Phylogenomics offers the possibility of helping to resolve the Tree of Life. To do this, we often need either very cheap sources of organismal genome data or decent methods of subsetting larger genomes (e.g., vertebrates; 1 Gbp) such that we can collect data from across the genome in an efficient and economical fashion, find the regions of each genome that are shared among organisms, and attempt to infer the evolutionary history of the organisms in which we’re interested using the data we collect.
Genome reduction techniques offer one way to collect these types of data from both small- and large-genome organisms. These “reduction” techniques include various flavors of amplicon sequencing, RAD-seq (Restriction site Associated DNA markers), RNA-seq (transcriptome sequencing), and sequence capture methods.
phyluce is a software package for working with data generated from sequence capture of UCE (ultra-conserved element) loci, as first published in [BCF2012]. Specifically, phyluce is a suite of programs to:
- assemble raw sequence reads from Illumina platforms into contigs
- determine which contigs represent UCE loci
- filter potentially paralagous UCE loci
- generate different sets of UCE loci across taxa of interest
Additionally, phyluce is capable of the following tasks, which are generally suited to any number of phylogenomic analyses:
- produce large-scale alignments of these loci in parallel
- manipulate alignment data prior to further analysis
- convert alignment data between formats
- compute statistics on alignments and other data
phyluce is written to process data/individuals/samples/species in parallel, where possible, to speed execution. Parallelism is achieved through the use of the Python multiprocessing module, and most computations are suited to workstation-class machines or servers (i.e., rather than clusters). Where cluster-based analyses are needed, phyluce will produce the necessary outputs for input to the cluster/program that you are running so that you can setup the run according to your cluster design, job scheduling system, etc. Clusters are simply too heterogenous to do a good job at this part of the analytical workflow.
Short-term goals (v1.4.x+)¶
We are currently working on a new release (this documentation) to:
- ease the burden of installing dependencies using conda
- standardize parameters input to various analyses
- improve logging of what is going on
- improve and standardize the documentation
Longer-term goals (v2.0.0+ and beyond)¶
We are also working towards adding:
- simplify the CLI (command-line interface) of phyluce
- improve test coverage of the code by unittests
- SNP-calling pipelines (sensu [BTS2013])
- sequence capture bait design
- identification of UCE loci
Much of this code is already written and in use by several of the Contributed to the code. As we test and improve these functions, we will add them to the code in the future.
Who wrote this?¶
This documentation was written primarily by Brant Faircloth (http://faircloth-lab.org). Brant is also responsible for the development of most of the phyluce code. Bugs within the code are usually his.
You can find additional authors and contributors in the Attributions section.
How do I report bugs?¶
To report a bug, please post an issue to https://github.com/faircloth-lab/phyluce/issues. Please also ensure that you are using one of the “supported” platforms:
- Apple OSX 10.9.x
- CentOS 6.x
- Ubuntu 14.04 LTS