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next topic and that is cycles. Now, we have a thermodynamic cycle. By thermodynamic cycle we mean what we have defined in our basic introduction. A cycle is nothing but a process in which the initial state is a final state. So, any process which can be represented even crudely as a closed loop on a state space that is a cycle and a cycle requires just one state of equilibrium. Just to assert that state one is state two. Two states which are not in equilibrium, we cannot say that they are the same because we do not know enough about every state. So, a simplest depiction on a cycle on any diagram P v or T s. The minimal depiction will be some state from there start off and come back to that state. You need not worry what happens in between. But for our purpose most of our cycle will be depicted using some quasi-static processes. So, we will have any number of points of equilibrium in between. Actual cycles may not be like this because a non-isentropic turbine we will say that look we need to know only inlet and exit states what happens in between and we do not really care. But this is about thermodynamic cycles and now on one side we have thermodynamic cycles, on the other side we have actual plant which is made up of pieces of equipment and we have in between technical cycles. Technical cycles are thermodynamic cycles as implemented in a plant. So, now we have cycles from thermodynamic. We have cycles technical considered for implementation and we have plant that is actual equipment. And when we now talk of cycles classify them we are actually in this domain. When we do cycle analysis we use the ideas of thermodynamic cycle but now what we mean are the cycles which are technical cycle which are executed in equipment which is part of a plant. So, when it comes to classification there are number of modes of classification. One type of classification is based on usage. First one is whether it is a power cycle or power plant or a refrigeration cycle which is for a refrigeration. Thermodynamically these are power producing cycles, these are power consuming cycles. For these cycles W dot would be greater than 0, for these cycles W dot would be less than 0. You can say these are normal or forward cycles, quite often these are known as reverse cycles. So, sometimes the same cycle can be used for example a sterling cycle can be used for power plant. Sterling cycle when implemented in the other way can be used as a refrigeration cycle. In fact, sterling cycle more implementations would be as refrigeration, refrigeration, heat pumping, carnos or cryogenic cycle. We will be using refrigeration meaning all refrigerators, heat pumps and cryogens. So, in one case you say sterling power cycle, the other case you can say sterling refrigeration cycle or the reversed sterling cycle. But you generally do not see people do not say that liquid nitrogen blast based on the reverse sterling cycle because moment is a liquid nitrogen plant it is known that it is a reverse sterling cycle and anyway there are many power plant implications of sterling cycle anyway. But, Brayton cycle generally used in gas turbines and jet engines. You can use a reverse Brayton cycle for refrigeration reasonably common for aircraft cabins and many other simple implementation. And if the pressure for greenhouse gases becomes extreme then Brayton cycle because it can use ambient air will become a rather attractive option. Only thing is it is not as thermodynamically efficient as is a vapor compression cycle for example. So, the reverse Brayton cycle is so common that we have the name joule cycle for it.  So, that is based on usage. The second classification is mainly for power plants. For power plants traditionally the energy which is supplied comes from combustion of a fuel. And when this happens we have a classification when combustion of a fuel is involved of what is known as an internal combustion or external combustion. And naturally because combustion may be involved for only for power plants no combustion in refrigeration cycle. This is only for power plant and that to certain type of power plant. For example, a solar power plant no combustion is involved. So, there is no point in calling it internal combustion or external combustion. In internal combustion the process of combustion is within working fluid. So, no heat transfer process is involved. This happens in our car. We put in fuel air it sucked in the combustion takes place within the engine. So, that is an internal combustion plant and the cycle is implemented as an internal combustion cycle. Car or a jet engine all these are internal combustion. In external combustion combustion in a fluid then there is a process of heat transfer to the working fluid of the cycle. So, there is a process of heat transfer involved. For example, over steam power plant you have a boiler a separate furnace. There are coils of water which absorb heat and become steam. Combustion does not place take place within the water or within the confinement of steam. So, that is an external combustion plant. It should be noted that because of this heat transfer to the working fluid because of the heat absorption there is no change in composition of the working fluid. Here there will be a change in composition. First one was based on classification on music. Second one was based on the type of combustion. The third one we look at is the type of equipment. And for this we have a single equipment plant and we have, see although we have shown on a TS diagram just some cycle. In actual practice this particular cycle is implemented as reasonably distinct processes. Some at constant pressure, some at reasonably constant entropy, some at constant volume and all that. In a single equipment cycle all processes in the same piece of equipment. Our car engine is a typical example. The same cylinder piston arrangement does the job of everything. Sucking in the fluid, compressing it, burning it, expanding it, throwing it out, everything. Whereas a multi-equipment plant is different equipment for different processes and fluid flows from one to the other. So, such cycles can be shown as block diagrams. One piece of equipment, second, third, fourth and fluid goes. One could be pump, one could be boiler, one could be turbine, what could be condenser. So, our power plants, household refrigerators, these are, you know household refrigerator is a typical example of a multi-equipment plant. You have the evaporator, you have the condenser, you have the compressor. Three pieces of equipment very easily seen by everyone, except that condenser these days is integrated with the body so it is not so easily seen. But simple models of refrigerators still have a proper black-pented condenser coil. The capillary tube is hidden somewhere so unless you really hunt it out you cannot hunt it out. You are air-conditioner, multi-equipment. Then a closed cycle for an open cycle. A cycle is said to be closed when the working fluid sort of sealed in and except for leakage which have to be taken care of. There is no interaction of the fluid directly with the environment. Nothing comes out, nothing goes in. So, one typical example is our household refrigerator, air-conditioners, steam power plants. All these are closed cycles. An open cycle on the other hand is the working fluid is taken in known as intake or induction or inflow and the working fluid is then thrown out and that means that at least one process is left to nature to work out. Illustration, IC engines, all our car engines, jet engines, air breathing engines as they say.  