And we're back with Off The Price on PLOS TV Africa. G.D. Johnson joins us this morning. He's a senior lecturer of the Nigerian Institute of Journalism. G.D. it's good to have you join us. Can I say happy election eve? I'd like to say I wish you're happy something. Well I'm not sure G.D. can hear us but hopefully we're able to connect to him. I set up with the leadership news people now. We have several papers to go through and that's courtesy of our paper vendor. Now the leadership says 14 state and dramatic governorship contests and that's what it is. It's five to finish in Lagos. Bauchi, Kaduna, Gombe, Delta, Adamawa, Reavers, Kano, Eboi, Enugu, Ogung, Oyaan, Sokoto, Nassarawa. That's it. Eleven governors in race for second term. Ainek, Ami, Wern, Personnel and please restrict movement for 18 hours. That has always been the case. But Kofi, you know that that's also a point of dispute and conversation. When we have elections, what's with the whole shutting down the entire economy?  Well I can't wait to share the thoughts of G.D. on this one. President Mohamed Abouhari to success or sustain my anti-corruption fight and I saw some tweet somebody say bye bye you know do anything. Presidential poll I will win in court says OB and just before we move away from the leadership competency will be my basis for appointment. That's what the president elected Kota to say. And then again you find aviation workers secure federal government or sue federal government over proposed concession of airports. This conversation has been going on for a very long time. I owe you no apology go to court. Look man dares or more share it or me sure. I hope I got that correctly. That's about the crisis in the APC. Narrow and fuel scarcity inflation push 24 million Nigerians into hunger. It's not something you want to think about. That's it this morning on the leadership. Important questions that you ask mercy. Let's go over to the punch this morning with these headlines. Nigeria needs competence not national unity government as the president like Bola Mettinbo and still struggling trying and striving to improve its legitimacy because that's always going to be difficult even if he measures at the end of the day. More from the punch leadership not by force. Good luck Jonathan. It's a very funny one. Leadership not by force. So like we say Nigerian pigeon leadership not be by force. Poles legos cannot reverse governorship candidates face tough battles. Yes so Miki governor of Lake River State made a live broadcast yesterday and he got people talking threats ahead of Saturday Poles worrisome Abdul Salami. States insist on contempt charge against Malami. We have IPOB rejects 10th deadly as terror group ranking. Okay. Three feared killed as party supporters clash in Lagos Ogun or your and unions protest airport concession. Sue minister fan and we see a picture story there readiness of officers of the Nigerian security and civil defense corps. Remember when they were asking Nigerians to allow them use guns and you can see they are brandishing their weapons ahead of the state governorship and House of Assembly elections in Abuja on Thursday and they have their own I think their own local elections in Abuja as well. But remains to be seen what what are these two translating to any any actual security and protection on the ground. Those are the headlines on the front page of a punch. What we take our attention from the punch and let's quickly look at the daily trust newspaper. It talks about the elections tomorrow. Security operatives on red alert in state and peace committee worried over violence and intimidation and threats. Now and again there's also another video of MCO Luoma threatening a sect of people to be very precise. I mean let's say the way this he goes if you don't you don't go to vote for the APC stay at home. Now these also generating too many you know controversies and questions and people are saying what exactly is going on. Is this not a crime. What does the electoral act says about this. Now apart from the electoral act if you look at the constitution is there any provision. Is this not a crime. How come the police is not involved. How can we even you know have party members of political parties projecting this kind of narrative. I mean this is this would actually pass for voter threat and intimidation. If you look at the number of persons who actually register for voting election and then you look at the number of turnout over time we always say voter apathy and that's because people are not interested. That's the cliche but I think that we need to begin to expand but like again GD Johnson will be here to share his thoughts on all of this one. I pop ranked war that's thinned deadliest group. Very interesting. How I will form my cabinet that's what the president like to squatter to say 17 dying canoe auto crash and just before move away national assembly leadership while we're delaying zoning till after governorship polls. Adamou is quoted to say finance minister urges up incoming administration to increase that to 10% that of what you want to ask. Girl six mistakes petrol for kerosene and born self while learning to cook wow. Well that's it this morning on the daily trust newspaper. All right we'll take a final one. We have an interesting addition to the other press family. It's an environmental newspaper called Nature News lead story there Echo Bank IITA partner to support 16,000 youth through agric that is the the the world-renowned IITA is very important organization tropical agriculture the Institute of International of Tropical Agriculture sorry. Nigeria produced 1.3 million barrels per day accrued in February. Missed target by 500,000 barrels per day OPEC SPDC lifts force measure on Bonnie exports that's some good news. Rivers pipeline explosion shell fail to protect facilities CAP that's corporate accountability and public affairs organization all right. More from Nature News Nigeria 54 others suffer shortage of health workers. I think that reminds us of what the Minister of Labor Dr. Chris Inge said about the health workers in the country have more than enough. IMF approves first batch of climate resilience loans. We're going to probably go back for these loans and death toll rises Cyclone Freddie ravages Africa some really really worrying news there. Jede Johnson good morning once again. Let's start with your thoughts on the picture stories of the front page of the punch newspaper and indeed daily trust but I hope our guys can show us the punch newspaper. I mean we see the weapons weapons you can see this is not even we're not talking about army we're talking about civil defense here and if this can be what the civil defense will be using on election day then imagine what the Nigerian army and police will be using I mean this looks like some sort sort team from an American movie but Jede Johnson we saw a show of force before the presidential election convoys of Nigerian soldiers some of them even filming their own personal videos warning those who were coming out to to rig elections and to cause trouble with us to be to be aware that they were on ground but what did we see on election day these guys were nowhere to be found in most cases not all cases what are your thoughts on this Jede Johnson need these people in coalition centers the world coalition centers that's where you want these people to be but what you have just seen is just a PR for these security agencies to just fight their budget at the end of the day we send your reporter to go around during the election I'm not to show you are going to see this this and these security agencies in the polling unit I've always released this question where do we need to secure elections are not rigged at the polling unit even though it starts at those process we have seen that elections are rigged at the coalition center but what do we have at the coalition center if you have the coalition center you use the local government sectarian as the coalition center so you have given a head start an advantage to the party that controls the state and the local government structure so as far as I'm concerned this is just them these security agencies to justify to justify their their their budget that they are spending they are spending money to preserve and protect democracy as as far as I'm concerned I I'm just saying that they are just playing to the gallery and in any case what the security just able to prevent some places in in imposing curfew without having the authority to impose curfew on the rights of the citizen to move left at the center curfew you are them you are the messy I wonder how you people go to the office this morning because where you are I guess is one of the areas in which the traditional impose curfew on people getting going about their normal business too as far as the security pictures you see even the pictures if you know you know if you even if you understand what I mean if you engage all those pictures are paid for they are just pure they are not newsworthy they are not newsworthy picture they are paid for to justify the budget that these agencies are spending on security protecting democracy who audited the money that was spent for the presidential election I've got the election what happened we saw what happened in the coalition center we saw what happened because the end I'm bread of Nigeria so as far as I'm concerned I'm not impressed and I'm not better well I also like you to share your thoughts on the leadership I mean the headline that you know you have for 10 states which are really in a dramatic contest for the governorship elections and Lagos is you know one of it about Chikaduna, Gomber, Delta, Damawa, Rivers, Kano, Eboni, Enugu, Ogun or your Sokoto, Nassarawa among others now do you really agree with this caption is there really going to be any sort of drama? Yeah if you understand if you see that now this is when people say politics is local you see a lot of things we play up concerning gubernatorial election in actually it's not out of place for a party to have won the presidential election outrightly to win the state outrightly and therefore the party to lose out in the gubernatorial election because there are so many local dynamics to the election so like I've been pointed out based on the intelligence that I've encountered by the reporter of the leadership Nishpipa, Ogun state, Nassarawa state, Lagos is a dead eat, Kano is a battleground, Bauchi you know the gladiators contesting in Bauchi so all of these states that have been rivers for example rivers there's a lot of people that are grieved that are grieved one allegedly based on the on the manipulation of the February 25th general elections in the river state and there are people that have put their foot on the ground that they'll set the record straight so there's no doubt that this dynamics will clear out and don't forget that some of the governors are ending their tenure so as a result of the end of their tenure new governors are coming into there are a lot of things that come to play I agree with I agree with I agree with that report in totality because if you look at Ogun you look at you look at or you're for example or you're that what's a clear country for PDP anyone has told you uh I've spoken about this in in four months time four months ago six