cool stage. Yeah. So a key element of this new transportation landscape has been the emphasis on moving away from car ownership to this more shared model. Raj, could you talk a little bit about how how Lyft plans to do that considering that at least at the current time the company still relies on people who do own their cars? Yeah, so I think that first of all we're sitting at a point in time that is unprecedented where this massive industry of automobiles has been built and it happened faster than almost anything from 1900 to 1913. I think it was like 10 to 15 million cars that were built and we built our entire world all of our cities around vehicles and it's been tremendous in terms of the benefits but there's been some downsides around congestion around emissions and what we're realizing is the more vehicles that exist the more congestion there is. You can't just keep adding roads to it. There's two and a half billion people over the next 10 years moving into cities and we're not on a path with the current car ownership model to satisfy people's transportation needs so it's not a nice to have option in our opinion it is a must have option in doing it and it just so happens that it's also a great economic opportunity for consumers that if you look at a car in America it's used 4% of the time, 96% of the time it's sitting around and not being used. It's the second largest expense after housing. You spend more money in the U.S. on a car than you do on food so you don't use it that much you're spending a lot on it it's causing issues in cities there's something that needs to change there which is why we believe shared rides certainly electric vehicles all of that will contribute to this and there's a new model the same way that music has been changed from ownership to access video entertainment has been changed we think that car and auto and transportation is now in that zone as well. Boodle do you agree with that is that that's essentially the the emphasis of M. Is that correct? Totally I absolutely concur with a lot of what Raj is saying I think at the heart of it there are still not alternatives enough out there for people to give up their cars and especially when we talk about urban consumers or people living in cities they still keep their cars because there is no alternative and here we as providers need to really understand consumers and what they use the car for so there are so many different use cases and when we started to now develop a new concept for global expansion we really looked hard into that and we feel that the short rides the e-hailing the ride sharing that is responding to a kind of a now need whereas we are focusing on actively providing an alternative to ownership to make people be ready to give up their cars so therefore also taking out cars of the equation because there is simply too many physical vehicles in the cars right in the cities right now yeah and I mean you mentioned taking cars out of the equation so Lyft of course has bike share and scooter share I realize I'm just earlier this year unveiled that you would be doing a ride-hailing network but do you do you have alternative modes of transportation on the on the horizon we first we are really set on doing something that consumers love so we are not backed up with by a venture cap we are owned by all the cars so the the objective now is just to do it right it's not like a massive you know pushing out vehicles to prove something it is actually doing right by the consumers but we will be totally open for partnerships we believe partnerships and there are certainly more parts of the transportation ecosystem where we would love to tap in yeah because correct me if I wrong with Volvo is partnered with with Uber around autonomous driving but I suppose that doesn't affect M at this point nothing affects M apart from the opportunity we have to impact the way cars are built and that is one of the tasks that that also we have to provide insights to Volvo cars about this new type of consumer that actively have decided not to own a car and that's very different from where automotive companies come from you know Volvo cars have been around for 90 years developing and building cars for one person to buy and use so it was earlier this month or last month actually when Ford bought electric scooter companies spin I'm wondering what's your take on on electric scooters well the other day I was on my way to my hairdresser and there was this young dad who slided up next to the school with the scooter and I can clearly see that that is very attractive so that there is definitely something in the last mile but I'm a walker I walk everywhere and I kind of don't like tripping over the scooters outside my door neither do I kind of very much appreciate being run down by scooter drivers as they use the walkway so there is I put it like this we are early in the face there needs to be a lot of alternatives tested and that I embrace wholeheartedly but there's also you know a lot of stuff that needs to be figured out in order things to be flawless but Raj I mean lift I mean scooters and bikes are definitely part of lifts platform how do you kind of envision all of these forms of transportation coexist in yeah so lift and I think any large scale transportation as a service provider has to be multimodal the consumer has demanded it so we did a test a couple months ago we had a promotion in the US we said ditch your car and at the time we didn't have a full multimodal solution so we put together car sharing from zip car we had bike share at the time we hadn't closed motivate which is the largest bike share now is part of lift in the United States and we added in ride sharing of course and we put all together as a package we thought that there would be a couple thousand people that would sign up we had 150,000 people sign up within a week to ditch their car and to try putting together all these alternatives which today are not seamless in doing it so we are moving from a ride sharing company to a full transportation provider and that also gets that includes especially in Europe it's so important which is transit transit is the lifeblood of a city it is the most efficient way to move large groups of people from point a to point b and it has to be part of the solution even in lift today you can find nearby transit information in fact we use a European company Traffey who's built a lot of the data management to help us on that so we view it as whether you know you're trying to go less than one or two kilometers you could walk for sure you could also and we'll recommend you walk in the application actually you could also use a scooter you could use a bike share and you could use ride sharing and we're seeing that people are trading off and transit in their everyday use and putting it together so it's I think you're going to see 2019 where it's going to get significantly easier by the end of 2019 to stitch all of these modes together than it was right now okay and how does autonomous fit into this so you know a good question that people ask is why autonomous are we doing it just because AI is here and it's the right you know it's the next big hot thing that everyone's talking about if we think about our business around ride sharing every single element that a consumer cares about is impacted positively by autonomous so on safety we know that something like 94% of accidents are caused by human error and so if you can reduce that significantly which we believe in the long run as the technology develops that will have an impact in doing it to is the cost of transportation my guess is that the industry can lower prices by more than half of what they pay today when you have an autonomous vehicle at scale they're right now expensive but they're going to get cheaper over the years and doing it another piece is the experience cars have been developed and built around the driver cars have been developed and built to be the least common denominator to do all the things that you want to do in a day cars are going to move towards the airline industry and become cabins and you're going to have a sleep lift a work lift and entertain lift and there's going to be all sorts of different things you can do there because you're not constrained by the steering wheel and the controls in the driver you can use all of that space and of course another piece that's important is shared rides today lift 35% of rides are shared on the lift platform which didn't exist a couple years ago but it's super important otherwise we're not going to reduce the number of cars and if we can redesign a car so their compartments for people that can have their privacy and safety and security that's something that we can also bring forth with autonomous so do you think autonomous will have or what effect do you think autonomous will have on traffic and congestion so I think a key point as I mentioned is it's not enough in our opinion to just have autonomous cars we believe there are three factors that the world has to move towards autonomous electric which addresses the emissions problem but it's also an economic reason to do it it is cheaper to run an electric car in a fleet like ours maybe not for a consumer but it's cheaper for a fleet and then shared you need to have shared rides cities need to promote shared rides otherwise you can't guarantee that the number of vehicles will go down with autonomous so all three are important to bring together for the next generation solution okay Bodil is autonomous or electric shared autonomous vehicles part of m's future it will definitely be part as that technology becomes available our global world of cars global CEO today said that we shouldn't overestimate the difficulties in introducing autonomous drive I think the Raj spoke to a lot of the benefits of autonomous driving when it's finally there I think also in a business like the one we are looking upon there are other user cases which are really attractive like thinking that cars would actually go out or your physical or visible space like come to you when you need it and then quietly and safely go away in park so imagine autonomous drive also being used in in other situations not only to drive yourself or being in the car but just as a support to you because I think finally we would like to see less cars around us and just have like more space right absolutely yeah and I mean so there was a study in in san francisco last month that said that that uber and lyft contributed to more than 50 percent of the increase in traffic since since 2010 so would you say that autonomous is part of maybe is part of lyft's effort to reduce that just kind of what you were saying around these shared electric autonomous rides and maybe scooters and bikes also fitting into there I think that integrating multiple modes of transportation is important so this is we've also seen that people are choosing bike sharing and scooters and that's a way to also get large automobiles off the street as well and then we're we have a goal that by 2020 more than half of all lyft rides will be shared and we're at 35 percent and we have line of sight to get there the way to get there is to