Let nature worry about what to do with that. Naturally internal combustion cycles will have to be open cycles because you are changing the characteristic of the working fluid. You cannot go on using it again and again. So, you have to throw it out and let nature worry about how to give you fresh air out of the exhaust. Cloth cycle, steam power plant. But that does not mean all steam power plants are cloth cycles. Our steam locomotive is an illustration of an open cycle steam power plant. It takes in water. You have to continuously feed it water. That is why electric engines have essentially an infinitely long range. Diesel engines have a much longer range because diesel is a very compact fuel. Steam engines have a much shorter range because periodically you have to supply coal which is a huge bulk and also water because water is a working fluid and the locomotive does not have a condenser. It throws out the steam which comes out of the engine part. Those will also be open cycles. Even partial recovery will make it open because to work the cycle you have to keep on feeding the working fluid at some end and there the recovery is not there because perhaps it is not economical or technically easy to recover or maybe it gets contaminated. But if it does not get contaminated goes only through heat exchangers maybe you can recover it. In this case it will be and finally the fifth classification is the type of working fluid giving you a gas cycle for a vapor cycle. Unlike the second classification external combustion, internal combustion see the first is applicable to power plant refrigerators that makes a classification. Out of then this internal combustion, external combustion is applicable only to power plants whereas type of equipment, single equipment, multi equipment is applicable to both power plants as well as refrigerators and closed cycle, open cycle applicable to both power plants as well as refrigerators. Similarly gas cycle and vapor cycle is applicable to both power plants and refrigerators. In a gas cycle the working fluid is a gas. No change of phase is involved of any kind. Here the working fluid is typically in two states liquid and vapor and hence condensation and evaporation or boiling are essential part of the operation of the cycle whereas no change of phase is involved here. Now from a student point of view there is an advantage. Here for example in this case the gas in a simple situation can be assumed to be an ideal gas with constant Cp Cv. In which case we have simple equations to worry about and it is possible to do what we call quite often a standard analysis assuming the working fluid to be an ideal gas and you get neat expressions for efficiency at some function of P1 P2 or T1 T2. Simple expression just substitute them in a calculator within half a minute you have the answer for efficiency and many other parameters. Whereas here because a vapor is involved and condensation evaporation is involved there is no simple equation of state whether it is water for power plants or any refrigerator for any refrigerant for refrigeration plant. So every case will have to be worked out. So we have a formula for the efficiency of a standard auto cycle formula for the efficiency of the standard Brayton cycle but we do not have the formula for the efficiency of a standard ranking cycle or the COP of a standard vapor compression cycle of any kind. So no simple EOS so we have to work out each case. See when you teach cycles in thermodynamics once you realize that cycles are one major link between thermodynamics and further engineering and hence when you come to cycles you must spend time in explaining what a cycle is and the different classifications of cycles. Show them at this time pictures of various equipment and their cycles so that their interest immediately let them realize that the thermodynamics one of the direct implementations is in analyzing power plants and refrigerators and the link is through thermodynamic cycles. So coming back to this thermodynamic cycles, technical cycles which are implemented in plant there is a link between these two. We will analyze thermodynamic cycles they are implemented as technical cycles in appropriate plant and you will notice that some classifications pertain to these cycles as well as these thermodynamic cycles. Some classifications pertain only to these cycles and the plant. For example when it comes to open cycle and closed cycle it has nothing to do with thermodynamic cycle. Thermodynamics is let nature cool it it is a constant pressure cooling process but when it comes to actual implementation there is no piece of equipment executing that cycle. Now the next thing you do is talk about performance parameters. There are performance parameters which some provide you the goodness of the plant. These parameters are known as efficiency or coefficient of performance for refrigerators. Then there are certain parameters which talk about compactness of a plant. These are usually some specific output or specific refrigeration effect. There are some parameters which talk of the size for the capacity. Here we talk about the power output in case of refrigeration we talk what is known as the tonnage. And then there are these are the parameters which almost everybody will be talking about. But then there are other parameters which are of some of them will be economic, some of them will be purely thermodynamic. We will talk only about one of these. For example other parameters directly or indirectly related to these are work ratio in some cases, mean effective pressure. See the size and capacity is essentially a scale small plant or big plant. When it comes to real thermodynamic size and capacity does not matter. We analyze a 1 megawatt steam plant and a 2000 megawatt steam plant using the same principle, same paper, same pencil. So that is a minor parameter. We are going to look at efficiency and the compactness and then we will look at the work ratio. Efficiency is invariably defined is for power plant or engines. And this is simply our schematic diagram would be we have an engine producing some output W dot, some heat rejected. This would be the typical nomenclature rather than Q dot 1, Q dot 2. This would be the environment temperature T naught. This would be some supply temperature T sum. Efficiency will be defined as W dot net divided by Q dot supply and naturally higher the better. That is simply Q dot supply depends on how much fuel you have. Power output is what we want. Fuel is what we pay for. If you are a power plant engineer, any type of power plant, you will generate power and sell it. So this is the revenue department and this is the purchase department. You have to buy fuel. You should be able to generate more revenue with the least amount of fuel. So higher is this ratio, better. Then when it comes to refrigeration and heat pumping plants, there is a coefficient of performance. And here there is a small difference. A refrigerator plant will do the following. You have an environment T naught and you want a T refrigerated space to be maintained less than T naught. You cannot have perfect insulation. So from the environment there will be some energy leaking into it. In between you will put something at room temperature and into it and say cool it or put water into it and freeze it. So either to take care of this leak into it or to cool the temperature or reduce the energy of the stuff which is put in it, we will have to extract heat out. That heat which is extracted is known as the refrigeration effect. And naturally our second law tells us that you just cannot throw it out at a temperature higher than or equal to environment. You cannot throw it at a temperature less than environment because you just cannot do it. You must be setting, sending it out to the environment and wherever you do heat transfer must be possible. So temperature must be above the environment, at least by a small amount. And you cannot do this unless you provide a W dot, supply a W dot. This is the equipment known as the refrigerator. What you need is Q dot R. It is what you want. What you have to pay for is Q dot supply. Q dot R, if it is good then your refrigerator will keep things very cold, produce lots of ice. If Q dot supply is bad it will consume more energy, your electricity bill will go up.  