months ago three months ago but you saw what played out with presidential election result and then the national assembly election where APC almost cleared all all of the state so it went voting those that won the election and those that lost they will be leaking their votes so I agree with with that particular with that particular report it's going to be interesting but what we want to happen is for us to secure the critical infrastructure of democracy which is the votes let whoever emerge emerge to the ballot system that he emerge to the votes leaders are elected from the polling units not from not from the coalition center not by the electoral body the moment we start voting by the by the end of voting if you do everything under normal circumstances by the end of voting you know we love you much you know we have much because what the beauty of it is of this representative democracy is that when I'm going to vote I don't know the outcome but as I'm voting all of us are voting and whoever emerges to that voting process automatically becomes becomes elected representative of people and when people emerge to fear free and fair democratic process then you have a fear and irresponsible public governance but when people emerge through crooked means that's why you have crooked cookie cookie them delivery of of of delivery boosts of democracy because they are not accountable they knew that they did not get the mandate of the people they got through office through crooked means so they will be accountable to those that got them into power and I hope and I hope the security just we understand that they are critical to securing democracy and how do we secure democracy whoever the the people that we assume responsibility of leadership of state apparatus must emerge through a free and fair electoral process all right very interesting GD Johnson I'm just in journal analysis here it's like eating some nice delicacy PMB to successor this on the front page of the leadership Friday he's saying to his successor sustain my anti-corruption fight is this to you from what you see playing out going to be possible and is it feasible and does the president have a tangible anti-corruption fight to hand over to any successor in your opinion the president is living in self-delusion I've said it is in the public domain you see on the anti-corruption drive of this administration from inception I've done I can tell you for a fact I've done nothing less than 20 programs talking about the anti-corruption drive of this administration and I've said that they are just into the gallery that they were only fighting corruption on the pages of this paper what what successive prosecution do you have what institution did this we knew when Obasanjo came a team loving there were institutions that were established EFCC ICPC agency were established and we knew ministers under Obasanjo's that were prosecuted we knew inspector general of police that was Mr. Obasanjo we knew not that some we say that is witch hunting well that's that's that's it that's another perspective we saw all of that under Obasanjo administration now what kind of anti-corruption drive did you see that this administration this present administration that came into in 2000 of it what kind of a corruption drive we did all we see is that money were recovered yet we are borrowing money the money that were recovered from who are those that those money were recovered from who are those that have been prosecuted over corruption cases in Nigeria but what we see is that those stories were slammed on the pages of newspaper and the media were watched there there are spaces with those stories without any corresponding investigation to attest to whether in natural sense and those money that were recovered or those that have been accused of corruption were actually tried and prosecuted we have a situation under this administration where somebody was was was was was convicted was almost convicted and because of technical issues the person his case his case was thrown out and then the person later emerged as the chief whip as the as the chief whip of the of the of the of the night assembly he's even predicting himself to be the senate president so what type of corruption and someone said you know what you recall when people said that if you want your corrupt if the national chairman of APC then who are supposed to be a senator-elect and the former governor of those states said as long as you come to APC your sins will be forgiven if you have corrupt cases i'm saying what what is there in the public domain so what is the president saying it's then to the gallery the reality we're done on him because now we discovered that nobody's coming to asorok again the attention has shifted to the president-elect it's everybody will be going to the defense house and the president will live under the delusion that he has subjected himself to in the last eight years we respect to fight the corruption those those corruption were just fighting on the page of this you know the campaign on that and you have to justify why they got the mandate of the people for the last eight years well um i'd like you to also share your thoughts on this is on the leadership newspaper it talks about the fact that aviation workers in Nigeria have sued the federal government who are suing the federal government over airports concessioning and the fact that 150 billion our areas have not been paid uh gd how does this really make you feel then do you think that there's any hope in all of the suing is it even necessary in the force instance well government has a responsibility to pay the arrears of the workhouse that's the allowability issued but if you ask me these i'll tell you that there is a need for us there are a lot of things that we need to privatize in terms of government has no business in in the vision sector allow the public just regulate and allow the public um to operate and manage this sector you all you need to do if you traveled through the length and breadth of this country and then if you traveled outside of Nigeria and you see the way we run we run our vision sector you just wonder whether something is actually wrong with us as a people as far as i'm concerned if you see what happened in telcos in in the telcos and then if you see what happened in other sectors that we allow the private to participate in we see that they are better service delivery and reduce a lot of wastages that you have and a lot of redundancy that you have in in government managing situation so i want us to privatize if they said the privatize nmpc not that we have not seen what has been done to that effect so let's really privatize the vision the vision sector but whatever allowability the government is going to work as the workers we're not going to privatize because they are feeding fat from from from from the redundancy from the mismanagement and from the unprofessionalism that characterize the management of this of this of these various institutions that are involved in the vision sector so as far as i'm concerned i want us to privatize but government must also fulfill his own part of the bargain pay the areas and don't give them the reason for them to go to court if there is an appeal they have a right to go to court in it they have a right to approach the court for the court to adjudicate on the matter and the court should look at the merit of the case and adjudicate as proper however is he a government that respect the decision of of the court system which is therefore it's an open debate everybody knows whichever side he belongs to there's a particular story which in one of the pages of the news we provide the the state governor insisting that the attorney general of the federation and the central bank governor should be tried for concept of court and i agree with them in totality when the chief law officer of the state is himself the person that is not actually putting in place instrument to ensure the enforcement of decision of the court what do you have when the chief law maker the chief law enforcer is the chief law breaker then you have you have chaos in the in the society so i agree let's privatize okay um a lot to look at um let's talk about concession of airports so you know try to you know reduce that talk on the parties uh today but the airport's concession is a big one that some are looking forward to with some sort of expectation so that it may be a way of having these airports that government has us you know invested money in through huge loans have them work but the airport workers the vision workers are not uh in support of this and they have proceeded to sue the federal government over the east proposed concession of airports um are you i do you think that uh the the workers have a point here that you know they may become the victims if this concession goes ahead they have a right they have a right to go to approach the court and the country also has a right to allow the private sector to participate like i've said they in nigeria own one of the largest airports in great between i can recall his name now so we look if you look at if you look at mm2 which was um which was considered to buy cotney you compare mm2 to mm1 if you have traveled to the local wing and you look at what is available in terms of facility in terms of operation and in terms of management um you you understand what we are saying there's a need for us to consider if those ones want to go to court they have a right to go to court and let the court look at the merit of the case government should pay the areas but there's a need for government to take his hands off a lot of things like i've said earlier if you look at the telcos industry where is nitel today nitel moved to mtel where is mtel today compared to the likes of other other other dozen players in the telcos industry so i don't want to mention that because it didn't pay your organization for at but thank you thank you for that well just quickly as we course this conversation down again there's several interesting headlines that we need to look at another one talks about the issue of niren fuel scarcity inflation that's been pushed as 24 million nigerians uh are going to be moved into hunger do you think that this is actually real or that's just uh you know a pepper statement 24 million nigerians getting into hunger there's an environmental news there's an environmental newspaper which is a fresh which is a fresh bridge to what we are doing which you read where ecobank and iit partners to support 16 000 using of the culture messy kufi you and i will go to the market with new what has happened to the price of goods and services we name it's clear and and and then so it's it's it's it's very clever for us to understand that when you have scarcity of of goods and too much funds are running after those goods you have you have you have you have you have inflation every of the actions of the government in the past has contributed for us to have hyperinflation we are even having to double digit inflation so a lot of people have been pushed down into into into into poverty line so it's clear it's clear for everybody to see except you don't go shopping except you don't go to the market if you look the price price index of commodity products it's clear for anybody to see that there is