have the more people that use a service the more density there is the more likely you can pick up people along the way the other thing we've done is introduce the concept of walking into the ride which is a you know amazing people didn't think about this but the idea is that if you can get several different people along the route to walk you don't have to do a point to point detour and you can make it a lot more efficient and you can lower the cost and also lower the number of vehicles that are there but you know as far as there are studies that come out in both directions in terms of what the impact is on a city so I don't think we fully understand what it is there's a lot of variables that are there but the most important thing is that we all do have to work on solutions to reduce the number of vehicles everyone is in agreement on that and I mean how do you generally incentivize people to take these shared rides as opposed to just requesting their their own individual either lift ride or m ride so you know the economics are powerful your lift ride if it's shared can be 25 to 30 percent below the price of a normal ride that's probably the biggest driver that's there but what we're finding is when two human beings get in a car they stop looking at their phone and they start talking to each other and there's actually human beings are social animals and what we're finding is that they're having a pleasant experience in a shared ride with some of that they don't necessarily know and that's another factor that people are are actually enjoying the concept of sharing a ride with another human being amazing but it's true I can't say I agree I generally don't like sharing rides but maybe an introvert I am it's true but Bodle how do you how do you kind of kind of see shared rides fitting into this and incentivizing people to take them people like me maybe who just who just want to look at their phone I don't agree I mean go to any restaurants and even people knowing each other they are sitting fiddling with their phone so I don't believe in in the social aspect of share ride I do believe that share ride is the biggest problem because autonomous won't deal with the number of cars as long as we are still one person in a car so I think there's a fundamental shift of understanding and supporting people to understand the different modes and that's it's a very very important thing and what is really needs to be bumped up is good public transport that's the that's the the biggest part of it because it's it's that number of people that now live in in the metro in the big cities around the world is just gigantic I think also the risks you know a situation today where a lot of the largest cities get such an influx of cars to serve the the cities during daytime so that is even more of a problem for us it's it's really about enabling a part of our behavior that is we see not yet a good alternative out there and I would argue that there are alternatives for what we want to provide you know to become an alternative to ownership we have tested everything that is out there and it's so clear that that it's we are still early days and that is why we also believe that even a small actor like like m can provide something as long as you understand something critical about the consumer and address something that is you know making them reconsider their current behaviors yeah and I mean Emma is definitely a bit of a of a latecomer to to ride hailing I mean how do you plan to differentiate yourself from the likes of Lyft and Uber and the ones that have really already gained quite a lot of market share well the first thing is that we aim not compete in in the areas where you are strongest the the the e-hailing and the the short trips we see that that is very very much answered to those needs are answered to what we see is the way you use your car when owning one is a very different from the one that is driver supported or yeah primarily driver supported or even the shorter rides you you use cars in very different situations when owning one it and their that behavior is what we are really digging deep into that's also something we have really a lot of experience on given where we what our owners have been doing for 90 years and then when looking at the alternatives we see that that there is very rudimentary services out there they are and I think people don't give up their cars because there's still nothing good enough you know it's both in terms of dependability to really become dependable but then it's also you know what do you expect from a really good service it's not just a car from a certain time you have bigger expectations if you are ready to give up your own car okay but so but you were saying that that really you're you're targeting people who are who are owning their cars but I mean Lyft and Uber drivers they often own their cars granted like Lyft and Uber have some leasing leasing options available but I guess yeah could you elaborate a little bit more on how on how it'll be different I mean a lot of us use the e-hailing and ride sharing services and own a car still we have been we have done so many studies and people all over the world actually in all the metros are now at the place where they rather keep their own car parked and use another service because they know that the difficulty of finding a new parking space would they come back after having used their own car so there is something like totally locked in but they need their car for certain things in their life could be their hobbies could be their children it's about life ultimately and what you do in your life what are the things that are important to you your family children hobbies things you do go and see people use you pick up several you know kids on the way to hockey training or you go and stay with friends for a while so a lot of the use of cases you actually want to to bring your own car not go with a driver for example and you keep your own car for those those times or those events and it's so few it's totally as Raj say the private cars aren't used it's just they are just standing idle for a very very short time but people need that certainty or like they want to to know that if I need a car I need to get one and also it's very you know they're not really on the level of service that you can expect okay so I mean so it sounds like your target your target consumer for M's ride-hailing network is people who already own their cars but maybe don't want to use them quite as often also people that do not use them but want the freedom that a car can provide okay but I mean but couldn't they also just use uber or lift because there are different behaviors because we don't always I use uber and lift another for certain rights but I use I don't I gave up my car when I got this job so I can't say I I have my opada use other services now for other ways of moving around okay okay and I know that this is probably a few years out well at least at least a few years out but let's get into flying taxis um Raj I know so you're one of your competitors uber at something that that they're talking about what's what's your stance on flying taxis especially as you think of this like multi-modal platform yeah I mean I think the concept is definitely interesting the the science seems to be there I think that we're far away from the entire system to be able to work at a price point noise issues and then of course there are rights of way issues that have to be resolved so I would bet that we'll see self-driving cars scale up before we'll see flying cars scale up as an opportunity in fact one of the things is we are live in Las Vegas with the largest autonomous self-driving fleet right now we've done over 20,000 rides and consumers love it it's been rated about 4.95 out of five stars over 90% would come back and do it again so we're seeing in 2019 it'll be a slow rollout but it's starting self-driving cars are starting to come out and one point around self-driving is that it's a lot more economical for a service like Lyft to launch it because self-driving cars are not going to work in all conditions all time when they start they can basically do block by block city by city there are certain scenarios they can work so we when a consumer hails a ride we can decide if it's safe to send out an autonomous vehicle or to send out a driver it's like a hybrid network just like if you were a 4g operator and you have 5g you can put a few 5g towers because you know you can fall back on 4g we have drivers and autonomous so we will see that rollout well before we see things like flying cars in my opinion it's fair but all probably true all the way it would be cool it would be really cool but yeah we'll see we'll see if we get there um all right i'm gonna take a few questions from the audience thanks to good ol slido um so what about the role of insurance in in self-driving vehicles and liability yeah how do you know there are going to be massive changes first of all just to take a broad view on this transportation in the us alone is a two trillion dollar opportunity when you look at all the businesses that are going to be impacted by the move to self-driving insurance um auto financing um even things like organ donation uh because there's less accidents there's less organs that are going to be the right when you add up all those effects it's another two point one trillion dollars is what we estimate which includes a fundamental restructuring of insurance because right now our premiums in the entire industry is geared towards a relatively high accident rate which we are not going to see going forward into this new world so every industry gas stations auto repair insurance everything is going to be restructured in this new world and we're going to see insurance have to change and accommodate as well i think the liability question is going to remain just like most liability product liability questions that are there who's responsible for the product you know and it's and if it is Lyft who owns the vehicle that we're going to look back and say just like in a car where in the supply chain was there a problem if there is a problem that autonomous vehicle and that responsibility will be there amongst the shared parties and going forward so i don't think there's any magical difference uh that's there it's that now you are getting a service and whenever you consumer service there is a liability question and the provider of that service it's either them or the supply chain that's responsible to be the same thing is any any thoughts to add or well volvo cars said some years ago that the day that volvo cars will be fully autonomous volvo will take liability and i think from a safety point of view i think it's interesting already today when we talk to the cities that are now our aim to launch they see very positively to get a service with volvo cars that has so much higher um safety um because of the the technology that we have been in in developing over many many years and that is actually part of the the journey towards autonomous yes and so when when launching these new technologies whether it's um ride heling or autonomous what are your what are your