This morning, we talked all about manageromic taxonomic annotation. Yesterday, you talked all about taxonomy. And finally, you're going to not talk about taxonomy. So learning objectives is going to discuss what just broadly we mean between functional composition versus taxonomic composition. I'm going to try to give you a really overview high level of the fact that there's different functional databases out there and what might take is on some of them. We're going to talk about the pros and cons of maybe assembly and gene calling when we're doing functional annotation. I'm going to walk you through how HUMON actually works. And then you'll use HUMON and STAP in the tutorial this afternoon to functionally annotate the same samples that you taxonomically annotated this morning. OK, so functional composition. What are they doing? So obviously, this morning, we talked about taxonomics. So you get the list of the microbes and who is there, whereas functional composition really answers what are they doing. So manageromics provides the opportunity to catalog the entire set of genes from entire community. So you have to remember that these reads come from all the microbes. And some of these reads will actually contain parts or entire genes, but usually just parts of genes. And the idea is try to annotate those fragments with some sort of functional annotation label. So what do we mean by function? I think this kind of came up maybe yesterday or this morning. It's all blending together. So by function, we could be just talking very general about very loose terms like photosynthesis or nitrogen metabolism or glycolysis. And that's really obviously function at a very high level. Or we could be talking about really specific groups of orthologs. Everybody remember what an orthologue is? But you got a definition for me? Come on. Shout it out. Same function, different organism? Yep, pretty much. Anybody else? That's good. Yeah, the phylogenetic in me, sort of says. So usually they'd have to be phylogenetically related. You could have convergent evolution that would lead to the same function in different organisms, but that wouldn't be typically an orthologue. But yes. And then technically as well, orthologs don't necessarily always have to have the same function. But anyway, groups of genes across families. And so this could be, say, something like NIF-H, which is involved in nitrogen fixation. It could be, say, an EC number is a different type of classification, which is from the enzyme classification system. So 1.1.1 is alcohol dehydrogenase. Or it could be a different database like the K-ortholog database, which is K00929, which is butyrate kinase, which is involved in the production of butyrate. So often when we're talking about function, we could be specifically talking about a particular gene or a particular enzyme, or we could be talking about something more general like a pathway or a module. And so we're going to kind of relate these things together. So there's lots of various functional databases out there. I'm just going to run through a few of them. So the COG stands for clusters of orthologous groups. It's been a classification that's been around for a long, long, long time. The actual database hasn't really been updated since 2003, which is like, I don't even know. Could you even sequence back then? I mean, I was joking. That's a long time. But you still see the annotations. So COG annotations are the ones with those letters that usually have R, T, S. I don't know what they are anymore. But kind of a breakdown into about 12 to 15 different groups. They're still actually used quite a bit. So people annotate things and annotate them with COG categories. The seed is another classification system. So the seed is actually used by systems like RAST and MG RAST. So MG RAST is this metagenomic server where you can upload your data. And then RAST is like a genome annotation system for microbial genomes. And so those systems uses the seed system so it's their type of functional annotation. PFAM is more focused on protein domains, although they do have whole genes as well. And they usually use HMMs to describe the PFAMs. Eggnog is another one. It's more comprehensive. It has about 190,000 different gene families. Uniref is becoming, I think, more and more popular more recently. So Uniref is a prediction, sort of an automated clustering where they keep updating these clusters. And their cluster, kind of like O2Us, are at different percentages, right? So Uniref 100 means that everything within that cluster is 100% identical. Uniref 90 would be 90% or less. Uniref 50 would be 50% or less. So if you go to Uniref 50, there's less families because they're grouped into bigger groups. And then Uniref 90 would have more gene families but with tighter groups. So it's kind of nice because they're constantly updating it with all the new genomes that come out. And so I'm not going to talk about it much more today, but there are systems that are starting to use that in more detail in the future. The keg is very popular still. Each entry is really well annotated. So if you've ever read papers, you'll probably often see these nice metabolism charts. And they link these keg orthologs, which I'll talk about in a second, into higher things like modules or pathways. Full access actually requires a fee now. They went private, I don't know, three or four years ago probably now, if I had to guess. So people are still just kind of using the last snapshot of the database since then. But you can still go to the database and get annotations from the system. That's all open. But sort of the back files that includes all the sequences with those new keg orthologs isn't updated unless you have access to the private site. So people basically use the last snapshot and continue developing tools for it. And slowly I think we're starting to migrate away a little bit away from the keg. And more people are starting to use things like, say, MetaCyc, which is the next one. It's more microbe focused than the keg, which is kind of nice, whereas the keg has a lot of human functions. So MetaCyc is an annotation system and classification system that's more focused on microbes. Okay, is there any favorite functional database that I missed in this one? What's that? Gene Ontology, of course. Yeah, Gene Ontology. Yeah, absolutely. So Gene Ontology is another really common system. And the nice thing about Gene Ontology is that it's an ontology, so you have this hierarchy structure where genes get mapped into more general and general categories. Yeah, absolutely, I need to update this with Gene Ontology. Gene Ontology's becoming maybe a little bit more used with microbes, but for a while it was a bit, it was very useful for human and other larger eukaryotes. But for microbes, again, some of the things, kind of like the keg, some of the genes aren't really well represented within microbes, but still, it's still pretty widely used. Yeah, that's a good one. Okay, so... Which ones would you recommend? Which ones I recommend? Yeah. Yeah. So I think the, so the last three are probably the most, okay, so I would recommend, so I'm gonna talk about keg today. So keg's great because whatever you find is really well-described. So if you find something significantly different, it's well-described, you can map it to modules or pathways, and you can make these nice, pretty pictures, it's awesome. But the problem with the keg, and I don't know if I discuss this later, but whatever I was discussing now, is it's not as comprehensive, right? So the keg database has 10,000-ish keg orthologs, you know, gene families, whereas eggnog has 190,000 different families, and like UNRF90, I think has like 250,000 or more gene families. So what that means is that if you take your genes and you blast them or a similarity search against the keg orthologs, you're only gonna annotate a certain percentage of those reads. Now, that happens for various reasons. One, maybe because there's no gene there, or two, because there's just not a significant finding, but it could just be because the keg ortholog database is not very comprehensive. So there's lots of genes out there we talked about this morning that we don't know their function, right? We don't know anything about them. So there's sort of left out in the dust of the keg database. So I think it makes more sense to move in a direction where at least we try to characterize gene families as most comprehensive as possible, and if they're unknown, well, at least we identify them, and then later on we can try to worry about annotating them. So personally, I think UNRF is a really good solution for this because they're automatically updated. It's not someone's database that they made once and it's gone obsolete. It seems to be has staying power. And I'm starting to see new tools. I'm gonna talk about humon one today, but I know humon two is released, but it's not published. So I decided not to describe the algorithm, whereas we're gonna dig into humon. But I know with humon two, they're using the UNRF 90 as their database, which is much more comprehensive. And I think that's much more satisfying, especially if you're working in an environmental microbiome where you might have a lot of unknowns. And then MetaSyke seems really good too. I'm just not as familiar with the system. So MetaSyke has nicer, in more depth annotations for microbes. So the direction I like to see is UNRF and MetaSyke. But for now, I think keg's still a pretty good popular tool. So it's satisfied. So can you plan to adapt? Oh, yeah. So PyCrust basically uses, it doesn't really matter that what we do is we take all the genomes that have been sequenced. I'm gonna use the annotations for those genomes. So if we can get this massive table of genomes with different types of functional categories, and it's reliable, then PyCrust works with it. So we focused on keg just for our validation, but we do have cog predictions. We do have RFAM predictions. And we started to play with MetaSyke, and we actually made it possible, but there was no validation. So we didn't really say that, it's one thing to make a prediction, but when you have nothing to validate it, it's like I don't really wanna let it out into the world. So it's definitely possible. And we can actually make the predictions. It's just we have to do some validation to make sure that it's accurate, like it was accurate for the keg annotations. Yeah. Okay. So I'm gonna describe the keg, but I think this applies, a lot of this stuff applies to other databases. So it's just good to have sort of an idea of how these things are assembled. So with the keg database, the most specific unit is these keg orthologs, then the most specific. And they're thought to be a particular gene that's doing some exact function. So now I've quoted like three different numbers of how many KOs are in the database. I should just look it up. So now there's about 12,000 KOs in the database. And then these are linked into keg modules and keg pathways. They have identifiers like K with five numbers after it. And it's slightly confusing because people refer to them as keg orthologs or KOs. But then the identifier for keg pathways is actually KO, which is super confusing. But keg orthologs or KOs are actually just a big K. And then pathways are actually these smaller prefects. But so if you go to keg database, if you get like a prediction from pie crust or you get an annotation, it often has an ID. And you can just go to keg database and it'll tell you that's the entry. There's often a common name for the gene there listed. There's a longer definition of the protein products. And then it lists different pathways as associated with. So remember I told you that keg orthologs are often grouped into multiple pathways, right? So this shows that for this particular KO, it's involved in all these different pathways. It's also involved in these three different modules. And then it's also linked to this disease, right? So the keg database tries to organize stuff into these as well. And it also has these bright hierarchies which just basically breaks some of these pathways into more general categories as well. So KO is the bigger top hierarchy. And then you have these modules. No, the opposite. So keg orthologs are actually the most specific, the smallest thing at the bottom. The modules are bigger. The modules are bigger. Modules subset keg orthologs, and then pathways are even bigger than that. Yeah.  