So this is the ratio that you are provided. Coefficient of performance is what we want divided by what we pay for. It is a dimensionless ratio, but unlike efficiency which the second law says should be less than or equal to 1, this can have a value less than 1 equal to 1 greater than 1. That is why it is called coefficient of performance. And out here your W dot net is usually denoted in terms of megawatt, kilowatt, maybe gigawatt for big fans. Although this can be in compatible units to give you a ratio, quite often Q dot R is measured in tons of refrigeration and the conversion factor is 1 ton of refrigeration, 3.5 kilowatt. Tell them that this is an approximate number. Historically the value may be slightly different, but for our purpose remember that 1 ton is about 3.5 kilowatt that is to remember. Let them read some book to find out the history, what it was meant initially by 1 ton of refrigeration. Do not tell them stories about that short term 9 from 0 degrees C to 0 degrees C. Let them read something on their own, they will find it out on the internet in a. W dot net is measured in kilowatt and hence quite often C O P may be written in terms of tons of refrigeration plus kilowatt. It is a dimensionless number, but now it has a unit. We talk of energy efficiency of engine as 30 percent, 40 percent, but when it comes to an air conditioner the supplier may say that look it provides you 1 ton per kilowatt or a more efficient one will provide you may be 1.2 ton per kilowatt that means it provides you this much of refrigeration effect consumes this much power. And there are other units BTU per kilowatt hour or kilo joule per kilowatt hour whatever they are all dimensionless numbers only units are different. So, do not ever insist that C O P must be given to you as a pure number it may have a unit associated with it, but the unit should be such that it should be a dimensionless unit. But if somebody asks you what is the C O P you should give him a pure number meaning it is kilowatt per kilowatt or ton per ton. This is for refrigerators when it comes to heat pump it is the same reversed cycle which does pumping of heat, but here the job is to keep a space at a temperature T higher than T naught and then you say that rather than directly heating up the job of a heat pump is to take heat from the environment and supply the required amount and run it as a essentially as a refrigerator. So, looking at it it is a reversed cycle takes it from a lower temperature dumps it at a higher temperature, but here we are interested in the refrigeration effect the amount of heat extracted from that low temperature system. Here we are interested in the amount of heat pumped and hence the C O P here is defined in a slightly different way instead of Q dot R by W dot supplied this is the definition of C O P here, this is the definition of C O P. The denominator remains W dot supplied the numerator now is the amount of heat supplied and why do we need to supply this heat because there could be some heat losses to the surrounding you cannot have perfect insulation or you may keep something there and expect it to be heated up by absorbing heat. So, this is the heat pump load heat pumping load not refrigeration effect that divided by heat supplied C O. So, these are the three parameters and you must spend time in explaining them the differences and similarities between the two particularly the differences between the C O P for a refrigerator and C O P for a heat pump and take this opportunity to tell them what is a turn off refrigeration means at least approximate. Then the something about compactness and size see whatever plant we have it has a working fluid and whatever we do the working fluid usually flows from one equipment to another this could be a power plant this could be a refrigeration plant does not matter M dot working fluid. Now, we may have a given capacity of the plant for example, you take a power plant it produces some W dot net and we can compare two plants one from company A another from company B both producing 1000 megawatt. So, they will be different differences and one company will say that look for 1000 megawatt we need to circulate steam 800 kg per second another company says we need to circulate steam 900 kg per second both may have the same efficiency but the fluid circulation rate is different and hence we define a parameter which is known as there are two parameters defined one is the specific output which is W dot net M dot working fluid and the other one is the reciprocal of it M dot working fluid divided by W dot net the unit here will be kilowatt per kg per second or megawatt per kg per second or it could be kilo joule per kg here it could be the other way round kg per second per kilowatt per megawatt per kg per kilo this is known as specific output this does not have a name but in steam plant this is known as the steam rate or specific steam rate and what is the effect if specific output is higher that means for the same working fluid flow rate I take more output from the plant or looking at the other way for a given output I need to circulate less amount of fluid this fluid is not consumed it is only circulated but if you need less fluid to be circulated that means you have smaller sizes of equipment smaller duct smaller duct sizes smaller boiler sizes and hence something which has a smaller steam rate or a higher specific output will be a more compact plant it has nothing to do with efficiency it has something to do with compactness of the plant similarly your refrigeration plant once in a while something leaks out okay particularly from the split air conditioners because these are not sealed system there are ducts there are flanges there are connectors so something leaks out and you have to fill in now suppose you have two such split end air conditioners both of 5 tons but once you fill in one requires more filling one requires less filling the one with less filling is naturally more compact works with less fluid may be circulates less fluid so this is something to do with compactness and size and this is what the technical name should be specific output or steam rate or working fluid rate steam rate is a very common in steam power plant so going to our parameters compactness size capacity is a scale so we have now these are the parameters which we talk about and these are specific to certain type of engine for example work ratio can be applied to engines and is usually applied to engines but usually is applied to but can be applied to refrigerators also you will find that many text books show an engine you supplied you rejected w net q dot supply w dot this is a very simple schematic but in many text books you will notice that it is shown like this what is called a Sankey diagram q dot supplied w dot net q dot is applied w dot net q dot is it really so is it like a highway one lane going there and other lane going here it is not so because we know that to keep the plant working there are certain internal exchanges which are not seen from outside so show them something like this you show them that look this network comes out but this networks come comes out from a work which is flowing inside the system something like this you show maybe I will do it on a bigger diagram on the next page you say that look this is the heat which is supplied I will show it horizontally I have a larger part of it is definitely rejected no doubt but it is not that the remaining becomes work this becomes work no doubt this is supplied but during working much more work is created and is recirculated within the system like this so there is a positive work flow in the system there is a negative work flow which is fed back and network goes out and you can show them this on a PV diagram and also on a TS diagram for example they generally have an idea of the Carnot cycle on a PV diagram and Carnot cycle on a TS diagram so show a Carnot like cycle on the PV diagram these are the four processes let me say A B C and show the whole thing on a TS diagram. On a TS diagram the Carnot cycle would simply be a rectangle A B you ask them what is the area under the curve A B for A B C on the TS diagram what does it represent q dot supply for q supply what does this area represent heat rejected so this area represents the W net which is q supplied minus q rejected so the ratio