conversations like with with cities and start with you about all they are amazingly open-minded and they do see the situation with the complex situation today with transportation with a lot of cars coming into the cities private cars being underused and they want to see more options and most importantly they want um providers like us and others to work with them and that i think is is absolutely paramount because it's such a big big change we are uh that we have ahead of us and they all state that we've been trying to build ourselves out of this now for 50 years and it's not working so we need to find uh different ways and in this um in this for me it's really critical to be humble about what you do and not just flood the city with another 400 cars that is you know already really really um weighing under the the the burden of too many cars at the same time at the same plies but but really listen to what what is the consumer looking for what how can we help what is broken that is not yet uh solved and talking and working closely with cities Raj what's what's your approach to to working with cities so um our founder Logan the CEO um had worked when he was in college and people were out at beer parties he was actually on the transportation board of the city trying to help them figure out how to run buses more efficiently so it's been in our DNA to really partner and work closely with the city and try to solve this problem because we can't do it alone there's no way it impacts the community so much so in 2012 when we introduced peer-to-peer ride sharing it wasn't allowed in any city or any state in the country and we had to work collaboratively to get all 50 states on board and work closely with every city to help redefine what the rules are it took a while this time we're seeing with scooters and bikes um and other forms of transportation and autonomous we're seeing a very open and fast moving city much faster than was in 2012 because they recognize it's not just about innovation they have a problem to solve they know their city's growing they can't have more cars and they need to work with companies uh to solve that so we're seeing a very different stance and we've been absolutely working with them on for example scooters every time we're working them on permits and helping them understand how to roll that out we view the same thing around autonomous as well so it is i think the entire industry whether a startup or a large auto manufacturer has realized you need to be working together on this it's it's not an option to be rogue absolutely we are unfortunately out of time but thank you for joining me thank you thank you thank you


Everybody forced here repping 22 23 upper deck trilogy hockey 10 box number two random teams I'm gonna show you the results right here. I'll scroll down in just a moment I'm gonna get the checklist up or Trillo three wise as it says on the box here. All right now I'm gonna scroll down to the rest I'll drop the link again for you and You can find the results in a template sports cards.com. We do ship to Russia. Yes, it's once per month for free shipping international Go good luck everybody Yes, we do ship free internationally that does not include any import fees tariffs Taxes whatever it may be that would be on you, but we ship it from us to you for free. So It was going on so many texts Brad just texted me. I know Maybe now then All right, let's see some nice trilogy stuff here. Good luck everybody. Yeah Come on Jake Have a knife fight Jake probably thinks I'm too crazy Trillo three Y. Good luck. See this is Trillo three Y Crazy, I love the original Star Wars Trillo three Y Tigers only one game below 500. Well, I guess the world will end this year Cam York There's a rookie renditions of Lastic to 199 for the Blackhawks patch card It's a 99 is Seth Jarvis for the Keynes Seth Jarvis Colleen President Futures Jack Quinn your savers There's two two ninety nine Markstrom flames. Yeah, Markstrom And a Vasilevsky But I've Steve Sorokin that's a nice one Jack Lafontaine and 999 for the Hurricanes as well rookie Premiers, that's a really sick living card plus a Kachetka for key premiers to two ninety nine to the ice Chetkov there. Yo Keynes Guy could end up being pretty elite In a lot of base Salutations. Oh boy. Oh Steve Good day to you sir There we go Islander signature pucks number just 23 is Kyle Paul Mary Big poem as they call him big poem where's Joe Joe would like this card Paul Mary. Yeah, like Josh Palmer big poem No, it's Donnie Yeah, Donnie's here today. Jamie Drysdale the 499 for the Ducks 100% Donnie Yeah, he's asking strikingly less questions today. I'm really good at golf Timi Salani on the past Future Got a Lindross number two six. Yeah, six ninety nine flyers Donnie doesn't know what I'm trying to think of what he wouldn't know vine is Castle like rookie renditions for the Senators Yeah, it was yeah to 99 Ronnie added level one Rookie premiere for the flyers Nikolai Eler's Triloh three why? Got one more of these tomorrow as well My make Donnie pronounce all the hockey names I'm gonna make him do it. So select a La Liga has seven to go break in and just a bit here Or box like La Liga Dude, I was just thinking that you guys stuck in my head now future Eklund for the sharks And Jake's on a mission everybody Jack McBain level one rookie premier to 999 for the coyotes Clinton byfield The New Guardians you should do it You got a Jacob mover air to 149 Kings auto rookie auto Kings level one Got a ever ZC to 799 rookie renditions for the Leafs There's a Gunther for the Yotes rookie renditions And a Jeremy Swamin future to 299 Bruins nice one there Bruins So Brian, how are you doing? Doing well With a hard hit for a test ball dude double off the wall. No way. Yeah There's a Ken Korsak to 299 level one rookie for the Knights and a 25 a Sebastian Aho for Carolina You guys think anyone could beat the Panthers Trevor Z grass future looks There's a rookie premier to 299 Marco Rossi nice card. Wow. That's a level two as well Good stuff for the wild nice Rossi We're looking for right there. Just saw it rock. I like this. It's been a while since group trilogy Imama level one rookie to 699 for the Yotes and a Jersey card to 499 for the pens is Philip Hallender level one rookie premiere Jersey card that number Was that the last one today I see if I saw the the star factor Allison That was yours Got to 99 a Carlstrom level one rookie premiere for the stars that green color match to 99 Jersey card Dylan a heatly or Danny heatly sorry on the past Senators and game used Jays have not been playing well as recently Suje stink Forsberg pass for the abs Now we've got to 999 Bobby Brink level one rookie premier flyers 799 bad so guard rookie renditions for the Senators and a frozen in time to 275 is ove for the caps Terrible is being nice. Oh boy Yeah, they do stink right now. Hey, they're going through rough patch. They'll get they'll get back at it They got good pitching good hitting she's a come around Well baseball season a long season, so there's a special to call out. That's really nice Keynes with a special to call out Oh This one is not number best to get out of the way early and a plastic to 999 level one for the Hawks It just feels worse because the AL East is a juggernaut, and if you slip up like you're in the basement Trust me. I totally understand where you come from Bradley Walsh rookie renditions for the Devils Followed by to 399 Dylan Holloway rookie renditions Oilers So I'm gonna rip some personal tonight. Whatever was ordered earlier and wasn't finished by Kenzie. There was a lot going on A lot a lot orders. I'll get to tonight any new orders will go tomorrow night after group breaks President Zuccarello Wilds Got Trilogy to 49 Carlstrom level one stars Ricky premiere.   Do you mean like gaming and sports to 499 Lucas Riker level one rookie Jersey? Black Hawks Ricky premieres to 999 Rodney Adder for the Flyers level one got a D on past Kings Marcel D on and a rookie premier to 999 Dylan Gunther Yotes level one Lou J's Boman's best eight box Forest burg the 299 for the Predators to 99s Vashnikov Keynes Abra ZC rookie renditions for the Leafs 799 rookie renditions Jack Quinn Saber's nice numbered Quinn honor McDavid Otto. That's awesome. That's how we start the night right here Yeah, here make David Going to the Oilers Kevin big congrats to Kevin P To 699 as a forceburg passed for the abs Just brought let me check that out. I see this one here. Maybe Matt. I just them way on sale It's a lot though. It's a big difference. Let me see Why can't I find it? There it is. I don't know why they're as high as they are here What must happen is I don't think they're supposed to be 54. How can I find out what happened? I don't know why they would be something must have happened It's probably been a long time since we've done a Bowman vest PYT on the site on the the main channel I'm guessing that's What would have happened? So they shouldn't be 54 hmm. Well They're where they should be now. I don't know any other explanation All right, let's keep going. I mean, yes Some of them went up Carl like the box price went up So like some of our teams went up and not all the teams went up if that makes sense Like from like release time to now like the angels have gone up I think like some expensive teams stayed the same some teams that weren't quite as expensive went up just based on how they were selling and how they were hitting and Pro and you know names that have changed What am I trying to say values have changed It's 299 no a kates level 1 rookie premiere for the Flyers and a trilogy generations 299 present Adam Fox rangers Dude, it's great. Thank you. Yeah, exactly exactly Carl. Yeah, Red Sox went up quite a bit Future to 599 is Isaac Ratcliffe Flyers and a Jersey Carch out of the Tays Hawks No numbering on that Should be game worn with the Taves Yep, I want to check my Liga Who to go on La Liga? muchelle And a rookie premieres boldy level 3 to 699 ice-card wild Second boldy Rookie premieres castellic level 1 to 999 senators with a rookie premieres mad boldy to 299 on the acetate here the ice-card That's a thin sleeve. I need a thick sleeve Wild doing super well here A lot of boldy a lot of numbered boldy van beesbrock on the past rookie premieres at 299 is Coli Uchannock for the The coyotes in the 299 present Sam Bennett enters Yeah, just about Carl just about Redemption is a rupee hints for the stars. Very nice.   I'm not on Facebook. I Can go I can go look at it though since I'm in the face of Facebook here rookie renditions chase Pearson wings the 399 499 Elmer Sorreblum on the rookie Jersey wings Koyvu in the past for the Montreal Canadians rookie premieres to level 2 Jack Quinn nice card for the savers to 299 Jack Quinn Van Beesbrock past 699 Panthers to 299 is a mama rookie premieres Well, come Bokongi a mama Yotes, I don't know Which one the late in the regular late in one or the the collector community. Oh There it is I find it That's awesome Carl.  