So it's a specific gene. OK, so then you can group these keg orthologs into keg modules. These are sort of mainly defined functional units. It's thought that there are small groups of KOs that function together. There's about 750 keg modules in the database. And they're labeled with this big M for module. What's cool about keg modules that's different from keg pathways is that they have this really well robust definition of what makes up a module. So it's not just a bag or a loose clustering of keg orthologs. So in order for a module to be present in a genome, but in a genome, it has to have a certain selection of keg orthologs. So the way that they do this is in a graphical representation as well as just a line you're describing it. So to cover this particular module, which is white boxes, you start at the top and you have to have at least for sure this KO 1803. And then the next step, you can either have K11389 to get through this process, or you have to have one of these plus this one. The next step, you just have to have one of these to get through to the next step, this one for sure, and then one of these two. So that makes sense. And then if you have that and any sort of mixture of those to complete that, then you have that module present. And then so they just represent that with this text for the destination. The nice thing about that is you actually have some structure to it. And these are a bit smaller. Whereas with keg pathways, they're basically just these groupings of genes that are within a pathway. So there's about 233 different pathways. What's nice is that they do have these really nice cool graphical maps. So you can pull them out. And then what's also really cool is you can often highlight your genes that are maybe significantly different across your metagenomes. And then you can over-represent that in a nice graphical figure and relay it to the pathway. So these are usually a little bit more general. And then even these pathways, you can collapse those into even more general terms that aren't really biologically useful, but things just like amino acid metabolism and carbohydrate metabolism. All right, does that make sense? Rockin. OK, so for annotation systems that are out there, basically here's some of the ones I've listed. So for web-based, there's EBI on a genomic server. So this is one of those servers where you just upload it. They do annotation. They use, I think, InterPro and behind-the-scenes, which is quite slow if you try to do it on your own. Or I guess they have these massive servers that just crank out the data. MGRAS, I mentioned it a couple of times already. So MGRAS has been around for a few years. Their server tends to be, I'm really scared now that the audio's gonna be online. I just realized, anyway. But MGRAS tends to be pretty good, but the server's often down recently. So I think it's because a lot of people submit data and it sometimes goes down. And sometimes you don't know how long it's gonna take to get your data back, so it could be a week or two or more. There's also a metagenomic annotation system through IMG slash M. And through that, I believe you have to sort of go through an application. I think it's usually free, but it's through JGI, and I'm not quite sure how to go through that process. But I think it's free as far as I know, and then you can upload it. You have to have a read to keep copy of data. For IMG, yeah? Yeah, join Gino, yeah, yeah. Through the JGI, right? Right, and that's all though. That's not too bad. Sometimes. Well, I'm just saying that's bad for you. Right, yeah. Anyone have experience with those? Thoughts? Do we base, which is sort of more on your system, as Megan, I mentioned this morning has been around for quite a while.  Clover, I should really just strike this out. It was a virtual machine base that contains sort of an SOP, but it hasn't been upgraded for a while now. That's gonna get scratched soon. And then sort of local based is there's MetaMOS was made where you can sort of customize different things, what database you wanna search with, do you wanna do assembly or not, and lots of different options in there. It's pretty cool. When I was testing it, some of the features are a little buggy and I think they've probably ironed out some of those, but if you're looking for something that's kind of, they've automated a lot of it, but you can maybe change different options. It sounds like a pretty good one. The do-it-yourself option, which is probably still massively done by a lot of people, is you just come up with your own pipeline and that is super popular and I go to posters and people will always have a new MetaGenomics pipeline, it's pretty amazing. And then Heman, I'm gonna talk about later on. Okay, so why the heck is this functional stuff so complicated? Why don't we just blast the NR database with my reads? So there's a few problems with that. One blast is really slow, right? So you have millions of reads, probably, and then you have the NR database, which is millions of genes, and it's gonna be just really too slow. Next thing is that the top hit may not be the actual correct annotation for function, right? So we're thinking about functional annotation here now. So there's always problems with, there's two different reasons why it could be wrong. One, it could be just that the annotation's on the database, or two, it could be that the top hit is 100% identical and then you'll often get these ties between genes and the top one doesn't actually match. The second one, even though they're like 100% identical. So you'll get these sort of bad annotations and it's hard to choose which one is which. There could be a bias from gene lengths or the database. So this has to come down to if you have a long gene. This is kind of like our discussion this morning about the size of the genome. So you can imagine if you have a bigger gene, the chance of hitting that gene with a read is actually more than a gene that's smaller. So again, if you're counting different functions in your table, you wanna down weight those longer genes. So if you had a gene that's four times as long as another gene, then the chance of just hitting that is four times higher and so that's gonna be overrepresented when you're doing your counts. So some people will try to normalize for that gene length. And also there could be bias from the database where the database has been obviously based on whatever's been sequenced in the past. And so if all your top hits are basically the same thing over and over again, you actually don't see that it could be something else further down your list of hits. Another problem is that sequencing depth is usually too shallow to cover all the DNA in the sample. So it's not like genomics where you would sequence a genome to say 30 times coverage and you have reads representing every single nucleotide in that genome and also all the genes. Since we're doing metagenomics and we have sometimes thousands of different O2s, the chance of actually getting a read from every single gene in your metagenome is unlikely unless you're doing a crazy amount of depth or the metagenome is fairly not too diverse. And then the other thing is that if you wanna then collapse these things into say, cake modules or cake pathways, how do you really determine how do you relate that into those things from those individual gene abundances? And a good example is if you have one gene from a whole pathway, does that mean I should count that pathway as one or do I divide by say the number of genes in that pathway or do I wait until I have most of the pathway before I should count it at all? Okay, any questions about that? This is metagenomics after assembly. I'm gonna talk about assembly after. Is that all right? So I was just wondering if not for sure if the last is how do you differentiate several genes in it on all different parts of the quantity as well? Yeah, that'd be a problem. So I think if you do assembly and you have multiple genes from a single thing, people will count each one of those hits. So doing the tools like NG-Rast and whatever, do they take that into account there for a quantity that might be 2000 bases long that might have multiple genes in there? I think so, yes, yeah. And then I think they'll collapse it so that they know that these hits are all to the same area so we don't count those multiple hits. It's the hits to different areas in the gene, yeah. Okay, so this is the sort of humane pipeline and I'm gonna sort of break it down step by step. So the first step is the step that basically all functional annotation pipelines will have is some similarity search to a database. So in this case, they're doing a translated blast against this cake ortholog database. Does everyone know what translated blast is? No. So blast by itself is there's different flavors of blasts so blast N would be a nucleotide versus nucleotide search. Blast P would be a protein query search against a protein database. Blast X, which is a translated blast, is a nucleotide query and then you search it against a protein database and so you search all six frame translations of your nucleotide sequence. Three in forward and then three in the reverse. And then the last one is T blast X which is translated query and the translated database. Does that make sense? So often people are doing functional annotation protein space so they're searching a protein database which if you're interested in non-protein genes then you would not want to do this, right? So it's something to keep in mind if you're interested in RNA genes or other interesting things that you can annotate DNA with that we're mostly talking about proteins. The reason that we're doing a protein search is just because one we can make it a bit quicker, I'll show on the next slide but also proteins allow for more conservation over time so it would be more noisy with a blast sort of end search. All right, does that make sense? Yay. Oh yeah, sorry, so is that a... Geez, that's kind of brutal aren't I? Yeah, sorry, so is that a Curtis Hutton-Hauer's group? So yeah, that's really weird. Sorry, I thought I had it listed. Okay, yeah, so sorry, humon is out of the Curtis Hutton-Hauer groups. It sounds like human only. I should have mentioned that as well but people do use it not just for humans because it is the keg database so there's nothing really focused just on humans.  