Hi everybody, welcome all to this Virtuosy Computational Biology Seminar Series. Today we have the pleasure to welcome Mark Robinson from the statistical bioinformatics group at the Institute of Molecular Life Science at the University of Zurich. Mark studied applied mathematics and statistics in Canada at the University of Gelf and British Columbia. Then from 2001 to 2005, Mark worked as research assistant and research scientist at the University of Toronto in Canada on micro-aid data analysis. Mark completed his PhD in bioinformatics and biostatistics in 2008 at the University of Melbourne in Australia, working on methods for the analysis of DCMS-based metabolomics data. He has also during that time been involved on different other projects, such as comparison of anthropometric platforms, differential expression for count data and normalization of RNA-seq data. From 2008 to 2011, he worked as a postdoctoral research officer in cancer epigenetics at the Government Institute and at the Water and Eliza Hall Institute. He then moved to Europe and set up his own group as an assistant professor at the University of Zurich and is also a group leader at the SIB at the Institute of Bioinformatics. His group developed statistical methods and software tools for interpreting high-support sequencing and other genomic technologies in areas such as genome sequencing, gene expression and regulation, as well as analysis of epigenomes. So today Mark will share with us his work on modern RNA-seq differential expression analysis. Mark, thank you again for accepting this invitation and the floor is yours. Thank you and thanks for having me and thanks Diana for the invitation to come and also for the introduction. So I would like to talk about modern RNA-seq differential analyses and I guess, well, I explain what that means, what I mean by modern, but there's been a few recent innovations that are worth discussing. They introduced some new and interesting challenges for us. Just before I begin, I just want to highlight the people that were involved in this work and of course in Lausanne, people are very familiar with the first author of this, Charlotte Sunson. She was here for a few years and it's great to have her in Zurich. And also just to say that everything I'm going to talk about today is more or less in this paper and also just to point out this is kind of a new model or a new way for my lab is this open post publication of you. So this paper that I'm going to talk about in this sort of modern differential expression analyses is published in F-1000 Research and this is an interesting model that we just tried out for this project and it's been quite a success, it's been quite a positive experience. And also to acknowledge Mike Love who in theory is my competitor, he's one of the main authors of the DEC2 package, but as we were going through this project we found that he was working on something similar a few months previous and so we got together and tried to bring a unified voice to some of the opinions that we have which I'll discuss throughout the talk. Okay, so here's the unanswered talk so I want to give a brief history of what we do with RNA-seq data and I'm really going to focus on differential expression analyses, of course there's lots of other things you can do with RNA-seq data, but I won't speak about those today. And then of course modern is in the time also I want to tell you what is new with RNA-seq analyses, that's kind of the main point here. And what this brings us to is well, I guess what I hope people will take away from this is that they really think about what you actually want to ask of your RNA-seq data and it's not so easy when you really think about it, it's just a bit complicated. And then also I want to highlight some of the other things, some subtleties that come up in the analysis and I'll give you some of my opinions and then talk about a couple of open questions. And so just to motivate this, and I don't want you to think that my research program is motivated by social media, but this was an interesting blog post that came about late last year and it caught our attention because it's kind of a shock and awe title but it's also something that we've been thinking about and studying in our lab and so I guess what I hope to answer or show to you today is that we're not really doing anything wrong, there's not a major crisis here but there are solutions that are worth discussing and worth kind of teasing out and of course the story's not over, even though RNA-seq has been around for quite a long time, there are still some open questions. And just to jump to the spoiler here, at least this controversy is really about how people count their reads and quantify transcriptional outcomes and we'll get into this in more detail. More about union counting versus transcript counting and that'll be clear in a few minutes if you don't already know what that means. Okay, so here's my brief history. And so I think people are probably familiar with RNA-seq but I'll just step through it just for completeness. So you start out with some kind of population of cells and I guess nowadays you can even do this at a single cell level and then you need to make a few choices, I guess the two common choices are you select for poly-ray transcripts or you deplete the ribosome learning. So you make one of those two choices depending on what you want to see and then go through all the preparation steps, fragment into small pieces, reverse transcribe, add some adapters and sequence on a very large scale. And this is where I guess we kind of take over, although in theory we're also involved at the design stage and I've condensed RNA-seq differential analysis into three main points. So there's a mapping process and then there's this counting, I've got counting quotes because while there's different ways to do that and there's some details there. And then I guess my research is in the statistical side, computing differential expression statistics and that gets a little bit complicated because we're often working in very small samples and so we want to do the best we can with very small numbers of experimental units. Typical is three replicates of one condition versus three replicates of another condition and that's kind of a hard statistical inference problem but we do what we can to improve on those. And also just to say briefly that of course RNA-seq is used for lots of other things, you can discover new transcripts or new isoforms of existing genes, you can do some analysis but you can partition the reads that you see according to ALEO and look at a real specific expression and even look at RNA editing and various other things. So let's just jump right to the heart of the bioinformatics problems and so this is where people start to, well, disagree. And so there's various things here. So if you look at the mapping, I'll get into all of these steps in a little more detail, you can, you have maybe roughly two choices there, you can do full alignments, so take your reads and fully align them. Or there's this relatively new business of serial alignment and that is just simply 10 to 20 times faster, so that's quite an innovation at the stage of the game. There are some caveats of both of these and so we'll discuss a bit of those. I guess one of the main issues is counting, so there's really two distinct schools of thought here and some bioinformaticians get quite energetic about expressing their views on this and I must say that I guess I'm kind of an excellent union counter and I've now moved over to being a transcript counter but I should say that most of that is because it's faster, not because it's necessarily mind-blowingly better and I'll get into that a little bit later. And then, so the statistics are this, this is my main research and so because the data accounts, it's a digital technology, you're counting things, things like negative binomial or Poisson models are prominent here and I'm not going to go into all the details of those but there are various related statistical issues that come up. Okay, so let's talk about the first thing and that's this mapping business. So on the left-hand side is just a schematic of the CDNA that you sequence and the small reads that you get and really the trick there is people have been doing sequence alignments for decades so there's not anything remarkably new here other than we want to do this on a very large scale and so there's some advanced data structures that people use and I must say that this is also not my area of research, really a beneficiary of very good methods that are available to us in the public domain and I guess the one main trick there is that you need to have an alignment with GAAP aware such that when you have a read and it goes over an exon exon junction it properly maps that and there's various ways of doing this you can map to the transcriptome, map to the genome and add some extra pieces to genome and do it that way there's lots of ways to do this this is kind of like I said there's lots of nice tools available to you so relatively new is this approach here where everything's kind of shrunk into camers so you take your original transcript catalog assuming you have a transcript catalog but for organisms like human, mice, drosophila, they have very good catalogs take those reference transcripts there condense those into camers and then also take the reads and condense those into camers and then do all the matching on the camerscale and the reason you want to do this is because it's a lot faster and so just to give you a perspective of the speed on the left hand side here we're talking for some of the samples we have it's probably an hour worth of compute time across a multi-core server whereas on the right here just your average laptop would be about 10 minutes so I mean this is a pretty big innovation for 2014 and so I guess the main players there are the cell-fish tool but also the same author has a tool called salmon and then Leo Popter's lab has Callisto so there's a few methods with this kind of ideology that are getting used and so the main thing is that they're fast and we'll talk a little bit later about their accuracy