It’s OK to omit -g refGene.txt.
It’s OK to use gzipped (.gz) files.

Output

The output looks like this:

chr22  41914573  41914611  GCGCGA  SHISA8  coding  -2,-2,0,0   -2,-2,0,0,0
chr22  41994883  41994923  TG      SEPT3   3'UTR   -7,-3,-1,0  -3,-1,0,1

Each line shows one tandem repeat. The first 3 columns show its location in BED3 format, column 4 shows the repeating unit, column 5 shows the gene name, and column 6 the gene part. Column 7 shows the copy number change in each DNA read that covers the repeat’s forward strand: for example -2 means the read has 2 fewer copies than the reference. Column 8 has the same thing for reverse strands. Here, the first line is a nice example with 2 clear alleles, and the second line is a nasty example without clear alleles.

The output lines are in descending order of “importance”, based on size change and gene part.

Drawing histograms

You can draw histograms of the output like this:

tandem-genotypes-plot tg.txt

This will make a file tg.pdf with histograms of the top 16 repeats. The x-axis is copy number change. Each histogram bar has a red part indicating the number of forward-strand reads, and a blue part indicating the number of reverse-strand reads. Instead of 16, you can get (e.g.) the top 50:

tandem-genotypes-plot -n50 tg.txt

You can see all options like this:

tandem-genotypes-plot --help

You can choose subsets of repeats with standard command-line tools like grep:

grep "coding" tg.txt | tandem-genotypes-plot - coding.pdf

Expected coverage drop for longer repeats

Longer repeats are likely to be fully covered by fewer reads. You can show the expected drop in coverage, for a set of reads (in FASTA or FASTQ format):

tandem-genotypes-plot --reads myseq.fa tg.txt

This shows the expected coverage drop as a gray line (whose absolute height is meaningless, only its relative height within each histogram is meaningful).

Crude allele prediction

You can predict 2 alleles per repeat, with option -o2:

tandem-genotypes -o2 -g refGene.txt microsat.txt alignments.maf > tg2.txt

This adds 2 extra columns to the output, with predicted copy number change per allele. These predictions are crude. If there is really 1 allele (homozygous), but the DNA reads vary due to sequencing errors, probably 2 close alleles will be predicted. The possibility of strand bias is not taken into account.

Merging allele reads into a consensus sequence

You can extract the DNA reads of each allele (for the top 16 repeats by default), and merge them into a consensus sequence, like this:

tandem-genotypes-merge reads.fq myseq.par tg2.txt > merged.fa

This uses 3 input files: the read sequences (in fastq or fasta format), myseq.par from the alignment step, and the tandem-genotypes output file. You must first run tandem-genotypes with option -o2 (to predict alleles) and option -v (to show read names). This merging requires lamassemble to be installed on your computer. Run tandem-genotypes-merge --help to see the options (most of which are described at the lamassemble site).

Tandem repeat input

You can supply tandem repeat locations by any of these files (which can be got from the UCSC genome database): simpleRepeat.txt, microsat.txt, rmsk.txt, RepeatMasker .out.

simpleRepeat: repeats with unit length from 1 to >1000. Made with Tandem Repeats Finder.
Direct links: hg19 simpleRepeat.txt.gz, hg38 simpleRepeat.txt.gz
microsat: a subset of simpleRepeat with perfect di- and tri-nucleotide repeats. Not comprehensive, but small and fast to analyze.
Direct links: hg19 microsat.txt.gz, hg38 microsat.txt.gz
rmsk: includes tandem repeats with unit length from 1 to ~13. Made with RepeatMasker.
Direct links: hg19 rmsk.txt.gz, hg38 rmsk.txt.gz

You can also supply repeats found by tantan -f4.

You can also supply repeats by the first 4 (or more) columns of the output format:

chr22  41914573  41914611  GCGCGA
chr22  41994883  41994923  TG

If you are using the “hg19” or “hg38” human genome, you can supply repeats with the included files hg19-disease-tr.txt and hg38-disease-tr.txt, which have a few disease-associated repeats:

tandem-genotypes hg38-disease-tr.txt alignments.maf

These files have “gene names” like: ATXN7:SCA7. The 1st part is the actual gene name, and the 2nd part is the disease (e.g. spinocerebellar ataxia 7).

Gene input

You can supply genes in these formats: refGene.txt, refFlat.txt, BED.

Joining and re-ranking `tandem-genotypes` outputs

Suppose you have DNA reads from 1 patient and 2 healthy controls. You can join their tandem-genotypes outputs like this:

tandem-genotypes-join patient.txt : healthy1.txt healthy2.txt > out.txt

Each output line shows: 1 tandem repeat with copy number changes for all inputs (in the order that you specified them).

