Welcome to this tutorial on how to effectively search for social work resources at the Tulane University Library. In this video, we will cover various search techniques such as Boolean operators, wild cards, phrasing symbols, filters, reference tracking and the virtual book browse. First, let's understand the basics of conducting a search in the library's catalog. This is the library's homepage. On this homepage, you will find the search bar. We'll begin this demonstration by searching for resources on counseling. If you type in the term or the terms that you want to search on, you can press the magnifying glass. This will return a list of all of the resources that have the term counseling on what's known as its records page. Each resource you see here has a records page. To access that page, you just click on the resource that you like. This page will give you information about the resource, including a summary, some subject terms, a link to the resource, as well as related readings. Think of this as the landing page for each of the resources in our collection. Now that you understand the basic search functionality, let's move on to some search strategies. Phrasing symbols allow you to search for exact phrases, ensuring that your results include the precise terms you're looking for. Let's see an example. Using the phrasing symbols to quotation marks. We will search for resources that discuss family therapy. By placing the phrase within quotation marks, you can search for the exact term in the exact order it appears. If you don't include the quotation marks, the system will find resources that have both of those words, but in any order. For example, you may see resources that have therapy before family, or there may be multiple words between the term family and therapy. If we press search, you'll see that the resources specifically mentioned family therapy in the order that we have provided. Remember to use this search strategy whenever you're bringing together multiple terms into a phrase, and you want the system to search on that phrase, not just each individual term. Next, let's discuss Boolean operators. As you can see, Boolean operators are useful tools for combining and excluding terms in your searches. Let's explore some examples. Using AND, suppose you're interested in finding resources that focus on both mental health intervention and adolescence. By using the AND operator, you can narrow down your search to include only resources that mention both of these terms. Be sure to provide the Boolean operator in all capital letters for it to work. As you can see, the results now show resources that specifically discuss both mental health interventions and adolescence. You will notice in some examples that the terms may not appear in the brief description. You will have to click on the records page to see the term. So we'll see here mental health intervention is in the title. We don't see the word adolescent. If we were to scroll through this page, however, you will see that the word adolescent is actually located here in the subject terms. So, once again, you may not see both terms in the title or on the brief description that you get on the results page, but you will see the term somewhere on the record page. Now let's take a look at using the Boolean operator OR. Let's say you want to find resources about mental health intervention or substance abuse intervention. We'll press enter and see what we get. Now, before we move on, I do want to make sure that we understand the difference between using AND and using OR. As you can see here, if we were to use OR, we're going to get 33,000 results. If we were to place AND here instead of OR in search, we're going to get a lot less results. And the reason why is because when you use AND, you're telling the system that both of these phrases must appear versus using OR, as we'll do again. You're getting more because you're telling the system you either want to see the phrase mental health intervention or the phrase substance abuse intervention or some combination of these two phrases. So again, OR is going to expand your search, whereas AND is going to limit your search. Sometimes you may want to exclude certain keywords from your search. Let's say you want to find resources on mental health interventions, but want to exclude anything related to substance abuse interventions. In this case, you can use the Boolean operator NOT. By using this operator, you are telling the system to return you resources that only discuss mental health interventions. And any resource that has both mental health interventions and substance abuse interventions must be excluded in this search. So once again, you're only looking for things in this case that mention mental health interventions. Anything that also says substance abuse interventions will not be loaded on this results page. While cards are special symbols that can replace letters or parts of words to broaden your search. Here's an example. Suppose you want to find resources that discuss color. This would be considered the traditional American English variation of the word color. So if we search color, as it is represented here, we'll get six million results. Some of us may know that the word color can have another variation from our friends in the UK and our friends in Canada.  So to capture both spellings of color, we're going to replace that U with an asterisk. And as I say before, this is going to put any variation of letters after O and before R to return as many resources as possible. So let's see. You see we have more resources than before, because we have in some way increase the variations that can be returned. And as you see here, even by the third example, we have a different variation on the spelling. This technique can become very useful whenever you're unsure about spelling or maybe even when you're thinking about differences and how things may be spelled across cultures and across languages. If you find yourself having trouble creating search terms, visit the strategic search page on the social work library guide. Library guides can be found by visiting the library's webpage and selecting the library guides button. Search for the social work guide. Once you find it, click on the page and then select search strategies. At the top of this page is a video describing how to find search terms and how to select search terms. I also have information on some of the strategies you've seen so far, as well as a list of search terms that are relevant to social work practice. To help manage your search results, you can use our built-in filters. As graduate students, be sure to pay close attention to the journals and databases that surface from your searches. These could be potential publication venues for your current or future research endeavors. Once you find resources that may be useful to your research, check the resource references to find other potentially useful materials. You can achieve this in our catalog by visiting the record page for a resource and scrolling down to the citation section. Here, you can view resources that were referenced in this resource and those resources that have referenced this resource. This information can be gathered natively in our system through the siding this and sited in this buttons. Additionally, you can visit indexers like Elsevier to find other cross citations. Similarly, since books housed in the library are organized by subject, you can also search for books related to your selected book of interest. To browse in this way, select the physical book from the catalog. Physical books will have a call number and available at marker. To browse in this way, select the virtual browse option or scroll to the bottom. Scrolling from left to right will show you the books related to the book you just viewed. These books can be found right next to the book you have selected here on the bookshelf in the library. When looking for books at the library, it is important to remember that our books are mostly classified by Library of Congress call number. The call number arrangement on the screen should be consulted when looking for a physical book. Congratulations. You've learned valuable techniques to enhance your library catalog searches. Remember to experiment with Boolean operators, wild cards, phrasing, filters, and other techniques to refine your results. Good luck with your research. For more information, contact us at library at Tulane.edu or visit our website library.tulane.edu. Thank you.