and so on okay so here's where it gets controversial and people get really excited about their particular method and so on and so like I said there's kind of two schools of thought here and the first school of thought is the union counters which is kind of the no-brains method just some other reads that you see as they land in different places of the gene so it's kind of taking the union of all the axons of the gene and counterreads that follow those places and then there's the transcript counters where they try to basically come up with a model that allows you to basically portion off the reads so just some of these examples here on the left here you've got some reads here you've got two different transcripts of a given gene these reads here you don't actually know whether they come from the red transcript or from the blue transcript by using an e-mail algorithm and a model for this and you can model various features of the data various biases and so on you can basically portion off your reads and estimate what the transcripts are and one of the main claims of the transcript counting approach versus the union counting approach is that the union counting is going to be bad in certain situations and here's a couple of those situations so for example what you can see here is there's 10 total reads here and 10 total reads here so if you're a union counter you would say okay there is no difference so it's just a log of 10 over 10 the logical change is zero now if you were to do proper accounting of the transcripts where you know that there's kind of a sharing of blue transcripts and red transcripts here but then over here you're shifted all the way towards the red transcripts and I guess one thing that's of course well known with RNA-seq is that the longer your transcripts then on average the more fragments you're going to get from that transcript assuming the same expression level and so if you're willing to count the total output of this gene you should do a proper accounting of this and so in this case this is where the expression is actually more coming out of condition B than condition A after you account for the fact that the transcripts are different sizes so the total output of the locus in condition B is higher than in condition A and that would be missed if you just do the simple counting and so that's a fair point and the reverse also happens so there's cases where in this case we have 10 reads here and 5 reads here and so that says there's a lot of full change there's a 2-fold change if you just do 90 accounting but in fact if you count for the length of these transcripts blue is a lot longer it's exactly twice the length of the red transcript you can see that well in fact there is no change in expression and so that's so if you do this proper counting then well there's no change whereas if you do simple counting this is the whole argument and I think well I'm not sure the reason why people stick passionately to union counting but I guess it's just been easier until recently when these new tools came along so that's the kind of overview of counting so just so you know that these issues exist now I'm going to kind of jump to the differential expression problem and here's where it also gets a bit confusing and so I just want to lay that out in a simple terms and basically encourage you to define what the problem is and so I'm going to throw around a lot of terms for the rest of the talk things like differential transcript expression versus differential gene expression and then I'm going to use these symbols DTE and DGE and these are going to come up again and talk so spend a few seconds and try and memorize these with me the idea for differential transcript expression is you want to ask the question does the blue transcript change from condition A to condition B and then repeat the same task for B, does it change from A to B and then do that for all the transcripts that you see that are expressed in your data set that's one question you might ask but you could also ask a different question from the same data what is the total output of this locus and that's what I'm going to call differential gene expression so essentially you sum these up and the trick here is to sum it up in an appropriate way so that you get the total output of this gene in condition A and condition B and it looks like in this case I've made a guess here but there's definitely more coming out of condition A than in condition B assuming these are on a scale compared just to turn the screws a little bit what if you think about differential transcript usage now it's a slightly different different thing and what I'm thinking about here in differential transcript usage is well if you look at proportionally how much comes from the blue versus the yellow transcript I'm not sure you can see that so maybe two thirds of it is blue in this case whereas in this case maybe only one third of it is blue and you can also take this and also kind of translate this to differential gene expression so what I'm really trying to point out here is what is the question you actually want to ask of your RNA-seq data because different questions will lead to different statistical tools and statistical methods and maybe that's completely obvious but it wasn't even to me when I first saw it kind of expressed in this way and so basically the point is there are a lot of different ways to ask the question and once you know what the question is there's even a lot of different ways to answer that question with all the statistical methods and just to take this one step further although quite related there is for people that are interested in differential transcript usage and what I mean by that is you're really interested in whether the proportions change then one fairly good way to do that is actually project everything into exons and start looking at exon differences and just to highlight that Charlotte also just finished off her study to compare different methods to do differential transcript usage as a separate problem and it turns out that the exon counting business seems to seems to work quite well in that situation and so I was just to say that there's all these different ways of doing the analysis there's one more coming but just to say just to plan out differential transcript expression it's different from differential gene expression it's different from differential transcript usage it's a different way to ask the question okay so so I guess the question then for getting a little bit closer this is what influence could you do or should you do and I'm probably not in a position to tell you what you should do but I can tell you what you could do so I guess what you need to ask yourself is what do you want to know and so I've kind of spied out a few of these so there's differential transcript expression so that's doing everything at the transcript level there is differential gene expression so you kind of collapse all the information you get from the transcripts to the gene you just need to do that in the most appropriate way because different transcripts have different lengths so you can't just add the counts like I showed you in the schematic example before another thing that we tried didn't know how well it was going to work is you can ask a slightly different question so from your transcript level information you can say well is there any transcript in this gene that is differential expressed I have to slightly different question and ask is every transcript different in this gene and so what I'm talking about there is that's collapsing also to the gene but collapsing it in a different way that's more I guess you could think about collapsing transcript level p values into a gene level p values and that's maybe a reasonable thing to do depending on the question that you want to answer and then differential transcript usage or differential exon usage now that's more about asking the questions the transcript proportions change and so as we come back to our schematic example before I basically answered this has the blue transcript changed from condition A to condition B yes it has it was here and it was absent here has the red one changed yes it was absent here it was present here have any of the transcripts changed well yes that's kind of a natural follow on from this one if there's a yes here then there's a yes here has the overall expression changed no the overall expression has not changed because 10 reads for a long transcript is equal to 5 reads from a short transcript so the overall outcome the overall production of transcripts from this focus have not changed and then have the proportions changed yes well it's 100 and 0 here and it's a 0 and 100 there okay so this is obviously a very easy example in most situations it's going to be a lot more difficult than this there's going to be a lot more transcripts there's going to be a lot more uncertainty in all of this so what you could do I'm going to later make a few recommendations about what you should do but I may not really be in a position to tell you what you should do okay so there's lots of subtleties here in case you haven't noticed them or picked up on them so I guess one of the main reasons people stick to union counting is that it's easy one kind of a quick verse if your transcript catalog is incomplete you might question how well the estimation of transcript expression is I don't share that later but there are certain concerns there's probably lots of cases where we don't know the full transcript catalog so how well do all these transcript estimating work if you don't have a complete catalog we can simulate a bit of that and we'll do a bit in that study on the flip side I think it's quite clear the transcript counting is the way to go it's more accurate and it's more precise but you could also imagine in very complicated genes think about some MHC genes you have hundreds of isoforms pretty hard to distinguish from which of those isoforms is really different so we'll come back to that a bit later so there's kind of some trade-offs here but it's worth thinking about what you actually wanted another thing is that there's more transcripts than genes so what does