there is there is in the last in the last eight years we have we have we have moved further down the the the poverty the poverty line and it will not get better it will not get better next year you see it with let's talk about the currency exchange you know the value of money that people have lost i i use we use money to buy money doing that doing that until the this until the judgment of the supreme court and the compliance with it this week a week after by the by by by by the actors and players in the banking industry everybody use money to buy money you need five thousand you use use one thousand one thousand five hundred to buy the money so over time people are i think i what whether it was your program where one of the public commentator said he got a hundred thousand for he got a hundred thousand for whether 125,000 i have to pay 25,000 so where is that 25,000 coming from all of this money we are going to come from and even look at this policy i've said it over time look the money they are collecting on trying to for us to be cashless it is putting for that body is hitting deep into your money because you're not paying attention you pay six you pay six nine seven to cover for telco charges and i tell you if you do if you do let's say if you do a 100 billion transaction in a day that's six naira 70 cover times 100 million transaction in the day plus the 29 or 25 naira that's the banks will collect as their charges if you put that over so who is paying for who so we have a system that has that has been put in place to take money from from the mass and put it in the hands of the elec elec so the rich are getting richer and the poor are getting poorer so there are a lot of things a lot of conversation we are going to have as soon as we are through with the electoral as soon as we are through with the election and the government comes into place because when people begin to look at some of these policies of government they have different forms of indirect taxes upon they have different terms of indirect taxes that have been imposed on you and i that is taking our money but because you cannot pay attention because those money and you need six naira 70 cover it makes no difference over 20 naira but if you take 20 naira from 100 million nigerians you know what it amounts to in every of the transactions they make and if you are getting quality service in return for some of these three revenue the government is making if you are getting quality service in return there's no doubt about that you pay for what you have in other clients you pay 40 45 percent of their income as taxes but they get that in return in terms of good services in terms of good infrastructure in terms of good health care in terms of other things that they can enjoy in the society but what do we have to show for all of the money that they are collecting from our hands is for us to have people threatening you is for non-state actors to have more power over the cities in than even state actors it's for a ballot but but you know johnson you you would also johnson you'd also want to agree with me that the tax our tax collection regime is very weak i mean i'm not sure we're collecting you know the taxes that we should collect i'm not talking about the extra but uh if you look at the one the one we are collecting i agree with you the one we are collecting what are we doing with the one we are collecting i'm asking you the one the revenue that have been generated what are we doing with the revenues that have been generated it's the question i'm asking if it's okay our tax regime is poor is this is that okay no doubt about that but the little one you are collecting justify why you are collecting it so that people will know that you are spending this money judiciously and you prosecute whoever is not paying the taxes the interesting thing is that it is people that are not paying the taxes that are also in charge of leadership you know that uh you're telling me i recall the i recall the president elect said in one of his colloquium that we need to we need to expand the the the the tax the tax dragnet to capture a lot of people and he mentioned his name that even i but that you know if i'm not paying i should be captured so that i should pay so is there is there in the public domain it's not uh whether you are for this or you're for what we are saying is that let there be a justification for i'll give you an example what's the justification for the collection of the toll that have been collected until like it took it for many many years for many many years it's it's an axis you go to messy you live in natcha so it's an axis you are you working via so it's an axis you go to you've gone through many many many many many years once you leave the toky tell me tell me in fact you even go to ashi in going through the payment is it an idea that the cost of collecting the taxes is even much more than the taxes that are collected all right all right didi john so we'll just take a a final one before uh we say goodbye to you and of course um look forward to having you uh back on the program they now feel scarcity uh an inflation is pushing 24 million Nigerians into hunger according to the f a of the food and agriculture organization so the front page of uh leadership friday uh while we have our eyes on the election this is a very important aspect of our national life that some people may go to bed hungry uh tonight and even if you know the elections are concluded and the best candidate wins at the end of the day we'll still have to grapple with uh with hunger um jillie johnson 24 million um Nigerians what hope do these people have um i mean the the hope is definitely not coming from the government is it in narav scarcity and fioske see uh driving factors including inflation to push these people into hunger jillie what what hope you see they are just the conservative which we got it's not going to be a mother to the four million Nigerians and it's really interesting thing you could feel if i have been able to convince at least uh nine million of those people you know have been the president elect today if i've been able to convince nine million out of that 24 million um and that's why you see that the incidence of food buy is very very hard and that's why it's a key component part of our democratic experience because one of the things that we have since is 1999 to date is not an exception to to the apc comments what we have since 1999 to date across the state across the 36th of the federation including the federal capital territory one of the nine german is trying to call the federal capital territory one of the states in one of the first congress in the state that at seven states i didn't know where where he got that from is to perpetuate poverty and once you perpetuate poverty an hungry man cannot think straight it's clear an hungry man cannot think straight it's so we have a situation we are by people justify collection of revenue people justify that they are investing in projects and you see a lot of people working on the street living in hunger living in abject poverty but you know jidya jasin when we talk about poverty i mean because this is you know another dimension to this jidya if you can hear us it's the fact that if we look at this has been several policies of government asked to uh i mean this policy is a targeted tackling the issue of poverty remember you know several statement and policy statement has been put out you also have different programs social intervention programs by this government eight years you know almost eight years already because few moments before the handover and all of these programs are you know were made and formulated to take care of the issue of poverty and so why then do we still talk about you know 24 million persons getting into poverty is this new 24 persons or what exactly is it and then you know it feels like the government has not been thinking because that's how you're sounding i think that there's been several policies that the government has put in place and you know there's been statement that's been put out also to tackle the issue of poverty for the past eight years let's call it eight years already uh jidya can you hear me i can hear you messy um there's no poverty that there's no poverty that has been tackled by any atmosphere but but you agree with me that there's been policies of government i mean there's so you know them for instance a social intervention program they succeeded in doing is to what they've succeeded in doing is to perpetuate poverty and as a result of that we have seen a lot of Nigerians in public under this present democratic dispensation which are called civilian administration and not a democratic governance because what we have seen is a transition from military government to people in civilian races they still have been the detentions of all the military they still have all the tendencies they still have it all right jidya we have to go at this point in time we do appreciate you uh for being part of our show this morning the breakfast and off the press to be precise we look forward to sharing your thoughts on more national critical issues as you know we'll proceed in 2023 thank you so much once again all right still ahead on the breakfast we have uh conversations concerning the elections a governorship and state houses of assembly elections especially in nigeria's commercial nav center of lager so look at what lies ahead expectations and of course um what are some of the fears that voters may have ahead of the lager's governorship polls and also look at what's happening in other states around the country please stay with us


I'm Lieutenant Colonel Mario Iglesias, I'm the product manager for the Bradley infantry fighting vehicle and with PEO Ground Combat Systems here today, fielding the Bradley 8-4 system. Right now we're in the middle of doing the operational new equipment training for the Bradley crews, so we're going to learn about all the new capabilities and how to operate this new Bradley 8-4 and they can finalize that with a gunnery and they will be fully qualified to be the first unit in the army to operate the 8-4. The main difference that the units are going to see right away is the powertrain. The new engine we put in there, by a significant amount more horsepower, there's a new transmission. So the whole new powertrain is going to allow the vehicle to be much more renewable. As the vehicle has aged over the past decades, we've added additional armor onto it, so now we need to give it additional engine power in order to make it so we can keep up with the other units and Navy's team. The new Bradley 8-4 also has new power generation systems, so as we put new electronics in the vehicle, we've had to have more power to distribute it throughout the vehicle. Technology's gotten a lot better, the way we use power is a lot better, so this new Bradley can support a bunch of new survivability, like new counter-IDD technology that we've put on there, potentially in the future, new active protection systems and new network capability. The technology in this new 8-4 will make sure that it is a dominant infantry fighting vehicle for many years to come while we wait for OMFV. This will give the capability to soldiers to ensure that they can maintain combat dominance.


