I live in Vancouver, Canada, where I’m a PhD candidate studying bioinformatics at the University of British Columbia with my supervisor, Inanc Birol. I work with the genome sequencing data of a variety of species, including human and the white spruce tree. I develop software that takes fragmented genome sequencing data and attempts to reassemble the original genome from which the short fragments were derived. My undergraduate degree is in computer engineering. I’m a programmer, an avid traveller, a singer, and an experimental amateur chef.
(explained using only the ten hundred words people use most often)
The written plan of living cells is very large. Computers read these plans, but they are not very good at it. They read lots of small little bits of the written plan at a time, and that makes lots of problems. I make a computer thing that takes the small little bits of the written plan and puts them together into larger bits. People look through these larger bits, because it’s easier than using the small bits, to look for interesting stuff in there and learn more about living cells.
Written using The Up Goer Five Text Editor and inspired by Keith Bradnam’s blog post How would you describe genomics without using any scientific jargon? and Randall Munroe’s excellent xkcd: Up Goer Five.
Linuxbrew is a fork of Homebrew, the Mac OS package manager, for Linux. It can be installed in your home directory and does not require root access. The same package manager can be used on both your Linux server and your Mac laptop. Installing a modern version of glibc and gcc in your home directory on an old distribution of Linux takes five minutes. Over two hundred current bioinformatics software packages are available in Homebrew-science.
Organellar Genomes of White Spruce
Chloroplast genomes of gymnosperms, including conifers, are well studied, but little is known about the mitochondria of gymnosperms. In fact, only a single gymnosperm mitochondrion is found in NCBI GenBank, and no conifer mitochondrion genomes are to be found at all, until now. Roughly one percent of the whole genome sequencing reads of white spruce are from its two organellar genomes: the chloroplast and mitochondrion. We assembled these reads using ABySS and found the mitochondrion genome to be nearly six megabases, which is unusually large for a mitochondrial genome. Although many genes typical of mitochondria were found in the genome, most open reading frames had no similarity to any known gene.
Shaun D Jackman, et al. (2016) Organellar Genomes of White Spruce (Picea glauca): Assembly and Annotation. Genome Biology and Evolution, doi:10.1093/gbe/evv244
The genes of the white spruce mitochondrion
My research looks at decoding the genes of the cellular components involved in photosynthesis and energy conversion of the trees grown for softwood lumber, called conifers. Conifers such as spruce, pine and fir are the primary product of the Canadian lumber industry. As lumber they are mainly used for building homes and are an important part of the Canadian economy. A cell has inside it small components named organelles that each perform a particular job. The organelles called chloroplasts absorb light and produce sugar, and this process is called photosynthesis. The organelles called mitochondria are responsible for converting sugar to a form of energy called ATP that is usable by the other organelles of the cell. Most genes of an organism are found in the nucleus of its cell, but the chloroplast and mitochondria have their own genes that are separate from the nucleus. My research will determine which genes are found in the nucleus of the cell and which are found in the chloroplast and mitochondria. This knowledge is helpful in breeding stronger trees that may be more adaptable to climate change or more resistant to insects, such as the mountain pine beetle that recently devastated large forests of British Columbia. Breeding trees that are more adaptable and pest resistant is critical to ensuring the health of our forests and maintaining a prosperous forestry industry.
UniqTag is used to abbreviate gene sequences to unique and stable identifiers. It selects a representative k-mer from the sequence of each gene to be used as a systematic identifier for that gene. Unlike serial numbers, these identifiers are stable between different assemblies and annotations of the same data without requiring that previous annotations be lifted over by sequence alignment.
Shaun D. Jackman, Joerg Bohlmann, İnanç Birol (2015) UniqTag: Content-derived unique and stable identifiers for gene annotation. PLOS ONE, doi:10.1371/journal.pone.0128026.
ABySS is a genome sequence assembler that distributes the computation of large genome sequence assembly over a cluster of computers using MPI.
White spruce genome sequence assembly
The SMarTForests project used ABySS to assemble the genome of the white spruce tree, which is twenty gigabases—seven times larger than the human genome. I’m currently working on assembling and annotating the organellar genomes of white spruce, the mitochondrion and chloroplast.
and a hands-on activity
‘Automating’ comes from the roots ‘auto-‘ meaning ‘self-‘, and ‘mating’, meaning ‘screwing’.
Bioinformatics analysis often involves designing a pipeline of commands and running that pipeline on many data sets. There are many ways to tackle this common task. Running commands interactively at the command line has the downside of being terribly unreproducible, unless one’s memory is fantastically infallible. Recording the commands in a shell script certainly beats storing the commands in one’s leaky brain, but is not particularly well suited to resuming the pipeline at a particular point, as is necessary after making a change to one step of the pipeline, nor to running independents steps in parallel. The venerable UNIX Make program is surprisingly well suited to describing bioinformatics pipelines. Make can resume a pipeline after a failed command without needing to start over, and it runs independent jobs in parallel. A Makefile describes a pipeline of shell commands and the interdependencies of the input and output files of those commands. A Makefile can be easily displayed as a graphical flow chart of files and shell commands, and such a visualization is a pleasing and powerful way to interpret a pipeline oneself or to communicate a pipeline to a collaborator.