And it was used though for the human microbiome project as their main annotation tool. Okay, so I said earlier blast is slow and so people have been developing faster methods to do faster similarity search. So after blast came out, there was faster tools like has anyone heard of Blatt maybe? Blatt's a tool that makes nucleotides searching a lot faster. There was this other tool called Wrap Search and then Wrap Search 2 which does protein searches even faster than Blatt. And then more recently, Diamond came out, man I don't have my references in here, sorry. Last year. And they claim that they had a massive speed up over blast x. So this is a figure from the paper and they show that compared to blast that you get almost a 20,000 fold speed up over blast x which is pretty amazing. And then they also show in these two figures that hopefully they still get the right answer. So this just shows different types of data and the bar is to show the correct queries mapped using the search methods. So they compare against Wrap Search 2 as there may be a different comment. So they're doing fairly well across the queries mapped and then if they look at the matches recovered from blast that they do fairly well. As well. Just from our own tests, basically Diamond does really good if your similarity is fairly high. So it seems like anything above sort of 80% would do very well. If you're looking for really divergent sequences say in the 30 to 50% identity range, Diamond doesn't always give you the best hit because it does do things to speed it up. So if you're looking for very diverged proteins, you'd be probably better off sticking with blast and just waiting. But with 20,000 fold increase, I mean basically this means that we can get stuff done without a massive cluster sometimes. So in the example today, you'll run things running in about a few minutes and then we calculate it one time and it would take blast about a couple of days to do that same search. So when you have so much data, sometimes you just have to use whatever will process the data. Okay, so the next step that, so the first step is to do that search and that currently is outside of Humon right now. So you'll do that search in your tutorial and then the second step is then to start to process these searches and then start to come up with hopefully what you want at the end is both pathway and module information at the end. So the first step they do is they try to normalize and wait the search results. So you can imagine they have all these different hits to different things in the database and they try to normalize those hits using a few different approaches. So one, they obviously take into account how many reads map to the gene sequence in that KO and then they wait those by the inverse p-value of each mapping. So the lower the p-value, sorry, the higher the p-value, then the less weight it will have to normalizing that data. And then the other thing they do is they take into account the average length of a K-gorthologue and then they divide by the average length of that K-gorthologue to come up with a normalized score. So right at this stage already you're going from real read counts to all of a sudden some decimal number that's been altered based on the similarity to the K-gorthologue as well as the gene length. Does that make sense? Right. Okay, so the next step is then this problem where I mentioned how do you take into account if you have one or two K-gorthologs of a pathway, how do you start to count that? So as I mentioned already, a K-gorthologue can map to one or more K-gorthologies but just because we find one K-gorthologue or a few in a pathway, it doesn't actually mean that exists in the community. And that's important because maybe that pathway represents something that's not actually actively occurring. So for this they just basically wrap a previous tool that was developed called MinPath and MinPath attempts to sort of remove spurious hits. So their idea is that you have all these K-gorthos mapping to different pathways and you try to remove pathways that aren't well covered. The next step is that they use organism information from the K-gorthologies. So they look at pathways that are not found to be observed in any organisms and are also made up mostly of K-gorthos mapping to a different pathway are removed. So this is the idea that if most of the K-gorthologs look like they belong to this pathway and they're not known to be associated with other pathways, they just basically remove them. The other thing that they take into account is this estimated copy number. So this kind of relates back to six-to-nest copy number. So you can imagine if a genome has multiple copies, they actually try to normalize for that. So they're only representing a single copy of a K-gorthologue within a genome. And then finally, they do one last step where they try to do some smoothing and gap filling. And this is the idea that they know that people with metagenomics have not usually not done enough sequencing to sample all the genes. And so just because you're missing a gene from a pathway, it doesn't mean that's actually missing your metagenome. It could be just that you've had low sampling depth. And so they try to increase K-gorthologs that they think are actually present that just haven't been sequenced. Okay, and then the last step is that they basically have now two types of data. So one is pathway coverage, which is a presence absence. So this is just saying for the metagenome, do we think that this pathway is at least covered completely and that's just a presence absent thing. And then the abundance actually takes into account the relative abundance, which is much more informative. So usually I don't use coverage for anything. And I want to look at the relative abundance of both pathways and these cake modules. All right, does that make sense? So that's Humon in a nutshell, great. All right, so what about assembly? So I think we'll get us to go to assembly a little bit yesterday. So just as a quick recap, assembly is the process where we have these fragmented reads and DNA. The idea is we're gonna find overlap between the reads and the whole thing is that we assemble these reads into context. And then later again, you can do assembling context possibly in the scaffolds where you don't know for sure if something, what the sequence is between the context, but you know the relative difference. So what about assembly? So assembly for metagenomics. So there's pros and cons to whether you wanna do assembly first and then do functional annotation or whether you do functional annotation directly on the raw reads. Okay, so this is a lot of my thoughts, but this could be interesting if you have questions. So on the pro side for metagenomics, there's less computational time for that similarity search. So since you're collapsing now millions of reads into an assembled contig, that means when you actually do your functional annotation search, then you're only comparing this collapse thing to your database. So instead of millions you could have maybe thousands or 100 to a thousand less fold. The easiest is if you can imagine if these are all 100% identical reads, then you wouldn't wanna have to do that search over and over and over again. You could just do it collapse them first and do the similarity search once. So that's a benefit. If you have short reads, especially this is a little bit more worrisome when high sequence generating like 75 base pairs. But since you assemble these reads into longer contigs, it could increase your ability to actually assign annotation. So you can imagine if you have short reads that that limits your ability to actually find a search to a protein. The other cool thing about it is that you can actually sometimes reconstruct genomes. So that's useful somewhat if you just do assembly on the whole thing, but some people will try to do some bidding first and then assemble the leftover with the hope of actually pulling out at least partial genomes from your metagenomes. Okay, so the pros and cons is that although you save time on the similarity search, assembly on full metagenomes is really computationally intensive. So you actually need a pretty big machine with a lot of memory and those aren't always available for all people in all labs. So unless you have access to machine with at least 128 and probably more like 256 or 512 gigs of memory, assembling a full metagenome is probably out of your luck. But if you know a buddy, then you're good. You have to keep track of the collapsed reads. So this was kind of a weird point, but for a while people were doing assembly and then they would just throw out the fact that multiple reads map to different assembled pieces. But now there's some assemblers that will keep track of the reads back to the assembly or you have to do it yourself. So you can imagine if you collapse all these reads into the assembly and then you annotate that, you'd actually want to keep track of the reads that originally mapped to it. So some assemblers will do that, but in others times you just have to do the mapping back to yourself and keep track of those counts. Low read depth and high diversity can cause assemblers to fail. So again, you have this problem with shallow sequencing. You have thousands of different things. The chance of it just to fail and not work very well is very high. And what else do we have? Chimeres. So the hope is that the assembler would do a pretty stringent job of only assembling things that are from the actual same genome. But there's a chance that it could assemble things from different strains or even maybe different species if it thought that the overlap was good enough. And so you actually could get these chimeres of things that don't represent sequences from the exact same genome, which could cause problems downstream. Okay, and lastly, there's a chance for this, although I still haven't seen any study to show if this is a big problem or not, is that if you do assembly on whole metagenomes, you can imagine that you would be able to assemble some of the more dominant things in the sample where the more rare things would be not assembled as much. And so if there's, that could change how you do functional notation. So it could bias your ability then to label functions in the more rare things because those are shorter reads. Although I don't have any proof that that's actually effective or a problem, but just something to keep in mind. Genome complexity with. Genome complexity? Yeah, the bigger regions and stuff like that. Right. Hard to assemble. Yeah, absolutely. So, and then that would affect your functional annotation as well, right? So you, right, you would miss. So 16S ribosomal operons or ribosomal operons are really hard to often annotate or assemble. And so because there's multiple operons usually, and so they'll get left out of assembly and then all of a sudden you can't annotate those genes if all of those are RNA genes. But yeah. So yeah, it's just something to be cautious of, but still assembly is still quite often they use by a lot of metagenome pipelines. So as far as I know, JGI still puts all of their metagenomes through a gene assembly process to get contigs before moving on with their parts. My preference right now is to just do it on the raw reads and it removes some bias that way. Any thoughts on that? Any thoughts from the speakers about assembly versus not? John, you're an assembler, aren't you? Okay, do you guys assemble your gene? No one? No, I do. Well, so either, so there's two things, we just didn't assemble very well. And then the other thing is that I'm also worried about that last one, right? We have, especially if you're trying to do comparison between two environments and you have one that has more organisms that are simpler and you have one that has really large organisms that aren't, you know, that are having more variety, one is going to assemble much better than the other. And then that could be attributed to amputation points as well. Right. So. I can see it comes down to how well the actual, how we're going to perform across different ways. The assemblers. The assemblers, and the assemblers are much better than the assemblers. So there's great matches in there now. Right. So that they perform to connect to get over some of these issues. I've used a few different assemblers, because that's the negative, it's another one, feel it for assemblers, that's really good. For viral data, we assemble them because we have short reads, it was high-speed. Right. And in that case, you just have to accept that your assemblers will be mined and then do your analysis knowing at a time that the assembly might be mined to results. But as long as we can keep in mind your biases during analysis, right, you just have to do for the viral data you had to assemble. Yeah, and if I want to come back to the pro that I think there's useful things to do with assembly and metagenomics, but if you're really interested in just getting to sort of the taxonomy table and then the functional table by themselves, I think you can do that with a raw reads and that's what the tutorial show you. But there's other times where you might really want to do something that requires