that mean? that means that if you were to do testing at the transcript level there's more tests there's possibly there's definitely a higher multiple testing penalty problem or testing penalty and maybe that leads to lower sensitivity but it also again depends the actual situation that you're looking for whether that affects sensitivity or not one question I get a lot and I'll come back to this later is do the standard statistical methods that we've been using for kind of raw counts do they apply to these estimated counts? so with this transcript level counting you basically have an estimated count so it's not the simpler situation there's some uncertainty involved there and the short answer is yes, they do but there's more to the story and so here's getting to her my argument later if the transcript changes so say you were doing analysis and you were doing it at the transcript level and one of your transcripts came up as differentially expressed I've heard many biologists on this and they say when that happens they probably might look at the other transcripts with imaging because they want to know is this transcript changing because the whole output of this gene is changing so all the transcripts are going up or is it changing because it's switching from one to another and so I'll come back to that a bit later and another thing is this whole business especially with the union counting versus the transcript counting how big of a deal is this and I mean I guess there's a lot of exaggeration on social media and I'll try to answer this further but it doesn't matter in practice and the spoiler here is there's not so much but there are certain situations where it's worth thinking about okay so in the paper that I presented at the beginning we made some claims and so I'll just jump into our response so we're going to start thinking about maybe we should be thinking about our analysis at the gene level depends exactly on the situation so I'm going to say the actual estimation problem is much easier at the gene level so that's one argument I would make I'll give you a little bit of evidence for the fact that the statistical engines work well whether you do transcript level or gene level even though there's law or estimator counts and I'll come back to this interpretation and I think it's a lot easier to interpret things at the gene level than at the transcript level but I'm also welcome to hear other people's arguments and then the difference between union counting and transcript counting is mostly small okay so first claim is this and so what we did is we simulated some RNA-synth data and the way that we like to simulate RNA-synth data is we take a real dataset get some empirical distributions of abundances that we see and kind of build that into our to our dataset and so what I'm plotting here is the estimated transcripts per million versus the truth because in a simulation we know the truth and so I mean basically when you do this at the transcript level there's a set of transcripts that are just really difficult to estimate so there is some non-truly amount of expression that we put into the dataset but the algorithms we're using salmon underestimate the expression and I think this is just simply because these genes are really hard to estimate it's not really a problem in salmon, it's a problem in the information that's in the data and so if we do something a little bit easier and that is take the TPMs and then sum all the TPMs for the transcripts of a gene into a gene level somewhere then things get a little bit nicer we still have a few genes that are hard to estimate and we'll come back to those later but this is a little bit better and so if you want to I mean this maybe argues for the point that maybe you want to do things at the gene level because the estimation is easier but it also argues for answering an easier problem so this is better because it's easier it's easier to get overall summarized gene level expression than it is transcript level and on the right here is the devil the union counting business and well it's actually not so bad, right? highly expressed genes attract a lot of reads lowly expressed genes don't attract a lot of reads so compared against the truth, union counting is not so bad and you might even favor union counting in these cases where your catalog isn't complete because if you have at least a transcript that gets most of the X on the gene then you're not going to go too far or as good I mean there are situations where we will but there's a lot of situations where you okay so that's the first time maybe we should just be doing things at the gene level because it's an easier problem now of course people in certain situations and I'll come back to these later we'll have to look at specific transcripts and whether they change so my second claim is that the statistical methods that you use for raw counts and for estimated counts are I'll say equally healthy here I'll put healthy quotes because it's not a perfectly healthy p-value distribution I'm looking at p-value distributions here the five different panels are five different ways to do this counting there's two arrays that are called the simple I guess there's the feature counts and the simple counting so these are the kind of union approaches and there's a few different ways to combine the data and do the kind of proper aggregation of transcript level to the gene level and so what you see here is that I mean this is just a diagnostic that we do in every differential expression analysis just to see whether we get this kind of flat p-value distribution then a spike at zero that's what we hope to see there's a little bit of stuff happening here where we get a little bit perhaps a little bit of inflation of the false discovery rate and so on but this is reasonably healthy I've seen a lot worse than this basically but I guess the point here is that whether you do this on the estimated counts or the raw counts you get basically the same profile so that's an argument for suggesting that the methods that you use the count methods that you use are still okay for these estimate counts they're still count distributed and so the assumptions that you make are still reasonably appropriate the other diagnostic that we look at is dispersion wean plots and we don't need to to lay around too much about this but just to say that regardless of how you summarize the data you get sort of roughly similar profiles and this is sort of this business of trying to moderate estimates in the data so to get slightly better inferences by sharing information and so we use this as just a diagnostic and it's not so different across the different types of counting okay so let's come back to this again one thing I want to point out again is this little business here of taking your differential transcript expression but then returning the results at the gene level so just be careful of the question that we're asking here you're asking whether any transcript in a gene is differential now one result from the paper is this and these are plots that we look at a lot and what you see here is on the y-axis is the sensitivity the power of a true positive rate and here is the the false discovery rate and what a statistician once used to control the false discovery so you want to keep to the left side but then you want to be high and so these three dots here that you see these three open circles are three different cutoffs of the false discovery rate and then the dot is actually pointed at the actual false discovery rate so for example this one here I know corresponds to the third cutoff so I'm setting a 10% false discovery rate and in this particular example I'm getting a 12% actually of false discovery rate so I want to keep things to the left but keep things to the top and what I'm comparing here is a gene level analysis versus a transcript level analysis and so there's some statistical advantage to doing things at the gene level but also keep in mind that what we're doing is your answer and use your problem again so it's more powerful to answer an easier question and answer things at the at the transcript level and that's part of our argument and so these are the two questions we're answering okay and so I want to I just make the argument for which you might choose which might be more informative to you and I want to make the argument that to me it's better to answer two clear questions than one broad question and what I mean by that is that when you are doing differential transcript expression you're just saying does any transcript change whereas to me it's a lot clearer if you focus on gene as a unit and answer more specific questions about that gene does that gene overall change in its abundance does that gene do they like to form within that gene switch and not everyone's on board with this and I accept that but this is my thinking that it's easier to answer two precise questions than one broad question now the counter argument to this is that there's of course some situations where we specifically want to do transcripts specific transcript level differential expression analysis this is a paper about a very specific isoform and prostate cancer that's very predictive response to treatment to us and this was made a little bit of a splash published in 1814 but basically the presence or absence of this error 7 very specific isoform and prostate cancer is very predictive of whether that isoform is whether the patients respond to the treatment and so this would be a very good example where we want to do a very specific analysis on those particular those particular variants I'd still question whether you want to do this on a genome wide scale but there are cases where you definitely want to do this ok so for my last time I need to tell you a little bit about our simulation because we basically made it as extreme as possible to kind of really tease out where things where this unique counting versus transfer counting is really causing effect and so what we did is we took chromosome 1 just to make it a little bit smaller we chose a very typical condition that we see, two experimental conditions each with 3-dialogical replacements that's the