That's awesome That's awesome Carl rookie renditions Owen power savers. That's so sick Matt boldy to find that now again future for the future generations for the wild six 2006 that's neat. That's pretty neat. That's a dope find Rookie signature pucks for the Red Wings is Jonathan Bergren go Red Wings Rookie signature pucks Dylan Sandberg level one rookie premier to 999 Jets a lot of rookie number and stuff in this What's up OC? Maybe you did how it was moving. Well, good luck and that'd be all that's gonna be a fun break We got it. I'm I think we're really gonna hit a wanderer J rod this time I've been saying it past few breaks as it happens. So I think we're like I really do think we're due I know I'm I saw your broker. I said that Friday, but I Think I think we're hitting a wanderer J rod or even like a Marta We need a big hit out of 19 jumbo Colgren to 399 rookie renditions for the Leafs and to 10 a pasta Bruins 10 of 10 David posternock. All I feel is pain when I see Bruins cards now You paid $3 eBay has over 250 Geez dude, but why would why would somebody sell that for $3?  Yeah was his birthday. What do you mean? I'm not giving him more cards Got past chichu trying to cheat you Rookie premieres cold. You're not level once a 999 Yotes Caulfield future to 299 Canadians I was lucky to get what I got last weekend Lucky is a gift dude acts.  First of all I don't think I have any other cards that Jake would like appreciate Lucas, right? Oh rookie rendition to Blackhawks. Oh, give me a Corey Kluber card I'll get you that red sauce Corey Kluber RP patch on or something 299 D on the RPA D on pass for the Kings to 299 Birthday week. Oh, yeah, I guess there is it. There is a cake here today There is a cake here today. Although it says it says good luck Kenzie on it and say hi birthday Jake Although we could just put candles in and sing. Hi birthday again McLeod to 499 Oilers Jersey card Dylan Sandberg rookie renditions Jets You don't want to call. That's right We've eaten cake here every Sunday Dylan Holloway future Oilers Cake and pizza the pastor's Sundays Rookie premieres the 999 solderblum level 1 wings Jake needs a wander. I don't have a wonderful. I don't I don't own a wander I'll give him like series 2 series 1 2023 base card I'm sure jake already has that he's a race fan They'll stall rookie renditions for the Ducks Yeah, I'm sure they weren't too expensive in series 1 Rookie premieres the 999 Nathan Smith for the Yotes. I pulled a majority on I don't know anybody. I don't know anybody that claims a birthday week I don't think anybody does anybody actually do that Zucrello to 499 for the wild Jersey to 499 Perot level 1 Ducks ricky jersey Call coffee old future for the canadiens now. We've got a rookie premieres 2 999 Lucas rickle level 1 hawks To 299 Dostal level 1 rookie premiere Ducks And frozen in time patrick wada 50 there you go abs. That's a cool one What was it in time patrick? Wow, jake says patrick roey, but that's okay. Oh, that is roey Sorry guys patrick roey jake corrected me. Thank you jake. I know all Marca Rossi were here renditions He's way jakes like yeah, right dude. I got an uncle's name is roey. That's how you spell it Stoods a lot of 299 future for the senators. I have a friend whose last name is right You choose it to be called in rare zgross that's 25 on the future auto ducks Nice hit ducks. This is awesome, man Look at that mag for you anaheim to 699 Gomez pass for the devils We hit a mickey d We got a zgross. I got you brian Got triple relic that is sick to 25 for the pens afghani mulkan honorary triple swatches That's so cool. Yeah, these will be game worn as well. Very nice penguins card. It's a great break Two at 25. Look at that three color pass on the left Gino victor loading level one to 999 ricky premier's senators Yeah, I don't I I don't have time for that car all unfortunately ricky renditions kachet cough at the canes Unfortunately, I don't Dawson mercer to 599 devils future Ricky premieres to 699 flasik level one hawks feel like a whole bunch of those presents is 75 aho canes Again guys, we have one more trilogy break on the schedule. It's for tomorrow. It's for tomorrow You got soccer tonight next that has spots left A coli oceanic level one auto to 449 There you go. Yotes rookie level one auto ricky premier ricky premier to 299 uh pustanin for the For the pens Where is that? Is that something california? I guess it is present chuck senators brady chuck Nico he sure to 499 devils olifson present for the uh savers Followed by rookie premier matt boldie again level one to 999 There you go again wild in last box Lot of spots coming in very very nice That is a triple patch For who this the canes? Yeah aho sveshtikov and frederick anderson trilogy triple relics Sick card is there numbering on this? Yeah, that's 25 great card for the canes now Three color three color two color aho sveshtikov anderson james van reamsdike to 499 for the flyers I like this stuff a lot. This is cool stuff Seth jarvis future followed by ricky premier's fajimo level one kings the 999 No, it kates to 799 rookie renditions for the flyers in a troy terry to 99 ducks Weasel rise See you brayston Don't bother me Yeah, that's gonna be a jordan harris level one ricky premier auto to 449 montreal followed by zoo to 199 wild trilogy matt zuccarello matt's uh Zucchini it's kind of I always think of that vegetable present generations patrick kane Dominica shek past to 699 that's going to the sabers Dominica shek dominator the goat Jordan spencer rookie renditions followed by it's a 99 kachetka level one for the canes canes did really well here And that is going to do it It's going to do it for trilogy 10 box number two Let's do a recap. So the liga looks like it's sold out The next break would be bowman best has spots Inception division says three eight to go on fast break And it's Nice All right, so low numbered cards and relics here you've got pasta to 10 jack quinn 299 Rossy to 299 Uh, there were some bouldies as well that were higher numbered relics you got samberg taves perot solderblum rickle hollander jarvis heatly Gino to 25 is a really nice one for Uh pittsburgh and triple patch was a host fashion to kava anderson for the canes also to 25 Again, the last two there these are both game used Really cool triples there autos we had a king's redemption jordan spence gold auto rookie tier one to 449 And he had stars rupa hints trilogy signature pucks no no numbering on that one stars rupa hints He had a special to kava auto for the canes for the um red wings was a jonathan bergen ricky pucks signature jordan harris colio chinook jake of mover air remover air And a signature pucks paul mary to 25 for the Islanders also a trevor zegras the 25 future generations auto The ducks and a connor mc david auto For edmonton. Thanks again everybody. We're on to some soccer now


On Tuesday, oil turned positive. We found out that the French consumer confidence saw a record fall in April, so the French Prime Minister said it's time to emerge from the coronavirus lockdown, but the UK said it's not ready to change social distancing measures. Welcome to the TICML Update. I'm Kiana Daniela, the founder of the InVestiva movement. Make sure to subscribe to the TICML YouTube channel and support us by liking and sharing this video with your forex trading friends. On Wednesday, we'll be looking at the all-important FOMC rate decision as well as the NBS manufacturing PMI from China. Today, I'm looking at the dollar yen pair, which is just testing below the daily Ichimoku Cloud. The pair has been under a bearish pressure as of late, and if a break below the key support level of 106.45 is confirmed, we could see even further drops towards 105. However, keep in mind that a break below the cloud is also normally followed by a correction, which could be ideal for the bears to short at a higher price and gain more profit. How low do you think the dollar yen pair will go? Head over to the comment section and let me know. Of course, trading in the financial market involves a risk of loss, and you should only trade the money that you can afford to lose. If you liked this video, give it a thumbs up and subscribe to the TICML YouTube channel. I'll get back to you with more updates tomorrow.


And in Lagos, the state governor of Babaji De Sao Lu has presented a total budget of 1.692 trillion naira for the year 2023 to the state House of Assembly. This is 66 billion naira lesser than the 2022 budget, which stood at 1.758 trillion naira. The budget tax, the budget of continuity, is said would unlock a greater Lagos and consolidate the efforts of his administration in his first four years, according to Sao Lu. Our correspondent love Ikuku Oyedokun reports. The Speaker of the Lagos State House of Assembly, Munda Shiru Obasa, arrives at the chamber to wait in time. This was followed closely by the arrival of Governor Babaji De Sao Lu. The arrival of the governor signifies the commencement of the day's business. Straight to the podium, Governor Sao Lu gives a breakdown of the budget. Capital expenditure takes 932 billion naira, representing 55 percent of the total budget, while the requirement expenditure gobs 759 billion naira. Please to present to you the year 2022-2023 budget of continuity as proposed with a total budget size of 1 trillion 692 billion 617 million 753 thousand 894. This comprises of a total revenue of 1 billion 1 trillion 342 billion 617 million 649 thousand 640 and a deficit financing of 350 billion. This comprises a total internally generated revenue idea of a 1.1 trillion and total federal transfer of 250 billion. We go on course for a new revenue sharing formula and a special status for Lagos. Lagos is too big for this country to allow it to fail. Lagos is too strategic for us not to see that the wholesome of Lagos is the wholesome of this country, that the benefit of Lagos transcends one part or one scope of this country. As a microzone of the entire country, Lagos it deserves all the support it can get at the national level. Receiving the appropriation bill, Speaker of the Lagos State House of Assembly Mudashiru Obasa lists three major areas the budget must address. The year 2023 budget of Lagos state should have more human face be targeted at reducing poverty and improving the welfare of the residents of the state as part of the things agenda that guides this administration. We will also as usual continue to give immediate support to you our dear governor as we all march ahead to solidify the achievement since 2019. Obasa also stresses the need for the federal government to approve the allocation of one percent special status for Lagos state. Lagos account for about 20 percent of the national gross domestic product and 10 percent of the nation population. This is not however to gloss over the impact of such massive movement of people to Lagos. The result is evident in state infrastructures that are persistently overstretched, thus requiring constant attention. This is the reason why we continue to add detail for allocation of one percent special status for Lagos state. Aside the budget presentation, the Speaker, OGIS Nigerians, to vote for all APC candidates at the state and the national levels, describing the presidential candidates of the party as the only one with the capacity to make Nigeria great again. Love you Kuku Uyedukum, plus TV news.


