WEBVTT 00:00:00.404 --> 00:00:02.132 ♪ (music) ♪ 00:00:03.320 --> 00:00:05.993 Probably the most appealing part for me was 00:00:05.993 --> 00:00:10.540 answering these long-standing questions that I've had since I was a kid. 00:00:10.540 --> 00:00:14.334 Evolutionary biology helps us understand the nature around us. 00:00:15.544 --> 00:00:18.824 First and foremost, I'm interested in evolutionary questions. 00:00:18.824 --> 00:00:23.076 I'm very interested in the biodiversity that we see on Earth. 00:00:23.076 --> 00:00:27.285 Everything from species identification to deep, evolutionary questions 00:00:27.285 --> 00:00:29.017 can be addressed with DNA, 00:00:29.017 --> 00:00:32.740 and the CCG provides all of the resources necessary. 00:00:32.740 --> 00:00:36.880 So if someone's out collecting birds or reptiles or whatever, 00:00:36.880 --> 00:00:39.347 they bring it to the lab and extract the DNA. 00:00:39.347 --> 00:00:43.576 They purify the DNA, separate all the cell material from it, 00:00:43.576 --> 00:00:44.962 and then you have pure DNA. 00:00:44.962 --> 00:00:48.273 Once you have pure DNA, you can do all kinds of things with it. 00:00:48.273 --> 00:00:50.899 You can sequence that gene for many different organisms, 00:00:50.899 --> 00:00:54.299 then compare them to each other and build an evolutionary history, 00:00:54.299 --> 00:00:58.080 or a "family tree" for genes and species. 00:00:58.080 --> 00:01:00.026 For the past 30 years, 00:01:00.026 --> 00:01:03.448 the main platform for sequencing, is Sanger sequencing. 00:01:03.448 --> 00:01:07.858 With that method we look at one section of the genome at a time. 00:01:09.115 --> 00:01:11.240 With next-gen sequencing methods, 00:01:11.240 --> 00:01:13.718 the data we can get is massively increased 00:01:13.718 --> 00:01:16.784 because we can do a lot of the sequencing in parallel. 00:01:16.784 --> 00:01:18.775 We have the MiSeq sequencing machine here, 00:01:18.775 --> 00:01:22.869 and we can produce 25 million sequences in one read. 00:01:23.785 --> 00:01:26.666 More recently there is a third generation sequencing. 00:01:26.666 --> 00:01:30.655 Here we have an Oxford Nanopore MinION machine. 00:01:30.655 --> 00:01:35.411 So, by reading those electrical signals, we're able to read the DNA. 00:01:35.411 --> 00:01:37.173 It fits in my pocket. It's amazing. 00:01:37.173 --> 00:01:38.378 (laughs) 00:01:39.709 --> 00:01:43.107 Matt Van Dam is currently working on weevils, 00:01:43.107 --> 00:01:48.090 using this new technology to try to understand their evolutionary history. 00:01:48.090 --> 00:01:50.921 Weevils are a particular family of beetles. 00:01:50.921 --> 00:01:52.968 One of the problems, in the genome assembly, 00:01:52.968 --> 00:01:55.596 is that you have all these little bits of information. 00:01:55.596 --> 00:01:56.948 And then, sometimes, 00:01:56.948 --> 00:02:00.687 sticking them together in the right way is extremely complicated. 00:02:01.072 --> 00:02:04.924 The Nanopore does quite well for these longer reads. 00:02:06.040 --> 00:02:08.499 A group of us here, at the Academy, are sequencing 00:02:08.499 --> 00:02:11.682 the complete genome of the Pygmy Angelfish. 00:02:11.682 --> 00:02:15.594 And that includes all of the chromosomes, all of the mitochondria, and everything. 00:02:15.594 --> 00:02:16.972 It's very exciting work. 00:02:18.204 --> 00:02:23.040 Lauren is trying to look at which genes are active or turned on, 00:02:23.040 --> 00:02:26.970 and what kind of combinations can be produced by these different genes 00:02:26.970 --> 00:02:28.847 being turned on and off. 00:02:28.847 --> 00:02:30.237 One of the craziest things is 00:02:30.237 --> 00:02:34.172 we've only characterized like 1% of scorpion venoms. 00:02:34.172 --> 00:02:36.445 A single individual scorpion might have 00:02:36.445 --> 00:02:40.682 150 unique types of venom in its venom gland. 00:02:40.682 --> 00:02:44.380 And so it has genes to create all of these different venoms, 00:02:44.380 --> 00:02:46.720 and those venoms are highly specific. 00:02:46.720 --> 00:02:49.991 There's active research on using scorpion venom 00:02:49.991 --> 00:02:55.492 to treat cancer, to treat arthritis, to treat multiple sclerosis. 00:02:55.492 --> 00:02:59.089 So she is using something called RNA-Seq or transcriptomics, 00:02:59.089 --> 00:03:03.079 and what you do is you sequence all of the proteins. 00:03:03.079 --> 00:03:05.797 That's a way to sort of skip the whole genome sequencing 00:03:05.797 --> 00:03:10.564 and you can focus just on the RNA, which is what produces the proteins. 00:03:11.580 --> 00:03:14.916 I've been involved with the seahorse project, for many years. 00:03:14.916 --> 00:03:18.356 We've been trying to understand this very complex group. 00:03:18.356 --> 00:03:22.597 They apparently evolved very rapidly and created many different forms, 00:03:22.597 --> 00:03:26.430 so we have seahorses, pipefish, sea dragons, 00:03:26.430 --> 00:03:30.200 all these wild looking fish, and nobody really knows the relationships 00:03:30.200 --> 00:03:34.312 because they evolved and radiated very rapidly 00:03:34.312 --> 00:03:36.191 and in a very short period of time. 00:03:36.191 --> 00:03:39.916 We're using a new technology called ultra-conserved elements, 00:03:39.916 --> 00:03:43.769 and these are parts of the genome that are unchanged 00:03:43.769 --> 00:03:47.931 across hundreds of millions of years to reconstruct those branches. 00:03:49.371 --> 00:03:51.132 Our exhibits have lots of amphibians, 00:03:51.132 --> 00:03:53.687 so when we bring them in, we have to make sure 00:03:53.687 --> 00:03:56.617 that we don't spread chytrid fungus to the rest of the others. 00:03:56.617 --> 00:03:59.142 If we put it in with the rest of the exhibits, 00:03:59.142 --> 00:04:01.255 they would probably all die. 00:04:01.255 --> 00:04:04.521 We essentially create these probes, which are pieces of DNA 00:04:04.521 --> 00:04:07.761 that match those unique markers to the chytrid fungus. 00:04:07.761 --> 00:04:10.110 If the probe matches, we know it has this fungus. 00:04:10.110 --> 00:04:14.059 If there's no match, we can pretty be sure that there are no fungus infections. 00:04:15.248 --> 00:04:17.019 I think that the role of the CCG 00:04:17.019 --> 00:04:19.956 is to help every scientist answer their questions. 00:04:19.956 --> 00:04:24.924 And there are very few questions you can address without genetic data. 00:04:24.924 --> 00:04:28.743 We have all of this information, that's accumulated for decades 00:04:28.743 --> 00:04:30.343 by scientists and naturalists, 00:04:30.343 --> 00:04:32.526 and they're depositing it in our collection 00:04:32.526 --> 00:04:36.204 with very good ecological data that's associated with it. 00:04:36.204 --> 00:04:39.922 It's very important that we can also unlock that knowledge. 00:04:39.922 --> 00:04:41.877 ♪ (music) ♪