Lift over is a way of mapping annotations from one genome assembly to another. The idea "lift over" is same as what tools like UCSC LiftOver, NCBI's LiftUp web service do. However, NCBI and UCSC's web services are available only for a limited number of species.

To perform lift over locally, one can use UCSC chain files (Kent et al 2003) with programs such as UCSC's liftOver or CrossMap. A chain file captures large, homologous segments between two genomes as chains of gapless blocks of alignment. One way of generating chain files is using this bash script and UCSC tools.

flo is an implementation of the above script in Ruby programming language. Further, both liftOver and CrossMap process GFF files line by line instead of transcripts as a whole. This results in some non-biologically meaningful output. flo provides a basic filtering of UCSC liftOver's GFF output.

We created flo for our work on the fire ant genome. If you use flo, please cite the following paper:

The fire ant social chromosome supergene variant Sb shows low diversity but high divergence from SB. 2017. R Pracana, A Priyam, I Levantis, Y Wurm. Molecular Ecology, doi: 10.1111/mec.14054.

Using flo | Results & discussion | Tweaking flo

To use flo you must have Ruby 2.0 or higher and the BioRuby gem. Ruby 2.0 can be installed through package managers on Linux and is available by default on Mac. To install BioRuby gem:

sudo gem install bio

flo additionally requires a few programs from UCSC tools, GNU Parallel and genometools. These can be installed in any directory by running 'scripts/install.sh' script after you have downloaded flo:

wget -c https://github.com/yeban/flo/archive/master.tar.gz -O flo.tar.gz
tar xvf flo.tar.gz
mv flo-master flo

It's best to run flo in a new directory - we will call it project dir:

mkdir flo_species_name
cd flo_species_name

Copy over example configuration file from where you installed flo to project dir:

cp /path/to/flo/opts_example.yaml flo_opts.yaml

Install flo's dependencies in ext/ directory in the project dir:

/path/to/flo/scripts/install.sh

Now edit opts.yaml to indicate:

1. Location of source and target assembly in FASTA format (required).
2. Location of GFF3 file(s) containing annotations on the source assembly. If this is omitted, flo will stop after generating the chain file.
3. BLAT parameters (optional). By default the target assembly is assumed to be of the same species. If the target assembly is a different (but closely related) species, you may want to lower minIdentity.
4. Number of CPU cores to use (required - not auto detected). This
   cannot be greater than the number of scaffolds in the target assembly.

Here, it's important to note that flo can only work with transcripts and their child exons and CDS. Transcripts can be annotated as: mRNA, transcript, or gene. However, if you have a 'gene' annotation for each transcript, you will need to remove that:

/path/to/flo/gff_remove_feats.rb gene xx_genes.gff \
> xx_transcripts.gff

Alternatively, if you have more than one transcript annotated for each gene, you can select the longest transcript for each gene to work with:

/path/to/flo/gff_longest_transcripts.rb xx_genes.gff \
> xx_longest_transcripts.gff

Finally, run flo as:

rake -f /path/to/flo/Rakefile

A common problem encountered is that 1st column of GFF file doesn't match chromosome, or scaffold, or contig id in the source assembly. In this case liftOver will generate an empty output file. flo stops at this point. You can fix the GFF file and resume flo by running the above command.

flo writes all output to a directory called run/ in the current directory. The chain file generated by flo can be found at run/liftover.chn. If flo completed successfully, a directory is created for each given GFF3 file in 'run/' that contains:

1. lifted.gff3 and unlifted.gff3 - liftOver's output
2. lifted_cleaned.gff - lifted.gff3 cleaned by flo -> final output
3. unmapped.txt - id of all transcripts that were not lifted and whose coding sequence before and after lift are not identical. Non-identical coding sequences can be the result of SNPs and short indels between the samples used to construct source and target assembly; it could be due to sequencing error in the target assembly or annotation error in the source assembly, or it could be that the transcript mapped to a duplicated region. These transcripts are included in the final GFF, but their ids are also listed here to signal lower confidence due to the difficulty in separating true polymorphism from assembly errors and paralogous sequence variation.

Both strengths and weaknesses of flo largely reflect that of the underlying tools - the chain file and UCSC liftOver. In general, gaps and errors in assemblies may split a long chain. Gene models that are split across different chains as well as those that are duplicated in the target assembly are not lifted.

• For an ant genome (~350 Mb) we saw 90% annotations map identically to the new assembly (unpublished result).
• flo has been used in:

1. Genomic architecture and evolutionary dynamics of a social niche polymorphism in the California harvester ant, Pogonomyrmex californicus
2. Improved contiguity of the threespine stickleback genome using long-read sequencing
3. Aethionema arabicum genome annotation using PacBio full-length transcripts provides a valuable resource for seed dormancy and Brassicaceae evolution research
4. Reconstruction of the origin of a neo-Y sex chromosome and its evolution in the spotted knifejaw, Oplegnathus punctatus
5. An ultra-high density SNP-based linkage map for enhancing the pikeperch (Sander lucioperca) genome assembly to chromosome-scale
6. Whole genome analysis of water buffalo and global cattle breeds highlights convergent signatures of domestication
7. Chromosomal assembly of the nuclear genome of the endosymbiont-bearing trypanosomatid Angomonas deanei
8. Meta-analyses of genome wide association studies in lines of laying hens divergently selected for feather pecking using imputed sequence level genotypes
9. Benchmark study comparing liftover tools for genome conversion of epigenome sequencing data
10. PRE-1 revealed previous unknown introgression events in Eurasian boars during the middle Pleistocene
11. The major histocompatibility complex of old world camels — A synopsis
12. Construction and comparison of three reference‐quality genome assemblies for soybean
13. Chromosome-scale assembly of winter oilseed rape Brassica napus
14. Genome improvement and genetic map construction for Aethionema arabicum, the first divergent branch in the Brassicaceae family
15. A single SNP turns a social honey bee (Apis mellifera) worker into a selfish parasite
16. Updated annotation of the wild strawberry Fragaria vesca V4 genome
17. De novo genome assembly of a Plasmodium falciparum NF54 clone using single-molecule real-time sequencing
18. Chromosome-scale scaffolding of the black raspberry (Rubus occidentalis L.) genome based on chromatin interaction data
19. First draft assembly and annotation of the genome of a California endemic Oak Quercus lobata Née (Fagaceae)

If you would like to optimise how chain files are created:

• UCSC wiki and website is an amazing resource to learn about BLAT and chain files. Don't forget to read Kent 2003 paper cited above first.
• Read the Rakefile from top to bottom. Ruby is similar, yet simpler compared to Perl and bash.

You can test things by lifting annotations between the same assembly.

Copyright 2017 Anurag Priyam, Queen Mary University of London