Hey, Susan, I think we're on Facebook Live now. And it's almost 10 o'clock. So maybe we could just get started. And you would probably do a better introduction of yourself than I would. But Susan, Thomas Ebi, it's wonderful to be with you. Thank you so much for joining Code Pink in this half-hour webinar on what is happening inside Iran, as well as US-Iranian relations. And yes, if you could tell us a little bit about yourself. Sure. My name is Susan Thomas Ebi. I'm an Iranian women's rights activist. I lived and worked in Iran from 1999 until 2010, supporting civil society, working to build the civil society, and working as part of the women's movement. In 2010, I came back to the US. Actually, I had migrated into the US at the time of the revolution. And I went back to Iran in 1999. But in 2010, I came back to the US. And since then, I've been working regionally in the Middle East and North Africa to support women's groups, women's movements across the region on their demands for rights and peace. Right now, I direct an organization called FEMENO, which works in the Middle East and North Africa region. And a lot of our work is with civil society groups and women's groups in closed and repressive contexts. So places like Iran, Egypt, Turkey, et cetera, where women are working hard to make their voices hard through civil society organizations. And I'm very happy to be here today to talk about what's going on in Iran and how it impacts women. So we'll spend most of our time talking about what's happening in Iran. But of course, as Donald Trump sucks up so much energy, I thought maybe we should at least talk about his first, the aggressive tweet in all caps and followed up by a response he gave to a journalist saying that he was ready to speak to the leaders of Iran and how this kind of back and forth craziness is felt inside Iran and among the Iranian-American community as well. Sure. Of course, we were very nervous last week when tensions rose to possibly their highest level at least in the last few years when there were a number of very aggressive and threatening tweets going back and forth, starting with the all caps tweets of President Trump telling Iranians that they can't threaten the US. And he was reacting to statements made by Rouhani who had said, war with Iran would be the mother of all wars. Peace with Iran would be the mother of all peace. And so I'm not sure how threatening that was intended to be, perhaps it was intended by Rouhani to draw some lines against increasing US threats that were a result of the US pulling out of the JCPOA and deciding to implement sanctions again. JCPOA meaning the nuclear deal. The nuclear deal, exactly. But nevertheless, the tensions were high and there were a war of wards exchanged by both sides. And we were very nervous. We didn't know where this Twitter war of wards would lead. And then all of a sudden, in an interview in Italy, President Trump said that he's willing to speak to Iran with no preconditions if the Iranians are willing to come to the table, which was rather strange because Secretary of State Pompeo had actually listed a 12-point plan after the US pulled out of the nuclear deal talking about the preconditions of the US for engaging in new negotiations with Iran, even though Iran had lived up to its side of the deal in terms of its nuclear inspections and its requirements with respect to the nuclear deal the US had pulled out and they had instituted a 12-point plan which made it extremely difficult for Iran or any other sovereign nation really to come to the table because they were asking Iran to give up quite a bit. So I don't really know how to read Trump's offer to speak with Iranians or engage in negotiations with Iranians with no preconditions because immediately after he sort of made the statement, which was news for two whole days, both in Iran and around the world, Pompeo again stipulated a few more requirements. So I don't know. I don't know. Certainly I think it's better than the tensions that we were seeing before that. I think we were moving towards potential conflict, even if it wasn't necessarily intended. That level of verbal tension and threats could lead to some sort of conflict. So I'm happy that we've maybe moved away from those very difficult times to a time where leaders of both countries could perhaps have a more civilized conversation about the path forward. And I hope that the offer for discussions and negotiations is a real and genuine offer. I would think that President Trump and his administration would have to take some steps to let the Iranians know they actually do want to engage in respectful and equal discussions with the Iranians because it is the US that pulled out of the nuclear deal. It wasn't the Iranians who pulled out of the nuclear deal. Well, yes. And as you well know, sanctions is war by other means. And not only have there been, since the time of the revolution, sanctions against Iran, but the sanctions that were lifted when the nuclear deal was signed have been reimposed. And now coming up very soon on August 6, there will be yet another round of sanctions. So can you tell us a bit about how these sanctions affect people inside Iran and how you feel this new round of sanctions will affect them even further? Well, the last round of sanctions were pretty serious. I mean, the US managed to get the world's community on board in terms of the sanctions the last round. And they impacted Iranians very negatively. But they were intended to bring Iran to the negotiating table. So we could say that perhaps they, even though they were very difficult for Iranians and impacted most ordinary Iranians, they had a very distinct goal. These sanctions that President Trump promises to implement don't have the same sort of clear policy objectives, because we'd already met the policy objectives of the sanctions. So and Iran was obviously complying. But the stated intent of these new sanctions is by many people associated with the administration is to create chaos and to basically destroy the Iranian economy and to in some way indirectly overthrow the regime in Iran. While Pompeo and his speech in California talked that he officially said that they're not after regime change, a lot of the people closely associated with the Trump administration have advocated for regime change for many years. And it seems that these particular economic sanctions are actually intended to destroy Iranian society from within, which is already struggling in terms of its economy. So obviously sanctions, I think people who follow sanctions, I'm not a sanctions expert, but because my country has been sanctioned for 40 years, it becomes an important issue for you. People who follow sanctions, they recognize that they have usually have the most impact on the most vulnerable groups of people, ordinary citizens. And they tend to strengthen the hands of hardliners. The reaction to the offer of Trump in Iran has actually been that at least from a couple of hardliners with a flat-out no response and a very threatening response. It's not the official reaction, but it shows that perhaps there are elements within the Iranian government or Iranian state, hardline elements that don't want normalization of relations, that don't want discussions, don't want the economic sanctions necessarily to be lifted or this threat of war to be lifted because they benefit from it. But what you have or you have ordinary people who don't benefit from it, already the Iranian economy is suffering greatly because of the speculations because of sanctions and increasing isolation of Iran. And so the last time we had sanctions, we talked to a lot of people. And I'll talk about the impact it has on women. One of my colleagues did this great research and hopefully we'll be able to translate it and update it and make it available to people who are interested. But talking to women, and what they did, what she found in this research is that what happens to women when they suffer economically and a lot of the economic setbacks that they suffered, they'd actually attributed to the sanctions last time around is that they cut from their, first they cut from their leisure activities, right? Then they cut from things like beauty supplies and things like that. Then they start actually cutting from their medicine. Then they start cutting from their food. So a lot of the women that she interviewed had actually stopped eating vegetables, eating meat, eating dairy products so that they could feed their children. And they were suffering greatly because of the sanctions on a personal level. Some women had talked about how a few of the women that she talked to actually talked about had they taken on, had they'd been forced to do the kinds of work that they would never do otherwise in order to meet their economic needs. And so given the fact that Iranian economy is suffering so much, I think that this round of sanctions will have great impact, especially on women and other vulnerable groups at a much faster pace than the last round actually had negative impacts. So people have told me that it's hard to find some medicines in Iran and yet medicines are supposed to be exempt from the sanctions. So how does that happen? Well, there are a couple of reasons and I'll go back to what happened last time and then I'll talk about what's happening now because the sanctions haven't been fully implemented yet or they haven't really been implemented yet. So what happened last time, immediately in 2010 when they announced the sanctions, the shipping industry was sanctioned. So Iran couldn't get insurance for its ships. It couldn't cause the insurance has to be bought through. So basically no shipping was actually allowed to the country. So immediately, almost immediately you could feel the absence of medicines in Iran because there was no shipping. There was nothing being shipped. Later on with the central bank was sanctioned and these sanctions sanctioning the central bank is unprecedented, it had been unprecedented. It had never really happened except for perhaps very targeted for weapons productions. But this is a blanket sanctioning of the central bank which meant that Iran couldn't pay for anything. So we couldn't use Swift, you couldn't pay for anything. So therefore, even if medicine is not being sanctioned as sale of medicine is allowable that companies inside of Iran or the government couldn't actually buy and pay for medicine. A lot of pharmaceutical companies inside the country who make medicine couldn't get their material because they import the material from outside. They couldn't get the material to make the medicines that they needed. So this had a great impact in terms of medicine. And towards the end of the sanctions period people weren't getting medicine for illnesses such as cancer. There wasn't even, at one point there was an anesthesia that was being imported from China that was defective and people were dying for simple operations because they were having reactions to this bad medicine. So it was really terrible. Now what's happening is that in anticipation of sanctions people who have hard to find medicines aren't making them available for sale or others who have access and have money are buying them up ahead of time. So what's available on the market is not available to everybody, right? So people are hoarding it basically. And of course, those who are producing medicine are gonna have the same problems. Given the fact that the real has lost its value it's over 50% in the last few months it's become very difficult for those who make medicine inside the country importers to import the goods, the material to make the medicines inside the country. So already we're seeing some level of shortage even before the sanctions kick in. And these protests there were some in December and January of last year and beginning of this year and then there've been sporadic protests. How much do you feel that they are genuine grassroots uprisings versus being fomented from the outside? And how likely are these to grow to be significant threats to the government? I think definitely the protests are genuine protests. I wouldn't say that certainly there are some fomenting there's some encouraging but I do think that it is a general dissatisfaction and in each area I read a really good article that talked about how really this is a failure of governance because in each area the demands were different. So if you go some areas it was for unemployment some area was in terms of the environment or talking about corruption, et cetera. So I think that the protests were genuine in that sense and people are demanding their government to be responsible to them for them to meet its demands. The fact I can't deny that I'm sure that some of the protestors are being egged on, et cetera but they're also being crushed as well. So I think that the Iranian government has a very, very difficult task in terms of meeting its addressing sort of the international crisis addressing regional issues but then addressing the demands of its public. So it has a huge, huge task ahead of itself. So there's somebody who wrote in a question and asked about how strong is the secular movement for democratic reforms inside Iran? I mean, we have a long, long history of sort