The story you are about to hear is a true story, a war story. It happened only fourteen years ago, but already it seems a little dated, almost quaint in these days of an ever bigger and bigger bomb. But it wasn't quaint to the men who lived through it or died during it. For no matter how huge and lethal our weapons become, our real strength lies in men. The men of Baton and Normandy, of the Argonne and Seol. The men who today stand constant guard upon our far flung ramparts. The men of the armed forces of the United States of America. To them, this historical footnote is dedicated. To them, this historical footnote is dedicated. Tarawa was tough, starring Mr. John Lund. Now, John Lund stars in Tarawa was tough. A tale well calculated to keep you in suspense. This is the way it was when they went island hopping. This is the way it was at Tarawa. But you can call it Quajolaine, or Macon, or Namur, or Los Negros. This is how they fought a war where it's tomorrow tonight. The training's all over now. You're ready to ship out.  But wars move slowly, even the best of them. And so you sit and wait. Crowded into a shed on a pier in the harbor of Wellington, New Zealand. You've been laying around this shed for 12 hours now, waiting to get aboard your transport. And you'd go box sock if it wasn't for the radio that the Red Cross fellow rigged up. It's tuned to your favorite station, Radio Tokyo. Hey, what a tune. Ah, what a tune. Can you imagine what Sinatra will do to that? Sinatra would murder it. Listen to Bing. Bing, Bing. You're getting old, lus. Sinatra's a kid nowadays. Well, I reckon Moish is right, lus. We've been out of the states too long. He's a whole new generation grew up since Pearl Harbor. Like these two booths. Can I help it if I was born later than you? No, something I wished I was born even later. Shorty, what are you going to do down in the canal to get a boat like Odie assigned to my squad? You shouldn't have let your squad get cut up so bad at the tenor room. Hey, look out what you're saying, Shorty. Lus is the best sergeant in the whole maraud. Now, Odie, just keep your coveralls on. I sit at the tenor room myself. I know all about how Lus got those rhythms. Yeah?  Well, listen to what's talking. The commander of Camp Lejeune. I wish I was listening to that record of Eddie's dog cart back in Allentown. I'm sick. Good? No. I was just thinking. Hey, Lus. Hey, ought I get them new platters like that in Tokyo? By a midget submarine, direct from Catalina Island. Oh, come on. I'm no kidding. How do they... What do you think Argentina stayed neutral for? Huh? Just so they could send the latest American zipe to Japan as our Tokyo Rose complained for it? Gee. You know, I never was able to understand that international politics. Well, boys, how did you like that one? Pistol pack and mama sung by Bing Crosby and the Andrews sisters. And this is your chin-up girl in Tokyo. Get her. Who writes her material?  It's kind of cute, huh? Pistol pack and mama. Your mama in Tokyo is packing no pistols tonight.  They're selling you down the river back home. Four more strikes were called today.  Oh, I went online. You guys aren't like that all the time. You get used to it. It's too bad, you boys. All of you couldn't be back home tonight where you're really needed, protecting those near and dear to you. And now I have a special word for the brave Marines down in New Zealand. Hey, that's awesome. You boys are waiting now for transports. You don't know yet where you're going, but I do. You will fail in the morning to attack the Gilbert Islands and the Central Pacific. But you'll never get there. We're waiting for you with hundreds of submarines. I'm sorry for you boys. None of you will ever see land again. And now a recording which seems appropriate to the occasion. A sleep in the deep. I don't like that. How does she know where we're going? Ah, Moise, you remind me of that tired old gag about the guy who reads a sign on the door that says, keep out this means you. And he asks, how did they know I was coming? Oh, very funny. Sure, that's just a lucky guess. Nobody's one to ask. In case you boys waiting on the piers in Wellington think I was kidding. She means us. In case you think I don't know what I'm talking about, the time is now five minutes after eight. If you'll look out the window at the clock in the customs house tower down there in Wellington, New Zealand, you'll discover that it stopped 20 minutes ago exactly a quarter to eight. What? Moise? Huh? Think again into that window, will you? Yeah. Yeah, she's right. Made her hand support a quarter of eight. My watch says eight-five. There you see, boys. Don't ever doubt your Tokyo chin-up girl again. Chin's up. And bon voyage.  When you pull anchor early in the morning and meet your naval escort outside the harbor, you forget all about that dizzy broad on the Tokyo radio. Let them send all the subs they've got left, you'll be ready for them because you've never seen so many carriers and cruisers and battle wagons in your life. You know you're on your way to something big and they don't leave you in the dark about it. New Zealand is just slipping under the horizon to the south when they herd you together for a chart talk. Man, there's been a lot of scuttled butt about where we're going. And it's time we give you the load down because all of us have got a lot of work to do before we get there. Our job is to take Tarawa. Ever heard of it? Tarawa is the principal atoll in the Gilbert Islands. The Gilbert Islands? I didn't know it took you a long time. She was only guessing. Part of the task force will attack Macon Island to the north. I think some of you boys are pretty well acquainted with Macon. Tarawa, another force will attack Abba Mama in the south but our job is to take Tarawa and we expect it to be plenty tough. Now, take a good look at this map. Study it. Study it hard. By the time we go over the side, I want you to know it as well as you know the drill ground at North Island.  The island of Badio. Badio. Here on the extreme southeastern tip of the atoll is where the nips have their strongest fortifications. Now you men who are at Guadalcanal will find this a much different operation. There's no jungle on Tarawa. That's a break. You mean we'll be able to see it a little before we shoot them? I won't guarantee that. Here a reconnaissance shows that they're strongly entrenched. The airstrip is surrounded with pill boxes and echelon. That'll take flamethrowers. You'll be supported by flamethrowers and light and heavy tanks. Brother, this is going to be easier than the dry run in Paris Island. Don't be too sure about that. You'll have the strongest support any Marine force has ever had. The island will be softened up for us by naval and air bombardment and all that. But remember, you're going to have to take the island yourselves on foot with the weapons you can carry. That's okay with me. Now, the beaches will be designated red, yellow, and blue. You men are assigned to beach red. Hour after hour, you go over the maps and the charts and the air photographs, printing each detail in your memory, cramming like a kid before a geography exam. You only take time off to chow to hit the sack and to take your turn topside on the sunny deck.  Can I get paid for laying out here, getting this lovely tan? Huh, that's what you think. Oh man, I hope I'll be able to keep tan like this until I get back to Brooklyn.  You look like a bad case of the Fourth of July. Nah, nah, nah, I'm okay. I always get red like this here at first, but don't mean nothing. Now listen, Moish, we're getting near the equator. This sun is different than the sun in Canarsie. What are you talking? I don't see no difference. There ain't two suns, is there? No, it's the same sun. Well done. You know what I'd like right now. Huh, what? A bottle of Mexican beer and a bowl of chili. Hey, chili. Hey, did you ever know any Mexican girls, shorty? You know, if I try real hard, I can see a connection there. Don't try. I knew a Mexican girl in Diego once. You know, while I was at North Island. No, kid. Yeah. You ever know of Mr. Smith in New York? No. Oh, I never run into him. That's funny. Yeah, a friend of yours? No. Well, a Mexican girl, she was. What? A friend of mine. You cat, Casanova Greenberg. Huh? It was just you calling me odious on a dirty. Oh, not necessarily. Casanova was a guy who had her away with women. Oh. You notice that about me too, huh? Did I ever tell you about that Aussie girl I met in Sydney? She had false teeth, uppers and lows, full set. So you talked. You might think that. But just to talk. Because nobody really listens. You talk because you don't want to listen to the pounding of your own heart. Racing against time, against the too soon time when it may stop forever. You talk or you just lie there and look out at the blue, blue water. And you wonder, what are you doing here? How did you get here? And you wonder if Tokyo Rose was guessing or did she really have the word. And you wonder most of all, if you ever will reach dry land again. We continue with Suspense. Power was tough, starring Mr. John Lund. A tale well calculated to keep you in. Suspense. 