most popular experiment that we get to analyze and so we as we deal with the simulations we put in truly differential things and what I mean by things is sometimes we put in overall differential expression so we take all the transcripts and overall change or we do it such that the isoform proportions change but the total output is kept at 0 or we just randomly select transcripts and put them in as differential and so this is essentially where we can now tease apart where the unique counting versus the transcript counting really makes a difference and it's pretty clear and so again same sorts of plots here true positive rate versus the achieved false discovery rate and what you see is that the methods break into two groups so the two lines that are here are the two what we call simple counting methods the union counting whereas the three methods here and the differences here in these three different methods are just slightly different ways to take the transcript level information and put it into the inference machinery and so essentially the union counting is where it goes wrong it's not very good, the performance is really degraded but when you really look at it it's really due to differential usage so the plots on the right here are this but split according to the truth where you have differential transcript usage or not and basically if you have no differential transcript usage then all these methods are pretty similar, there's a few that kind of fall out a little bit but there's several here that are basically indiscernible whereas the ones that you do have differential use when usage the proper counting you're in pretty good shape there you're getting decent enough power you're controlling the false discovery rates whereas here the union counting you just really massively throw out the false discovery rate so this is I guess what partially motivated the switch to transcript counting but I should also say a lot of it was due to the fact that these things just run a lot faster just to follow this on one thing you could do and you can do this for any dataset is calculate so you've got this simple situation where you've got two different experimental conditions you can calculate a large full change with the simple counting with the kind of union based counting and you can calculate a large full change by kind of properly adding that up the transcript expression to the gene level and doing a large full change there and so we did this for our dataset and that's up here in the top corner that's scene 2 and you can see the differential transcript usage is in red and the rest are in blue of course we don't know what's truly differential transcript usage in the real datasets we only know it for the simulation dataset and what you can see is this is where things are going wrong so you can see here that on the raw axis here this is the transcript counting the large full change you get and there's a bunch of large full changes here that are basically zero because remember in our simulation we put in no overall change in expression but we put in change of the isophones and this is where I mean this is where the simple counting kind of derails you get lots of these really extreme lab ratios that I really just do to the differential transcript usage and when you look back on this it's actually not too surprising what I'm showing you is not too surprising what was interesting for us and I don't think the story is finished here is that what if we did this for real datasets because we don't know if we've been doing union counting for the last 6 or 7 years how could we trust our past results and I don't think the problem is so bad I mean there may be a few instances where this really goes wrong but I mean these were just 3 datasets that we had right away available but you can do the same exercise on any dataset that you want but we don't see the same kind of derailing of the large full changes in the simulated dataset than in the real dataset and well of course we have to accept that the simulation that we did is pretty extreme in the end we put in a lot of differential transcript usage so this is I guess part of the argument for saying well it's not such a big deal in many real datasets now there are going to be datasets where there are cases where we switch from a long leciform to a short leciform or something like that where it really changes things just to summarize I just want to talk about some other considerations I mean this is RNA-seq data differential analysis and just some news some old things that you may know about or may want to think about if you were doing these experiments so I mean one thing that makes the transcript expression challenging is that a lot of times you have fairly low expression and so this is just a review and in my abstract it says genes of interest to pharmacologists are frequently expressed at such low levels that are not adequately represented in genome-wide so this may be true and for a grant application I just looked at some datasets and said well how big of a deal is this and this is what I came up with and so what I did is I just ranked the genes according to the number of reads they attract and I did this across a couple different datasets and what it means is basically 80% of your reads go to the top 10% of genes so that's not a very efficient way to collect data unless you're really interested in all the high-level expressions but if you're interested in the more low-level expressions and probably there's a lot of situations where you are not a lot of your real estate goes there and so there's some experimental ways around this I don't know how well they've been received or how it'll work overall because it adds another step in the lab and some other cost and so on it may not work out in the end but essentially if you could deplete some of these really high-level transcripts and some structural proteins that you're not really interested in maybe you could get a little bit deeper for the same sequence and depth so I'm not sure that's all that well appreciated but it's something that seems to stand out pretty strong a recent paper from McWatson's group just coming back to a point that I discussed earlier some genes are just super difficult especially if you have very similar gene families and he did an experiment with a thousand reads onto every gene or every transcript and try to estimate them and what you see is there's a big spike at a thousand but then there's a long tail to the left where the reads just cannot be kind of unambiguously mapped and so he makes an argument that they make an argument that some of these genes are all of disease relevance and certainly that's the case another point that's kind of lingering in the back of my mind is that maybe in a few years we're not going to be doing any of this fragmenting into small pieces we're just going to sequence for white transcripts either at a CTM or an RNA level this is probably not so far away this is just a plot of some of our first experience with some Pac-Bio or iso-seq data and it was just brilliant we had a reference chemo and this particular example at the top here we had a this was the annotation which was inferred by partly RNA-seq data some computational algorithms and you can see that with the computational algorithms that was two transcripts it's quite clearly one transcript and you can see these are reads just across the whole transcripts at the price point that something like Pac-Bio is if you want for discovery but as these technologies get higher and higher throughput and maybe Oxford Nanopore is really getting first at that point now maybe we can do this in a qualitative way and sequence our CDNAs and just essentially count at the transcript level makes some of this business a little bit easier and so I'm going to just wrap up here to say that there's no real crisis here we haven't really been doing our RNA-seq all along but there are some cases where this business of transcript counting and unique counting is large enough to think about this is just my view unless the need really dictates I would suggest answering an easier question answering questions at the gene level because it's easier to do estimation at the gene level and so are any transcripts differential because that's an easier question answering very specifically about every isophone and so that's why I say I find it easier to interpret two precise questions but that also doesn't mean that this isn't important so the analysis methods for differential transcription are actually essential for building into some of these analyses and so there's a little bit of work to be done transcript weather estimates improved gene weather inferences one question people ask me is what do you do in your lab and so I guess I would say that if you want a fast pipeline I would combine salmon with edge-r and now you have to take into consideration there's a bit of a bias there since I'm the co-author of EdgeR but that's definitely what we do in our lab most of the time now one caveat there is that well actually sometimes these four alignments that are computation costly they're actually really useful to look at and of course I always encourage people to look at the data and with these fast pipelines you just take estimates out so these four alignments are actually really useful especially if you're interested in spacing so I mean you might want to slow it down if you want to be clear about things one thing I haven't really commented on but I should say something about is that these methods are so fast salmon and caristo that you can do bootstraps of the containers to get uncertainty estimates of your transcript level estimates and so the question is what to do with those and I don't have a full answer because I mean basically my argument earlier in the talk is that well actually you just throw away the uncertainties due to your standard EdgeR analysis and it's still fine and the reason I think that it's still fine is that most of the genes are actually really fairly easy to estimate so there's not so much uncertainty it's a very small proportion of the genes that are a bit more a bit more uncertain to estimate but of course you should think about or I mean me from a method side we should think about propagating those uncertainties into the differential expression calculation and we're working on this I'm sure lots of other people are working on this and we think we have kind of a good approach to make use of the bootstraps I'm not going to tell you about it today but there's more to come there and of course we're not the only ones working on this okay I already mentioned this part the failure elements are useful last thing is that as part of this collaboration with Mike Love we created when I say we I mean Mike Love created the TX import package now this TX import is the bridge between the estimators, the transcript level estimators so Sam and Felisto, RSEM all these estimators and the other layer which is the statistical layer which is DC2 with EdgeR or Voodmina so just to let you know that that exists and I think it was just last week it's now available in the developer version of Bioconductor okay so I will end there and thank of course Charlotte Sonison who did the majority of the work here also Mike Love and then there's lots of other people in my group that are kind of related in this project Shadei and Gosia and Lucas that are involved quite heavily in RNA-seq data and there's the Agile Development team which is Gordon and Yunshun and Melbourne Davis is in Cambridge now and I also thank the funding agencies for supporting these people in these projects and thank you for this one