assembly, right? So this idea of binning reads possibly and then reconstructing genome somewhat or if you need something where you actually need the longer reads, like if you're trying to find LGT or something, then that really requires an assembly. But you have to remember that assemblers work great on genomes where you know that they're supposed to fit together most of the time. I mean, assemblers have to handle chromosomes, right? But originally they weren't really designed for metagenomes where you have hundreds or thousands of chromosomes or different genomes, right? So you just have to keep in mind that and these things can be really similar. So if you have a population with different strains, you could get these chimeric assemblies that kind of scares me a bit. Okay, so what about gene calling? So that's the next step. So in genomics, normally you would take your reads and you would do assembly and then you would do a gene call. And so for gene calling, if you're not familiar with it, this is just the idea that you're gonna try to predict the start and stop side of an actual gene within your genome. So you can imagine an open reading frame is identifying nucleotides that code for amino acids and then you gene callers will look for open reading frames over a certain size without a stop codon. And then they use gene models from other sequence genomes to identify the probability of that gene being a real gene. So for metagenomics, some people might then wanna do a gene caller first before then you try to actually assign functional annotation. So the pros in that approach is that you may result in less false positives from not annotating non-real genes. So you can imagine if you have a DNA sequence, you find a match in some open reading frame, but it's mostly like a domain or something to another gene, but it might not even been a real gene in the first place, especially if it was a shorter hit. Whereas you did a gene caller, then maybe you would have less false positives that way. Again, it would slower the number of similarity searches. So it cut down on computational time on that end because you actually have less things to do the functional annotation with. I began again, similar to assembly on the cons, the gene callers are quite computationally intensive by themselves, right? So it takes a long time. I like my English, no good learning dataset. So usually with genomes, people would build gene models based on sort of known model organisms. And so they would train these models. And with metagenomics, all of a sudden you have things that people have never seen before, right? So genes you've never seen before or organisms that you've never seen before. And so there's not great gene model. It's really hard to come up with a nice learning dataset to build those training programs for those. The other big thing is that, often if you're looking at raw reads, right? A gene call, you wouldn't actually necessarily have to start, might not have to stop, or you might even have to start or stop. You might just have the fragment from the middle. And so that causes the problem for gene callers. So because of that, people would often do assembly first. So the people that tend to assemble, they sometimes often also use a gene caller. And so the gene callers that are out there, specifically for metagenomics, will allow for, say, the starter stop to be missing so they don't actually have that restriction. So I mentioned metagenotator here, as well as frag gene scan. And I think there's been others as well. So the alternative is to not do gene calling at all. And you just say, do your six frame translation using BlastX or Diamond. And that's sort of the approach I do. Yeah. This is mostly for vectoria, yes? What if you have to do it? The gene calling? Yes. No, I wouldn't say that. Actually, I don't know. I think it would work. Does anyone have any thoughts about that? So there's gene callers for, obviously, for eukaryotes. I'm just, I don't know if they've been adapted for metagenomes. Yeah. Yes. Interesting. Is there a specific organism you think? No. Well, because we're like, so I, I guess the issue that might be how much gene I have in the gene signal, so it's one of my favorite signals. And that's just specifically for me. Yeah. Rich. Yeah. Yeah, I think that might be at a better approach where you, exactly, you try to filter your eukaryotic reads first, and then say these are eukaryotic ones, so we're gonna do something different with them. You can still carry out assembly and then at the counting level, identify whether you carry out it. Yeah, it's a good point. I think if you, if you thought, if you were going after eukaryotic assembly and then trying to do gene-calling that way might be the better approach for you carrying out. So we work with some Jordanian samples that have very few introns, but nevertheless do. So we treat them set, well, not that we treat them set, so we have to use first gene finders that are more specific and do eukaryotes and the ones that are really interesting. Right. Anyway, good point. Yeah. So how do I get changes in parameters in my data? So I'm starting to run and get that out of there. So as we do this bit of a thing, what's the difference? Something that can be totally different with the stuff you're talking about here. How does it show in the data? So if you assemble it, what does it show? Really important. So that's the thing. So the classic example is you would get to this point where then you have a table, right, with these functions, and then people group the functions into their different categories, right? So similar with what we got with PyCrust with this K-gorthal log table with multiple samples. And then people will do statistics that say, oh, that's interesting. We see the microbiome has a greater proportion of these particular carbohydrate metabolizing genes, right? And so we think. That's right, yeah. So the classic one is something like butyrates, right? So as humans, we know that butyrates are kind of important for its link to colorectal cancer and it's known across the intestine. And so it's seen as a sort of healthy aspect. And so you can look for genes that are in butyrate production, right? So butyrate kinase or something. And then people will then show that maybe the microbiome is making more or less say butyrate, which has been linked to other diseases. So that's interesting. But it could be other functions too that people have no idea, right? So you're going to the pie crust data. But so you start to make decisions about that data based on what the microbiome as a community is doing. So that's in the sort of what I would call the most straightforward analysis. That's what you're gonna be doing in the tutorial. And then there's other things that people start to do maybe with the genome reconstructions where maybe they're really trying to describe, say, a new genome that's never been described before, and so they try to do metagenome reconstruction or they're trying to link the functions back to the specific organisms through the assembly or something like that. But I mean, there's all kinds of cool stuff you can do with the sequences beyond just making the functional table. But that's what I'm focusing on here. That make sense? Okay, cool. Okay, so this is my sort of pet peeve number two. It's kind of like pet peeve number one this morning. So just so when you're running papers again, just to make sure that if you're doing a metagenome, we're not talking about actual transcripts or proteins, right? We're measuring genes. So the big thing around that is we're talking about sort of function potential. So just because of course we see an increase in the relative of genes within the microbiome doesn't necessarily mean that those are being actively transcribed or then translated into proteins which are then having an actual impact in the gut. That being said, I think people tend to think, this will be interesting to have opinions about this, that the community in general is pretty selective on the organisms and that if you do see an increase or decrease in maybe the presence of the genes, you can assume that those things are just actually being transcribed. Not, I don't know, that's not the right way. That they could be useful and that it does show sort of potential for a gain or loss in function. But you're not actually measuring the transcripts without actually doing the metatranscriptomics, right? So you just have to be aware that you're not actually measuring, say, butyrate levels, right? In any way, you're just saying, you see an increase, say, in butyrate, the ability for the microbiome to generate butyrates and you would have to measure the metabolites separately or the transcripts separately. Does that make sense? Okay, good stuff. All right, so that's my summary. I have a quick summary slide. So remember I showed this before, but I think this is where we're at now over the two days. So the hope here is that you can generate both OTU tables using 16S data or CHON. This morning, I showed you how you take metatranscriptomics data and use Metaflan to get the same sort of taxonomic tables, so it's not OTU too, but it's the same sort of data, right? Where you have things grouped into a species or a genus or family. And then from there, you can use STAMP to generate these types of plots. And then yesterday, I showed you how to get from the OTU table from 16S data using PyCrust to get to the functional. And then now, we're gonna show you how to go from Chocoman genomics data, and using Qmoh and Ndiamond to actually get the K-Borthalart table through that method. And then you're gonna use STAMP to check out some of the functional differences. Does that make sense so far? Okay, any questions? Those are not STAMP's output, I think. Sorry, what was that? Those are not really STAMP's output, I think. No, that's a bit of a lie. This one is, that's not, this is, that's CHON. That's CHON. I made this an error, this is CHON. Yeah, this is like a hand waving STAMP idea, not like STAMP, sorry. STAMP slash other tools I should put in here. Yeah, try it. Well, they do quite different things for visualization, you mean? Well, so they just give you sort of different things. That's not a very good answer. So for like, CHON does a great job with Unifrack and their PCA plots, you know, with emperor and stuff, that's pretty slick. But if I'm looking at separate taxa and separate functions, I would like STAMP for that, if you can go through it and it draws it for me, whereas with STAMP, with PyCode of CHON, you can do that as well, but it's a little more clunky, you have to make each plot from the command line and it's a little more clunky. You can make key maps in STAMP and I don't think you can make key maps in CHON. That's what I remember. And then really big key maps, I would prefer to do in R, because that's just what I like to do. Set bar charts, I often do in CHON, when you're looking at multiple, but you can do those other things too. So I think it's just what you kind of use too, yeah.  There is a step where for all the samples, we mentioned that you don't actually have to run all the samples through diamond because it would sort of take too long. You can give it a whirl. Actually, I put how, I think I update how long it is. I think it's actually only about 10 minutes for 10, 15 minutes.  So I'd encourage that. So the Amazon cluster has eight threads, right? So eight different threads and quite a bit of memory. So you can parallel things up to eight times and really punch through the data. It's kind of satisfying those two. I don't know why it's as fine to me. I bought this machine that has 48 threads. I just love just loading it up with data. I can't even see the machine, but I just picture it in this place just going. Anyway, it's just me, I guess. I'm just trying to figure out if there's any other questions or comments. No, yeah, so I'm sure there's questions or comments. Oh, there is one thing I wanna show you. So that's the end of the lecture, okay?