This morning it's the very exciting theme of Metatranscriptomics. And again hands up for those of you who are interested in doing some Metatranscriptomics.  Yes, yes you should all be interested and hopefully part of my goal is to convince you. Why is that popped up is to convince you that we should be thinking about doing Metatranscriptomics in our datasets. And Ryan who's just walking in there Ryan will be taking you through the tutorial. At the end of this talk. Right, so module six Metatranscriptomics created commons open source free to share use everyone is see fit these materials. Okay, so what are we hoping to learn today. So, I'm not wearing the mic so I'm going to raise my voice then I'm fine without mic. So at the end of this module. As I say one of the main things I want to get across is maybe a little bit more of appreciation for Metatranscriptomics understand its capabilities what it can do that say 16S and metagenomics can't do. I want to go over some ideas behind sample collection experiment design and so forth. And then hopefully picking up. We're going to go through our pipeline which called Metapro. It finally got published last month hooray for us. Obviously we promote our pipeline but you should also feel free to think about creating your own pipelines as well because each of us may have slightly different needs. And the tutorial itself will take you through processing a simple Metatranscriptomic data set. And then at the end we have this kind of visualization kind of tutorial which is based on side escape. Who's used side escape before. Okay, well that's great that there's only three of you. So, so it should be new to everybody. We just find it. It's generally really useful tool for just during visualizations of all sorts of different data sets. And so if you can think about a heat map, rather than presenting a heat map for your data you can actually present it as a network. And so it gives you an alternative way of kind of presenting your data helps you maybe interpret your data a little differently as well so beyond justice Metatranscriptomics we think putting you through this kind of exercise working with side escape. You might again just find generally useful in your own research if you're playing it in other context so it's a tool that's been developed by a colleague at U of T, as well as a somebody I think he's at UCSD Trey Ideca Gary Bader is a colleague at U of T so he's been working on side escape since about 2003 2004 very well supported lots of plugins and different stuff that you can do with that but we go through that in the tutorial. Okay. Metatranscriptomics why Metatranscriptomics so 16 s, we know it's good for telling us who's there, but it only gives us relatively limited mechanistic insights. So then Metatranscriptomics, which was all day yesterday we're very excited about Metatranscriptomics costs of it are starting to come down a little bit. The ways that we're using Metatranscriptomics data is starting to become a little bit more standardized as well. And it's really good at identifying functioning determining differences in functions across samples. As I say what I want to try and get across today is Metatranscriptomics where it's not just what functions are present in a sample. It's which ones are actually active so it's telling you something about what is the active function of a microbiome, or rather who is doing what. The idea behind Metatranscriptomics so similar to Metagenomics where you're just doing whole shotgun DNA. Here we're doing whole shotgun RNA. And so this can tell us which genes which pathways are actually being actively expressed from in the community. So here is a this is a science, science, a visualization these are genes that are involved in cell wall biogenesis. These pie charts, the size of the pie charts are representing the relative expression of each of these genes and cell wall biogenesis. And then the breakdown are showing the taxa that are contributing to those functions. So these kind of visualizations really give you an idea of who is actually doing what within your community. So once you're looking at relative expression you can look at changes in relative expression across to the data sets and so the red arrows here indicate those genes that have been upregulated in this case this is a sequel sample from a chicken. And this is between chicken which has been given antibiotic growth motor so these are these antibiotics that you put in livestock feed. So if you give the chickens these antibiotics in their feed and no and behold a lot of these cell wall biogenesis genes expressed in the microbiome actually going up which is potentially not that surprising but it's nice to see this kind of demonstration that you can see how a microbiome is responding, not necessarily in composition but in terms of its activity to some kind of external perturbation. Right, so to give you an idea of the sort of things we can do with metatranscript, metatranscript so it's what we can learn using these kind of data sets. This is a study that we published back in 2017. It's looking at this peri-lipin-2 gene so peri-lipin-2 it's a gene that's involved in lipid uptake in the gut. So we have a lot of colleagues, Dan Frank, at the University of Colorado, and they have this mouse model, and they found that in this mouse model deletion of this plin-2 mice largely abrogates the negative consequences of a high fat diet. So we're interested in understanding well what impact does this plin-2 knockout have on the microbiome.  We had four sets of mice.  And then we applied this whole microbiome RNA-C, this metatranscriptome, so we generated 20 to 30 million reads per mouse.  We saw that there is no difference in community composition. So they effectively had under a high fat diet, both these types of mice seem to have similar community composition. So just at the gene expression, we found that despite having these almost identical communities, they did have a significant difference in terms of the genes that they're expressing. So we identified about a thousand highly expressed microbial genes that were differentially expressed between these two genotypes. Okay. So this genotype, this genotype is having a difference is altering the function of the microbiome. So the microbiome is the same, but the functions, what's the pathways have been expressed are actually changing. Many of these differentially expressed genes were associated with amino acid metabolism, energy metabolism, and we were particularly interested in this particular pathway. And what these kind of dots represent here, these are enzymes, and the size of the enzymes represents the average expected. Oh, okay. Oh, I'm not allowed to move. I see. Okay. So when we, when we map these kind of gene expression differences in the context of this like this pathway what we see is this large track, going from, what is it one fructose six phosphate, or the date all the way down to pyrovate this is where a lot of ATP is being produced in this particular pathway. These two knockout mice are actually down regulating this part of the pathway. They don't seem to need as much ATP production. And so this helps us kind of think about building some kind of model where in a wild type mice, you have these facts in the diet these facts are being absorbed by the intestinal cells that you have this clean to this wild type clean to you have this regular clean to these liquids again absorbed. So not much is necessarily left over for the rest of the microbiome and the lumen of the gut. However, in the plane to knock out mice what's happening is that you're not getting the same uptake of facts. As a consequence you have more these triglycerides in the lumen of the gut. And because you have this extra availability of these triglycerides, there's more than sufficient energy for the micro for the converter. And as a consequence they can dial back their energy metabolism because they're already producing a lot of energy, and they can switch. They can switch their expression into other types of pathways. So this is an example I think, and hopefully a convincing example where we've used metatranscriptomics to actually understand and starting to get at some mechanism of why changes in in this case it was a different gene type in the mouse a knockout of mouse, how that actually results in changes in the expression of certain pathway functions. Okay, so hopefully that's a little bit convincing for you. So we look at the uptake of metatranscriptomics so on the left here we have publications with word microbiome on the right publications with the word metatranscriptome metatranscriptomics or metatranscriptome. And if you look at the y axis, we can see it's still a very slow uptake. Okay, in terms of how many publications how many people are actually using metatranscriptomics in their data sets. So maybe it's starting to catch on. And I think maybe one of the biggest drawbacks of metatranscriptomics is one it can be a little bit challenging because you're dealing with RNA and not DNA and secondly, it is more expensive. So library preparation costs are probably the main expense and it's about $250 per sample at the moment. Now we're hoping that these costs can come down. And the Sanger Center who are doing very high throughputs, tens of thousands of samples for metatranscriptomes. And so they're really motivated to try and bring these costs down. And hopefully if they can they can come up with a new ways to do these like preparations that will bring the cost down for everyone else. So, I guess we're at a stage now where it's a balance between well how much do we want to spend how much can we learn versus the actual cost of these kinds of experiments. Okay, so I have examples of metatranscriptomics on the left this was a paper that was published last year in Izmi Journal. Here they were interested in looking at soil microbiomes to isolate genes that would be useful for the processing of arsenic. And relying on metagenomics, they actually didn't care if an arsenic gene was actually present or not they wanted it to be actively expressed and they use that as a criteria for saying this is the kind of gene that we want to focus on because it seems to be expressed it seems to be used within the context of this microbiome. On the right hand side, this is from Curtis Hutton hours group this was from 2018. Here they applied metatranscriptomics to IBD samples they found that specific taxa contributed unique pathway expressions they're starting to really did dissect the individual contributions of different taxa. This is a taxa that are very abundant from the metagenomics point of view, but are actually quite quiet, they don't seem to be very active. And so there's this concept with metagenomics that we may be able to sample all the DNA in the sample, but is that really reflective of the activity is this DNA that's just been shared how reflective is metagenomic analysis of the metagenomic kind of data, telling us in terms of what is functionally happening in in that particular data set. The other thing that metatranscripts a metatranscriptomics enables us to access that metagenomics doesn't are RNA viruses. This is a, this is a study that we did recently on mice and we recovered these, what are they astroviruses. And it turns out that our pipeline does a really good job of out of these hundreds of millions of sequence reads actually reassembling entire viral genomes. So it seems to be a really good way of being able to identify novel RNA viruses, and a colleague from U of T are Tim, but by and he's just new faculty member. He published this paper just last month where he used all these metatranscriptomic data sets to expand this universe of RNA viruses and obviously there's big implications for future pandemics and so forth. So it's kind of a novel aspect of metatranscriptomics given as access to information that metagenomics just isn't able to provide us with. Okay, so how does this work well it's pretty much the same as an as an RNA seek experiment to extract the RNA, your fragment is sequence, you align to know in transcripts, and you end up this digital read out of gene expression so it's very similar to single organism RNA seek. But it does have its own challenges. So in a typical kind of RNA seek experiments you apply RNA seek to maybe a single eukaryotic organism. And if you're dealing with eukaryotes you're able to isolate the RNA through the poly A tail. Now unfortunately bacteria don't have poly A tails. So this means that you have to sequence all of the RNA, and all of the RNA includes ribosomal RNA which makes up about 95% or so of the RNA in a typical bacterial cell. And you've got to get over the fact that how do you enrich for this messenger RNA. Another factor in RNA seek is you generally have a reference genome and so you're able to map your sequence reads that reference genome. So for applying meta transcriptomics we generally don't have that reference genome, and so doing this mapping identifying what the source these transcripts from can be quite challenging. So the plan of challenges that we're facing is, first of all, compared to manage no mix DNA is relatively stable RNA, not very stable so you have to be, when you're doing the isolation of the RNA happy working in very very clean conditions, making sure all of your, all of your surfaces are wiped down with agents that are going to contract RNA is. We also have this lack of poly A tails is also host contamination which was briefly touched on yesterday so the host is going to have a lot of RNA as well