2,000 Marines aboard this transport. And below decks it isn't exactly a cruise ship. The bunks are stacked four deep and there isn't any head room. You have to slip in sideways. Your gear hangs on the steel upright. Your netting covered tin hats swing slowly with the motion of the ship. It's hot down here and it stinks. Stinks of the sweat and cigarette smoke of hundreds of men. But you're not noticing any of this now. You're lying on your bunk with Moyship above you and Shorty below, studying that map of Tarawa. I sure wish we were going into beach yellow instead of beach red. Why? Well, beach red's next to that pier. I bet the Japs will have guns all over it. Kid, by the time we get ashore, that pier just won't naturally be there. The bombardment will take years. The brass has learned a lot since Guadalcanal. What is a canal tough, Shorty? Oh, that's never tells me much about it. Kid, it was tough, it was rugged, it was hell. This Tarawa thing is going to be a breeze. I hope you're right. Oh, boys, I just got the dope from the skipper. What is it? When do we go over? My back. Hey, Odie, don't sit up so sudden. How's the sunburn, Moyship? Terrible. My back is just killing me. Why'd you lie on your stomach? Oh, it's burned worse.  But I did. You wouldn't have listened. What did the captain say, Les? DJ is the 20th. Hey, Chower is 8.30 in the morning. Tonight's the 18th. And we go over day after tomorrow. We go over tomorrow night. And if you guys got any letters to write, now would be a good time to do it. You're ridding them out, Odie? No, not today. You better do it, kid. They're going to be pretty busy from now on. But you can't write. What can you say? You got too much on your mind to write. They spring a boat station drill long about 400 hours. By the time that's over, you've got to line up for Chow. You spend the rest of the day checking over the maps and photographs and your equipment down to the last cartridge and grenade. And as the day passes, you start to tighten up. You can feel the other guys tightening up, too. It's all over the ship. Everything's the same. The placid blue sea with the sun sparkling across it, the flung-out lines of transports and carriers and battle wagons. It's just like it has been for six days. But inside you, inside every man on board, it's different. It's hard to get your breath.  Your nerves stretch tighter and tighter. And by nightfall, you feel that something has got to snap inside you. And then along about midnight, a loud speaker box. All hands secure condition three. Set condition 1A.  Relax, kid. That's for the Navy crew that's running this tub. The hired help. Yeah, you got plenty of time, Odie. That's the most you got of anything in a war. Time. Yeah, but maybe they changed the plan and were going in earlier, huh? Now, you know with Admiral, it wouldn't go and do anything like that without consulting you, Moish. Odie, did you write your mind tonight? No. I've been too... Well, I've been getting my gear together. Listen, kid. You better write your mind now. You still got time. I don't know what to tell her. Tell her you're having a wonderful time. Tell her about how pretty the palm trees look in the moonlight.  Tell her anything to keep a morale up. While the Marines lay around on their bunks down below in the blue battle lights, there's plenty doing topside. From the boat deck, Davits Creek and Gron as the LCPs, the landing craft personnel go into the water. From the forward and after decks, the winches sputter and choke, dropping the big LCMs landing craft mechanized over the side. Finally, they're all away and cruising astern in a loose turning circle. A board ship, the loudspeaker is squawking. Evisions 15 and 16. Hey, hey, 15 and 16. They ought to be calling us pretty soon now, huh? Call us when they call us. Relax. Well, you can't take that island all by yourself, Moish. You got to have some help. Oh, it ain't that. I want to get it over with so I can take this equipment off. He straps a motor in my sunbind. Oh, don't you worry about that. Japs will take a sunburn off you, man. Won't they, Les? Yeah. How do you feel, Odie? Okay. Scared, kid? Me scared? Heck no. Listen, brother, let me tell you something. You look scared. You act scared.  The only difference between you and Les is Les don't look or act scared. Are you scared, Les? Sure I am. Scared to death. Les, I'm glad you said it because honestly, I'm so scared. I know, kid. It's just natural. Hey, you guys, where's the chaplain? Well, he ain't there in the head. I'm looking for him everywhere. Last I saw him, he was after the sick bay hearing prayers. Well, thanks, Mike. You better hurry. He's got to land down that bank as long as a gene artery picture in Abilene on a Saturday night. Divisions 17 and 18 report to the parking station. Hey, 18. That's us. Come on, Les. Hurry up. Come on, shorty. You better save your strength, kid. You're going to need it later. They're crouching in the landing bards now. 36 of them have come tumbling over the side and down the cargo net and now they huddle in the bottom of the LCP. They don't look like the ads in the slick magazines back home. They're just three dozen American kids with assorted sizes, creeds, and color of hair. They aren't quite as slap happy as a football team on its way to the Rose Bowl, but they aren't acting like they had a date with Destiny either.    Yeah, that's important, Odie. You ain't got a tin hat on your tail. Another thing. First time on the fire, maybe you'll freeze. Well, don't let that worry you. Just freeze. I'll be there to look after you. Oh, I should treat me like a kid. I can take care of myself. Now you hear me. I'm looking after you, see? Who's going to look after you, Sergeant York? It's not yet done. And the boats are cruising around in a circle at the rendezvous point a mile off the beach. It's quiet except for the purring of the engines and the low talk of the men. And then way off toward the horizon is the jack-of-all-a-sparks. Hey, what was that? The Navy! Milet ships and cruisers off shore. You and I work for us. Brother, when they get through pounding that beach, there won't be a jack left to rig a booby trap. That, boys, is what it's known as. Softening up.  It takes hours to organize and land an invasion force. You don't rush off toward the beach at top speed yelling, Gung-ho. You wait. You circle and wait while the big guns of the Navy pound the 800-yard-wide island of Beijing. And then the sun bounces up out of the ocean off toward Hawaii in its daylight and hot. And still you wait, circling slowly. And when the battle wagons leave off the dive bombers take over, swarming up from the flat decks of a dozen carriers getting behind the horizon, roaring down in the Japanese gun positions, crumbling the pillboxes, pulverizing the concrete ramparts in the most intense of air bombardment in history. The you-crouching mouth that gave off shore in the LCP. It looks as though nothing could remain alive under that murderous fire. It's getting close to a shower now. And you get the flash from the flagship. And the circle breaks up and the boats fan out heading for beach red. But you don't go in full throttle. You sweep in slowly, feeling for a safe channel through the treacherous coral reefs. Praying the high tide will hold its promise four and a half feet of water that you can unload these kids on the page of a history book. You peel your eyeballs, searching the coral formations beneath the rippling green water. And then suddenly something's changed. Something is not quite the same. And then you get it. It's quiet. The Navy guns have ceased firing. The Navy planes have let up so the men can go in. It's a shower. And no sound comes from Bejo. Not a single rifle barks a challenge to your coming. How you doing, kid? Okay. Look at that. Look at that. Not a peep out of them and only a couple of hundred yards to go. Yeah. We run aground. Yeah, you've run aground. You can back away all right, but you can't go forward. Your bottom's crunching coral. This is the end of the line. All right. This is as far as we go. All right, boys. Over you go. And keep those rifles out of the water. All right. Japs went to school in the States. Too many nips read American history. They know about Bunker Hill and don't fire until you see the whites of the enemy's eyes.  All up and down the beach, the men are pouring out the LCPs into a hot wall of death. This is Beach Red. The brass had to make up the code named it well for this day. The beach is red all right. And the surf breaking red upon it. Blood red. Getting off the reef, the landing barges are helpless. All except one. Finds a curving channel through the coral and pushes its front nose in shore to pick up the wounded. It's tough going waiting for water just pulling a gun over your head.  You're not scared now. You've only got one thing on your mind now. You've got to get to that beach. No, you're not scared.  Come on. Get to that beach. All right. Don't see free duck. They're shooting this way. There's another CP over there. There was Helandia Bay and Guadalcanal and Saipan and Iwo Jima, Okinawa and Awituck. But they were one and held by men. Even the final island of Honshu on which the two first adolescent bombs burst was finally one and held by men. The men of the armed forces of the United States. The men whom America honors. Which Mr. John Lohn starred in, Tarawa was tough. Written, produced and directed by William M. Robeson. Listen. Listen again next week when we bring you another tale well calculated to keep you in. Suspense. Supporting Mr. Lund in Tarawa, West Top where Ellen Morgan, Jack Krushen, Dick Crenna, Joe DeSantis, Robert Easton, John Daener, Larry Thor and Lou Krugman. Suspense has come to you through the worldwide facilities of the United States. Armed Forces Radio and Television Service.