of a movement for democracy inside the country. We had a constitutional revolution in 1906. So it's over a hundred years that we've been working in and I think we've had, so we had a secular movement and then we had a national movement and then we had an Islamic movement. I think right now people are from within are working to create reform. The problem is that perhaps the reformists have lost the message with which to really to gain the public support that they had. We can't forget that a year ago, a lot of people went to the polls to vote for Rouhani. This last year has been extremely difficult on the Iranian public because Rouhani hasn't been able to deliver economically because those sanctions that we're supposed to snap back, they haven't snapped back. There's a lot of mismanagement. There's also a lot of corruption that through use of social media is sort of people are finding out about it and it creates a lack of hope and a great frustration among the public. But I think that there is still a genuine civil, there's still a genuine effort within the country to create reform and to work for democracy. There's a genuine civil society that has it very, very difficult. We don't hear the voices of civil society very much because they inside the country, they face backlash when they speak up. Many of them face backlash when they speak up. And so people outside speak on their behalf. One of the young men, Zia Nabavi, who was in prison for nine years. And then finally they told him, actually, the imprisonment was wrong but he'd already spent nine years in prison was just arrested again yesterday which was devastating for me to see but today I read a piece of news that he was released but he's one of these young people who even though he was in prison for nine years he does believe in reform and he does believe in making change from inside the country. And he, as a very sort of moderate, as a very moderate vision he thinks that change needs to happen slowly and deeply. So could you talk a little bit about what you would call a women's movement in Iran, what gains have been made over these years? What are women still fighting for? And what kind of, what ways do they use to struggle for more rights? Okay, so I'll give you a very, I'll try to make it very condensed and quick because it's a long history but at the time of the Iranian revolution the revolutionaries adopted a very conservative version of their interpretation of Shadiah law and we lost a few of the rights that we had gained not very many, but we had struggled women had struggled for them and we lost them such as checks on polygamy, increase in age of marriage even abortion rights. So for the time Iran was actually very progressive even though we had still a lot of restrictive laws on the books. So women lost a lot of their rights they've been fighting for it ever since the very conservative interpretation of Shadiah law imposed certain requirements on women such as allowed for polygamy. Of course, polygamy was allowed before as well but it reduced the age of marriage to nine years old it's been increased to 13 since then and now there's discussion for it to be increased to about 15 or 16 years back and forth on that. So women are fighting for their rights within the family they're fighting for reform criminal law that impacts them negatively they haven't made much headway in terms of actually changing the laws they've had some small successes like I said the increase in age of marriage and child custody for example, issues like that but women have actually gained quite a bit in terms of their social status. So if you compare women's legal status with their social status it's hugely different and divergent. So women are, they're present in the workforce they're doctors, they're lawyers they're even truck drivers, taxi drivers, right? While the age of marriage is very low the actual average age of marriage is 25 and 27. The rate of divorce is very high even though it's very difficult for women to get divorced but they're still getting divorced and the rate of education for women is very high women are educated, college graduates the majority of women even though there's been attempts to sort of push women out of the workforce during Ahmadinejad's period there was a pushback on these social gains but women have managed to hold their ground. Women are fighting, the women's movement suffered a great setback after our 2009 protests a lot of people left, the economy was really bad sanctions made the economy worse Iranian women became even more isolated there was a sense of hopelessness and there was repression and crackdown but they're slowly re-builded after the green movement yes, after the green movement, after it was crushed but they're slowly rebuilding their movement and I'm actually very, very hopeful. On March 8th, we saw sort of this very broad example of the different types of work that women are doing and they held many, many events which was very, for me was very hopeful they had a number of conferences and meetings That's International Women's Day Exactly, International Women's Day we celebrate it we've been celebrating for many years it's a big deal inside of Iran and beyond the conferences and the events that they held they also had a number of street actions women going out into the metro women talking about the economy, et cetera the right to choose their veils, how they dress and also there was a protest planned inside of the, in front of the ministry of labor to object or to bring attention to women's economic situation a lot of people were arrested I think 80 people arrested finally 20 people were charged but they were all acquitted last week which was, to me, it was a very positive thing it shows that at least some part of the security apparatus is for civil society being active and is willing to create that space and I hope that the women's movement can use that space to continue building on the successes that it's had the slow successes it's had in the last few years And what we hear about in the United States are perhaps the more superficial or less in-depth kind of struggles of women to not wear a headscarf or to be able to attend sports stadiums how important are those issues? They're obviously they're very important because they're important to women's daily lives women have been struggling against the official state form of veiling and hijab from day one really they have been they've never given into the official sort of government mandated form of hijab so they've been struggling against that and they're struggling for it even more more loudly these days the stadium, of course, this is a movement that's been going on since the early 2000s and there seems to be some finally the government is finally giving into it a little bit more so we do hear a lot about those but there are also a lot of other activities that women's groups are engaged in that we don't necessarily hear about and they include working on prevention of violence against women there's a group that's working they came up with a draft bill a draft law that they the parliament is actually looking at a draft law on prevention of violence against women so hopefully they'll be able to provide feedback and work with parliament to adopt a better a good law, a better version of the law that's being presented there's another group that's working to prevent street harassment they just launched a harass watch website where people can go and report and they're working with a municipality of Tehran to post their informative posters in the metros and in taxis there's another group that's working on prevention of harassment in the workplace so they're working with factories so there's a lot of different kinds of group work that's going on inside the country that we don't really hear about ever sorry, when you say harassment on the street or in the workplace is that verbal harassment or is there actually physical grabbing of women on the street? Well, the harassment on the street yes, it's verbal harassment it could also be physical harassment, et cetera and this was always so yes so they're working to prevent this they have some great posters that they've developed that really talks about how people should sit in the taxis how they should sit in the metro cat calling isn't a way to it's not flattering for women that are especially threatening so it's an educational and preventative and there's actually a law on the book that's a pretty harsh law I don't necessarily advocate for it against harassment so it includes lashings but there was a recent court case that actually requires a harasser to do community service which we think that's good we don't want people to be lashed for harassing you might want it we don't really want it in law you might wish for it if you're going through harassment but we don't really want it in law so that was actually a big it was a big achievement that somebody was sentenced to do community service for harassing a woman this is great the harassment in the workplace that we're talking about so there hasn't been a lot of work on this there's no encompassing law that deals with this so the people who are working on this issue started with blue collar workers and started working in factories they've done a number of trainings and actually the factory workers and all the local officials they've talked to have been very supportive of the effort because harassment in the workplace is a big issue and they want to prevent it they don't know how to prevent it so it can be harassment you know harassment so lack of boundaries and borders between co-workers in terms of you know how do you define appropriate relations and appropriate behavior but it could also be your boss and these women they did something really amazing they said since we don't have a law we're going to work with women to help them come up with strategies for resistance how they can resist and how they can create spaces in their workplace where they can bring this to the attention of management and make management responsive but it's they're doing they're doing great work yes but we don't we don't hear about those those kinds of things yes and wouldn't it be lovely if we could work together on these kinds of things women in the U.S. and women there and focus on how we make all of our lives better instead of the way it looks right now and in the remaining minutes we have maybe we should go back into the big picture how much do you fear that the U.S. policy will be successful in creating chaos inside of Iran what do you feel is the end game and what do you think that we as Americans who want to help create more space for Iranians to change their own society what can we do to be helpful yeah I mean I am fearful about the future of Iran Iran is a big country in the region its instability will not help the region it won't help U.S. interests or European interests in the region it won't help the security of the region I'd like to see an Iran that's engaged both with its neighbors in a positive way but also the international community Iran has been isolated for 40 years it's probably one of the most isolated countries it hasn't you know and I think even despite its isolation it has a strong civil society we've tried not speaking not talking I think it's time to sort of for full engagement and when I talk about engagement I don't just mean engagement of government and business it should be engagement at all levels so at a cultural level academic level civil society level right sports etc that's that's the future I see that's what I would like to see I think these stabilization efforts will not serve anybody's interests I hope that the Iranian government and the U.S. government take the notion of negotiations and discussions seriously and move forward with it no one can stand bear the consequences of a failed state such as Iran in the region not Iranians can't you know withstand that level of you know destruction the region can't either and I don't think it would be good policy for the U.S. or for Europeans so I hope that we can move past the stalemate to have constructive dialogue and discussion wonderful so as we close here just to let our listeners know that you and your colleagues in the Iranian American community have been working with us at Code Pink in lobbying our members of Congress to speak out against the sanctions and certainly push back against any kind of military attacks on Iran and we also are creating some exciting programs that you are helping us with in terms of these people-to-people ties and so anybody who wants to get involved with us please contact us you can write to info at code pink.org or go on the website sign up for our weekly alerts but thank you so much Susan for the wonderful your work you're doing to help try to keep and under these very difficult circumstances expand the space for civil society inside Iran and of course that means that we have to stop the attacks from the U.S. so that that kind of opening can flourish thank you for being on with us and we look forward to continuing to work with you. Thank you for having me it's been a pleasure and thank you for all the great work that Code Pink is doing to support Iranians and to prevent war and sanctions. Great. Okay. Bye-bye. Bye-bye.