of W net to q supplied is the efficiency and you can say the ratio of q rejected to q supplied is the say the heat transfer ratio and efficiency higher the better heat transfer ratio lower the better because heat transfer ratio is nothing but 1 minus efficiency. Now you ask them what is the area under the curve A B C this area work of expansion what is the area under the curve C D A work of compression what is this area in between this is network A B C D this you say is work of expansion which I call W plus magnitude this I call work of compression which I call W minus by magnitude so that W plus minus W minus is W net but do we call W net by W plus as the efficiency it is not the efficiency in fact W plus W minus have nothing to do with efficiency but now consider two engines with different ratios W minus W plus this what we call the work ratio what is the significance if there are two engines with a work ratio of 0.9 and another work ratio of 0.2 which one would you prefer 0.2 why less overheads that means well in either case suppose I am supplying 100 kilo joules my efficiency is 50% I am taking out 50 kilo joules of work but to take out 50 kilo joules of work internally I may be doing 500 kilo joules of less than 0.5 right. So this work ratio is better and for us this is lower means better this is usually not worried about in thermodynamics because this has nothing to do with thermodynamics and this is useful because when we start discussing cycles the first question which comes up is if Carnot cycle is the best why not use Carnot cycle it turns out give them an example and it is there in the illustration a Carnot cycle has one of the worst work ratios and hence we get attracted towards Sterling cycle Erikson cycle which are modifications of Carnot cycle. Then there is another parameter which comes into operation and which is the mean effective pressure this has something to do with reciprocating machine either power plant kind or the refrigeration kind we can give an illustration of power plant you say your car engine everybody knows that your car every student may not own but I think every student or his or her family owns at least one two wheeler these days most probably all students have a two wheeler these days and everybody talks of a 100 cc engine 200 cc engine typically in that range what does this 100 and 200 mean they say it is a displacement it means that you have a cylinder piston arrangement in which if you take the piston from one extreme end to another extreme end the amount of volume which is swept which is displacement is that V which you talk of V displacement VD technical name is a displacement volume and then you say that one cycle of working would take the piston from one end to other end and back at least once in some old engines it could be completed in one back and forth most of the modern engines it is implemented that you do it twice some cranky engineer in Kochi has implemented it by doing it thrice six stroke engine and I am sure there are other possibility now you say the work done per cycle and by this cycle I mean the technical cycle not the thermodynamic cycle of just going back and forth and at this time tell them that this movement is known as a stroke and this movement is known as a stroke a stroke one way the stroke another way and if your thermodynamic cycle is completed within two strokes it has it does require an even number of strokes right you have to come back to the original state then you say that it is a two stroke cycle if it is completed in four strokes it is a four stroke cycle during cycle analysis rather than straight away go into diagrams and formula it is worth spending a few hours explaining all this this way you are making the life simpler for people who will teach them IC engines and steam turbines provided they realize that the students already know this and just revise this quickly and go ahead otherwise they will bore the students so you determine the work done per cycle let us call it let me say network done per cycle is Wc now if I assume because this is moving by PD if I say that let me assume that this is done in one stroke of the piston what should be the pressure that should be acting on that stroke that pressure we call the mean effective pressure the displacement volume or you may call it stroke volume sometimes this is also known as Vs for stroke volume for engines the mean effective pressure would be the work done per cycle per unit stroke volume because work per done per cycle is mean effective pressure multiplied by the stroke volume at this stage there is no need to split it into break mean effective pressure and indicated mean effective pressure we are looking common thermo dynamic point that we can come when it comes to IC engines and similarly you say for an engine this will be the work done per cycle so multiply this by the number of cycles per second and you will get the power output of the engine you can even set up a small problem based on that for for example you tell them that our household refrigerator also as a compressor it consumes power so work consume per cycle if you calculate and divide it by the stroke volume you will get the mean effective pressure for that compressor for any reciprocating machine you take a reciprocating compressor you use that small pump for filling air into your foot ball or basket ball or even bicycle tire you can determine the mean effective pressure for that how much is the work done per cycle divided by the stroke volume which is a geometric feature find out the area of the piston how much does the piston move from one end to the other you have the stroke volume you get the mean effective pressure once you do this after this it is our routine formula but tell them this and the next more in next most important thing to tell them I will put this if you want in the final version is why not carno cycle in fact if you go through the exercises the first two exercises 8.18.2 now I will call it CA1 and CA2 are essentially this the characteristic of a carno cycle and its modifications the sterling cycle sterling and Ericsson cycle in the carno cycle I have asked you to determine efficiency all state points of course all state points come first only then you calculate the efficiency oh temperatures are given so efficiency comes straight away work ratio work done per cycle mean heat supplied per cycle and mean effective pressure for a carno cycle you will notice that although the volumetric compression ratio is 20 so the actual pressure will be 20 multiplied by 360 bar maximum pressure the work ratio is so near one that the mean effective pressure is pretty small mean effective pressure should be high because in a given volume you are doing more work so a higher mean effective pressure means a more compact machine either a more compact engine or a more compact compressor tell them that why did I write about mean effective pressure here it should be the higher the better so efficiency should be high mean effective pressure if it is a reciprocating machine should be high specific output should be high or specific working fluid flow rate should be low work ratio should be low it should not be near one.