Well, I think we can start. So I want to welcome you all to this SID Virtual Computational Biology Seminar Series. Today, we have the pleasure to host Guna Rech, who is professor of biomedical informatics at the computer science department of the ETH Zurich. So Guna Ern is PhD at the German National Laboratory for Information Technology, where he also did a postdoc. Then from 2005 to 2011, he led a group on machine learning in genome biology at the Friedrich Michel Laboratory in Tobingen. And then in 2012, he joined the Memorial Sloan Kettering Cancer Center as an associate faculty. And then in May 2016, he and his group moved to Zurich to join the computer science department of the ETH Zurich, where he's still is. So his group's research lies at the interface between methods research in machine learning and sequence analysis and relevant application area in biology and medicine. Current research focuses include large-scale machine learning, accurate transcriptome reconstruction, identification of RNA processing regulators, developing clinical decision support systems, and developing method and resources for international data sharing of genomic and clinical data. Zurich Laboratory has extended expertise in analyzing the RNA-seq exome and oral genome data and has contributed to major discoveries in RNA processing regulation, RNA alteration, and the relevance in cancer. So today, Guna will tell us more about these novel approaches to identify, understand, and take advantage of RNA alteration in human cancers. So thank you again, Guna, for accepting our invitation. And the floor is yours. Thank you so much for the very nice introduction. It's a great pleasure to be here and speaking here, talking about our recent work. Some of that work has started while I was at Memorial Sloan-Kettering Cancer Center in New York. And we are about to finish some of this. And I'm happy to talk about this here. OK, so let me start with some more general motivations before I go to specific topics. So I think it's clear that some pieces of medicine are relatively imprecise. There's medications which are on the market, which make a lot of money, which are not helpful for some of the patients, which take those. What you see here is a figure from 2015. For instance, the first drug, which has the largest market share and income, only benefits one-fifth of the patients who actually take this drug. For the next drug, it's one out of 20 people who benefit from this. And 19 people don't benefit from this. So there's a great imprecision to some parts of medicine. And I think we have the understanding today that phenotype, namely whether the drug works or not, is influenced by a genomic part, but also by lifestyle, the environment, and so on. So it's a complex system, which we are, and it's influenced by many different factors. So the genomic part, there's a promise that the genomics actually helps to understand when certain drugs work, when certain treatments work, and so on. And there are some initial successes of genome-based medicine. One is in pharmacogenomics, where you see certain mutations in the genome, which lead to a different binding pocket. And if you have that mutation, then this may change the efficacy of a specific drug. For instance, here with clopidogrel. Another case is rare diseases. Here's an example of a 20-month-old girl, which has a rare neurodegenerative disease and has a strange gait, impaired vision, and so on. And only by exome sequencing, it was found that this girl had a deficiency in a vitamin B, a transport mechanism. And this revealed how to treat this patient, in this case, by giving more vitamin B. And this, I think, almost completely cured that patient. And then in other cases, we have another example as cancer treatments, where we look at specific mutations, which may be common even among different, across different cancer types. And if you have a certain mutation, then you can treat patients for these specific mutations with specific drugs. So I mean, there is a great need for large-scale clinical genomics. So I think it's clear that rare diseases, most people who have rare diseases, will get their genome sequenced relatively soon. It will become common practice to do so, similar for patients who have a cancer. Their cancer genomes will likely be sequenced very soon. So the estimates are that within five years, probably, that 15% of the developed world population will be sequenced either for rare diseases, or for cancers, or for some other reason. So just thinking about the data sizes which are involved here, so this is quite drastic. So maybe a cancer genome is 300 gigabytes total in sequencing, and a germline genome, maybe 90 gigabytes in sequencing. So if you multiply this with 150 million people, that is about 28 exabyte of data. So if you would all put it in one place, that would be roughly the size of what Google stored maybe a few years ago. So it's quite large. So that needs new algorithms, and it needs new concepts, how to work with these data sets, and how to distribute them, and so on. And we did some work, which I can't go into detail much here, on genome graphs, where we can actually collapse a lot of the genome sequences, which are very similar to one data structure to more efficiently access and store such large data sets of genomes. OK, but the genomics part is just one part. We have the other challenge, that we have the lifestyle, the environment. We have clinical data, which we also have to capture. And a few years ago, or maybe 20 years ago, the data looked like this. It was in non-digital form. Nowadays, the data is maybe in digital form, one mostly digital form, but it is in some databases. And these databases are hard to access, and they are not designed for research purposes. They're designed for clinical purposes. So it's really hard to actually get the data out for research purposes. These databases are not fast enough, and they don't deliver what we actually need, and so on. So what we really would like to have is data in a somewhat more intelligent form and a much more accessible form in the future. So obviously, this data is already used in clinical studies, and these clinical studies are needed in order to show the efficacy of certain treatments. And usually, what happens is that you have this institutional database. You have a patient cohort, and then you have people, people who go into this database and read of what's stored in the database, read the medical documents, and extract certain pieces of information which are needed for that clinical trial. And then they enter this information into the clinical research database. At a hospital like MSKCC, there's lots of clinical trials going on, and there's 500 full-time employees who do this all the time. So that's why this little cubicle farm here, right? So because it's really many people who actually do this who are involved in extracting information out of electronic health records. So this is, I mean, from a computer science perspective, that's totally inefficient and totally unacceptable to go this way. And I think we have to solve this as computer scientists. So I think there's quite a few challenges, data science challenges, which we have to solve at medical centers. Just thinking about efficient search and information retrieval, I mean, we know how to do this in principle. Google can do this, right? But it's not done on medical records. Right now, it's very hard to visualize something like a patient electronic health record, as we can do, for instance, visualize something like a piece of genome that displays quite complex data. So we cannot do this with patient data as well. And obviously, we need high performance data access and computing within the hospital's efficient data structure, scalable computing, all within the hospitals. And we need some data science mindset, I think, also in the hospital. And this is kind of a long way we still have to go, but I think that's where we have to go eventually, right? So I think the challenges which we have to solve as researchers and maybe also as people who push research into hospitals as we have to develop, of course, new data science approaches for medical data. So maybe we need new methods for medical data. But also, we have to provide tools for the community so the hospital could actually use this and reproduce some of our analyses and use our tools. Then obviously, we have to solve biomedical problems. So solve cancer or solve a specific part in that quest and usually do this through collaboration. But I think a big challenge is also to create an environment which allows us to do this. So where we can actually solve these first three challenges. So an environment where the medical data is in one place, where the researchers are in one place, and everything can come together. And that's, I think, an important step, an important goal. And that's one goal which is attacked by the Swiss Personnel Health Network and by the Data Coordination Center is to create a network of compute systems which are interacting with each other. And these compute systems provide means to bring in hospital data, to bring in research data, and to perform the research on that data within a secure IT environment. And there's people in this room who are involved in this to provide this kind of platform. And I think this is a very important effort and without which it's going to be very hard to do any meaningful research on medical data and genomic data. OK, so this is ongoing. The SPHN network has started last year. The first projects start this year. And we will see in the future how much this will develop. So now let's get closer to what MyLab is doing. Very brief overview before I get into specific projects. So as I mentioned, we work on data structures for genomics. We try to develop genome graph data structures so we can actually encode and store information about thousands or millions of genomes in an efficient way. We also work with clinical data. One project which we work on is on intensive care data. The data here is from the hospital. We get data from about 60,000 patients. And we would like to predict, develop a early warning system for kidney failure or for heart failure. So you can actually predict maybe a few hours in advance that something is going to be wrong with that patient. So doctors want early about this. So we are interested in developing methods for heterogeneous biomedical data analysis for patient and disease modeling. And we work on cancer genomics. And most of what I'm going to talk about today is about cancer genomics and anything related to cancer what we work on. So I will need to come back to talk about the other topics at another opportunity. OK, so project one. This is a project I started when I was at MSKCC. At MSKCC, we had access to some medical data and to some genomics data from the same patients. And here, we performed a joint analysis of clinical notes and somatic mutations. So a clinical note looked like this. It has different sections. For instance, chief complained, history of present illness, and so on. And then there's a summary of what the doctor wrote at a certain point about this patient. And in this case, we had about 2 million electronic, these kind of documents, from a total of 200,000 patients. Now, the question is, how do we extract information about this? Because it's quite heterogeneous. Every doctor writes something different.  