Recently, I've been seeing a lot of changes happening here and some new additions. You know, like the Hollywood studio sign that's missing now and some Christmas decorations in October. So, this is going to be fun. Let's go do this. It's so nice to see those Christmas decorations starting to come out of Hollywood Studios. Like last year, it was amazing over here. I absolutely loved Sunset Boulevard during Christmas. And I am so excited for Hollywood Studios Christmas this year. I mean, I just loved everything last year and it actually pulled ahead as my second favorite part. Magic Kingdom will always be my favorite part during Christmas, but last year, Hollywood Studios really stepped up their game and they jumped above Epcot, so I'm excited. And I mentioned some signs that were missing. And right there, that iconic Hollywood Studios sign right before you go back to Star Wars Launch Bay is gone. I don't know if they're redoing it or if it's gone permanently. I don't think it'd be gone permanently. I mean, I really like that. It was actually my thumbnail in a couple of videos. I have not seen them shooting out water from the SS down the hatch in such a long time. Look at that. Really awesome. And once again, not a single person waiting to see poor Olaf. Only a five minute wait. So sad, isn't it? Oh, JoJo, what's up man? One place I want to go kind of explore a little bit is the Backlot Express because I haven't been down here in a while and frankly, I don't even know if they're open. Look at this. Not a single soul down here. I used to love this. This used to be my favorite quick service actually at Hollywood Studios. And notice there's no loafing around in here. The inside of the Backlot Express has definitely changed over the years, but they still have some cool little tidbits that I always remember to check. The time clock is still over here. I still wish I could just actually take one of these out and just stamp it for somebody. Maybe A or K Ford, Carly Ford. I'm just dicking. Right here are the used to actually have the original stunt car used for Benny the Cab in Roger Rabbit. It's nice that they kept some of the artwork up though. You can see that. Isn't that pretty awesome? You can see a little more over there. Pretty cool. Even though Benny is no longer here, it's still a pretty awesome quick service place. I came to Hollywood Studios not only because I wanted to see the things that were changing in the new Christmas decorations that were actually popping up, but I wanted to ride Toy Story Midway Mania. So I'm going to head on over to Toy Story Land, but before that I think I'm going to stop at baseline, see if they got any new beers. On our way over to baseline, I did want to point out that they did announce that we are getting new scenes for Star Tours when the new movie drops in December. Very excited for that. Ah, here it is, baseline. Definitely top five of my favorite places to get a drink at Disney. And I guess it's safe to say it's beer o'clock. I decided to go with the Golden Road 329, actually give you a little fanciness and I like it a lot. Well, this is kind of fun because we're going to walk through Star Wars Land to get on over to Toy Story Land. I think that's interesting. Rising Moons and Till the Spire. I love Star Wars Land at night. I mean, this is amazing. The only time I've actually been here is during the morning time. I did early, early magic hours once, and then also I did the annual pass holder preview with Jackie, but wow, at night this is phenomenal. I mean, look at that. Look behind me. All right, it's time to transition from Batu to Andy's backyard. And look at this, Alien Swirling Saucer is a five-minute wait. I say yes. Oh, when Alien Swirling Saucers is five minutes, you hop on that and you ride it. I'm so excited because during Christmas they actually do a ride layover here and it's all Christmas lights and Christmas themed music. Oh boy. Gotta find, gotta find our spacecraft. I'll take this one. This one! Oh. Attention toys, please remain seated. No lab seating. Absolutely so much fun. I love this ride. Isn't it so pretty here at night at Toy Story Land? Now we're gonna head on over to Toy Story Midway Mania because, like I said, I've been really wanting to ride this ride for quite a while. But I'm not gonna film much on there because I really want to see what kind of score I get. I think that happens every time I come to Hollywood Studios. I'm like, I'm gonna ride Toy Story Midway Mania, but I'm probably not gonna film it because I'm gonna be too busy trying to get a high score. I will show you the results though, only if they're good. But in the slight chance that I end up doing not so good, I'm basically just gonna act like I never even rode it and you guys wouldn't even know. Didn't do horrible, but I didn't do my best. And with that poor performance, I think we're gonna leave Toy Story Land for the night. Bye Woody. Bye. There is another thing that I have not done since it changed. And that is Star Wars Launch Bay because Darth Vader swapped with Kylo Ren and I haven't been over here since then. So we're gonna go visit Lord Vader. And this time of the night, there are no weights. Look at that. Look at this, no weight. I think we're gonna go do Darth Vader first and then Chewbacca.  Rebel transmissions have intensified. Tell me what you know. Are they preparing an offensive? Yes. Very offensive. If you speak to an Imperial commander, you will tell him what you know. Expect to be summoned and questioned. Thank you. You have to go now. Thank you. I love Darth Vader. But I'm also very excited to meet Chewbacca. And it's awesome that both of these characters are no weight. Like literally, we are walking right in to see them. What's up man? Oh, handshakes only. Thank you. I appreciate that. He likes the beard. I like your beard. Goodbye. You're my favorite. And since we met Darth Vader and Chewbacca, we might as well meet BB-8 because there's no weight. Hey! Look at that little guy. Hello, this is Patrick. Hi. Thanks for your hospitality, BB-8. Have a great day. Safe travels. So that was pretty awesome. We got to meet all the Star Wars characters in Launch Bay in literally like 10 minutes. All of them had no weights. Nobody was waiting. We literally just walked in.  But how about a change of subject? Let's get some ice cream. And normally when I'm at Hollywood Studios and I want to get some ice cream, I usually go to Gertie's for the soft serve. But since Gertie's is very rarely open, kind of seasonal, we're going to head up Hollywood scoops. Look at that. Hollywood scoops, Hollywood Tower of Terror. So I might actually get this Apple Chris Alamo. That sounds pretty delicious. But they have plenty of good options. Brownie's Sunday, a hard root beer float, plus your traditional ice cream sundae. Decisions, decisions. Wow, look at this. There it is. That looks amazing, doesn't it? I've never seen anything like that. Seriously, I really enjoy this. It's really good. I mean, the ice cream is just ice cream. But honestly, that Apple Crisp and this strudel, that's what is the big seller. That poor Apple Chris Alamo didn't even stand a chance. Wow, it was so delicious. There's only $6 too. That's not that bad of a deal. But there is only a couple minutes left. Actually, two minutes left until the park closes. And the Tower of Terror is a minute wait. Oh man, it smells so good in here. Whole cart, all to ourselves.  Seriously, that was fantastic. If I can recommend one ride to actually ride the last of the night and try to get something all to yourself, it would be the Tower of Terror. Fantastic. The park is closed now. I think I'm going to go home. Love the life you live. We'll see you next time. Bye bye. We walked out just in time for the finale. One more thing. Thanks for saying hi, Bryson. You made my night. I hope you have a great time here.