Alhamdulillah, Allah SWT says in the Qur'an Allah has given you the prescription of fasting Just as He prescribed it for the peoples before us All of the previous religious traditions had some element of fasting in their tradition The Christians used to fast for 40 days The Jews also had fasting And Allah SWT gave us fasting And then He gives us the reason, it's called When Allah tells you why He's doing something In order that you learn piety This is one of the most important elements of fasting Is to learn piety and from piety is sabr And that's why Ramadan is the month of patience It's learning to be patient Allah SWT also says The command that He gives in al-Baqarah Is that He created us And those that were before us And then He told us to worship Him That we might be pious That we might learn this taqwa And so Ramadan is a madrasah It's a school, it's like continuing education That you go back to, like a physician Or anybody that's in a type of practice That needs the skills to be upgraded, enhanced Ramadan is the yearly time That we return to this school of taqwa And of sabr The Jumu'a is the weekly time The prayers are the daily time That we return, we realign ourselves with the Divine Human beings are in heedlessness Allah SWT says You laugh and you're in a state of heedlessness That you forget your Lord This is one of the deepest illnesses of the human being Imam al-Junaid when he was asked What he thought the foundational sin of the human being was He said that it was ghafla It was heedlessness because all sins emanate from heedlessness But there's another reason why Allah SWT gave us this practice of fasting In the verses that follow Allah SWT says First of all, Ayam is called It's a plural of pausity It's a plural that indicates that there is not much of it Like amwal Because wealth, mal, there's not much of wealth People don't have a lot of wealth So amwal is the plural of wealth To indicate that wealth is something that is limited Ayyamin Ma'adu, that's limited days You have a few days And then Or on a journey But you can make up those days if you were sick or traveling That if you're able to do it Then you can give fidya So you pay That was abrogated by the verses that follow So people used to be able to do fidya If they were wealthy, they could do fidya But that was removed And everybody has to fast Unless you're sick And then you can do fidya for that But other than that, everybody has to fast So Allah SWT said About this Fidya And whoever does extra This is better for him Again, those were abrogated But they still apply to nafila fasting You draw near to your lord with nafila With extra acts Until Allah loves you And so fasting is one of the ways Three months Three days out of the month It was the practice of the Prophet SAW Some of the Sahaba practiced the fast of Dawud Which was every other day Some practiced Monday and Thursday But the Prophet himself His practice was three The three days of the white day The white days So the fasting woman And you do fast for her But the fast is better for you That's if you have a choice Like if you're traveling It's better to fast Some say though If there's difficulty That you should leave it Then Allah SWT says This month Is the month that the Qur'an was revealed The Qur'an The inzaal of the Qur'an Allah SWT On the 27th According to most of the scholars But the Prophet said Look for it in the odd days Of the last 10 days The Qur'an came down to the world The entire Qur'an Jibril A.S. Brought the Qur'an down to This earthly canopy The heavens The earth of the heavens And then over 23 years It was revealed Peace meal to the Prophet SAW Based on the circumstances And the incidents Over the lives of the Sahaba And the Prophet SAW So the Qur'an came down Inzaal one time And then over 23 years Allah SWT revealed it Through Jibril A.S. Peace meal So this is the month of Ramadan It represents the gift Of the Qur'an Which is why we return to the Qur'an In the month of Ramadan The Prophet SAW Said in a sound hadith He said The fasting And the Qur'an Will intercede for you Allah gives them a voice On the Day of Resurrection And they intercede For people that practice it What does Siam say? He said I prevented him From his appetites In the daytime And from his food And so Allow me to intercede for him I prevented him from sleeping at night These are the things that we do So fasting is not just in the daytime There's an element In the night time Which is sleeping less Than we normally sleep So we eat less Than we normally eat And we sleep less Than we normally sleep And what is the gift? Allah SWT Accepts her He grants them their shafa'a For the servants that do this Look at the Prophet SAW He didn't increase his tahajjud Woman and lady He didn't increase his prayers In Ramadan We do To get closer To the practice of the Prophet That he did all day Every year He didn't increase his practice Of prayers That was his practice The whole year The Prophet was practicing He didn't increase his practice Of prayers Aisha One of the great salaf Aisha said Tell us the most wondrous thing That you saw From the Messenger of Allah And she said What thing that he did Wasn't wondrous He came one night to me And he entered into the bed with me I could feel his flash up against my flash He said Oh daughter of Allah Leave me to go And pray to my Lord And she said I love to be close to you But I prefer what you prefer He got up To the place The water container And he did wudu And it was a light wudu So he began to cry Until the tears covered his chest And he continued like that Until Bilal came And he told him The prayers come in In other words Fajr So Aisha said What is making you cry When your sins Anything you've done And his sins are Doing a virtuous thing When a more virtuous thing could be done When all your sins have been forgiven What past And what is to come And the Prophet sallallahu alayhi wa sallam said Should I not be a grateful slave Should I not be a grateful slave In a similar hadith in Al-Bukhari He stood until his feet had the edema From standing so long And he said Shouldn't I be a grateful slave Gratitude Allah SWT says Allah wants ease for you In this fasting He doesn't want hardship for you He didn't reveal this Quran for you to be miserable He wants ease for you And for you to be grateful This is the time to be reminded O you who have believed Say From the good things we have provided you Thank Allah O you who believe Eat of the good things of Allah But show gratitude for those things If you are truly worshipping Allah SWT One of the most blessed things That we have in this world Is food It brings us together It nourishes our bodies It sustains us And then The pleasure of food itself This is a great gift Allah could have made brackish water That we had to drink He could have given us rocks That we had to crush as our nutrition But he gave us cherries And grapes And figs He gave us varieties of meat He gave us all of the blessings That the earth brings forth What does Allah ask us? Shukr Work Do things out of gratitude O Al-Dawood And how few of my servants Are always grateful Always grateful He didn't say Shouldn't I be a grateful servant? Shaker You can be shaker One time or another time When you're shaker It's called It's the form of hyperbole It means you're always grateful Our Prophet ﷺ was always in a state of gratitude This was his whole experience Of the world's gratitude One of the worst things about modern times Is in gratitude Is cultivated in people They're ungrateful for the police They're ungrateful for government They're ungrateful for their education They're ungrateful for everything People just complain all the time And Allah SWT Your Lord has declared If you're grateful I will increase you in blessings To be grateful for But if you are an ingrate If you lack gratitude In fact, if you show ingratitude I will give you more reasons to be ungrateful I will give you more reasons to be ungrateful This is a metaphysical equation Gratitude equals increase in blessings Ingratitude equals decrease in blessings This is a qaida It's a law It's a metaphysical law that's as true As the Newtonian physics that you learned in high school If you're ungrateful Then Allah will give you more to be ungrateful about More to whine about You think it's bad now? You have no idea how bad it can get Read history to know how bad it can get You think Syria is bad? Read about the Mongol invasion They didn't have any place to flee to You think the Muslims are having tribulation in America? Read about Nazi Germany And what happened to the Jews? We have to be grateful Because if we're ungrateful and always complaining Allah is going to give you more to complain about I was in one of the Gulf states And somebody was complaining about the price of gasoline Nazi driver 25 cents at the time Now it's a lot higher Why?  