And we came up with this relatively simple idea, which I'm happy to explain here. So we thought a sentence is a good unit of information. So a sentence is a statement. It says something about the patient. It's relatively short. It's not too heterogeneous. And we thought we could just analyze sentences. So it has limitations, of course. But if you just count how many different sentences we have, we have about 100 million sentences, which we found in these 2 million documents. So what we did is we removed a kind of stop words, which happened a lot. And then we took these sentences and thought maybe sentences which share similar words probably mean the same thing. So we clustered these sentences. And the idea is when these clusters probably have a lot of sentences which mean roughly the same thing. And this is kind of a description of a phenotype or of the medical condition of that patient. So and here's just a small piece of that graph which we generated. So each dot here is one sentence. The color are different clusters. This is just one little piece of a big graph where the nearest neighbors of sentences are connected to each other. So just to give you an idea how similar or how different these are. In the end, we chose, I think, 10,000 cluster centers. We essentially reduced these 100 million sentences to 10,000 clusters. And then we had for each patient a description, whether they had that sentence or they didn't have that sentence. So it's a binary vector. And that is something which we can then analyze. And we can connect to genomic data, which you also had for these patients. And in those cases, in the case of MSKCC, there is data from two more sequencing. There's a 1,000x sequencing of about 400 genes. So 342, I think it got a bit larger by now. And about 100x of germline genome. So we got a list of somatic mutations for a subset of the genome. The idea was, can we connect the data which we get from the text and associate that with the genetic information, maybe conditioning on some clinical variables, like which cancer type and so on. So we get more interesting correlations. And here's just one basic result. There's still a somewhat preliminary. So here you have the gene. Here you have the sentence prototype, which describes this cluster center. And for instance, he underwent colonoscopy, which revealed a polyp in the colon. And that is associated. The presence of that sentence is associated with a somatic mutation in APC, which is not totally surprising. But we found some of these cases, we could easily validate in the literature. They have been published about. And in some cases, we're much more recent or we're not published about yet. So we see this as a great source of hypotheses, which we can then go and check with the doctors to discuss those and see whether they mean something meaningful. So something which helps maybe the treatment of the patients in the end. So this is ongoing work, but it's one way where we connect clinical data with more genomic data. I will take questions at the end. So the second two projects, which I'm going to describe is mostly on genetic data, where we don't have clinical data. These are from large cancer genomics projects. This is from TCGA, the Cancer Genome Atlas. This is a large US project from the National Cancer Institute. It has about 12,000 donors. It's about one petabyte of data. It has exome data. It has RNA-seq data, some whole genome data, and a few other modalities. And the second project, which I'm going to describe, is a subpart of ICGC. That's an international effort. It includes at least 20 countries, 25,000 donors, and also about one petabyte of data in total. So there's probably hundreds of groups generating this data and also analyzing this data. And you could say, well, why do we look at this? Probably everything has been done by now. But we have certain ideas how to analyze this data. And we are particularly interested in transcriptome changes. So my group has been working on RNA analyses and RNA splicing. And our view on this data was, can we see anything in the transcriptome, which is maybe cancer-specific, or how can we exploit that? And that hasn't been done so much. People have mostly looked at somatic mutations themselves. And these two projects, which I'm going to describe, look at two different aspects of that. So the first one is really about alternative splicing. Here, we look at cancer-specific splicing and the implications of in about 8,500 samples. And we wanted to find cancer-specific splicing patterns, differences between cancer and normal. And we also were interested in identifying variants, so genomic variants, regulating splicing in cis or in trans. So we learned something about gene regulation. And we also, as you will see, we thought about immunotherapy, whether this can be used for immunotherapy. So, and we need two data types for this. We need RNA-seq data to describe splicing and the RNA, the gene expression and so on. And we need exomes, we used exomes, which describe the variants near the exons. So typically not deep into the intron, but it describes the variants next to the exomes and maybe a little bit into the intronic regions. And these are the interesting regions when you look into splicing. So it's a good data set to look at. And it's a relatively large data set. So it's an association study with 8,500 samples. It's one of the biggest data sets you can look at, which is relatively homogeneous. Okay, so it's a relatively complex project, I would say. So we had the pancan atlas data, which is, I think, 30-ish cancer types. Each of the cancer types needs to be processed in some uniform way. We analyzed the RNA in a certain way, in a uniform way. We looked at the exome data. We did the bearing calling in a certain way. And everything was processed in a uniform way. And then we performed a different analysis. So we performed the splicing QTL, which I'm going to describe, some different splicing analysis. Then we identify neo-introns, introns which are very cancer-specific, which you only can find in cancer. And then we show that these introns generate peptides, which can be validated by mass spec, which may be used for immunotherapy in the end. So I will get to that. So there's a quite complicated data flow, and it's quite massive in scale to do this. Okay, so the main tool which we use is a tool for the analysis of alternative splicing. It's called splatters, splice adder. And the idea is that you look at an existing annotation, you look at the alignment of RNA-seq reads. So it takes any alignments, and it takes an existing annotation, which may not be perfect. It uses that annotation and annotates it, extends this annotation with additional connections between axons, which we find in the aligned reads. Okay, so there's a read which starts on one axon, and it goes into the next axon when we see, oh yeah, that gives evidence for an intron which connects these two axons. And it also detects new axons and new introns which have not been annotated before. So it finds novel and can also quantify existing splicing events. So in the end, we detect splicing events, and you quantify splicing events, you can also do some differential analysis, which is optional. So we stopped for this analysis here. We run this tool on all of the 8,512 samples and quantified splicing. So then you get essentially a quantification for each splicing event. Is the axon included or not included? And this gives you a vector for each sample, and then you can visualize this vector, for instance, by T's knee. And then you see, okay, actually each dot here is one sample, and then you can see that there are major differences between the cancer types, which is not totally surprising. If you would do the same with gene expression, you would also find a similar effect. So for instance, here you find the breast cancers, here you find the ovarian cancers, and so on. But you can also look into more detail and look into different subtypes of cancers. For instance, on the right of this panel D here, you see that the subtypes of breast cancer, so the basal is here, the luminal A and B I think are here, and this I believe is the HER2 class. No, this is not sure. So luminal A is here, luminal B is this one, and this one maybe here, right? And I think these are the normals, if I see it correctly. So you can actually see a good spread of the subtypes. You could actually say for each sample, just by looking at splicing which subtype it is. So that's quite good. But it's fully descriptive. I mean, there's limited things you can learn about this. So we're interested in quantifying how much maybe novel splicing is there, and also is there more splicing in some cancer types than in others? For this, we did one analysis where we normalized for the different number of samples we have for each cancer type. Obviously, when you have more cancers in one cancer type, you find more splicing. So what you did here is we just randomly chose 40 samples for each of the cancer types. So now it's comparable. And now you see that, for instance, there are some cancer types. This is the number of events for each of these cancer types. For some cancer types, you find a very large number of splicing events. So this is exonskips. So you find 40,000 exonskips for this cancer type. And this dark bar here, that's the number of exonskips which have been unnotated before in the annotation. So it's actually a pretty small part which is unnotated. And 80% is new. But there's very good evidence for it. We see it in multiple samples. We see it with many reads. And there's high confirmation rates for these splicing events. So there's a lot of new splicing going on. It is highly variable across cancer types. And we thought, OK, that's interesting. What can we do with it? And what we also see is that there's more alternative splicing going on when you compare normal samples versus cancer samples. So, for instance, here you have the number of alternative splicing events in average for the tumor samples versus the normal sample. And for some of the cancer types, there's a big difference in alternative splicing between tumor and normal. So, for instance, for lured lung adenocansidoma or lung squamous, there's quite big differences in the amount of alternative splicing which we see. And in some, maybe, thyroid cancer, there's not such a big difference. So the hypothesis is that the splicing mechanism is somewhat disrupted and this leads to additional splicing events which are spliced in a certain way. And that's, I think, that's one interpretation of this data. So we're interested in finding genetic reasons why certain splicing happens. And you can do this by looking at an association study where you look at a correlation of a somatic mutation with a splicing event somewhere else in the genome. And for this, you usually use generalized linear models. I don't want to explain in detail here. So this is the somatic change here and we try to explain the splicing change. And there's some additional factors which are compounding factors which we try to factor out wherever possible. So, and we did this in cis and in trans. And in trans, this is actually quite a bit of compute which we have to do. We have to correlate, essentially, every position in the genome with every splicing event which we find. So maybe somatic mutations, we have thousands of them, maybe 10,000s. And splicing events, we have 100,000s of events and we have to see whether there's a correlation between these two. And after proper filtering and a lot of manual work to get this clean, we find, first, not surprising that there's a diagonal, right? So because you have a splicing event which is next to an exon and that exon may be affected in its splicing. So this is this diagonal. But you also see kind of stripes here, right? And this means that there is a somatic mutation in a certain place, for instance, here in chromosome 2 and this affects splicing in many different places across the genome. And some of these cases are known. For instance, I think this stripe here, this is SF3B1, is a splicing factor. You have a mutation in the splicing factor and this leads to splicing changes all over the genome. This is actually where it's not known, right? So here's the count of how many events are actually that a somatic mutation affects. So for SF3B1 maybe you have 500 targets where we see a splicing change when you have a somatic mutation of a certain type. But there's other cases where we are not known, which are not even RNA binding, it may be a secondary effect. So there's a lot of food for thought how to interpret this. But I think we have done the study here quite thoroughly and I believe these stripes are probably really there. So maybe not totally surprising is the cis splicing effect when you have a somatic mutation or a germline variant which is close to an exon boundary, then this can have an effect on the splicing of that specific exon. So and here's a distribution of somatic or germline variants which we found associated. But it shows that somatic, that variants even maybe 100 nucleotides away can have a significant effect on splicing. It's not only the first two nucleotides or something of the intron, the AG or GT which affects splicings, it's the maybe much wider region of the intron which is influencing splicing. Okay, so we looked another way at the data and asked is there something maybe specific to cancer. So we had this idea that maybe splicing is disrupted in some cancers, in some samples. And what we tried to define or what we defined is a cancer specific splicing burden. So how much additional splicing do we have in a specific sample which goes beyond normal splicing? So essentially look at all the splicing which you see in normal samples and be subtracted from this specific splicing which we see in a sample. So we remove essentially the annotated transcripts, we remove junctions which we see in GTACs and then we only keep junctions which are commonly observed in pan can atlas. And then we count essentially how often do we see a certain junction for one sample. So and one dot here is one sample, right? And here is the splicing burden was the number of additional introns which we find confirmed in that specific sample. And you say if that number is very large then there's many additional splice events are going on and hence the splicing is probably somewhat disrupted. And what you can see here is in purple you have the tumor samples and in green you have normal samples you generally see the normal samples are much less disrupted than tumor samples. So there is really something going on in tumors.  