Welcome to the walkthrough part of this tutorial. We're going to start off with a quick sort of schematic overview here to kind of get a general sense of what we'll be going through and we'll refer back to this a couple of times during the tutorial to sort of keep track of where we are in the process. But the basics of what we're going to do today are we're going to first access the metadata from SRA and then transform it into a searchable format and run a search on it and that's this set of steps over here on the left. The result of that will be a list of runs that we're interested in and we'll use that over in the second step to download the objects, the data objects from the cloud into galaxy. We've got two examples we're going to download the aligned read objects and we're going to convert those to fast day in galaxy and we're also going to download the VCF files and show that you can run your tools of choice in galaxy we're going to use snippet as an example. And then I'll also talk briefly about if you wanted to start in the cloud in BigQuery or Athena you could run your queries there and get a run list out of it. And that would replace this whole set of steps over here on the left the download transform query steps on the left and you could take that list of runs and use it to directly import these data into galaxy as well. But we will start here in galaxy I'm at use galaxy.org. You can use whichever public instance of galaxy you'd like they should all work fine for this. And I've got a nice fresh history over here on the right side. If you need a fresh history you can click this plus button up here to create one. And I'm going to name this I'm going to name it serfs, but you can name it whatever you would like that's not actually important for the tutorial. So the first thing we're going to do is download an index file. I mentioned that these these metadata files are available in the cloud, they're split up into smaller pieces to make them more efficiently queryable on the cloud and they're updated every day. The names have the date on which they were generated, as well as a number representing which, which piece they are. And so, we can download an index file first to tell us what the most current up to date set of these files is so that we can download them. So if you go over to the upload data button up here and click that we're then going to go up here to these tabs up top and click on rule based. And we want to make sure that we have upload data as data sets selected and load tabular data from pasted table there. And then you can go to the actual written tutorial and the text need you need to paste in is there. Click the copy button, then click in this box, hit control V, and we've pasted in this, this set of URLs and each each URL is the path to one of these index files for one of the metadata tables. And we're going to click build. And this brings up the build rules for uploading data sets. And from here, we're going to click on the column menu down here. And you can select this top option base name of path of URL and select it. And from column a is correct. So we're going to click apply. Then we're going to go to the rules button here and click, we're going to click add modify column definitions. This is where we tell galaxy what these columns represent and you can see that the first thing we did split out the individual file names over here into a separate column, and that just sort of gives a unique identifier to each of these for galaxy. Now we're going to tell it galaxy what these columns represent. So we're going to click the add definition button. And we go down here to URL selected and column a is automatically selected here and that's correct. This is our URL. So we're going to leave that as is and then click add definition again. And here we're going to choose name note there's also something called name tag. That's not the one you want you want name so select that. And this is column B, that file name that was that was split out over there. And those the only two we need to be click apply. And you can see our two rules are set up here. If I mouse over them it kind of highlights the column that it refers to. And now we're going to click upload. It's okay to go ahead and just close this box. And so you can see over here on the right in our history we have a set of files that are queued up for download. This usually won't take very long they're just small text files that are really just a list of all of the metadata files for, in this case say the Contigs metadata table, or the end table variation metadata table. One thing I will note is that these files are updated daily and that's usually happens around 530pm Eastern time. So if you're doing this around that time it's possible that you could kind of get a race condition where you download the index file. Start this import process. And if the files on the server get updated well between say now and when you actually download the files you could you could try to do the download and realize that the metadata files have changed their names have all changed.  Do something else come back and start a little bit later just to make sure you don't encounter that. And in the tutorial we have a link to a time zone converter if that's helpful. But you can see these have all downloaded they turn green over here on the right. And if we click on one of them click on this view data icon. You can see it's just a list of file names until this is the date on which they were generated, and then we just got an integer over here. So now that we have the list of file names we're interested in we're now going to actually download these files. And to do that, we are once again going to go to the upload data button. We're going to go to rule based again. And up here we're going to upload data as collections. And we're going to load type of their data from history data set. Click that. And the one we're going to choose is this context file list. We're going to import the context table. So we select that and it shows us the contents here is the file names like we expect. We're going to click build. And once again we're back at the building rules for uploading collections box. So first going to click the column button, and we're going to click on fixed value. So we click that, and the value you want to put here it's sort of the first part of the URL to download these files and this is present in the tutorial so I recommend you just copy and paste that I've copied it from the I paste it here. Click apply. And you can see this is showing up in column B now that that prefix prefix of the URL that we want to download. And now we're going to go to the column menu. Click concatenate columns. And we're going to it's from column B, you're going to pick this is the beginning of the URL. And then the second one is a that's the actual file name. And as you can see we now have a third column which is column B and a concatenated together and this is a URL and these URLs can copy and paste this. And if I just paste it up here into a browser or you can see it downloads the file right there. These are just URLs you could download in a browser or from anywhere if you want to download these files they're freely available. But we're going to put them in the galaxy. So to continue that process, we're going to go to the rules button here and click it and go to the add modify column definitions option. We're going to click add definition, select URL. And this is going to be column C. It's the column we just created. Over here that's our URL and then we're going to click add definition we're going to add one more. We're going to select that first option list identifiers. Click it. We're going to select column a. And this is the file name column that just gives it a unique value to each of these. Then we need to enter a name for our new collection. So I'm going to call this contigs.json and click upload. And again, I'm going to close this. You can see we have a new history item over here on the right and this is going to download this full set of files. It's going to download each of these files. And then we'll have them in our history and be able to work with them. And so, going back quickly to our overview. The first thing we did was download this index file. And now what the step we're currently doing is downloading the JSON metadata files themselves. And since these are split up for efficient querying in the cloud, we're going to get a group of these files. You saw there were about 45 there, I think. And the next step is going to be for us to combine these into a single JSON file. So that we can further work on the my galaxy kind of all in one piece and look at all the metadata together. And if we come back here we can see that my download jobs have finished. It can take a few minutes usually for these jobs to finish under certain circumstances it might take longer depending on the load on galaxy and other factors but it's usually not too long. And we can see it's