How's our response? What's our response? We're in Tasbiru And if you show patience And show piety Restraint Control your body Control your body Control your body Control your body Restraint Control yourself Because that is at the essence Of this matter That is at the essence Of this matter This is our deen Allah SWT said And the village was For example The village was safe And safe And it came at its own risk From everywhere And it refused to worship Allah Allah's will Allah's will And fear For what they used to do Allah strikes a similitude For you to think A similitude Allah strikes A similitude For you to reflect A township A city A hamlet That was Peaceful It was tranquil It had provision In abundance Coming from every place Like we have today We eat Berries from chili And we drink tea from China And we have rice from India Basmati rice In your homes from India You get all this Blessing from all over the world So what did they do Instead of saying Alhamdulillah Alhamdulillah Alhamdulillah They were ungrateful This is the meaning of kufr Kufr is in gratitude You can be a Muslim And be a kafir In the meaning that You're an ingrate Right? This ingratitude That's the essence of kufr The essence of Islam Is gratitude Shukr A feeling of blessing That Allah has given The biggest blessing Is that We exist Niamat al-Ijad When Niamat al-Imdad The blessing of giving us Our existence And then sustaining our existence He could take away that Sustainance at any time So Allah SWT says That he enveloped that city In hunger Look at the blessing of food In hunger and terror Hunger and terror Why? Bima Ba'a sabbiya What's the cause? Because of their ungratefulness And this is what we've lost An understanding of our religion We look at all these events We don't see the real source Of these events In gratitude If we want to change the world We have to change our state And our attitude About all these blessings That we've been given Government is a blessing Even the worst form of government Is better than anarchy Even a tyranny is better This Malik Ibn Anna said this These weren't ignorant people These were genius people Because he lived through civil wars He saw what happens When things break down The people of Damascus Six years ago Were eating beautiful food They had good clothes It was one of the few countries On the planet That was actually self sustained And now all of this tribulation And if you say Why is it happening to them And not us Maybe because Allah loves them more Because we know Ahlus Sham Have a maqam So Allah will put All of the Torments of the Akhirah He'll put it in the dunya For a short period And then they go back to Allah Completely free Of any tribulation Or punishment Maybe it's the places That aren't being afflicted That deserve to be afflicted That should be worried This is the way Muslims traditionally Look at things When the maqul came down When the Mongols came down They didn't say These evil Mongols They said This is a punishment God has sent to us Because of our wrong actions That's how they looked at it Muslims don't look at that anymore Evil America Evil Israel Evil Okay good Allah put them in power He says He gives power to them He pleases Muslims had it If Muslims had nuclear weapons Right now with the idiots That we have We would have nuclear Conflagrations all over Allah knows what He's doing Allah knows who He gives power to Yes, they abuse their power But to what degree Who would be worse? People want the destruction of America Well, let's see what happens When China gets the world power See if there's going to be An Abu Ghraib that you hear about See if there's going to be Any way, recourse To redressing any wrongs Allah ta'ala anum I don't know But sometimes The worst things Are answered prayers We don't know The different way of looking At the world People don't want to look at it That way But I read the Quran And the Quran It comes back to me Where did this calamity come from? Allah says It's from your own selves It didn't say Oh, it's from those evil Quraish Those evil Quraish No Allah wanted the Quraish To become Muslim He entered them The Messenger of Allah What do you say?  At a test that God sent me Whom am I really angry at? See, you can be humanly angry But who are you really angry at? If everything is a test from Allah Then who are you really angry at? That's a question Allah said May we thank you May we thank you May we thank your Lord May we thank your Lord No, thank you very much These are days of shukr For the sake of another happiness You have two joys He has a joy when he completes He breaks his fast Because he's completed the Ibadah And then the delight of the food Most of the big ulama said It's Mam Ata'a It's completing That's the farha It's not the food It's actually finishing the Ibadah That's Imam Nohoi High maqam Allah SWT says وقليل من عبادية الشكر The Prophet was shakr But he told us to be shakr One of the A'rifin said هَا دَلْضَمِرُ with that هَأُلَا عِبَادُ with that هُنَكَ عِبَادُ الْأَسْمَ So that's a high maqam It's where you're always grateful وَالْشُكُرُ صَرْفُ الْعَبْدِ مَا أُولَاهُ مَا أُولَاهُمْ مِنْ نَعْمَاهُ فِي رِضَاهُ This is what all the ulama taught And Muhammad al-Musawi said Is the meaning of shukr Real shukr Shukr is to utilize what God has given you اِعْمِرُوا عَلَى الدَّوُودِ شُكرًا To utilize what he's giving you Out of gratitude He gave you an eye to see Not to look at pornography He gave you a tongue to speak the truth Not to lie Not to cheat Not to backbite He gave you a hand to work And not to steal Or to embezzle Or to take things that aren't yours He gave you feet to walk in righteousness وَعِبَادُ الرَّحْمَانِ الْلَذِينَ يَمْشُونَ عَلَى الْأَرْدِهُونَ The servants of the merciful Who walk tread lightly on the earth You know they talk about carbon footprints now Carbon footprints اِبَادُ الرَّحْمَانِ don't have a carbon footprint They tread lightly on the earth وَإِذَا خَادُبُوا مَنْ جَاهِدُونَ قَالُوا سَلَمَا Allah says اِنَّا انزلناهُ فِي لَيْلَةَرْ قَدْرِي وَمَا أَدْرَاكَ مَا لَيْلَةُ الْقَدْرِي We revealed this book on لَيْلَةَرْ قَدْرِ And what will convey to you what لَيْلَةَرْ قَدْرِي is لَيْلَةَرْ قَدْرِي خَيْرُ منْ أَلْفِ شَهَرْ It's better than 83 months of worship The Prophet was because he was so covetous about solicitous for his ummah He was concerned that his ummah would not have a lot of rewards when he knew that the ancients had much longer life spans So this is من خَصَائِسِ رَحْمَتِ وَمَنْ خَصَائِسِ عُمَتِ This is a specific gift to this ummah that they have a night that is worth months of ibadah And so in a lifetime you can literally get years added onto your ibadah This is a great gift from Allah And this is why Allah SWT the Prophet SAW said من قام رمضان إيمان و احتساباً غفر لهم تقدم من ذنبه Whoever stands doing extra acts and the tarawih stands doing extra acts he has his sins forgiven of what passed وَمَنْ صَامَ رَمَضَانِ إيمان و احتساباً غفر لهم تقدم من ذنبه And whoever fasts the month of Ramadhan believing in Allah and believing that the reward is from Allah أصيام لي و أنا أجذي Fasting is my own And I reward it myself That's what Allah SWT says وَمَنْ قَامَ In the hadith وَمَنْ قَامَ لَيلَةَ القَدْرِ إيمان و احتساباً غفر الله لهم ما تقدم من ذنبه و ما تأخر Or as Allah SWT said Whoever stands on the night of Qadr in belief and expecting a reward from Allah he will have his sins forgiven The Prophet SAW said In the hadith الصلوات والجمعة إلى الجمعة و رمضان و إلى رمضان مكفرات لذنوب That your prayers Each day you pray مشتنب or مشتنب الكبائر As long as he avoids major wrong actions The prayer The five prayers that you do And then Jumu'ah to Jumu'ah Praying Jumu'ah from Jumu'ah to Jumu'ah Friday prayer to Friday prayer And then رمضان to رمضان These will remove your sins They will remove your sins As long as you avoid major wrong actions That needs a specific type of Tawbah صغائر get removed from وضو From prayer, from fasting, from Jumu'ah All these things But the major wrong actions And the عدمة differ on them But the major ones are well known معلومة دين طرورة And drinking, stealing, cheating, lying Fornication, adultery, all those things That Allah has declared foul, fahshah So this رمضان is a great gift Of this Ummah It's a time of special gratitude One of the hallmarks of gratitude Is that you share your blessings with others You share your blessings with others كان رسول الله صلى الله عليه وسلم أجود الناس و أجود ما يكونوا في شهر رمضان كان أجود من ريح مرسلة The Prophet صلى الله عليه وسلم Was the most generous of men Always But he was even more generous in رمضان Why? زيادة الشكر The sense of blessing The gift The gift It's a great gift أقول القوري هذا أستغفر الله لوركم رسائل المسلم الحمد لله وصراته السلام على رسول الله وعلى آله ومن الله We have a lot of tribulation in our community At home and at abroad We have tribulations just Each person has their own Each one of you can give me a litany of tribulations This is just dunya دارو بلاعين دو دينك دنيا هي This is dunya So all those tribulations that you have If you put them next to all the blessings you have They're nothing And that's why if you focus on your tribulations You'll become ungrateful If you focus on your blessings You'll become grateful I advise myself and all of you To count our blessings This is my advice And رمضان is one of the greatest blessings People say, oh it's so hard Are you fast a whole month without water?  Really It just makes you appreciate the Cornucopia of food that you have والله So this is my advice We need to show more gratitude And stop complaining Complaining Iblis is the complainer in the Qur'an He complains all the time He's so upset about things And he wants to just make us ungrateful It's his favorite thing to do Make us ungrateful Make you ungrateful for your wife Make your wife ungrateful for you Microaggressions You know all these grievance theory And victimology All of them It's a disease That Iblis has spread amongst this humanity It's a disease Racism is a reality What? Racism didn't exist in the past How do people respond to racism?   