All right, let's get started. So the learning objectives of the module. Having done an experiment, a sequencing experiment, you have detected maybe dozens or maybe hundreds or even thousands of genome variants, and you really want to interpret them in the context of what they may be doing in the cancer biology. So the learning objectives of the module would be understanding what variant annotations there are out there and how we can use them. And how do we predict the impact of a particular genome variant given various machine learning tools and mathematical models out there? And then in the practical session, you'll learn to use a particular annotation tool called ANOVAR, which you can use in order to translate genomic coordinates to protein coordinates and then further understand of what the variants may be doing in the context of that protein function. So when we have performed next generation sequencing of cancer genomes and extracted all these variants and some of those variants will be related to genes and others will be related to intergenic regions, we have to consider information at two levels of organization or logic. One is on the level of gene. Once we have identified a gene of interest that seems to be mutated frequently, then we can ask whether the gene is related to a process involving cancer, such as apoptosis or cell cycle. And we can investigate whether the gene is known to be sensitive to perturbation. For example, we have CRISPR and SHRNA screens out there that we can use to understand the gene's function. But before going to the level of a particular gene, we really want to know the impact of a variant. The variant may affect the gene, it may affect the coding region of a gene, or maybe it affects a gene that doesn't even produce a protein, so it's a non-coding RNA. And all these things we can do using various bioinformatics tools and we'll walk through some of them in the following lecture. So when we have different evidence about variants in the genomes, in particular cancer genomes, then there are several different facets that we can look at. One is variant recurrence, so it's a very powerful concept that if you analyze many cancer genomes of unrelated individuals, these are the somatic genomes, if a variant tends to recur or occur in many of these cancer patients independently, then it might be a driver variant. This is because the genome is a large and low-list space, so if a mutation happens to affect the same region over and over again, then it might be a driver mechanism. On the other hand, once we have identified a gene, we may start to build biological stories or hypotheses of what that gene may be doing, if it's activated by a mutation or repressed by a mutation, and then we look at the pathway and network context, which is part of the second lecture. But right now, we're more interested in the particular variant, so a single nucleotide variant or an indel in a gene, and we want to predict what that indel is doing in order to affect that gene biologically, or maybe it has no role whatsoever. In the genome, we have many strange things, in particular in cancer genomes, and we can classify them by their size. That's one of the simplest specifications, so on the small scale, that's probably a single nucleotide, or maybe dozens of nucleotides, maybe up to 50. The most common variant type is the single nucleotide variant. It's a one-base pair substitution where one letter of the DNA alphabet is replaced by another letter. These single nucleotide variants are probably the most high-confidence data that we can extract from cancer genomes. Various algorithms are out there that detect single nucleotide variants, and many times these algorithms agree upon each other's output, so it's easy to form a consensus set of variants that appear in a genome of interest. Small indels are insertions and deletions, sometimes a few nucleotides, sometimes a little more. These are more challenging to detect, and various algorithms don't agree as well on their different outputs, so the more stringent analysis would combine the output of various indel-calling algorithms and get a consensus analysis. We are mostly focusing on the small indels in this lecture, in particular the single nucleotide variants, but also smaller indels. On the medium range of hundreds to thousands of base pairs, we have different types of elements such as insertions, deletions, inversions, translocations, and so on, and sometimes there are complex rearrangements that combine multiple types of these alterations in cancer genomes. These are the most challenging to detect because the algorithms look at various things. The detection of these different elements and modifications and rearrangements is also due to the sequencing length, so many times they don't cover the entire read. When you map these types of medium-sized elements to genes, you don't necessarily use entire coordinates of genes and rearrangements, but maybe you see if a particular rearrangement only captures a fragment of the gene and you could already start to hypothesize how it affects the gene itself. On the large end, we talk about copy number alterations. One of the thresholds could be 5KB or more, sometimes copy number alterations affect entire chromosomes or chromosomal arms, so we use cytobans in order to annotate them to the genome. Copy number variants are relatively easy to detect using microarray platforms and a little bit more challenging to detect next-generation sequencing. When we start to look at variant annotations, there are various steps that we need to do in these pipelines or various components that we need to understand. One of those components is the data that we use for mapping these different variants. When we find the variant in the cancer genome, we first want to relate that back to the existing knowledge, and for that we have various databases such as the 1000 Genomes Database, the ASP6500 project or the exact contortion dataset that essentially tell us how frequent the variant that we found has been found earlier in studies, in particular healthy individuals. Now there are other databases that capture cancer genome sequencing, Cosmic is one of them, it's a mixed bag of various genome studies, it could be smaller scale studies from earlier days or very recent whole genome with all the exome sequencing studies. Now step number two, once you figured out how frequent the mutation is in an existing dataset, you really want to map it to genes because the entire genome has many different things and genes are definitely among the more interesting ones, but you don't really map your variants only to genes but genes have different components to them as well, like untranslated regions and protein coding regions and promoters and perhaps unconscious. Once you've mapped them, mapped your regions to genes or decided that they don't overlap any gene at all, you may want to predict the impact of any given variant considering the features of DNA around that variant. And there's a number of different tools and algorithms that do that, for example SIFT and BOLUFEN and mutation assessor are some, these focus on protein coding sequence. And there are other effects scoring methods, for example CAD is a method that provides a score for every nucleotide position in the genome, so it provides literally billions of scores and then there are many other ways to interpret coding and non-coding variation. This is a very active topic of research, for example you could look at splicing regulator predictions or you could look at transcription factor bound regions in the DNA from ENCODE, make predictions of whether these mutations, perhaps all the gene regulator motifs and so on and so forth. Alright so the first step that we should discuss is the variant adaptation databases and allele frequencies. Now one of the first and most important large scale projects is a thousand genomes project, which is no longer a thousand genomes project because the latest phase contains 5500 individuals. The goal of this project is to capture human genome variants that occur at a relatively high frequency, so more than 1% of the human population in various ethnic groups, for example you know Europeans and Latin Americans, Africans, Asians and so forth. And then this project is focused on the entire genome, but the entire genome is large, so the coverage of the entire genome is somewhat lower and then the exonic regions are covered at the higher rate. Another dedicated project that's focusing on exonic regions is the ESP project, 6500 individuals have been profiled in that data set and it's important to know that that data set doesn't necessarily include only healthy individuals. The goal of the project is to discover cardiovascular disease and lung disease and blood disease variants that are perhaps low frequency, less than 1%. So if in your study you are using that data set as a filter you just need to pay attention to that this is not necessarily healthy variation, some of the variations that you capture may be overrepresented relative to the general population because you're ultimately looking at the disease cohort. Now EXAC is a very recently published data set that the goal is to capture the largest exome sequence in data set ever. This is about 10 times more data in individuals than the previous data set, 60,000 unrelated individuals and again these are not necessarily healthy people. The cohort includes various disease cohorts so they have tried to capture every possible exome sequencing data set and process it uniformly. So when you use that as a control group for example then just acknowledge that these variants may be disease variants as well. On the other hand, DBSNP is not a single study that has been processed uniformly but it's more like a meta database of various different studies that have been accumulated over time. That includes submissions before and after the next generation sequencing era so you could see that some of them will be small and based on maybe some ancient sequencing technologies and others are really high throughput next generation sequencing at home genomes. Now it includes polymorphisms found in a general population so it's a database of healthy individuals but at the same time it includes disease associated individuals and also cancer. So it is a good resource to look up your variants and maybe study them in detail but the moment you start using it as a filter you may you may be filtering out something important. For example because it contains somatic variants in cancer if you discard anything that comes from DBSNP you may actually you know remove some interesting results from your data. Moving on to cosmic, cosmic is a reference set of anything mutations that have been discovered in cancer genomes and similar to DBSNP it contains earlier submissions and later submissions and it's a mixed bag of various studies. So again when using it as a filter you should