just a list of JSON files and if you click on one of these, click on the little view data icon. We'll see the contents which are just rows of JSON data. And so now we're going to concatenate these into one single file. And again, you can click on the tools in the tutorial if you want to go straight to the tool that we're talking about but I'm going to type can cat appear in the little tool search bar. This is one to be careful about because there are several concatenate tools that do various things. We're going to use this concatenate data sets, tail the head parentheses cat. Click on that. And we're going to go to data sets collections here and click on that little button and our context.json is there. So we're going to select that. And then we're just going to click execute. This is going to take the files in that collection and concatenate them into one big JSON file, which we will use for further processing. I'm going to go ahead and rename this data set. So if we click on the edit attributes button right here. This brings up our ability to rename it. I will call this contigs single JSON and click save. And here we've got our concatenate job running. And once that completes, we will proceed to the next step. That tool is going to be called JQ. I'm going to type JSON up here in the tool search bar. And JQ process JSON. Click on that. And you can see our concatenate job over here is finished green on the right. So if we choose JSON input, we want the context single JSON that we just created and then under JQ filter. I recommend that you copy and paste the filter that's listed in the tutorial. I'm going to paste that here. And really all this this filter is doing is taking everything in the JSON and pulling it in as we convert to a TSV format and so convert output to tabular you're going to click this to yes, and then click execute. So this is converting that JSON format into a tab separated format that we can then query within Galaxy. So I'm going to go ahead and rename this and click on the edit attributes button, and I will call this contigs dot TSV and click save. And this warning is okay. The name will still be updated over here once it completes. This process sometimes takes a little while since we're converting for the large number of JSON files. So if you want to go make some tea or coffee for a couple of minutes and come back this is a good time to take a little break. The fact that we've just finished is combining into a single JSON and right now we're converting that JSON to TSV format and finally we're going to transform that to a SQLite database and run a query on it to get our run list a list of runs we're interested in that we can actually use to get the data. And so going back to Galaxy. My conversion process has now completed. And we're on to the next step. We're going to use a tool called query tabular. It says query tabular using SQLite SQL and click on that. And that brings up this screen. So the first thing we're going to do is click this insert database table. We're going to go for tabular data set for table. This is correct our context dot TSV files what we want to use. And we're going to go down here to table options, click that opens up a new set of options for us. There's a few things we need to fill out here need to specify a name for our table. I'm going to call it SARS contigs. You can call it whatever you want but it's important that what you name this table here corresponds to what you call it later on in your query. You can just copy and paste the query we've provided and it will work. We need to specify column names this tells it what each column represents here again I'm going to recommend that you copy and paste this directly from the tutorial. You just click here, control the got our column names all listed there. I'm going to scroll down a little bit further. There's the save the SQLite database in your history option. The false to know if you think you're going to run more queries on this database, like, say, let's say later today, then I would, I would select yes because you will allow you to skip this conversion step and if you want to run future queries on this, you can just do that there's there's a link on Galaxy that let you run those queries. If you think you're going to want to run queries again in like a week, you might want to go through the import process again to get the newest set of metadata, but saving this in your history will allow you to more efficiently query it in the future without going through all these previous steps again. But I'm going to leave this to know right now. The first thing is the SQL query to generate type of their output. We've got a query for you in the tutorial so please just click the copy button there, paste that here. Basically what we're doing is we're selecting distinct runs so we're going to we're excluding duplicates from this contigs metadata table. We're looking for things that have contigs that have a length greater than 20,000 and a coverage greater than 100x. Then I've got a couple of other filter and sort options here to make it a little more reproducible for people doing this tutorial in the future. And we're doing a limit 10 here that's just going to give us 10 results because we're just sort of demoing this if you were doing this for real with your own search you probably would want to exclude this and get the full set of runs that that your search criteria. So we're just doing 10s, doing 10 of them, and then include query result column headers, we're going to click no on this because really all we're getting out of this stage is a list of runs. Once we've set all of those we're going to click execute. You can see it appears over here on the right. And now I'm going to click the edit attributes button and rename this again to run list. Click save. All right. So this is another step that can take a little while to run. One thing I'll note is that we're going to take this run list, show you how to import the VCF files and the aligned read objects. So what I really wanted to do with this run list is just dump fast queue with the originally submitted quality scores and that's all you care about. You can take this run list and use it with the SRA tool cutting galaxy to do that. And so, just, I'm not going to do it but I can show you how if you were to type SRA up here in the tools tool search bar. This brings up some of the SRA tools in galaxy and you could use this faster download and extract reads and fast queue. If you click that. It's like the input type you would have list of SRA sessions one per line. That's the output that we're getting here with this run list so you could just feed this run list to it, click execute and it would create that fast queue with original quality scores. One other thing you can do here. I've mentioned before that you can query this metadata directly in the cloud so we've converted JSON to TSV and we're currently doing the transform to SQLite. This is the first step and this whole set of steps. The results is we get this run list out and this run list is what lets us do the further downloaded import steps. You don't have to do this in Galaxy. We're showing you how to do it for ease but you could do this in the cloud you could do it in BigQuery or Athena where you wouldn't have to actually transform the metadata it's already there and ready to query so you can just query it there. Get a list of runs out and you can use that list of runs to start at the next step that we're going to do directly directly import here. We've got a couple of minutes while this process right now completes. So I'm going to show you a couple of examples of how you can do that. So here I'm showing you the web interface in Google's BigQuery platform. And if you're interested in doing either of the things I'm going to show you here in Athena or in BigQuery searching in the cloud. We have step by step guides to get you started on that in our on our NCBI documentation and there are links to that in the references section of the tutorial. But once you have a login and are kind of up and running with the data, you can just type in your query and we have example queries on the website as well if you're new to SQL. There are a number of tutorials out there that are free online and that are pretty good and actually SQL is not that hard to learn if you don't have much much experience.  