Because clearly it's a problem on the planet It always has been How did the prophet deal with it? He educated people انت امروكم فيك الجهلية You could do better than that Abodar put his hand Face on the ground And begged Bilal To put his foot on his face كفارة Just for saying You have been a Saudat Which is like using the N word today That's what he asked him to do Forgive me Ignorance It's from جهلية That's what the beauty of Islam It teaches you how to be different All these problems have been around How do we address them? How do we look to the future? All this grievance from the past The English did horrible things They did horrible things too My answer is mostly from Ireland 700 years of oppression That's not the English people today They didn't do that They didn't conquer India Those people are all dead تلك الأمر قد خلط لها مقسم I just met an Armenian man He was in the doctor's office And he said I said Have you ever been to Turkey? He said No, I can't go there I said Why? He said And then I looked at him He looked Armenian I said Are you Armenian? He said Yeah And then he was like Why do you say that? I said Because that's That prejudice that you're displaying Is cultural Those Turks today Had nothing to do With what those Turks did 100 years ago Don't blame them They're good people I said War makes people do horrible things American boys that went to Iraq They were nice boys, many of them If you met them You'd find them pleasant people You'd sit with them Have coffee Talk to them When they went into that Horrific condition of war They became murderers This is what war does to people And now they come back And what do they do? They get addicted to drugs They commit suicide Because they cannot believe What they actually did This is what war does to people That's why it's such an evil We have to let these grievances go Kulukum in Adam What Adam in Turab We're all Benu Adam We're all children of Adam We're all cousins If we went back far enough We would see Somewhere down the line We're cousins We're family This is the human condition And we have uncles That people want to kick out of the family But you can't There's people They're terrible But this is the reality How do we make it better? Remember God and don't forget Him And be grateful and don't be ungrateful That's what we say The last thing I wanted to say Subhanakumma ahamdikashadun la ilahim anta Astaghfirukuratubu ilay Eid al-Fitr When we have the Fitr For people just a reminder Zakat al-Fitr is an obligation On everybody that you have nafaqa For In other words All of your dependence on Allah Is on you Is on you Is on you Is on you For In other words All of your dependence You have to give The Eid al-Fitr And the Sa'ad Traditionally it was given on the day On the way to the prayer The Hanafi's permit it Before Which is a blessing We can use these opinions Because it's difficult in America For people to do things always And they also permit it with money The other imams They say you give it with dates Or weeds Or rice or something So There's a monetary What is it? How much? Okay So it's between five and seven To get out of khilaf It's always good to go to the higher So I would say You know give seven dollars For each person in your family And they have the boxes And everything But do that because The fasting is muallak It's suspended It won't go to Allah Until you've done the Zakat al-Fitr Zakat al-Fitr is only for There's eight categories In Tawba In the sixtieth verse In Tawba Faridah So this is a faridah It's only for Fouqara and Masakin That's the Zakat al-Fitr That's the Zakat al-Fitr


All right, so I got something I want to show you guys to kind of rejuvenate you if you aren't already Kind of show you some may know some may not I'm hoping you don't know But check out this Three two one. Hey, what's up 8% nation? Hey, I'm Michael the playmaker number 88 and I'm out here NFL Combined, you know in the Bible 8th 8th stands for the number of new beginning These young men are trying to start their new beginning and become one of the very best in the NFL Just like you guys are the top 8% I will be your keynote speaker in Dallas, Texas July 27th for the 8% nation insurance wealth conference I'm looking forward to seeing you there from one playmaker to another Let's have a good time. I look forward to seeing you soon If that doesn't get us excited for 8% nation, I don't know what will I'm actually I'm impressed. We got shirts on We got a jacket on I didn't wear any of my we're gonna screen with your polo But what's you know, it's funny Baila Bethany has 8% nation all over her social media, which I was like I noticed this weekend. I'm like that is awesome Talking in the headlines under her Facebook and all that. I'm like that is it's unique You can tell she enjoys planning for that event, right? So This week we're gonna do something special ticket-wise. We're gonna focus on it promote it have a ton of fun. I Can't like even think straight what you know, I get excited for that because my obviously my whole family was a huge cow Whisper and we're all gonna be there I've also already got I've already got the speakers lined out for the whole event That will release over the next several weeks that I can't say yet. They know about that one. If I don't get you excited I will But we'll release some more Along the way We got something a few few special things in there So we'll start to focus on that the next several weeks or months start picking it up Start ramping it up start playmaking it up Anything else? You good? We're good. Okay. I didn't even comment on that That's awesome. All right, you guys good. We're gonna have an awesome week Michael Irvin jacked us up All right, let's do it It's one of the ways not only my super jack about that dude I mean look at this right here, man. I don't know my jacket about Michael Irvin but start off your day by Being motivated being charged being focused being freakin ready to run through any wall that's in your way That dude does it.  Michael Irvin be speaking live at 8% nation I want you guys to be there. I do he's got so much passion.  He's got energy and there's no doubt That's why he's been successful people that have energy the passion that love what they do and they're excited about what they do They do better than those that don't so when you're going out your week this week Think about how you can incorporate passion energy culture a team To have in the best week you ever had


All right. One last thing on iOS 15 comes from listener Greg. He says there's a great new feature for recovery options in iOS 15. It's iCloud Data Recovery Service. It's an interesting service, and you can look it up at Apple.com. They have a support article about iCloud Data Recovery Service. iCloud secures your data in the cloud by storing it in an encrypted format. If you forget your password or device passcode, iCloud Data Recovery Service can help you decrypt your data so you can regain access. And they talk about a few of the different ways that you can do this, and they talk about some things like keychain, health data, and a few others that use end-to-end encryption that is not able to be decrypted via the iCloud Data Recovery Service because only you can access that. However, they say to make sure that you can recover all of your data, you can also set up an account recovery contact or use a recovery key. Either of these methods will give you additional ways to recover your data, including that which is end-to-end encrypted. But of course, as we all know, if you lose the key, well, then you have a problem. So Greg asks, he says, I know I can store the recovery key in one password, but if I get locked out of my devices, then I may not be able to get at the recovery key. Also, I can store the key in a text document and make sure an encrypted sparse disk on the Mac and make an encrypted sparse disk on the Mac and store it there. But the message shows, the message that it shows before enabling that gives me pause. It says, are you sure you want to create a recovery key? If you lose your recovery key and can't access your devices, Apple won't be able to help you regain access to your account or your data. To your account or your data. Seems like, from Greg, he says, it's better to add a recovery contact instead of be interested in your thoughts. Yeah. As I was going through it, I had that sort of thought of beware the recovery key, because once you enable that, it's the only way you can get your data back. And if you lose the key, curtains. And so it's the trade-off between convenience and security. Recovery contact seems better, but what happens if you lose access to them? Or you have a falling out with them, then what happens? Right? I mean, like these are, there is no one answer to these things. It's just one of those things like we often do here on the show. It's like, okay, eyes wide open, make the decision and know what you've made. I think I'm pretty comfortable with recovery keys. I have a lot of things that require me to use recovery keys. And I do. I store them in one password. And then for some of them, I store them elsewhere. And I will leave it at that to keep things obscure. And I also store my one password, password, elsewhere, so that I can get into that. And so that my family, if something were to happen to me, could get into that. One thing that I've heard recommended is if you have like a one password shared family account is for everyone to store their one password, password in a shared family vault. Obviously, that requires trusting the family that you're sharing this with because they have access not just after something happens to you, but also at any time they could you know, they could decrypt your one password data. But as long as you're okay with that, it makes life way easier because, you know, you can, if you need to get into someone's vault, you can just get in as long as you're one of the four. So it's, I mean, obviously everybody's got to protect that data and you need to have some trust there. But if that works for you, that's a super easy way to do an end around on all of this, including Apple's recovery key. So I haven't set up a recovery key yet, John, but I probably will.