and depending on the sample that you are sampling from you can get a lot of host contamination from human's that could be problematic from an REB perspective in terms of screening out those kind of human reads before you're something to get. But at the same time there's a possibility of using the host RNA signal to get an idea of what is the host actually doing how is that responding to the microbiome as well. So it's kind of a, it can provide some some some benefits as well. There are very, very complex data sets featuring hundreds or thousands of different taxa. And so this is question of what is the depth of sequencing that we need to do in order to best sample from all the different taxa that are within our, within our particular sample. And also as I mentioned we have this lack of reference sequences with regenerating sequence from strains that we've never encountered before. And so how problematic is that when we're doing a mapping of these reads back to their original transcripts. So the first challenge that we face is the kind of instability of RNA so RNA quality and deteriorate very rapidly. And so the ideal is to try and get your sample to minus 80 as quickly as possible. And that we're involved in Pakistan with through the Aga Khan University they're working with these field sites, and they are able to get a stool sample to minus 80 within about two and a half hours of collection. Okay, so they have a really phenomenal way of being able to manage the collection and maintaining the integrity of the samples in Toronto. And unfortunately we don't have that same level of capability. And so we're working with newcomer communities a lot of these newcomers. And this is these are young women that are pregnant. A lot of these newcomers are living in shared housing they don't have access to a fridge and so we had to identify a solution, where the RNA could actually be preserved at room temperature so that they could send it through the mail to us. And there's a number of kits that have been developed so Zama research as this DNA RNA shield, nor gen has a kit the one we've selected is this omni gene gut kit which was just released last year, which is supposed to be very good at maintaining the integrity of RNA in samples, I think up to about a month or so. At that stage hopefully have them put on minus 80. One thing people have been trying to suggest is RNA later. We find that RNA later seems to interfere with the library preparation kits and so we don't advocate the use of RNA later in trying to store and maintain these samples. So that's how much of these kits perform so Zama research, nor gen this is interesting the color bars at the top are sharing, for example with the shield or with the kit that the kind of the breakdown in terms of taxa that you recover is as good as the original sample, what they're actually showing those DNA and they're not showing RNA it's only the bottom kind of. They're really showing how they're better able to preserve the RNA is only DNA genotech on the right hand side which is showing the RNA breakdown in terms of taxonomic composition, showing that this DNA genotech kind of kit seems to do a pretty reasonable job of maintaining the quality. So you might notice from this summer research for this RNA later here this thing here, no recovery of any RNA so it seems to really poor job at least under the conditions that they were doing their trials. So we think that this DNA genotech hit simply pretty reasonable and that's the one that we're currently using for our studies. One of the reasons I think maybe metatranscript tomates isn't adopted or isn't as widespread as you think it might be is that it is expensive it's largely because of this library preparation. So Illumina has this joint kits which does this kind of library preparation for you and it's about $250 a sample. And then on top of the sequencing cost it works out to about $300 $400 sample, but the main costs again is really the generation of the libraries. And if we can come up with some kind of homebrew solutions or better solutions that don't rely on these expensive kits and that could really drop the price down. One thing that can potentially cause a lot of issues in terms of cost is how many record cuts do we need. And again, this is a pretty nascent field. And so it's very hand wavy. We are going to have I think a little bit of a discussion about power calculations this afternoon with Anita. But these are kind of very challenging analyses to do because we just don't have the data there aren't the number of studies out there that are able to give us the statistics that we need to come up with more compelling kind of power calculations. So in terms of mice, we're suggesting at least four it's probably six is probably a good idea in terms of replicates. And these mice are under similar conditions and the microbiome isn't changing. If you're looking at individuals where there's a lot of variation, then you're probably looking at a minimum of 40 for the individuals in order to get at some kind of signal that you might be able to detect. This is very hand raving numbers and again Anita I think we'll be discussing this more this afternoon. The major challenge that we face with RNA data sets is this large amounts of this ribosomal RNA so as I mentioned it can make up as much as 95% of all of the RNA within a bacterial cell. And so there have been kits that have been developed, which decrease and filter out these highly abundant ribosomal RNA species. These kits do put in biases, so they seem to be better at some tax than other tax if we're removing the ribosomal RNA. Overall, they do a fairly decent job. So the current kit that we use is this ribo zero kit. So this is part of this Illumina library preparation so it does the ribo zero depletion and then it also does the library preparation at the same time so it's all in one $150. That's fantastic we should all be buying shares in Illumina. One thing we find with these kits though is again they're designed for bacteria so on the right hand side. This is a study where we're infecting mice with a protest. The first eight lanes. This is a naive mouse it hasn't been infected with a protest on the right hand side it has been infected with a protest, and that big kind of dark blue bar is ribosome and RNA associated with a protest. So when we're thinking about these kits that we're applying to deplete this RNA. They're not going to work for the certain taxa that they've been set up for but they're not going to work for other taxes so if you're interested in certain types of organisms that may not be captured by these kids then you are going to run into these problems where you are going to have a lot of ribosome and RNA in the future. And then just to mention that we do get hosts we do become a host message RNA is. And while this can be challenging, it can be informative so you don't necessarily have to throw out those reads you can actually use them for an additional part of your analysis. Any questions on any of this so far. I'm not seeing any hands get a yes, it comes down to your budget so you know I think most of our experiments we run a max of about 32 mice. And so it's okay what are we going to gain most from these 32 mice, because that's going to cost us about $18,000 or $20,000 right. I think the key is to think about this as hypothesis generation. The statistics aren't necessarily going to give you a definitive answer but if it's starting to come up with the pathways and I think this is more of the powerful approach is the individual genes could be very flaky when you start putting them into context of gene sets and I'll mention this and a little bit as we get to the end of the talk. I think that's where the power of these analysis coming because you can see entire pathways or entire complexes or biological process and again up regulated or down regulated. You may not care about the individual specific genes but you just see that entire wave of function going up and that gives you confidence that what you're seeing really is real. And then you can design for example specific DNA primers just a PCR up on some of the genes that you want to follow up on, maybe do a larger experiment larger number of samples route paying the $18,000 for this. So you can think of this more as a discovery tool, and then going back and reperforming an experiment with some more kind of focus but cheaper kind of experiments. Yeah. So you're basically like sending them off and then they're putting it in the kit and then dealing it. That's a really good question. So in our Toronto cohort, due to COVID slow down research coordinators, leaving us for more, more kind of richer pastures with private CROs. We have recruited total of one individual. I think it took one week through the post for that sample to actually arrive and then we've suck it at minus 80. He can always use FedEx. I'm not sure that, you know, with the added shipping. Yeah, so then again it starts it starts mounting up prayers in Pakistan you've got actual people in the field that are able to collect the samples and then take them to stick more minus 80 straight away so yeah we are at a bit of a disadvantage. The other thing I want to mention is this concept of absolute abundance and quantifying absolute abundance. So how many of you are aware of this issue of relative abundance versus absolute abundance. Yeah, so I think there's more and more appreciation that we need to consider this in our analysis, whether it's 16S whether it's metagenomics whether it's metatranscriptomics, more and more of these studies and reviewers are asking for well can you quantify what the actual level of material is in your actual sample and how does that go from sample to sample. So there's number of ways that you can do this. There's flow cells so you can actually count each of the individual bacteria as it as it's going through and that can give you an idea of the abundance of bacteria within that sample. Now they see if they see if you count so you could just play things out and look at one tax on that you know is going to grow under those plate conditions and see how that varies. One that we have used before is this ZYMO spiking kit and we had pretty good success. This was a trial of chickens and what this spiking there's two tax are in there. So these tax are supposed to be taxed that you would never find in a wild sample so you can I you can easily identify them distinguish them, and those are going to provide you with as you're doing your DNA extraction you're adding this spiking right at the very beginning of the extraction step to your stool sample, and then they're getting processed in exactly the same way at each step. And then during the bioinformatics you can actually identify which of your reads have come from those two tax ring use that to quantify okay given this level of reads that we spiked in. They're representing 10% of the sample 50% of the sample, then that gives them a way of saying well how many other bacteria are in that sample, and it does a pretty good job so this as I mentioned we did this in chickens. So the top the top row is a breakdown of taxa from a relative abundance point of view in the bottom set from an absolute abundance point of view so it really is able to quantify differences we find that for example the genome has roughly about 20% of the number of bacteria in terms of density relative to the cecum, whereas before we know you naively assume that they're kind of equivalent. So it's nice to see that you do get this kind of lower level, lower density of bacteria in the samples where you might expect to see that. So I think that this is important. So at the top, this is a readout based on relative abundance that kind of dark red tax on at the top might suggest that at 24 days post hatch. Those, those chickens have seen a decrease in bacteria AC which is the dark red tax on. However, when you account for absolute abundance is actually no difference. So unless you're accounting for this absolute abundance you can get very misleading results and you can identify taxa that may appear to be up or down regulated or a natural fact when you account for this absolute abundance. Then, there's actually no difference or they might be moving, even in the opposite direction. Yeah. You're ready to go. Possibly, I'm not sure I'd want to go that far just because of how things are collected at a later stage and patch effects and so forth but potentially it might give you a little bit of an ability to do that, but I guess we need to start seeing meadow and we need to do this based on several different studies before we can start making those kind of claims. Yeah. Yeah. And the other thing is that we were discussing a couple of weeks ago was, well, maybe it's not the density of bacteria. There are absolute abundance here, but we really don't know what the quantity of material is in the entire intestine and it's somehow where have you captured that. But that's a whole different question, right. So, I don't know if anyone's looking into the total quantification of stuff within somebody's guts, but I imagine that that is going to be kind of interesting and probably going to have some kind of functional ties associated with that. All right, generating reads, Hamley reads are enough. This graph on the left hand side this was from a relatively early study where we compared four different metatranscript tones and we're looking at how many enzymes do you recover at increasing depth so it's kind of a rarefaction curve to show when you get saturation of in this