The output lines are in descending order of “importance”: large changes in the patient are prioritized, and large changes in the controls are de-prioritized.

You can run tandem-genotypes-plot on this output: it will show the first (left-most) dataset.

You can use any number of patients and controls (separated by :). You can also use concatenated files:

cat healthy1.txt healthy2.txt > controls.txt
tandem-genotypes-join patient.txt : controls.txt > out.txt

Using a genome instead of reads

You can also find repeat length changes in a genome. For example, in a chimpanzee genome relative to human:

tandem-genotypes -g refGene.txt microsat.txt hg38-panTro5-1.maf > chimp.txt

These human-chimp alignments are available here. You could use the output as a “healthy control”:

tandem-genotypes-join patient.txt : controls.txt chimp.txt > out.txt

Points to be careful of:

Make sure all files use the same “reference” genome.
tandem-genotypes requires that the alignments are in the order produced by last-split. So hg38-panTro5-2.maf from the above website won’t work.

Control files

You can use control files in the controls directory:

tandem-genotypes-join patient.txt : hg38-microsat-control201808.txt > out.txt

Each file has 3 controls:

PacBio reads from a human (NA12878 / SRR3197748)
PromethION reads from a different human (NA19240 / ERR258112-5)
A chimp genome (panTro5)

No doubt this will soon be outdated, but it should remain useful, if not ideal.

These control files have been shrunk by keeping just one representative copy number change per repeat per control, which does not affect tandem-genotypes-join rankings. They were made by commands like:

tandem-genotypes-join -ss SRR3197748-rmsk.txt ERR258112-5-rmsk.txt panTro5-rmsk.txt > rmsk-control.txt

One -s gets representative copy number changes, and a doubled -ss also omits gene annotations.

`tandem-genotypes` options

-h, --help: show a help message and exit.
-g FILE, --genes=FILE: read genes from a genePred or BED file.
-o NUM, --output=NUM: output format: 1=original, 2=alleles (default=1).
-m PROB, --mismap=PROB: ignore any alignment with mismap probability > PROB (default=1e-6).
--postmask=NUMBER: by default, tandem-genotypes ignores mostly-lowercase alignments (using the method of last-postmask). This is because lowercase indicates repetitive sequence, and alignments without a significant amount of non-repetitive sequence are less reliable. You can turn this off with --postmask=0.
-p BP, --promoter=BP: promoter length (default=300). This is used for gene annotation.
-s N, --select=N: select: all repeats (0), non-intergenic repeats (1), non-intergenic non-intronic repeats (2) (default=0).
-u BP, --min-unit=BP: ignore repeats with unit shorter than BP (default=2).
-f BP, --far=BP: use DNA reads whose alignments extend at least this far beyond both sides of the repeat (default=100).
-n BP, --near=BP: count insertions <= BP beyond a repeat (default=60).
--mode=LETTER: L=lenient, S=strict (default=L). The non-default S mode has stricter requirements for using an alignment to a tandem repeat (such as requiring the alignment to actually cover the repeat at least a little bit). This might be good for high-quality sequence data of the future, but is not recommended as of early 2018.
-v, --verbose: show more details. -v shows the name of the DNA read with each copy number change. -vv shows output for all repeats, including ones not covered by any DNA read.

`tandem-genotypes-join` options

-h, --help: show a help message and exit.
-c N, --change=N: which size change to use for “importance” of each tandem repeat, for each patient or control.
- “0” means to use the most-changed DNA read. To avoid outliers: if the average number of reads covering each repeat (for repeats with at least 1 read) is ≥ 3, the most extreme expansion and contraction per repeat are ignored. This is the method described in the paper.
- “1” means to use the most-changed allele. But if this would have greater magnitude than the “0” change, use the “0” change.
- “2” means to use the least-changed allele. But if this would have greater magnitude than the “0” change, use the “0” change.
-s, --shrink: shrink the output (see above).
-m NUM, --mean=NUM: how to summarize the “importance” across multiple patients of changes in a tandem repeat. “3” means to use the cubic mean of each patient’s importance score: this was the method used before version 1.5.0 and described in the paper. “1” means to use the ordinary mean: this is now the default. The importance across controls is always summarized by cubic mean.
--scores=FILE: override importance scores for gene parts, with a file like this:
```
coding   50
5'UTR    20
3'UTR    20
exon     15
promoter 15
intron   5
```
-v, --verbose: prepend to each line: the importance score, the gene part importance score, the importance score from patients, the importance score from controls.

Limitations

tandem-genotypes doesn’t work for tandem repeats at (or extremely near) the edge of a sequence. This is because it uses DNA reads that clearly align beyond both sides of the repeat.