be paying attention on the other hand if you find a really interesting variant in your data as well as cosmic you should follow up on it because recurrence is such a powerful feature in detecting cancer genomes just go out there and count how many studies actually found that variant. If it's just a single study or a single patient it's likely to be less important for your research. If it was found in dozens of patients and many different types of cancer it's likely to be more important. If you find a mutation that overlaps with the cosmic database go and see if that particular nucleotide has been frequently mutated or if the nucleotide isn't a hotspot mutation and maybe the entire gene is frequently mutated. If a gene is frequently mutated it may be indicating that is important cancer driver gene. However there are some genes that are apparently very frequently mutated but they're not long they're not cancer genes. An example would be the Titan gene that is an enormously large gene and will contain a lot of mutations but none of them are statistical significance. All right so once we, yes? Okay just ask one question about the meta-databases. Like if the reference genome is changing over time are they updating the variants? I wouldn't be able to tell you for sure but this is an important aspect to pay attention to. If your variants that you have been annotating to one genome version you need to make sure that the database that you're looking at is also using the same reference version. I would say that the databases will come in different setups so that will be an ensemble database that uses the reference genome 37 and another ensemble version that will be using the reference genome 38. So if your annotation pipeline went through 37 you may need to make sure that you also use 37 for follow-up interpretation otherwise they won't match at all. Right any other questions? Just to make sure I understand you correctly, if you're using the DBSNIP as a filter to filter out the also interesting variants, DBSNIP also has some kind of lag. So that's what you said we should be careful. Exactly. You don't want to throw them out because if you took DBSNIP as a repository of healthy variation then that wouldn't be entirely correct because there is also disease and no data variation in there. Alright once we have established the frequency of variants perhaps in the healthy population or whether these variants have been associated to some cancer samples earlier we definitely want to ask whether which variants are associated to genes or various regulatory regions of these genes and which variants are clearly the genetic and perhaps noise or at least more difficult to interpret. So when we talk about genes most of the time and we talk about protein-going genes right there's about 20,000 of them in the human genome and that number fluctuates every once in a while but there's also a large number of genes that are not responsible for coding any proteins so there are non-coding genes that are further divided into classes like microRNAs or long non-coding RNAs or long intergenic non-coding RNAs and so on and so forth and I think according to the latest high confidence counts then the number of long non-coding RNAs is similar to the number of protein coding genes so we shouldn't underestimate these sets of genes. However they are likely functionally differently important and there's also vastly different knowledge that's available so much more is known about protein coding genes so much less is known about non-coding genes and probably some of those non-coding genes are also artifacts of just pervasive transcribed regions that are not very functional in cells and when we focus more on protein coding genes they come with different segments or elements of types of blocks in these genes that are responsible for various things so first on both ends of the gene you'll see untranslated regions that get transcribed but they don't get translated and part of the protein coding accents are probably the most interesting ones for variant interpretation because they end up being folded into proteins and active in cells. Intrans get spliced out they are not translated but they may contain some interesting regulatory elements and spliced sites are ultimately responsible for selecting which exons get incorporated in the particular cell type and which ones don't but besides these clearly protein coding elements we have other parts of the genome that may be relevant for our genes of interest for example upstream and downstream regions of the gene may be interesting in particular promoter regions that are upstream or transcription start site may be interesting for understanding genome variation but they are probably way more difficult to interpret and say exactly what they may be doing in order to affect the protein function and finally probably the largest amount of variants that you detect are intergenic you can't really say what they're doing because they're far away from any genes are non-coding RNAs. Alright when we do an annotation of variants to genes then many cases it's really simple there is a gene or there's no gene under your variant of interest but there are situation where that gets a little bit more complicated for example that could be a protein coding gene that partially overlaps with a non-coding RNA and then the question is which information do you really care about when you use the ANOVAR system the ANOVAR has constructed a set of priorities in order to annotate variants so the most important the variants in this priority system are those that affect protein sequences so exos and right after exons and splice sites come the non-coding RNA so if a particular variant seems to be affecting part of a gene but that's not a protein coding gene the next step would be to see if that overlaps a non-coding RNA and after non-coding RNA the next set of priorities lies on their untranslated regions after untranslated regions come the entrance then the upstream regions and finally anything that's intergenic but you can also ask ANOVAR to report all these potential effects so this is worth paying attention to if your field of interest is non-coding RNAs and you use ANOVAR out of the box you may lose a lot of information just because ANOVAR will give priority to coding sequence here's an example of how that really works on the upper panel you'll see a protein coding gene G1 the larger or taller blocks correspond to exons and the narrower blocks correspond to UTRs and then there's also another gene under the under the latter part of the gene which is a non-coding RNA gene NCR1. Now when we use ANOVAR in order to interpret the genome variation then anything that's in orange will be annotated as part of the protein coding gene so you see all these blocks that correspond to UTRs or exons or splice sites or entrance they will all the orange areas will be will be associated with a gene of interest if any variants occur in these regions. However in the blue areas the situation gets a little bit more complicated because the blue area corresponds to that non-coding RNA so the orange strip in the middle that I'm trying to mouse over represents the coding fragment of our genome interest and in any variants occurring in that region will be part of the protein coding gene but just just next to that there's the sequence which doesn't correspond to anything coding but it does contain the non-coding RNA so variants in that blue area will be annotated to the non-coding sequence and non-coding RNA and moving further right on the panel there's a yet another region that is annotated to both the protein coding gene in the UTR and the non-coding RNA and in this case the non-coding RNA will take priority because UTR is less less of an effect than the non-coding RNA and it's worth mentioning again that that if you don't want this behavior out of ANOVA there's a common line parameter that would force ANOVA to report every potential effect making you output a little messier but perhaps a little useful as well. All right so far we've spoken a lot about annotating variants to genes but we didn't mention that genes come from out of a database and that may actually make a difference. ANOVA uses the RefSeq database so all the sequences of genes, transcripts and proteins are all uniformly coming from the RefSeq database and that is the recommended behavior for this particular software. However other databases also provide collections of genes and RNAs and proteins and transcripts namely the UCSC genome browser and ensemble all have their own versions of the genome and the genes in these genomes. I wouldn't say that one database is clearly better than the other but what I would recommend for sure is that once you stick to one database use it throughout your entire pipeline because converting between these these different databases and the database identifiers can get a little messy you you can lose information in it and create situations that you really want to avoid. So ultimately besides mapping our variants of interest to genes and saying this particular variant is associated with that gene we want to predict an impact of what that variant may be doing in order to be able to dig a little deeper into the biology. In the context of cancer you would like to find out whether a variant causes an oncogene to become activated or perhaps it causes a tumor suppressor to be downregulated or inhibited or disabled. Generally this is difficult to do. Generally in the sense that most of the genome is very hard to interpret but the protein coding genome is somewhat easier to interpret at least a lot of good ideas have been implemented in order to understand protein coding variations. In certain cases in the regulatory genome it may be also possible to say something about the impact of variations. For example untranslated regions or UTRs are known to be regulated by microRNAs and if we can annotate microRNA binding sites together with our variants of interest we may be able to say that the particular variant was affecting a regulatory site in the UTR or maybe in the promoter region. However protein coding sequences are probably the first priority in any of these annotation pipelines and it may be easier to chase down these effects because of the protein codon and stop codons and things like that. So when we look at the genes that produce proteins and potential effects that variants may have then this is a list of variants that from decreasing in a decreasing order of impact. So probably the most impactful mutations that one can have in a protein is an early stop gain mutation that causes a stop codon early on in the protein and it truncates the rest of the protein. Perhaps there's only a little tail left of the entire protein which in the end leads to almost like a knockout effect and then that there are these studies showing that many individuals carry you know genes of genes that are truncated so