case enzymes we find that around about 90 to 95% of all of the enzymes as measured by these inside commission or these EC numbers can be recovered by about 5 million messenger RNA reads and it is going to depend on the complexity of the sample, but even a deep sea sample at about 5 million reads we're seeing that we get around about 1995%, however, depending on the complexity of the sample, given that the cost of sequencing have come down, we generally do in the region about 40 to 80 million reads per sample but again depending on if you're after relatively low abundant taxa that you think might be contributing important functions and obviously you have to sequence much deeper. So most of these experience with RNA seek with this RNA seek with this metatranscript and it's really reliance. Not so much on pack bio we don't care if the reads long or not I mean these are transcripts they kind of, you know maximum of maybe 2000 maybe 5000 base pairs. So we're really after the digital readout what is the relative abundance and so we just want these little tanks, these short reads and so we really rely on so on the luminous sequencing, and particular the Nova seek platform, just because of the quantity of data it it can produce. There's probably some application of pack bio and long reach it if you really want to get a good, I guess idea of the diversity of the actual transcripts. So most of what we've been doing I really relying on trying to come up with this counts for these kind of digital readouts of the relative expression of these genes. Okay, so we've gone through sample collection storage processing sequencing now we have the data. This is where we're coming in. We are generating data sets and Laura mentioned a publication yesterday with 300 billion reads. So certainly we've been a transcriptomics we're generating billions of reads per these experiments and so we really reliance on these kind of compute clusters. So some of our data sets. We run into problems we run out of memory. This is, this is a bit of a challenge. These are very complex data sets we have to do comparisons against very large databases, the amount of memory we need per node is hundreds of gigabytes. And so we really are reliant on getting access to these compute clusters and processing can be slow I mean we're talking maybe months for processing some of these data sets so there's a lot of compute involved. So the data set that we're showing you today again as Laura mentioned, a lot of these separate actually pre run for you, just because it's just impossible to actually run these on our laptops and desktops. So I mentioned that we had a meta pro pipeline just published recently there's a couple of other pipelines that we compared against so human three is one. And there's one called SAMHSA as well which is, which has been mentioned some comparisons next couple of slides. So my dear it's very similar to processing meta genomics reads you've got your, or your filtering of your low quality of your adapters but we also have an additional step where we need to filter out ribosomal RNA is because we know that even applying these RNA depletion kits, we're still going to get RNA creeping through. One of the tools that we use for this is one called infernal. There's another one called sort me RNA sort me RNA is great if you ask the quick, but quick could be writing a program saying move to the next step, and that would be quick but it wouldn't be very effective in the same way sort me RNA only gets about 50% of the ribosomal RNA that infernal does. And the problem with infernal is that it is slow. So this is again one of these steps which slows down this kind of processing and again it's one of these steps that will be skipping. So he uses these hidden Markov models to actually identify and it's very sensitive way of identifying ribosomal RNA is in within your sample. But as I mentioned, other pipelines are using this sort me RNA and I'm afraid it doesn't do that great a job. So we're going to compare our tool Metapro with human three with Sansa to and basically all of these tools are kind of wrapper scripts that are using established kind of tool so you could easily go ahead and build your own pipeline and maybe run it through step by step by step. We provide Metapro it's in this Docker container and this Docker container means that it should work in any architecture so you just copy the download the whole thing and it should just work this shouldn't be much in terms of getting installation to get it up and running. On these compute clusters there's a programming environment called singularity who's heard of singularity. Hey, some of you have heard of singularity so singularity enables you to use these Docker containers and have one of these Docker containers running on each node of your supercomputer. And so you can set up thousands of these jobs running at once so we've really set this up to do this is in as much in parallel as possible to reduce processing times. In terms of comparisons with human three. This was a kimchi data set that we looked at so you can see the ribosomal RNA contributes a large, a large number of reads in these particular samples and all three tools do reasonable actually same to two does a terrible job of doing ribosomal RNA and I think that's because it is using sort me RNA, yes. Right. So, with the ribo zero depletion. I think we are able to get down to about 30% of the reads are over some RNA. So that means you've got 70% of your say you're doing 50 million reads 70% would be 35 million reads are pretty good so that's kind of pretty saturated but again it comes down to the kind of questions that you want to address and I think because the cost of the the cost of the sequencing is not the limiting step here. So much as a library preparation costs. So if you're already spending $250 on library preparation, then spending an additional 50 or $100 to get from say 20 million up to 60 million reads is probably worth the extra investment. No, I think you just go ahead and aim for about 50 to 80 million reads because that seems to be the bar at the moment for other studies. It's not not very useful but you you are starting to see stuff in those samples that you're not able to see when you're just doing 20 million reads. Sometimes you get a bad sample as well as that even if you've generated say 50 million reads you still only end up with 5 million just because of something that's happened within that ticker sample maybe the RNA depletion didn't work quite right. So there's a rule of thumb re-aim for as I say between about 40 and 60 or 40 and 80 million reads and it again it just comes down to what you're happy with in terms of cost. Okay. The other thing to note with our pipeline so the things that are interesting are the things that we can annotate these represent real bacterial genes and we can see that metapro quantifies and annotates stuff that the other two pipelines just says I don't know it's something but it's not a gene or anything we metapro actually annotates 50% more in terms of the genes that it can identify. I should mention that human three is really built as a metagenomics kind of pipeline so I've been a little bit unfair doing this comparison with human three, but they are supposed to do this kind of capability of doing metatranscriptomics analysis as well one drawback I find with human three is that it's not terribly transparent in terms of getting intermediate data sets that you might want to get at in order to see. The enzyme here could be really interesting in terms of its expression. How many reads actually mapped to that particular enzyme and getting that information out of the human three data is not that easy. I don't know if Morgan has any comments on human three. Okay. So one important step that we find for metatranscriptomics is assembly and we find that if we can assembly this really improves annotation accuracy. So again the whole point of metatranscriptomics we want a digital read out of gene expression we reliance on these short reads. But because we have short reads that limits our ability to annotate them because you've only got 150 base pairs to map to something else. And by that you need to get in the region of about 200 or so base pairs, really to have a good chance of matching something accurately to something in the database. So we use RNA speed RNA spades to build these kind of transcripts and then we use meta G mark to separate these into individual offs, which can then be, which can then be annotated. And then we go into chimeras. These can occur because you get all logs from different species and they're kind of mixed together. However, we did an analysis of this number of years ago we found that in these data sets only about two to 5% of what we're assembling actually turns out to be chimeras so it doesn't seem to be a problem that we're too worried about. So that depends on the complexity and depth of sequencing in your sample. But for a chicken microbiome, for example a chicken metatranscript home. Assembled reads are in the region of about 10 to 20% and then unassembled reads are probably in the order about 60 70% something like that. So it is. It is the case that we do have a lot of these kind of singletons that we just aren't able to assemble. Yes, still try and take them absolutely. Yeah. Yeah. I think the cost is too prohibitive when you. I mean what is the my seek might want my seat run can do what about 40 million reads of that. My seek is quite expensive as well isn't it relative to some oversight. Yeah. Yeah. Yeah. Okay, so we've done the processing we've done some assembly we've identified the genes. So now we have the genes that are coming from the assemble conflicts that we split up into individual genes and then we have the singletons as well, which are also coming from their own kind of unique system. How do we annotate all of this. So our pipeline uses this kind of three tiered approach. The first uses BWA to try and identify these kind of strict matches so things that are pretty much identical to something in the reference database very fast. So diamond is black. This is fastest less strict than BWA. But it is better than blast or we actually use diamond. So diamond is slower than Latin BWA. It is somewhat. Yeah. It's less strict than black for sure. But use it in place of blast just because it is so much faster. But by using this we're kind of hoping to speed up this entire kind of annotation pipeline. It comes out with the sorts of matches that your teeth would curl at it's kind of like well this is, this is a score of five it's not e to the minus five it's a score of five so those of you run blast searches, and generally to get a match to something that you deem the homologue use a cut off at some like e to the minus five. Here you get a score of five it's kind of like well that is that really significant. We all rely on the fact that we look at percentage identity over the actual sequence. So these are relatively short sequences that's why our p values are not super great. But in a way, we would argue that it doesn't really matter if we're not getting to the exact gene that from the tax on that we might associate with. Instead, we're more reliant because we're using this more from a functional perspective. Then if we can get a match to a gene and that gene is associated with some kind of enzymatic function doesn't matter which which tax on it's coming from. We care more about maybe what that function actually is and so doing these kind of matches on this kind of peptide. So translating the DNA sequence into peptide space gives us with the kind of percentage identity cut offs and so on and so forth, gives us more confidence that we're mapping to something that is functionally, that is functionally related. And so we're really, I guess at this step really really focusing more on the function rather than rather than the actual taxa. In fact, we have a completely different approach. So this is purely in terms of functional annotation trying to identify genes and and what their functions might be. Now one of the problems that we're facing and this is this is why these pipelines are getting so bloated and slow down and taking months to run are the size of these databases so the reference genomes are always increasing. The memory to perform these searches is really increasing as well, which is requiring these large memory compute clusters, where each node has access to hundreds of gigabytes of ground. Now there are software solutions where we can spit databases. One thing that we are now starting to experiment more with are these custom databases. And so what we're showing here results from 100,000 reads from a sequel metatranscript home. And what happens when we're comparing against the trucker plan to database or the trucker plan three database from, from the human software pipeline. We find that our custom database of 500 genomes. So these are genomes, these are mags and genomes that have been assembled from previously generated sequel microbiomes from chickens. So to be small database just 1.1 gigabytes is in size. Compare that to 66 gigabytes is for the human three database. And then when we look at the quality of the alignments we get much higher quality than we do with these trucker plan database as well. So I think there's gathering appreciation that these custom databases are really important really driving, I think our ability to analyze these data sets, otherwise we just get lost within just the ever increasing number of genomes that have been generated. So how do we