But you can hear we're just going to run a really quick query to look for all the data that have a collection date greater than the start of this year. The query typed in here in the window. I just click run, and it starts to run. And these queries perform quite quickly as you can see they're optimized for running in the cloud. And the sort of preview results they give you here. You can see all the data fields that have come up for each of our, each of our queries. So it shows you the geographic location, the sample collection date. I mentioned that there were some sort of complex data fields. There's a sample of that here. So these key value pairs, these are hard to sort of query elsewhere. They don't have a clean analog in standard SQL database, but they're not too hard to query in the cloud. There's specific syntax for doing that. And we have examples again on our website for unnefting these and searching all the sample information in here. You can see a lot of other information like the sequencing platform. And there's other information like the library construct some library construction information is at Amplicon, etc. So you can get these, these data out of big query. I'm also going to show you quickly what the Athena Amazon's Athena site looks like it looks pretty similar. But you can type in your query in the little query box in here. And click one query to let this go. But here we're looking at the geographic location information and getting account for all the samples from this in the SARS-CoV-2 set. Where, where was it collected? And you can see this information down here below. There's 300,000 from the United Kingdom, 75,000 from the USA and on down. So if you are specifically interested in one of these locations, you can further drill down, you can just query for that specific set of data and look at it, or you can further combine this with the time it was collected. And just, okay, I want to compare time a to time B or see how it has changed over over a time period. And so you can get a lot of information just by querying on the database itself, even without looking at the data itself yet. I mentioned that you could also do your queries here instead of in Galaxy and export that run set, that list of runs, and then use that directly for your import. So here is an example if I run this query. We're just looking for samples with the geographic location with South Africa. And you can see we get a run list here. If you click this little download the results in CSD format, it'll download this list to your computer, and you can just insert this into the next step of our analysis in Galaxy and start from there. So if I'm back here on the BigQuery page, when I have a result that I've got down here that I want to save and let's this is not a list of runs but if it were, I could click this save results button. And that brings up the option for me to save this locally and you can save it as a CSV file or they have other formats as well. Click save and it'll just download that to your computer and you can use that in Galaxy to import at the next step. And now back to our Galaxy interface, and we can see that our run list is complete here that query is finished running but if I just click on the view data quickly. The result is just 10 running sessions. And that's because we specifically eliminated it to 10 for purposes of this example. But looking quickly again at our schematic. We've completed this step, and we've got our run list and now we're going to proceed to download some aligned read objects and then we're going to get the contig information out of them and convert it to past day. So the first step to doing that back in our Galaxy interface. We're once again going to go to the upload data tab or upload data button rather click on that we're going to go to the rule based tab again becoming familiar with this upload data as going to select collections load type of their data from here we're going to go history data set. And we're going to click that run list that we just generated and it shows us the preview here that it is our running sessions so we'll click build. And then here in the rule builder, we're going to make a couple of changes. We're going to go to the plus column button, and we're going to click the using a regular expression option. And it gives us three options up here, we're going to select create column from expression replacement. It gives us two boxes down below. Here I'm going to recommend you fill in these boxes by copying and pasting from the tutorial, but we fill in our regular expression here. And then we're going to put in the replacement expression, which again is copied from this tutorial and I paste that there. Once those are done, we click apply. And you can see it brings up a second column, which is a construction constructed URL that will give us the SRA aligned read object for each of these runs we're interested in. And again, this is a public URL you can plug this into a browser or W get it or anything from wherever you are and you should be able to just download this file freely. But to import it into Galaxy here, we're now going to go to the rules button and click add modify column definitions. Click add definition, go down here again to URL selected. We're going to pick B because that's the column that has our URLs in it. And then we're going to click add definition again. We're going to select list identifier. And we're going to use column A for that. This is our, again, our unique value that we're assigning to each of these objects. So we're going to click apply. And if we mouse over these, you can see that they apply to the correct columns. That's good.  I'm going to call it sarf path and click upload. We can close that dialogue box. And this is grabbing these files there in the cloud. It's going to download them to our, our little instance of Galaxy. One thing I do want to note is that there can be some lag in the availability of the aligned read objects, and the VCF files, this is specifically for like newly submitted data. We know that the metadata is present in the online tables, but the actual aligned read object or VCF hasn't yet been uploaded. There can sometimes be some delay in that processing. So if you try to download one of these files and you get an error, generally, if you wait a little while, like say 24 hours and try again, that will succeed. If for some reason it doesn't and you're having trouble getting a file that you want, you can always write into our help desk and we'll be happy to assist with that. The SRA help desk email address is in the references section of the tutorial if you need to do that. But we can see that this is still downloading over here. It doesn't usually take all that long. They're pretty small files as I mentioned previously. So we can see there with these runs that were interested in their pretty small files since they don't contain quality scores. And since they use compression to the reference and they're all aligned. So the final thing we're going to do here and ultimate actually it's not quite the final. It's going to use the SRA toolkit on these. I previously searched for the SRA toolkit that's still over here. And so if we go down to the download and extract reads and fast to a slash Q and click on it that brings up this tool. We're going to select input type. And here we want SRA archive and current history, because these aligned read objects are just SRA archive objects they're just like the format exactly the same as other SRA data. And then we're going to go to advanced options and scroll all the way to the bottom. So this this field that is table name within CSRA object and CSRA object is really it's a kind of an older name for SRA archive objects. And here we're going to type reference in all caps. This is telling it that