Hi everyone, Sean with jaspyscasebreaks.com here doing 2020-2021 upper deck artifacts golf two box break random pack number one where obviously all card ship and We're gonna open up two boxes 16 spots. Well first Number all the packs one through 16 then randomize the names and the pack numbers and you'll obviously get whatever pack Your name is matched up with so Here is the list of customers from Danny Giansopolis with last spot mojo back up to Alan Matsui and the numbers from one through 16 Here is are the eight boxes. We're working with on from this case So we're gonna do one two Three and four on the dice as far as the two box stacks. We roll a five or six. We'll just re-roll And it's a five back there three roll that Six that's a two so it'll be this stack here, which is That's number three We're gonna pop up these boxes and number of the packs I already have stickers made up for everything so Pack one pack two so on so for pack one pack eight. So that's packs one through eight there Byron Nelson, of course the great fire Nelson. Oh, this is a good a good time. Just like is no If you guys are local to LA area, we have an informal Jaspy's golf league That's literally just me and Oliver Soria at this point If you've seen him in the chat so stacks pack one through eight on left pack nine through sixteen on the right You guys are more than welcome to come golf with us anytime. So let's pop back over here to the randomizer You can see the stacks right there Dice roll to randomize both lists and pair them up a one and a one two times Dice snake ice so one two Alan down to Danny and two times on the Numbers one and two 11 down to 13 So Alan Matsui with packs 11 and one Danny Janesopoulos was 12 10 4 15 8 9 and 5 Alan Matsui with six Scott Blackwell with seven Danny Janesopoulos with 14 Alan Matsui with three Danny Janesopoulos with two and Alan Matsui was 16 and Danny Janesopoulos with pack 13 So you can see that's the packs Print that out real quick Sort this by name. We'll do everybody's packs together So we'll go Scott first since we've got one pack for you Scott Alan will do your five and then Danny will do your the rest for you Okay So Scott we're doing pack number seven to start pack number seven there for you There's our final list. Good luck first break taste breaks look at Look for autograph legendary PGA tour course relics cards every box contains three hits on average Tour worn memorabilia artifacts rookie autograph cards nice Byron Nelson arm Very cool. It's hard to see And you got Cameron Beckman on the back there It's so dark, but there you go Nice Byron Nelson gold aurum. It's so dark if you look at that way, but that's super good looking in person Very nice There you go Scott. That's your one pack Okay, next up. We're gonna do Alan with packs one three six eleven and sixteen Alan one three six eleven and sixteen pack one three Pack six eleven Danny the rest of those are gonna be yours. So start with pack one here Got Arnold Palmer the great David Duvall and a shei Revy 219 out of 249 red and Azara Munoz pack number three young Wa on Mark Wilson and A rookie of Colin more calwa to 199 and Tiger Tiger Tiger would y'all Adrienne Otsugui and Kevin Kaisner shirt relic big drive fragments and Pat Perez JB Holmes Byron Nelson David Duvall out of 25 very nice little black 17 out of 25 and Natalie Golbis and pack number 16 KJ Choi and Artifact rookies Adrienne Otsugui to 999 Mark Wilson and Nancy Lopez There you go Alan. Those are your pass for Danny to four five eight ten twelve thirteen fourteen and fifteen Good luck Danny with pack number two Yoast Luton Maverick McNeely to 999 Patton Kizir and Tom a love lady. Oh, you had Nancy low. Yeah classic lady Nancy Lopez and Ball marker relic artifact Kevin Kaisner. That is super cool looking Lori Kane and Lisette solace So Justin that is you said you had an autographed version of that But somebody else obviously pack number five Harris English and Austin cook rookie auto 99 out of 149 dual relic and auto Brandon Grace of upper-deck Paul marker Yeah, the PGA one. That's cool. That's so cool. Pack number eight Brittany Linsecom Doug Gim rookie to 999 Zee shang duo and Mariah Wanta Goran, but no check the webs the schedule Paul Check the schedule. It's probably a filler attached to it. Sue. Oh Blair O'Neill Maverick McNeely to 199 and Brett Wetterich Tiger Orem number 10 Danny Henrik Stenson Austin cook to 999 Tom Lehman and Emma tally Shea Ravie and Justin Sue two out of 149 dual relic and auto rookie and Michael Thompson 13 Francesco Molinari and PGA tour course relics. We got grass or sorry. We've got sand Kevin Keisner That is from yeah TPC sawgrass We got a sand relic That is super cool Anacasorns them and Arnold Palmer back 14 Jason Duffner and Hole-in-one remnants Yuli, Inkster shirt relic and David Duvall and pack number 15 to finish it off Morgan pressell And we got another gold Orem Natalie Gulbis used lutein and Yeah, young Juan on to finish it off and there you go Danny. Those are yours Thank you guys very much. That was our first Look at upper deck on case breaks That was 2020 2021 upper deck artifacts golf two box break random pack number one from Jaspies case breaks calm random pack number two in store now Jaspies case breaks calm. I'm Sean. Thanks for hanging out. We'll see you next time


Hey, this is Brett and this 2022 GMC Acadia SLE elevation is stock number 23J202A. I am here at Summit Automotive in Fond du Lac, Wisconsin, you're new and used SUV headquarters. This 2022 GMC Acadia has the 2.0-liter turbocharged 4-cylinder engine, puts out 228 horsepower. It's paired with a 9-speed automatic transmission and it is an all-wheel drive vehicle. This vehicle also has been fully safety and inspected by our service shop per the state of Wisconsin inspection process. It has a fresh oil and filter change, all the fluids have been checked and topped off and it is 100% ready to go. I'm going to go all the way around in this video. Inside underneath, start it up, take a look under the hood and just show you all the options on it. Cayenne red tint coat is the color. I shoot all my videos in 4K and if you like the video you can subscribe to my YouTube channel which is youtube.com slash Summit Auto. Click the bell notifications to get updates on the videos I do each and every day as well as having access to one of the largest catalogs, vehicle and SUV walk-arounds on YouTube, almost 8,000 videos and counting. This one comes with the painted black 20-inch alloy wheels and that's part of the elevation package. It has Michelin primacy tour 235 55R20 tires and these tires I would say have probably about 70-80% of the tread left on them so very good tread left on those tires. Front fender is in fantastic condition. This one does have the LED running lights and headlights. We'll turn all those on at the end of the video so you can see just how bright they are. Front bumper is in great shape. I didn't see any major dents or dings on there. It does have the front bumper parking sensors and that elevation package gives you the blacked out grille as well. The hood is in fantastic condition. I didn't see any major dents or dings on there and the passenger side front fender is in really nice condition as well. Passenger side front wheel, no major scuffs or scrapes on there and as you go down this side of this 2022 GMC Acadia, take note of how clean that body is, how reflective and mirror-like that paint is. I take these HD videos so if you are far away or even if you're close by and you just cannot make the trip down but you're still interested in purchasing the vehicle, you can see the vehicle, hear the vehicle and have confidence in what you're looking at before you even get here. When you do get here, there's absolutely no surprises and you can make a smart and informed buying decision from wherever you're at. If this video helps you make that buying decision, let your salesmen know that you saw the video, that it was helpful and that Brett sent you back wheels in excellent shape as well. The back tires have just as much tried as the front tires. You get this felt wheel well liner which keeps your frame and under body looking good. I'm not sure if this one has a frame or if it's just a unibody but either way it keeps it looking good under there. Note the blacked out SLE lettering, you get LED tail lamps as well.  It lifts up like so. Back storage area is very nice and clean. We do have the cover for that receiver hitch. We keep those inside so they do not go missing on the lot. Those seats do fold on flat and we do have the all weather mat that goes back here when those seats are flat. I have it out of the vehicle right now for the video because it's not very conducive to doing this video.  To get this gate down you press that button right there and it comes down like that. Coming around to the driver side just as clean as that passenger side, no dents, no dings on the rear quarter or the rear door and for full disclosure this back wheel just as nice as the rest. Down the rest of the side of the vehicle, doors look really good, does have the blind spot monitoring, built in directional signals, has the intelligent access button.  There are no rips or tears on those seats, they look really good. I don't think this vehicle has ever been smoked in, has the power driver seat, factory all weather floor mats, auto headlamps, power windows, locks and mirrors and rear gate control. Get the wood grain trim on the door there. Second row quad seating. Get the latch child safety system for any child car seats you may have and these seats fold down when the headrest is pushed in. Second here. I'm going to pause it and put that front seat up just a second. So these seats go down like that and you can also think they adjust as well so they go forward and back. You get all weather floor mats in the second and third row as well. Back here is your rear air controls, USB, USB-C and a 120 volt, 150 watt plug-in. I wanted to show you the back seats that they are in very nice condition as well. And you can see that that seat actually slides forward, that's what I was trying to do before. So that goes like that for easy access to that third row. And then that slides back nicely. Safety locks on the back doors and the inside and bottoms of the doors all look fantastic. Alright, let's hop inside. Check out the miles, the radio and everything that this Acadia has to offer on the interior. You can see this one has 41,171 miles on it. Instrument cluster is very nice and clean. I'm probably going to have to start it up here, which is fine. Starts right up. And yes, we are going to be getting some fuel in this vehicle right after this video. Digital spinometer on there. Once again, instrument cluster is very nice and clean, comes with the multi-function steering wheel, Bluetooth and information center controls on the right, cruise controls on the left and forward collision warning controls on there as well. This one has the infotainment, the GMC infotainment system. You get Apple CarPlay, Android Auto. This is also where your backup camera shows up. You can see that is working nicely. But you also get AMFM and Sirius XM radio capabilities. And then you also get all your different apps on here, including rear climate controls and all that good stuff. Down here are your more tactile climate and volume and tune controls. Your 9-speed automatic transmission is kind of a pull button system. Start, stop capabilities, stability control, hazard lights, parking sensors, you can turn those off and on.  