everyone is a knockout mutant of something. A slightly less impactful mutation is a frameshift insertion or deletion that shifts the entire logic of breeding frames by one or two amino acids nucleotides I'm sorry and that leads to a faulty translation early on perhaps and sooner or later a stop codon will emerge so that would also lead to a faulty early truncated protein. Another impactful event would be a mutation that affects the splicing site so splicing sites are small motifs that are near exon borders and when these splicing sites are altered that would could determine whether an exon an anti-exon is included or excluded from a transcript and you can see that how that could potentially affect a protein a protein function as well. Another slightly less impactful event would be an in-frame insertion or deletion that would either remove one or more amino acids so that that has to be an indel in the male multiply multiplied by three amino three nucleotides and it's more difficult to say what that particular change could do to a protein it's maybe perhaps easier to say if it affects a well-consumed regions that we will look more into in the next few slides and then finally you can have stop losses which means that the final stop codon is replaced by an amino acid and the protein continues to be translated from the RNA and maybe leading to a longer protein that that has a different function and then the broadest class probably is the set of misest SMVs where letters of the amino acid alphabet get replaced by other letters of the amino acid alphabet finally the the least likely functional mutation would be a silent mutation which doesn't lead to an amino acid change yet there are studies that some of those silent mutations in protein coding regions may be responsible for rewiring transcription at the binding sites so even synonymous mutations may have may have an impact so loss of function variants are a couple of variants from the earlier slides namely stop gain, frame shift and splicing that can be considered highly impactful and highly dilaterious in terms of protein function but even then before you jump into conclusions and start writing up your paper it's worth considering additional features of these of these loss of function variants for example consider the percent of sequence that gets that's get altered by this mutation it can be a stop gain mutation but the but if it occurs very late in the protein then the entire protein almost is functional and perhaps the protein tail is a disordered tail it doesn't affect the entire protein that much another factor that needs to be considered is alternative splicing so that could be a situation that there is a very important stop coding in one exon but in that particular tissue of interest say the cancer tissue that exon is never expressed so it doesn't matter whether there is a stop code or not and splice saving effect is in general it's quite difficult to predict because there's there's a large number of those splice sites and we don't exactly know the function so miss sense variants being the largest class and also the most reliable class of mutations how can we learn more about any individual miss sense mutation we can look at the amino acid alphabet and various physical chemical properties of these amino acids well they're not born equal they are distributed to families and perhaps a change across the families would be more disrupted protein function a very powerful feature of annotating and analyzing variants the genome is conservation if a particular position in the genome has been the same for millions of years and many different species then it's much more likely to be important in disease or maintaining the healthy state themselves you can look at also features of proteins for example protein domains the protein secondary structure whether it's a structured regions or a disordered region 3d protein structures are available but not for all proteins and there are software tools that allow you to simulate the effect of a miss sense mutation in a 3d protein structure assuming that there's good structure information available and then there's tons of other features some work in my postdoc and in the lab is revolved around post translation modification sites which are abundant in proteins and sometimes they are abundant in regions that would be otherwise considered pretty benign but the fact that there are post relation sites and disease mutation enrichment highlights that this is an area of study that's interesting and besides looking at all these different features separately there are machine learning tools that attempt to integrate information across these different facets of data and give either classifications or scores about any individual variants so there are tools that tell you this variant is more likely to be benign while this other variant is more likely to be harmful for the protein function. Here's an example of BRAF which is a very famous oncogene with a hotspot mutation in I guess in the kinase domain where BRAF v600d is seen in many types of cancer and it's a target for cancer chemotherapy while v600a is another variant that has been observed in somatic and germline genomes but we're not really sure of what it does and when you look at this chemical map of the various amino acids you can see how you could interpret the function of this missense mutation just by looking at the physical chemical properties of amino acids v and a are very similar amino acids and they are just right next to one another they share chemical properties while the v changing to an e seems to be way more disruptive the molecule have it as a different shape it has a different hydrophobicity if I'm not mistaken and you can interpret that information by just say you know imagining the possibilities how it could alter protein structure because it's a different molecule. Alright moving on to conservation and the ways various machine learning tools use conservation to establish which variants are more likely to be harmful than others. Conservation is a pro powerful and broadly used idea and we can ask whether for a particular nucleotide or a particular series of nucleotides or genomic regions how likely is that region conserved in human versus many related species and we can use that information by analyzing essentially multiple sequence alignments for either proteins or whole genomes and it does make a difference whether you look at the nucleotide sequence or the amino acid sequence and various tools give you a different analysis whether they focus on genomes or proteins and some of those tools are the following phylo P is based on based only on conservation and it's quite useful for assessing single variants fast cons is a different type of a beast it allows you to assess variants but also entire regions and it's useful for regulatory regions as well as proteins and then multi species alignment is something that you can visually inspect and study and it's useful for understanding how the sequence has evolved and which parts are more likely to be more fragile towards variation and which which ones are very more naturally variable in general. Here's a snapshot from the UCSC genome browser it describes the TP53 gene above and you see that they are quite well conserved and towards the bottom part of the the screen you can see how different protein residues are conserved across species and in human. So when you have your high confidence variants of interest you may want to go back to the protein alignments and look what these variants look in the context of various species in the evolutionary tree. So phylo P is one of these useful scores it's designed to test whether the nucleotide substitution rates are faster or slower in the particular region or site compared to a neutral drift and you can use phylo P in the context of cancer because a line sequence is available but you can use it less when you focus on exotic model organism. Phylo P scores are either positive or negative if it's a positive score it can be considered conserved and you can use a cutoff such as the value of two to decide that some regions are highly conserved. Zero means neutral and negative scores mean that these sequences are evolving faster than the background rate. Phylo P scores are available for various species and it will determine how deep your conservation is. You can look at the only primates or all vertebrates or something in between. The problem, yep. If it's a positive number it's more conserved. If it's a negative number then... So one of the main take-homes of conservation is that it will tell you that the position is important but it won't tell you whether the change in the position is important or which change in the position is important so only know that that region is important in general. Additional machine learning tools and scores will try to improve on that and provide something more. So when you're using some of those scoring models in order to assess liberation then you should keep a few of these ideas in mind. You can use conservation in a different way. You can look at protein level conservation or you can look at DNA level conservation. One of them will restrict you to protein space and the other one will perhaps misinterpret or discount features that are only focusing on proteins. You can also study amino acid physical chemical properties which is a different angle to the conservation. However these may be also more difficult to interpret. When you use some of those tools you need to understand which ones are based on scoring so some sort of mathematical model and which ones are based on machine learning and the machine learning ones are usually more dependent on the training data so machine learning will help you classify or score variants but it comes with certain assumptions of how the data sets were constructed and used in the beginning. And the choice of data set may actually matter a lot because different types of disease variants may have completely different properties and training your machine learning model on one type of data will result on that particular type of data or variant to be discovered more frequently. For example some tools may be distinguishing activating and data function mutations in a better way where others may be better at finding inactivating or loss of function mutations. And when some models have been perhaps trained on human minglilian disorders they may not be as valid for discovering cancer mutations because it's a it's a different piece that doesn't necessarily need to lead to truncating the mutations but it could activate a protein in a particular hotspot and so on. So one of the first algorithms or even the quite first one is called SIFT. It's very broadly used and it's quite old more than 15 years now. It's designed to find deleterious mutations that are disruptive of protein function and it's not a machine learning tool it's based on scoring and