we specifically want the reference table which in this case are the context. And then we're going to click execute. And then see, I forgot SRA archive and current history I forgot to tell it which SRA archive and current history so click on the data set collection button here. It automatically brings up soft path which that that last thing we did which is the set of aligned read objects we imported that's the one we want to run it on our reference table is still entered there so now we click execute and hopefully this is added to our history over here. And so this job is is dumping those context for us. One option you have here as well is that if what you really wanted to do instead of get these contigs was get out the reads in basket format and you're okay with not having the submitted quality scores getting placeholder quality scores in that format. You can just leave that that table name within CSRA object that field will be typed reference leave that blank and you'll just get the reads out in basket format. So that's another option that you have here that we're not showing actively, but we can see that these dumps generally happen pretty quickly and if we click the data button for one of them. You can see these are the context and they aren't fast you format because the tool we're using as a fast Q converter. So we're going to do one last step to make these into true fast day format. In order to do that. Go back to my history here, search, ask you to fast day up here. This will give you a lot of results. We scroll down a little bit, or you can of course click the tool directly in the tutorial is a couple fast Q to fast day converters. This is the one we're going to use fast Q to fast day converter. I click that. Then I click data set collection. I didn't I didn't rename this, but you can rename this as you would like or you can leave it I'll just leave it for now but this is the context we just dumped. We've selected that and I click execute. This is now going to convert that into true fast day. So you'll just have the fast day sequence of those contigs. And this is also generally a pretty quick step. If I click on this, we'll see if any of them are done yet. I click view data, not quite yet. But once you're at this step, you can use the fast day in your workflow as you would any other fast day or contigs that you had generated yourself. One thing I will note is that we don't generally consider these to be amazing or the best possible alignments that can be made and that's not to say that we think they're bad it's just that using an automated process the way that we do. No, no set of parameters would be completely optimal for all samples. Really what we're hoping to let you do is see what's in there get an idea of what's in here before you go through the process that can be somewhat costly both in terms of money and time of assembling it all yourself.  And so you may find that once you've gotten a sense of that, then you want to go back and do your own assembly with a specific set of parameters and that would that would be a perfectly reasonable way to use this. Let's see. We can see here that some of these are done and we've just got fast fast day format for these contigs and these contig names correspond to the contig names that were in the metadata files previously so that contained the covered information and length and other parameters about these contigs so you can cross sharpen that with those tables if you need to. And so looking again at our sort of overview schematic we downloaded the aligned read objects and then we then use the S-ray toolkit within Galaxy to extract the fast day contigs from those. And then what we're now going to show you is how to get the VCF files for these same runs and of course if you wanted to you could run a new query to figure out the VCF files you're interested in or run that on a different metadata table what we're just going to use our existing set of runs, their existing run list to do that in favor of time. So going back here to our Galaxy interface where once again going to go to the upload data 10 which is our rule based which it already has us that upload data as collections load type of their data from history data set and actually we're going to use run list again we're going to use the same list of runs to import our VCFs so we'll click build to get to our building rules for uploading collections. And here we're going to go column. Click on the column button. And we're going to click the using a regular expression option here. We did previously, we're going to click create column from expression replacement. Here again I'm going to recommend that you copy and paste the regular expression and the expression replacement from the written tutorial, but I'm going to copy in my regular expression here. I'm going to copy in an expression here and click apply. That's made us our new column that is the full URL for these DCF files, which again, you could download through any method that you particularly like to download with but to import them into Galaxy. We're not going to go to the add modify. We're going to click on the rules button and go to the add modify column definitions option. We're going to add a definition. We're going to click on the URL selected. And we're going to change this to be just the column we've constructed to make our URLs, and we're going to add definition again, select list identifiers. And here we're going to select a which is a unique value for each of these. And then click apply. We have our rules. As we've seen previously a few times, we need to give a name to this collection. We're going to call it VCFs. Click upload. We'll close this box. So that's going to go out and download those VCFs. And so at this stage, once you've downloaded the VCFs and ported them into Galaxy, you can sort of do whatever you want them you could insert them into your own standard pipeline or processes here at this stage, just by way of example, we're going to run SNPF on them. We're going to give a demonstration of something you can do, but really we expect you to do your own pipeline or use them as you would normally use them in this case. So if I search SNPF up here. We'll get a couple of options. There's more than one SNPF in Galaxy. And you want to use the one that's specific for annotate variants for SARS-CoV-2. So I'm going to click on that. And we will click on the data set collection option here under secrets changes and 91 VCFs. That's the thing we just made. This is our set of 10 VCFs. That is exactly what we want to use. I'm going to leave the rest of these options as they are certainly you could update them to make them specific to your personal interest or usages. I'm going to leave them as they are, including producing summary stats. This is going to create a second item in our history that will have the summary stats. And I will click execute. So that adds these two items over here. One is going to be the annotated SNPF annotated version of these VCFs and one's going to be the summary stats. Click on this VCF item, the thing we just sort of imported. Click view data. You can see that these are just VCFs we imported and you can just work on them as you would normally expect to. So going back one more time to our workflow overview, we downloaded the VCFs and now we're running your tool of choice in this case SNPF on them. And from there you can you can take them and use them as you would like in Galaxy. I go back out here. This is busy running getting close to being done but you can see we've got our outputs. VCFs here as well. And so this concludes this part of the tutorial. I if you have any questions or concerns that you'd like to write to us about the SRA help desk. The final address is in the references part of the written tutorial. Feel free to write us questions or comments or other updates you'd like to see that kind of thing. If you have general questions about NCBI resources, you can write to info at NCBI and that address is also down in the references so you've got both of those there. And let us know what you think. Thanks.