Keyless entry right here with remote start and power lift gate. Passengers side, floor mat and seat are in excellent condition as well, no rips or tears. And once again, I don't think this vehicle has ever been smoked and the headliner is in fantastic condition. And you get on-star and SOS buttons and map lights up there. We'll take a quick look under the hood and we'll also turn on all those lights so you can see how nice and bright they are. I would personally like to thank you for checking out the video today and hopefully from this HD video you've been able to verify the quality condition options and of course cleanliness of this vehicle all the way around inside. Now you can see just how nice and bright those LED headlights and running lights are. Under the hood we have the 2-liter turbocharged four-cylinder engine, 228 horsepower engine base. Very clean runs, very smooth. Once again, this vehicle has been fully-safetyed and inspected by our service shop. It has a fresh oil and filter change, all the fluids have been checked and topped off.  There's the emission sticker and this vehicle is 100% ready to go. I would highly recommend it from a quality and condition standpoint and to see more pictures of this vehicle in one of our 550 new and used cars, trucks, SUVs, minivans, Wranglers, you name it. We got to go to the website right there, summitauto.com, full pictures and descriptions of every single vehicle. All at summitauto.com and if you'd like to check out more HD videos you can go to youtube.com slash summitauto. Click the bell notification to get updates on the videos I do each and every day as well as having access to one of the largest catalogs of vehicle and SUV walk-arounds on YouTube, 8,000 videos in county. In fact, in a second you'll see a link to subscribe to my YouTube channel on the upper left. There's a link to more Acadia videos I've done in the upper right. A link to this vehicle on our website and lower left. And a link to one of our latest YouTube videos on the lower right. Click those, check us out. We're super excited to help you with this Ultra Clean 2022 GMC Acadia SLE elevation package in Cayenne Red Tin Coat. Thanks again for checking out the video. Remember to like, subscribe and share on the YouTube channel. I really appreciate it. Thanks again.


Okay, we're back. We're live at 3 p.m. on a given Wednesday. I think that Thursday. Orientation is time and space. It is Thursday. I'm Jay Fidel, and this is Energy in America. We're studying Energy in America every two weeks at this time, often with Lou Pulgerisi, but today it's with Pat O'Jarrell. And he is a research analyst for EPRINC, APRINC, which is the Energy Policy Research Organization in Washington that looks into all kinds of macro and international issues around energy, and that's where Pat is from. So, Pat, it's very interesting. The topic we chose today, which I suspect you chose today, is what stands in the way of our transition. And I take it when you say transition, you mean a transition to renewables, to clean energy, and, you know, greenhouse, no greenhouse gases of carbon, and so forth. Everybody has this on their minds. I mean, why are you thinking about this for 15 years and articulating policy about it? But, you know, things get in the way. And this show, this discussion will be about, you know, what the global obstacles that we are experiencing and will experience in terms of transitioning to renewables and clean energy. So you have identified a few. Let's go through them. What is your favorite one? So, yeah, thank you so much, first of all, for inviting me to your show. So today's topic is very interesting, especially, you know, given all this hype around renewables and also recent announcements by governments, including the Biden administration on net zero targets and then their infrastructure renewable and infrastructure plans that goes out to mid-century 2050, 2060. So it's going to be a very interesting to see how we can achieve those energy, you know, clean energy goals. Yeah, let me ask you about the two trillion dollar Biden plan that just been rolling out, rolled out now. And the details are in the newspapers, of course. But I have one question, maybe you can help me with it. How much of the two trillion dollars is energy? So I think the entire, I think that the entire two trillion dollar bill is in one way or another related to energy. And from the news articles I am receiving, receiving $600 billion for transportation, but not all of that is related to energy, of the $600 billion for transportation, $170 billion is for EVs, EV electric vehicle financing projects. So I don't know the whole picture or the detailed numbers, but it's, you know, all aspects of the bill is in one way or another related to President Biden's clean energy goals. Yeah, maybe you can help me with my eyesight also. I have a very strong recollection that this bill, which has been discussed by the Biden administration for the past couple of weeks anyway, or longer, was a three trillion dollar bill. Now, look again, Bingo, and it's a two trillion dollar bill. What happened to a trillion there? To be extremely honest with you, I didn't have an answer to that. Lou might be the better person, but yeah. I'm right though. It came down a trillion dollars in the past few days, am I right? Yeah, I agree. It's very hard to, you know, to achieve or implement, you know, the previously announced goals or stated policies. Just when it comes to real politics, it's just difficult and then that's something I would talk about later today. You know what Everett Thurston used to say, he was a senator years ago, used to say, a trillion here, a trillion there, after a while it adds up to real money. Okay, so yeah, so I have a few slides today. Can I see the first slide? So I wanted to, I first wanted to show you and in the audience what the energy, current energy system is, and then the current energy mix is to, you know, illustrate where we are now. So this graph, this Sankey diagram shows the current world energy system on the left hand side is primary energy supply by fuel type. And then on the left, on the right hand side is final end use by sector. And then if you look at the energy, the left hand side, it's clear that the world energy system is extremely, you know, very, very dependent on fossil fuels, which accounts for about 90% of the global primary energy supply or demand. And then the blue blocks in the middle is electricity. And I can, I'll talk about that later in the show, but it's less than half of the, less than half of the primary energy supply goes to electricity, meaning that there's a lot of, you know, a lot of room to grow electricity in the future power and, you know, electricity, sorry, heat and electricity, both of which constitute the power sector. And then on the right. So this chart, for each thing, it's not a dynamic chart. There's no time feature here. It's right now today. It shows you where the energy is coming from and where it's going. Yeah, it's the current, it's from 2019. And then you'll see in the chart that buildings, transportation and industry are the three, you know, important sectors that use energy evenly. So just, yeah, so if you go to the next chart, we chose the US energy consumption, and it is compared to the rest of the world, more reliant on natural gas and oil and, you know, still fossil fuels. So it's going to be, you know, a difficult challenge for not just the world, but also for the US to decarbonize its economy because the current technologies don't allow a lot of, you know, decarbonization, technologies, carbon capture, sequestration and hydrogen, they are not on the table yet. So if I look at this chart, I see a big green swatch there. Is that oil? What does that mean? That's oil, yeah. And then also, yeah.  