the scoring is relatively simple. So first you identify a query protein of interest then you use a blast-based tool in order to find additional proteins that are similar to that protein of interest. And once you have an aligned set of these proteins of interest for each position you determine the probability of any amino acid in that position. So essentially you build something that's called a position weight matrix which is as long as the sequence you have and as tall as the amino acid alphabet it's a square and every probability in that matrix will tell you how probability is to see a particular amino acid at that position. Then you normalize that matrix and you'll see in the end of how likely it is to see any amino acid in a particular position of that protein. And now they showed in a few case studies that after normalization proteins that seem to have a low probability in a particular position of the protein also correlated with with deleteriousness in that various diseases. So it's a simple score and it makes certain assumptions but it's robust because it doesn't really depend on the particular training set or set of variants that were used in order to build the algorithm. Polofen number two is another algorithm in that family. It's a machine learning based tool. It integrates various features in order to predict the function of a variant in a protein. There are several feature based sequence based features that are input into the algorithm as well as structure based for protein structures. There's things like physicochemical properties of the proteins and also protein domains, multiple sequence alignment metrics and so on. And then a machine learning tool called Naive Bayes classifier will attempt to build the best classifier between presumed disease variants and neutral variants. So they have two sets. The most stringent one will only look at damaging Mendelian disorders and their variants. And others will be looking at non-damaging differences between human proteins and related primate proteins. And the more broad set will look at all human disease mutations from the Uniprot database and the negative set will be any non-synonymous SNPs observed in the human population. So you can see how such a machine learning method can be very powerful but it will ultimately depend on the input data. For example, if you select a particular set of disease mutations in the training set, this is what will be captured most likely in your data as well down the road. The other problem is that there's far fewer well annotated disease variants compared to all the individual variation that's out there. So these training and test sets are likely to be very imbalanced. And then this is an active area of research. People improve these algorithms all the time and try to reduce the inherent bias from these training and test sets. Mutation assessor is another tool in the family. It is not based on machine learning. It's a director of theoretical model. It's kind of similar to an enhanced sift but it incorporates more information. It's based on amino acid conservation but it specifically attempts to model conservation that's specific to different classes of proteins. So proteins are known to evolve at different rates and then that is input into the model in addition to general protein substitution rates. And then they have an entropy-based score that attempts to predict whether a protein substitution is likely to be seen or less likely to be seen. And they claim to be performing really well for recurrence somatic variants which we need to understand when we look at cancer genomes. Another tool in the family which is relatively recent, I believe it's about three or four years old is CAD. And CAD is powerful because it allows you to score variants genome-wide. So for every position in the human genome it provides a score for every potential nucleic acid change. So any nucleotide in the genome will have a score for any potential nucleotide in the genome that gets changed to in a potential mutation. Therefore it allows it to score proteins and also non-coding areas of the genome but there's a trade-off. So it doesn't incorporate all the good information that we have about proteins because it's supposed to be generally applicable to any region in the genome. It is a machine learning model, a support vector machine that is designed to separate harmful variants from non-harmful variants. Their negative training set is a set from humans compared to the human ancestral genome. However the positive training set is based on simulation data and that seems to be deliberate rather than focusing on a particular set of very well-defined elements. They define a simulation model that attempts to generate harmful variants based on our knowledge of what harmful variants look like in general. And as predictive features they use, of course, conservation but also many other metrics namely and code tracks that annotate the regulatory regions of the genome, various tracks from UCSC and so on. And they claim to be performing a little bit better than just pure conservation because they add all these additional features. And then I'll just show you a couple of slides about their performance, where the thick black line that hugs the top edge the most is a area under curve metric, which appears to be the best for CAD compared to all the other algorithms. Okay, so so far, yes, can you repeat that? I'm sorry I don't think I know what you're talking about. Okay, that's interesting. So it performs a combined score of all these various algorithms in a predicated way. It's interesting. All right, so I don't know that specific algorithm in general, but that's a very good idea to have various tools to predict the outcome and if most of them agree then you are more likely to trust your outcome. The ANOVAR pipeline that we go through as a tutorial will allow you to do the same in a naive way because it will use these various sources of data and you can almost add up the scores and then rank your scores according to the best score. I'm about to wrap up this lecture. I just wanted to mention a few different approaches that are different from conservation. That's the main theme in annotating many variants. This is a study from Toronto a few years back in science where they were predicting splicing regulatory alterations or variants affecting splicing. So the goal is to predict how single-nucleotide variants affect the exon inclusion or exclusion and they used a machine learning based strategy to understand how splicing works in general. So they were looking at sequence motifs in splice sites and also the expression of various exons in the associated genes. And then once they had that machine learning framework in place they were able to predict what mutations may be doing in the splice sites based on the previously observed mutation, the motif composition in splice sites as well as the transcription levels of various exons. The feature or perhaps an advantage of this framework is that it does not learn based on known disease or splicing alterations but it's designed to be more robust and more general than the few examples that we know from literature earlier. Another aspect how you can interpret the cancer variation in the context of proteins is the idea of phosphorylations and other protein post translational modifications that are very abundant in proteins in general. Post translational modifications are like activation switch or disabling switch in proteins, they extend the protein functions. There's a large number of them that are based on experimental evidence, mostly mass spectrometry data about 130,000 sites in proteins representing about 12% of human protein sequence. It turns out that the many of these sites are enriched in disease mutations and especially in cancer and these sites seem to have less variation in the general population suggesting that these sites are important to be maintained without variation. What's important here in the context of this lecture, the figure that you see here shows you how known disease mutations would be classified using some of those tools that we discussed earlier. Black represents damaging mutations and this is expected because it's a disease mutation and they would be damaging. However, orange and red shows disease mutations that are predicted to be benign according to these various pipelines yet they affect these various post translational modification sites, which suggests that if you use post translational modification site annotations in your analysis of variants, you may be uncovering more information that wouldn't be shown in these various tools like polyphen and SIFT and then the reason for that is the following post translational modification sites are often not conserved. They are in the protein regions that are called disordered regions and therefore because they are not conserved, they are less likely to be called harmful by these various tools that rely heavily on conservation. So having additional layer of various signaling sites will help you to perhaps annotate some of the variants that otherwise would look benign. To conclude this lecture, the main feature in many of these variant annotation tools is conservation and the simple new that conservation such as the phylo P score is quite powerful in many cases and it performs not as well as the complex scores but relatively well and it's pretty general and when you analyze your data using conservation, it's always a good idea to check back to the multiple genome alignments or multiple protein alignments to see how it actually looks. When you have these machine learning based models that distinguish harmful variants from benign variants, they can be really powerful but you need to understand their certain weaknesses in particular that they have been ultimately trained according to either known disease variants that represent a pretty sparse data set or alternatively they may have been trained using simulation data and simulation data is always based on assumptions. And when you have your variants of interest, especially further down the pipeline when you have a set of really high confidence variants, you really should work on them just case by case as well as your bioinformatics tools. Just consider the conservation, the various scores, see how well do different machine learning models agree on the predictions and you can review the amino acid changes and you can look at the physical chemical properties of these various amino acids to think how they are affecting protein function. Sometimes you have access to three-dimensional models that you can simulate to see how the variant potentially affects the structure and so on and don't forget to think about the variant in the context of the gene and the potential networks and pathways that it's involved in and this is the theme of the next lecture after the tutorial.


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