My lab, my data, my life
I've put together a few pictures so you can see where I work, some of the people I work with, and what I spend my days looking at. I hope you enjoy this narrated gallery.
Here is the lab where I work.
This is my lab bench, where I work to do most of the processing of my samples. Outlined in green is my heat block, which I use to heat up my samples during different steps of my protocol. Just to the right of that and outlined in blue is a stack of petri dishes with nutrient rich agar in them. We can visualize and count bacterial viruses by using these to grow up bacterial cells that are infected with the viruses. Wherever there is a virus it and its offspring kill the bacteria cells and leave a clear spot called a plaque. Above these and outlined in purple are my micropipetters, these can accurately measure liquids smaller than a drop of water! All the way to the right and outlined in red is my bunsen burner. Flame is a great destroyer of life and I use this to heat up and sterilize a lot of glassware I use routinely.
This is Andrew, a fellow graduate student in my lab, working at his bench. He does really interesting work studying the effects of pleiotropy (when one gene influences multiple traits) and population dynamics on adapting viral populations.
Here is the chemical hood I use when I collect new samples. The first thing I do when I return from a wastewater treatment plant is to add chloroform to my samples inside this hood. This destabilizes the membranes of any pathogens that could hurt me, either bacterial or viral and helps me begin the process of isolating the ssDNA viruses.
I use several different centrifuges during the processing of my samples, but the one pictures below is by far the most impressive, in my opinion. It is called an ultra-centrifuge and I use it to spin my samples at 24,000 rotations per minute, and this isn't even the fastest speed possible! Setting up the samples before they go in here is a delicate and patient process as the samples need to be balanced very precisely to each other.
Below are two of the awesome people who work in the core facilities at FSU. Steve is the manager of the sequencing facility and runs and maintains our genome sequencing equipment. Margaret is the manager of the analytical lab and maintains all of the protein analyzing equipment in the lab. They have both been extremely helpful to me innumerable times since I started my graduate career.
Here is the Illumina MiSeq, which I used to sequence an earlier sample to determine the efficacy of my protocol. The sample I ran generated roughly 7.5 million reads of sequencing, but they have improved the system since I used it so it now generates more sequencing reads.
This is the Illumina HiSeq, which I am planning on running the samples on that I am currently raising money to finish processing. Currently the MiSeq can generate roughly 15 million reads of sequence data, the HiSeq on the other hand can generate 300 million reads of sequencing per lane! With the MiSeq I could only run one sample at a time, with the HiSeq I will be able to run all 15 of my remaining samples at once.
Now for a brief glimpse of what I mean when I say sequencing reads, and what I spend my days staring at and analyzing. This first picture is sequencing data obtained from standard sequencing technology, where you sequence one individual at a time. The row outlined in red is what we refer to as one read of sequencing. It is a string of nucleotides sequenced together that we know were generated from one strand of DNA. As you can see with this sequencing technology we sequence a genome by overlapping several reads of sequencing, this both ensures that we obtain the entire genome without gaps, and allows us to ensure that the nucleotide sequence is correct and free from errors that can be generated during the sequencing process. I apologize if these next two images are hard to make out the details.
This, on the other hand, is the sequencing coverage I obtained from the MiSeq. As you can see each region of the genome is covered by many reads of sequencing. This allows us to sequence an entire complex population of viruses and then determine if individual mutations that we see are sequencing error or a novel variant in the population. Boxed in red is an example of this. The whole visible column consists of A nucleotides, except that sole T. Computer programs can count and display how many times the T emerges at that site. If we only see a variant at that site a few times it is most likely a sequencing error, however is 30% of the sequencing reads display a T at that location than it is likely to be a true variant in the population. Over on the right you can see the different contigs listed, such as the one I have open that is highlighted in blue. Each of these contigs is a potential viral genome and you can see how long it is and how many sequences are assembled to it.
Finally, here is my desk. I spend most of my days sitting here, analyzing my data, writing, and grading student papers.