Some cancer types seem to be very much affected and some cancer types are not so much. But what I think is most exciting about it is that there are now specific splice junctions which are tumor specific. They're specific to that tumor which you look at and it turns out that some of these splicing junctions actually recur in different samples. So here's an analysis where we look at the recurrence of alternative splicing in different cancer types. Each column here is one cancer type. These are tumors. These are normals. And this is G-tex. And what you see as a color here is the recurrence. That means how many samples have that specific splice change. So there are some splice changes which are shared by 60% of all the cancer samples but essentially none of the normal samples are also not anywhere in G-tex. So that means this is extremely specific to cancer and it is highly recurrent. Because that is something which we can actually target with immunotherapy if we have a peptide or a vaccine against this specific peptide which could target that. And if it is highly recurrent then actually this peptide would be effective for many patients. Okay, so now the question is are these peptides really there? And so we have these splice junctions which are new, right? Which connect one axon to another and this is a new combination which is not seen normally. This generates a peptide. It's different from peptides which are generated by somatic mutations. So a somatic mutation would maybe be within an axon and it would change maybe one nucleotide and this changes the peptide. But a splicing change introduces a new intro and a new connection between two axons and that generates new peptides which have not been seen before at all. So we generate a list of all these peptides. We do MIT binding prediction and only let the strongest binders pass. Then we look into mass spec data which has also been obtained for some subset of the TCGA data which is part of the CPTAC project and validate, I mean, essentially check whether we can find the peptides which we predict by splicing. We also, in order to compare it against something, we also did the same for the germline or somatic mutations. We also checked whether we find those. So we can actually compare the number of peptides which are generated by splicing versus the number of peptides which are generated by SMDs.  Here is, each dot is one sample and this is the number of peptides which we can confirm by mass spec which is predicted to be MHC binding for splicing peptides and we can compare this with SMD-generated peptides. So in many cases we find exactly zero SMD-generated peptides which are confirmed by mass spec and MHC binding. So there's actually much more of those here and hence there's much greater potential to find something which you can use for immunotherapy, for targeted immunotherapy. Okay, so I think this is pretty exciting. So the next steps is to show that splicing peptides are actually bound by T cells. We have to show that splicing peptides can be used with immunotherapy, is that maybe the gene expression of those is high enough so it actually is detected. Then we have to show that patients with high splicing burden maybe respond better to checkpoint inhibitor therapies. So there's new work funded by SPH-NMPHRT together with Mark Rubin and George Koukos to do some of this work together with George Koukos in particular to show whether these peptides are bound by T cells. Good, so I'm running a little low on time but so maybe I do a little short version of this. Good, so I take questions later. Probably I'm too fast anyway. So project three. This is a project which is more of a consortium work where I mean the whole consortium is probably 800 people. So there's 800 people who try to publish at the same time which is about now. And some of that work will come out very soon on BioArchive and hopefully later in cell. Let's see. So I'm co-leading the transcriptome working group and this is one aspect of that work which I'm presenting here. And we try to integrate diverse transcriptomic alterations to identify cancer-relevant genes and signatures. And the idea of that is that there's many different ways to induce an oncogenic event or to disrupt maybe a gene, a gene's function. So for instance for MET there could be a non-sononomous mutation in MET which leads to an activation. It could also be an alternative promoter which leads to a higher expression of MET. It could be a fusion which leads to the higher expression of the relevant part of MET or it could be alternative splicing which changes some piece of the coding sequence which then leads to an activation of MET. So usually people have looked only at the somatic mutations to a certain extent at fusions but much less at alternative promoters, alternative splicing and so on. And part of that study is to analyze these different alterations types together and come up with a little algorithm which can find maybe genes that are recurrently altered across multiple alteration types. And that's the goal of that work here. So as I said ICGC is an international project. There's many countries involved if you don't want to read them. And we get data from different heterogeneous sources. We get whole genome data on one hand and RNA-seq data on the other hand. So the whole project has about 2,500 whole genomes. For this study we could only use about half of them because only some of them had RNA-seq data. But we needed RNA-seq data. So we came up with the pipeline which was a result of much discussions. We ended up using top hat 2 and star. We joined these results and flagged genes where the results were not agreeing and so on. So it will be part of another paper describing that. So then we looked at different alteration types. We looked at RNA-editing, gene fusions, expression outliers, alternative promoters, alternative splicing, alleles species expression on the RNA side and also DNA copy numbers and non-sonoms mutations on the other side. So we tried to put them all in the same coordinate system so we can actually do some joint analysis because RNA-editing is quite different from, let's say, copy number variation. So we had to simplify that. We had to summarize alterations per gene. And what we did is, for instance, for RNA-editing or for fusion, we said, well, we say this gene is modified if there is a fusion in that gene period. So it's a fusion, zero, there's no fusion. Similar with RNA-editing, if it's an RNA-editing event which leads to a somatic, to a non-sonomous change. Then copy number, we said, okay, it's one if the copy number changes more than, I mean, it's greater than four. So it's somewhat arbitrary. But we did the same with expression outliers. We say it passes the filter if the Z score is larger than something. Okay, so these are more quantitative filters. Then we took, essentially, for each gene, this vector for each sample, put it into this vector, and then we put it, essentially, into a cube. So here we have about a thousand samples. Here we have eight different alteration types. So six RNA-level alterations, two DNA-level alterations, and about 16,000 expressed genes. So this is like a three-dimensional matrix, and now we can slice this matrix in different ways. We can either project to the top. We can aggregate over the samples to do a recurrence analysis, or we could do over the, what is it, the alteration types to maybe look at different pathways, how they're disrupted, and we can also aggregate over the genes to see maybe whether there are samples which have maybe specific patterns. Okay, and here's a rough summary. When you look at cancer types, you see big differences between alteration frequencies of the DNA pieces. This is for the different, this alternate promoters, expression outliers, and so on, and here DNA, and you see significant differences between the different cancer types. You see, for instance, in kidney, there's much more alternative promoters, and maybe in lymph BNHL, there's much more alternative splicing going on. So there are specific characteristics of specific cancer types. So that's good. You can also look at samples which have, I mean, there's a certain way to look at somatic alterations in cancers. So, for instance, we can look at mutations in P10, MTOR, PI3, 3KNAs, and so on, for instance, for kidney, and you see that certain samples, the column here is one sample, are mutated. So we find certain mutations for a subset of the sample. So in 37 of these samples, we find somatic mutations or copy number mutations. If you now include additional types of mutations, so we include fusions and allelespecific expressions, splicing outliers, and so on, you can actually find quite a few more samples which have some alterations in these key pathways related to kidney cancer. So I guess we have an explanation for a larger fraction of samples, what's happening with those, except that the alteration now is not a somatic mutation, but it is something going on on the RNA side. So a new type of analysis we performed to identify known and newly recurrently altered genes. So essentially we were interested in genes which across the different alteration types, so we didn't care which alteration. So across the different alteration types is frequently altered in many cancers or many samples. So a similar type of analysis is done with somatic mutations asking, is that somatic mutation frequently or recurrently altered in specific cancer types? So we wanted to repeat that, but we did it across different alteration types. And so that's the result of that analysis. So here you have the ranking which you came up with, and what you see is, yes, it is highly enriched with cancer-sensors genes and with driver genes which are known, but it also shows that there is much, there's a few other genes which are very interesting which have not been picked up as cancer-sensors genes before. And I can't go much into detail here. So for instance, KLF13, there's some evidence that this is indeed involved in cancer, but it hasn't been picked up by other analyses before. More details about this manuscript or about this work we described in a manuscript which will come out probably next week or in a few weeks. Okay, so in summary, the group works at the interface of data science and biomedical applications. We do a lot of technical work or, for instance, on the scalable graph algorithms for large-scale genomics and new training algorithms for recurrent neural networks. I can talk about this another time. I talked about three projects. One is the unbiased analysis of EHR in the context of two more somatic variants. It needs more data, it needs more clinical data, it needs more somatic variants from Switzerland when we push forward to get this, but it also needs more discussions and validations with clinicians. So project two, I think I'm pretty excited about because we actually can show that RNA splicing leads to RNA alterations which are potentially targetable by immunotherapy. And that's a new work, an ongoing work, which I think is great. And project three is a new way to integrate RNA and DNA alterations to identify key genes in two more, two more agencies. So I think overall the collaboration with life sciences, with life sciences we need in order to translate the technology into new science and better healthcare and the initiatives like the SPHN Data Coordination Center or PHRT will make these kind of collaborations much easier. So with that I would like to thank my team, about half of that team has moved from New York to Zurich. I'm grateful for that and also for their work. And I would like to acknowledge a lot of collaborators in New York, also in Zurich and other places. Thank you so much for your attention.