So the green line is oil and it goes, most of it goes to transportation. And then, you know, within the three, among the three main sectors, although they use similar amounts of energy, their sources are very different. For example, buildings in the US came soon as a lot of electricity, whereas industry uses a lot of natural gas. But the US economy is not manufacturing based, so it's, you know, it's, you know, comparatively smaller than the other two. But for China, it's bigger and then the coal consumption that goes into industries way, you know, much larger than that. So, and if you look at transport, you know, although there are a lot of talks about electric vehicles and net zero transportation, electricity, you know, accounts for only a small, very tiny portion of the transport sector. So, so where is, where is renewables on that chart? If you go back to the chart, the renewables are the first one is hydro nuclear and the third one is the renewables. And then if you just compare the width of the lines of the first three, they are, they are much smaller than the, than natural gas, oil, biomass, and coal. Now, this is actually, at first, when you showed this to me, I thought it was the New York City subway map. But it's much more interesting than that, as a matter of fact. Can we go back to the first one? The first one was global, right? The first one is global, yeah. Yeah, let's take a look at that compared. So, there's plenty of oil there. And the coal is huge. That's the gray one. That's the gray one. Yeah, the gray one, half of which comes from China, whose China's economy is extremely dependent on coal, 60, 60% of its energy demand comes from coal and its power sector is extremely dependent on coal. So, and then other, you know, energy fuels are important and then renewables, hydro, renewables are growing solar and wind, but they, you know, the scale of that pales in comparison to the other more traditional energy sources. So, this looks fairly complex. It also, maybe it's just the way the chart is set up, but it suggests that to move these things around to change them is really complex too. It's a chore to change those pieces. So, I get a couple of things here out of your comments so far. Number one is, correct me if I'm wrong, but you need to have government support the change. In other words, these lines on the transition to clean energy, they don't, it's not really a matter of the utilities or the public so much as it is the government showing the way, establishing policy and programs, but also writing checks for tax credits or supports of one kind or another. And correct me if I'm wrong, but if you had the government do little or nothing to support the transition, that would, that would be a problem.  I guess one thing I get, the other thing I get is, as I said, is that it's complex and in order to change the chart like that, which is so complex, you may need time and be you need buy it by not only, you know, the government, but by various sectors in the economy, US economy, the global economy, the economy involving all the entrepreneurs, all the investors, all the management, all the workers, they all got to get together on the same page to make this change. Am I right about that? That's absolutely correct. Yeah, I agree with you. So first, politics is extremely important, political support, because the other two challenges, main challenges are costs and technology. Some, you know, some technologies are readily available, but they are not cost effective against the incumbent to current technologies or, you know, it's hard for consumers to adopt those technologies. And second, you know, these, most of the nascent technologies or even for more mature cost competitive renewables, it's hard to scale up because of their technological, you know, issues, intermittency. So technology and cost are the main issues, but without government support, it would be impossible to reach these goals. Even with government support, it's, yeah, it's extremely difficult to reach these goals. But let's talk about technology for a minute, that, you know, if we, if we look back to inverters, for example, on solar installation, it's back 15 years ago, they were primitive. Now they're not so primitive. A lot of them are made in China. It's electronics. And as the, you know, electronic industry, IT grows, develops, gets more sophisticated, manufacturing gets easier, keeper design gets better, may have better technology. However, it seems to me that, that you have to have an awful lot of people adopting the technology, and you have to have integration of the technology. All those wires have to go somewhere. You can't, you have to connect it up. And so would you see, would you see obstacles, that's what we're studying today, to the transition in some aspect of handling new technology, making it, making it to solve a problem instead of just theoretical for the sake of it kind of technology, and connecting it up to, you know, all the, all the other devices that must connect to, is that an obstacle? So yeah, that's an obstacle. So let's go over a list of technologies that need to be adopted at scale. I was reading Bill Gates' new book titled, How to Avoid Climate Disaster. And then he listed the technologies that need to be financed and then scaled in order to reach the, you know, 2050, 2060 mid-century carbon neutral goals. And then those are, they're a lot of them, but they're more than a dozen technologies and fuel sources from, do you want me to list all of, you know, go through the list or? How many, before I answer that, I need to know how many I want to look at.  Hydrogen technology is important, but, you know, currently we use gray hydrogen that is from natural gas, but it's not, its emissions are not captured and sequestered, but CCUS or, you know, carbon capture and carbon capture utilization or sequestration technology is important for that. And, but there's another option which is green hydrogen, which is also not cost competitive at the moment. And then grid scale electricity storage, there are a lot of talks about battery technology, lithium ion batteries, but, you know, it's, if you look at the EV price, it's still not cost competitive and then you're really dependent on subsidies and it's still a niche market to achieve grid scale electricity storage that is not only cost effective, but also is able to store, you know, electricity for long periods of time. That's a huge task. And some others are zero carbon cement and zero carbon steel.  And it's hard to find an alternative to natural gas and coal and other fossil fuels for those harder to obey industries. And then yeah, I can go through the list, but hold there for a minute though. Yeah. You know, Bill Gates was on 60 minutes, maybe three, four weeks ago, and he was touting his book as you mentioned. But he also has invested and it's speculative, to some extent, a system to infuse carbon that you don't want into concrete. So there's really a lot of concrete being made, you know, that's the metric of development, concrete. And when he's developing a system where you can put the carbon variant inert into the concrete, get rid of it. It's a very interesting idea. But what it raises is just the other thing I wanted to mention to you is, so a lot of this is experimental, really.  And a lot of the things we have out there, including the elements of electric cars, they're kind of experimental, except they're being practiced on hundreds of millions of people. But isn't that the only way we can get home? Isn't that the only way we can, you know, steer the course, spend the money, even if it is not terribly economic, even if it is, you know, cost you more, what we learn? Because I don't think, I mean, if you agree, I don't think we can develop these sophisticated devices, systems, materials, simply in the back end of a workshop. We have to let them breathe. We have to put them out there, into the community, see what happens, see if they work. And they're never going to be economic to start with. We can only hope that at least some of them will become, over time, economic. And that's the way to change society. Am I right? Yeah, that's right. There are R&D projects supported by DOE and then DOD in energy technologies. And then they, some of them have been extremely very successful in the past. And government direct support to those high risk, high reward technologies was, you know, was the major driver of these technologies success. I agree. But at the same time, it is also important to be able to see their limits. And also understand that they have, they are not cost competitive and many of them will fail eventually. As we've seen from the solar industry before cheap Chinese solar panels were coming in, there were a lot of ambulance hype about solar, about the solar industry. And then the US solar industry was trying everything out from PV to, you know, other, I can't recall some of them right now, but they were in a different technologies. So almost all of them failed. So it's also, I think, important to keep in mind that they are, there are limitations. Yeah, that's how you learn, but you can't pour money into it past a certain point. It's no longer a workable experiment after a certain point. You know, I got something in the email about the cost of a wind turbine. Because the wind turbine involves a lot of steel, they're bigger all the time, arguably the bigger they are, the more efficient they are, but it's an awful lot of steel and all kinds of other materials. And it takes a tremendous amount of effort to manufacture it and, you know, integrate it and fabricate it and deliver it and install it. And this article, I really don't know, I don't have any sense of the credibility of the article, but it stood for the proposition that wind turbines are simply not efficient in terms of cost on the environment. That there's more carbon going in there, more work, more effort, more blast furnaces, steel factories and all that making this thing, that it will ever begin to pay back to the environment. And that it is a modern wind turbine is never going to be efficient from an environmental point of view. Thoughts on that? On that, I, yeah, please send me that article. I, yeah, I, you know, it's, I'm not 100% certain about that, but there, you know, major drawbacks like siding and, you know, the wind turbines take up a lot of space and it's also harmful to vocal birds and, you know, et cetera. What I was going to say is that, so yeah, I agree that. So I think I forgot what I was going to say. We're talking about the economics and we're talking, and you have this list, maybe there are other items on your list we can talk about in terms of obstacles to the transition. Yeah, I'll say, I'm sorry, I just, yeah, remember to your point about steel that goes into wind turbine making wind turbines, you know, these new technologies, EVs and renewables, I'm not against them, you know, I support renewables and clean energy, the clean energy transition. But at the same time, many of these technologies are quite a lot of steel in minerals, including rare earths, which is, you know, which is controlled by a few countries, the sector, for example, is dominated by Chinese processors and also Chinese rare earth miners. So those will be a challenged, you know, political and environmental stakeholders will increasingly realize that there are other challenges, including supply of minerals and rare earths that go into these technologies. Yeah, and you mentioned an interesting point about China and, you know, its control of rare earths and manufacturing processes that are relatively speaking, economic, and that is the whole geopolitical thing, you know, the whole energy technology adventure is really a global one, and we need a lot of collaboration on that. And if for reasons that are entirely political and geopolitical, we get into an argument with someone, and maybe we cut off those minerals of rare earths, we cut off the technology of the maintenance or elements, components of the technology, and all of a sudden, where we thought we were being resilient, we're not so resilient, because there's a barrier created by, call it a failed geopolitical relationship. And so that's an obstacle, isn't it? That is, you know, from the environmental perspective, it probably is not an obstacle, or it might be because it, you know, cheap solar panels, for example, some according to some studies, cheap solar panels from China hindered further growth in U.S. solar innovation. And also, depending on a few countries on these minerals, for example, Congo, the Democratic Republic of Congo controls cobalt, you know, important component of, you know, new energy, sorry, new energy vehicles or electric vehicles. So those, and then the country is also known infamous for, you know, some other issues, human rights and child, etc.  