go about assembling these custom databases. So I was at the second center at the beginning of May and met with some of the magnify team. So, Morgan I think mentioned magnify yesterday so this is this seems to be a great resource what they're doing is they're compiling and they're generating mags for all the genomes that have been published, and they're starting to collect them into niche specific collections. So if you're interested, for example, in a acid mine drainage, metatranscript tone, you could go to the magnify database, identify all the mags and pull out a collection of mags associated with metagenome projects associated with acid mine drainage. Same for example for a chicken gut for the pig guts for deep sea. And so this seems to be a really useful approach for actually limiting the size of the databases that we need to be searching against and to speed up our actual annotation and make sure that we're not spending months processing these data sets but we can try and get this down to weeks or even days. So that's a really useful approach and I think that's a really good kind of really good innovation I think that that someone is actually doing that and putting those collections together. And then finally just to mention in terms of the actual processing of the reads. So if we are thinking of RNA-seq reads, if we are thinking of a typical RNA-seq experiment, we're converting our reads into expression values. So whereas in the old days you'd run a microwave experiment and you'd look at the brightness of the spot to quantify whether the gene was up or down regulated. So we're really looking at the counts of reads that are associated with each of these transcripts. But we have to account for the fact that these transcripts are different sizes. And so there's this concept of this RPKM reads per kilobase of transcript map. So here you're normalizing for the length of each of the transcripts. So in the part of our pipeline, we can incorporate tools such as bowtie and cufflinks to actually normalize for the length of the transcripts and come up with a more accurate kind of accounting of gene expression. Okay. So that's kind of functional annotation, that's kind of normalization of the reads. Just spend a few minutes on taxonomic annotation. So taxonomic annotation, we have alignment tools such as BWN diamonds. And I mentioned that because it's relatively short reads, it can be quite challenging just to use those kind of tools to identify taxa. And so there's a number of compositional methods that are available. And again Morgan referred to some of these yesterday morning. And crack and two, for example, is probably a good approach. We don't actually use crack and two at the moment, we will be putting that into our pipeline shortly. But at the moment, we're combining results of, I think, Kaiju centrifuge and actual diamond searches as well to give us a breakdown of which taxa are within our sample. So let's give you an idea of the performance in terms of taxonomic classification. So we have this majority voting rule, as I mentioned with diamonds diamond Kaiju and centrifuge. So this is from a data set of mice. So these are mouse cut taxa. And on the left hand side is the gold standard. This is where we know, within these mice these were infected with these ultra shedla flora. Under germ free conditions and we know that there's I think it's nine different taxa associated with these altered shedla flora. So that's all we should find in this mice and so we can use BWA to specifically come up with a gold standard to identify. Yes. Got five minutes left perfect I've only got half an hour left in this talk so that's great. So you can see none of the pipelines really perform as well as the gold standard but, and again I think this is something we need to be a little bit aware of when we're running these pipelines is they're not giving us necessarily the truth. And we do have to be a little bit careful about some of these areas that can, that can creep in and miss annotations and particularly in terms of tax on that we shouldn't take for granted that what the tax on that may not actually be the tax on that's actually present. This is just to show the difference between 16 s data and metatranscript tone data so the top is 16 s data the bottom is metatranscript tone data. And so we can identify groups of bacteria that are present, but apparently not active. We can also identify other tax so that may not be very abundant that can be very active so again just emphasizing that metatranscriptomics can give us information that metagenomics 16 s sequence income. They get roughly the same tax but this really tells you what's important within your data set. This was an interesting study that was published. This was 20. Yeah, just last year. So this was looking at using metagenomics metatranscriptomics look at the ecology of the vaginal microbiome. And looking at the evolution of the vaginal microbiome over time and what they found was that the expression from the metatranscriptomics was actually the better predictor of what the colony dynamics in terms of the abundance. That the next time step was going to be then the metagenomics. So again this just emphasizes that it's the activity, it's the actual expression of the genes and pathways, which is important for helping us understand how these kind of niches how these microphones are actually evolving. And part of the reason that they gave is that DNA can be slow to degrade and so it's hanging around and it's not really giving you an accurate reflection of what is happening at that point in time. So the last few slides I just want to mention, I am going to go five to 10 minutes over sorry Sydney. Just in terms of puncture annotation once reason being assigned to transcripts, these transcripts have hopefully been annotated with functions. Again Morgan went through some of this yesterday but I think it's worth just going over again. This concept of puncture annotation and what we do with this function annotation where we get that data. In addition to the Uniprop database as the eggnog database, and this again gives you mappings of things like gene ontology terms keg enzymes keg modules case items as well. We find that gene ontology terms can be challenging to summarize so there is a side escape tool and a plugin called bingo which enables you to identify over enriched gene ontology terms. As you see these papers and they just give these bar charts of these gene ontology terms can I, well, I have no idea really what that means. And so I think this gets at the crux of how do we interpret these really complex data sets. In some intuitive fashion that is not just staring at a list of bar graph showing us that one function is at some level or some other function is at another level. So this is where I think we're now starting to mature more and more as a field in terms of meta genomics meta transcriptomics so every time we do a new meta transcriptomics run in our lab. We try to identify a new functional category that we can add into our pipeline. So things that we've done recently are looking at things like iron capture so Siderophores iron storage kind of pathways. There's a nice tool called FE genie. So there's this concept of these other databases out there which are really good at focusing on subsystems that are associated with different functions of bacteria. And so can we start taking these kind of more specialized databases, putting them into our, into our pipelines that we can get a read out of, for example, not just metabolism but also I am capture iron transport. And Morgan mentioned the car database. So all biogenesis genes that I showed right at the beginning this is from protein protein interaction data sets have been generated for bacteria. So again there's a number of these kinds of databases and data sets out there that we can start placing these sets into kind of these broader functional categories to get more of an intuitive understanding of what are the functions that are up down regulated and what are the actual genes involved in these and isn't just reliant on these very broad gene ontology terms. We've done a lot of metabolic reconstructions in our lab it's one of the things we've been really doing for I think the past 15 years or so coming up with different tools one of our in house tools is detect. This is part of our pipeline we think in metapro we do really good job of annotating enzymes detecting enzymes with high confidence high quality. And this enables us to come up with, for example, sets of enzymes for each taxon. And I'm not going to mention too much of this day but again just thinking about future where we're going with these kind of ideas is we can start thinking from these data sets and again you could be applying this to metagenomic data sets and these mags is coming up with these metabolic reconstructions. So for each taxon we meet within your data set you can generate a metabolic reconstruction that metabolic reconstruction tells you what metabolic capabilities, each of those types that actually has. And this tool gaps he came out last year. It's, it's, it does a really good job of saying these are the enzymes that have been annotated. I'm going to fill in the gaps in these pathways because these pathways would not be functional without these enzymes there. And so it uses your initial set of gene predictions to fill in those gaps and come up with a model, a metabolic model of your of your particular tax on. And this is where I think the next generation is coming from for starting to interrogate these datasets is metabolic modeling so for example from a chicken data set. We can identify the tax or we can build metabolic models for these individual taxa. And then there are these metabolic modeling tools here this is one called back arena each these dots represents an individual bacteria. So we use these models to predict the evolution of growth, the evolution of the structure of this community. And these models enable you to do things like, what happens if I change the feed once if I add in this organism, this probiotic how's that going to change the community you can make predictions as to what kind of a tab lights are going to be produced and so forth. So we think that tying up these kind of metabolic predictions from your metatranscript own from your mags. So it allows you to start moving into these more sophisticated analysis where you can actually model what your data is actually telling you. And then finally I just want to mention a couple of slides and visualization of results. So here we use side escape. Again, we find this a generally useful network visualization tool, we can be using it for all sorts of our analysis it has figures for papers and so forth. And we think it's quite intuitive. So here, for example, this is from a mouse metatranscript term and here we've just mapped on the gene expression data from different taxa on each of these enzymes and then I'm just highlighting at the bottom there, the conversion this enzyme here, which is involved in the interconversion of fructose one six phase phosphate glyceride I had three phosphate seems to be largely mediated by bacteroids so that seems to be contributing to the function function and using these kind of visualizations you can see which taxa we're really doing which parts of the main function the pathway. One thing we, again with RNA seekers we're interested in differentially expressed genes. This is challenging for these metatranscript term datasets is very complex data sets. The statistics don't quite aren't quite appropriate for applying tools like DC to edge on so we really rely on some new methods being developed for doing differential expression. At the moment where we kind of rely on DC to we know that it's that it's prone to false positives. But when we've a tried alternative methods like Aldax tool and come we find that effectively don't get anything out. And so it's kind of like well that was a whole waste of time if we're just going to play out extra or ankle it's telling us that nothing is significant. So we go back to DC to and that's where the gene cell enrichment analysis then gives us more confidence. So one thing that these methods need to start taking into account is the need to normalize for tax on or gene abundance. So for example within your data set, if one of your data set doubles a tax on, you might expect the number of transcripts are also going to double but that's purely related to the fact not because that tax on increasing its gene expression. It's just that that tax on is increasing its abundance. So there's this need to kind of normalize for this tax on and so there's this concept of tax on specific scaling. This is very much a work in progress. I think people are starting to work on one of the best approaches to making sure that we can detect really significant statistically supported genes and differentially expressed. So finally, as I mentioned it's a little bit depressing in terms of the statistical measures out there what can we do about that so I think genes and enrichment analysis is really the way forward. So an additional layer of statistical support in terms of which functions are being up or down regulated. So the idea is you're collapsing rather than relying on an individual gene being up or down regulated. You're collapsing genes that are associated with one specific function into a group and then you're seeing if that group is enriched in that type of genes. So you've got a lot more confidence that a particular function being up or down regulated.