But I think Europe, the U.S., Japan, and other developed countries are more aware of the situation now, and then they are trying to circumvent this challenge by developing domestic industries or cooperating with other industries, other countries trying to less, reduce their dependence on these countries. Good point. So you have some more, you have some more charts. I know there are a couple more charts. Yeah, let's do that. Can I, yeah. So this is related to my earlier point about how hard it is to, you know, reduce reliance on fossil fuels, because, you know, this is a historical chart showing energy consumption of the last 50 years, and the share of renewables and other alternative technologies has grown, nuclear, for example, has grown, but fossil fuel, the absolute values of fossil fuels have never gone down. There was never real energy transition in that period. So, the idea behind energy transition is a switch away from fossil fuels to other zero carbon technologies that will replace the former. But based on the historical past experience, it's proven to be extremely difficult with all the incentives and policies that have been implemented, you know, in the past. So, and I can go to my next slide, which is not related to this one, but this is a chart. This is from Princeton University study showing what needs to be done for the U.S. only to achieve net zero emissions by 2050. And can you summarize this for us a minute? I'm able to read the words. Okay, so it's just to repeat my point, it's a huge challenge, you know, for the first point is that EVs need to increase to 200 million by 2050, for example. And then there are other, you know, challenges or solutions, including carbon capture storage, clean electricity, battery technologies, dear carbon fuels like bioenergy and bioenergy with carbon capture sequestration, which uses bioenergy to capture carbon, you know, bioenergy and then it captures carbon and then the capture carbon is sequestered. It's a combination of two, two approach. So it's going to be for the next three decades, we have to do a lot to invest a lot of money and also do a lot of reforms, political hard decisions to reach these goals, if we ever, you know, reach it. So it's a really, you raised an interesting point right there and that is that it requires political will. I mean, political money requires political will and action requires political will.   We never lose sight of our goal and our, you know, and we do not let obstacles stop us. And I suggest to you that maybe we have learned over the past 20 years that the world is really not well equipped to do that. Not only the United States, which has its own issues about political will, we know, but the world in general. So we're, I think we're in trouble on this. Have you any thoughts on that? So, yeah, political will is very important. And actually, we're seeing a lot of, you know, commitments pledges from governments across the world around the world to achieve net zero in the next, within the next 30 to 40 years. There are a couple of those in countries that pledged to do so, including the US recently, Japan, Korea, and even China. So China last year pledged to achieve net zero by 2060, not 2050. But these are long term commitments. And not a lot of, you know, not a lot of politicians will be in their current positions by 2050 or 60. So so many things come up and distract us, you know, you can make a list of things that might distract us from relentlessly following the goal. And it's an infinite list. So however aspirational or however moderated current policy makers can be, it's hard to foresee the future. And then it's, there will always be opposition. And then also other factors that will prevent us from, you know, reaching these goals. So one thing you mentioned, I think it's worth talking about is the electric vehicle thing. I think we need 100 million to really make a stab at it. Right now Hawaii has something like 1300 electric vehicles on the road. And it's going, I wouldn't say it's going geometrically, it's not. It's a straight line of increases, not all that fast. And people have been trying talking about it for, you know, since electric vehicles were available. And I'm thinking, if that's the model that is prevalent in the country, where the manufacturer is, although somebody told me, or I saw that General Motors said that by 2035, it doesn't intend to make any more, you know, conventional cars. That would be something. But I think a lot of the automobile industry is not committed to electric vehicles, because people don't necessarily want them. Because they already have the factories to make the conventional cars, they would have to spend any enormous amount of money to retool. And so it's not moving as fast as it might be.  But I don't think that's happening quickly right now, not only in the US, but elsewhere. And so I don't think we're going to reach 100 million right away. So that was 300 million, actually. Sorry, thank you. Just for the US. That's three times the number of doses or whatever. Yeah. He said he was going to do 100 million doses in 100 days. Let's see him do 300 million cars in 40 years. Yeah. So that's, it's a big challenge. And it's, you know, we're talking about the US, right? And then there is, I mentioned this earlier, but Africa, South Latin America, and Southeast Asia, South Asia, they are going to, you know, be growing. Their populations are going to be growing. Energy consumption is growing. So those are the countries that we have to shift our focus to more because, you know, consumers and policymakers in the US and Europe are already pretty aware of the situation. But, you know, there's a lot of uncertainty around how their policies will play out, but the real challenge will be in those less developed emerging economies. Let me ask you a Charles Dickens question. It's the ghost of Christmas future. Just suppose, hypothetically, that that last chart is what prevails, where we really don't have a transition. I mean, we are sure we have some renewables, yeah, but we're still using oil, we're still using coal. And in these developing countries, which require additional supplies all the time to meet the demands of its population, which is, you know, budgeting, what happens if you have increased demand, but you don't have a change in the essential arithmetic for what the supply is? What happens to the country? What happens to people? What happens to the economy if that happens? So can you repeat that question, sorry? We really don't make a transition. Okay, last chart. We just don't do it. Historical chart, I see. So what happens? What does your economics tell you about this? So from the economic perspective, if you don't meet the goals or Paris Agreement goals economically, you know, it's very hard to tell. It's hard to predict that. So I will probably be able to answer that. Then I'll make it more complicated for you. Okay. Suppose the same thing, but we start running out of oil and gas, or it becomes, you know, just marketplace more expensive. But these economies cannot afford it. So what happens is they cannot afford to get the energy they need, or should have to develop, or even to remain viable. What happens then from an economic point of view, they cannot afford the energy they need to grow or even maintain? Yeah, that's a very good question. That's a very good question, which I don't have answered to. It will be difficult, but also that question reminds me of peak oil demand. That was a big problem in the, you know, a few decades ago when there were embargoes, and then conflicts in the Middle East, but the US was able to improve its technologies. And now it's, you know, exporting oil and gas, and it has a lot of shale gas.  But I don't think I have an answer to your hypothetical question, which, you know, seems there's no... Oh, we're not there yet. Probably the whole world can afford to spend It's important to think about those questions earlier, you know, rather than later. Yeah, we should all be alive when the crunch comes. So let me, let me ask you, we're almost out of time here in a couple of minutes, and I wanted to ask you what you would like to leave people with, you know, the conclusion that you reach and that you would urge them to reach about this whole problem of the reality of emerging obstacles, existing and emerging obstacles to a transition to clean energy. What do you want to take away? So just to reiterate my key points, clean energy, the clean energy transition is good, good for the environment, and it's good for the health and well-being of the world, and the inhabitants of the world. But, you know, reaching the Paris Agreement goals and NetEar goals by these governments will be extremely hard. And then the main challenges would be cost, technological technology, and policy slash politics. So those are the main takeaways that I want the audience to get from this show today. Thank you, Vett. And Vett Augerelle from Eprink in Washington, D.C., an economic energy analyst. Thank you so much for joining us. Really appreciate your answering all my questions. And I hope that we see you again on the show in the near term. Thank you so much, Vett. Thank you so much. Thank you for your meeting. Aloha.