We are on the road heading three and a half hours upstate to a dairy farm that is for sale. Hopefully there are no sale towers otherwise you guys can watch me freak out again. So this farm is 180 acres. It is an operating dairy farm right now. It is organic certified and it's for sale. You know the property is currently being leased and it is up and running but what I'm more excited about are the two people involved. The real estate agent is actually a former dairy man. He's retired, he's older. I think he's in his late 70s and he was actually interested in consulting with us and helping us along. I mean I don't think there's a better guy to help us to be honest. This guy probably knows more about farming than everyone on YouTube combined and the other guy buys organic beef cattle for a butcher shop. So you know the two people out here that are selling the property and that own the property involved you know couldn't be more crucial to potentially helping us move along with this business. So I'm excited to drive up here today to meet these guys and see what they have to say. I didn't actually sleep at all last night which is one reason I'm not too enthusiastic about doing this drive. I was worried I was gonna be tired but the reason I didn't sleep I think was because I took a probiotic supplement and in hindsight the lactobacillus strains probably gave me an allergic reaction so that's probably why I didn't sleep. So when I woke up I had some supplements, some Nat Glutathione and ubiquitol and I feel like I could run through a wall so I don't think falling asleep is gonna be an issue on this drive. Losing my sanity however might be. I'm trying to stay calm boys. I've missed two exits so far because I've been filming videos in my car. So instead of a nice three and a half hour drive it's looking like it's gonna be four hours so we're gonna stop filming. We're gonna focus on driving because if I miss another exit I might actually lose it. Might actually lose it. Although there were quite a few Wi-Fi towers on the way up here as soon as we got into this specific town I lost cell service and it was kind of ironic because losing cell service was what I wanted but then I couldn't use my cell phone.  This farm is over 200 acres already certified organic. They already have some cattle. They already have so much dairy equipment. This is four hours upstate. Well three and a half hours from me I made a few wrong turns and it took me quite a while to get here. Four and a half hours to actually drive up here. Spent most of the day driving and then when I got up here a couple hours talking to the person that owned the property. A really cool guy talked to the real estate agent a bit but this guy that owns this property also has another farm that's a miler to a way that has much more acreage but that farm is about two million dollars way over our budget except you know in a year or two from now if we really want to grow we can buy that farm as well if we want more acreage. It looks like we have everything we need here to make this work on a small scale. Get you guys dairy, get you guys eggs sooner than later and when we want to scale up and get the full butcher operation out here and get things fully up and running it's looking good. So right now they already have these cows rotation grazing.  They have the cows out there. So it looks like they have the cows. What do they have like a one or two acre paddock right now and they just have them graze it all down and they haven't really lined up. That's all they need. All they need is 20 cows to do so on your cows and make it as big as a small scale. Oh how quickly things change. As optimistic as I was that this was not near any Wi-Fi or any cell structures I spoke to the couple inside. Thankfully they showed up while we were looking at the property because you know I took a look inside the dairy barn I took a look around I was like hey this looks pretty good I can definitely make this work although it is kind of dumpy. Really really sweet couple in their early 30s they basically told me look this property is way overpriced for the amount of money you're looking to spend you can get twice as much acreage much nicer facilities something that's really ready and up and running. So she gave me a list of websites that she's been looking at. I actually told her hey listen I'll pay you commission hourly if you want to give me a list of properties that you've been looking at that you have in mind too. So this is definitely not going to be the property I think one because they would probably have to give them a three or four month notice and two I don't think the guys are going to go low enough on this property for me to want to buy it and you know three if I can get a property that's closer to New York City that's higher acres that's a better fit we're going to find out so you know we're getting closer you know as we go on this journey looking through these farms you know we're learning a lot very quickly and I'm looking forward to seeing what properties I'm going to get a hold of within the next week or two and then hopefully we can close in on something before the end of July hypothetically get something that we can move into a lot sooner doesn't have people already living there I mean so that's the update we have more of an idea of what the market's like what is out there what we can actually find logistically I know exactly now what I need to start up the operation how much capital I need how much down payment I need for the initial farm you know what stuff we need to buy so I'm really optimistic you know once I get home I'm gonna I'm gonna look up some properties get a list of stuff see what we can dial in on if you guys you know want to support me of course there there's a stuff all down in the comments below you guys do want to support the farm venture in any capacity whatsoever you guys can go to the website skybluefarm.com and sign up for the newsletter and I'll try to get in I should have an email set up for the site already info at skybluefarm.com the only thing I really wish is that I got started on this a little bit sooner you know I wish I kept looking back in February and March and that I was able to figure things out and really dialing on a property I got another long four-hour drive home so we're gonna head back now overall thoughts on the day you know I'm not really sure how much I actually need these outside people and that I should be able to figure out a lot of this stuff on my own you know and if we get the right property that has a lot of the infrastructure already set up you know we won't have to worry about building too many facilities buying too much outside stuff and the initial operation shouldn't really take more than than one or two people just a quick update on the farm search when I got home I looked up some properties found something that was so perfect I called the real estate agent this morning unfortunately that property might already be sold the seller already accepted an offer I put in a higher offer so we'll see what happens if I can get that property which is a bit closer in Pennsylvania all new facilities new structures high acreage I could not ask for a better property but it's probably sold if we don't get that property which is seemingly perfect this might not actually happen this year because I know a lot of dairy farms are closing I know there's a lot of properties out there and I don't want to spend money on something that has you know facilities that need to be built because then I got to wait for a loan for the money to build the facilities or subpar acreage or land so hopefully things pan out and we do get something good within the next month or two if that doesn't happen though it's not going to happen this year and it might not happen for who knows how much longer I mean guys if I had you know two three million dollars we wouldn't be having this conversation but I only have so much money I can put down on the property and then we have to see if we get a loan if we don't get a loan I have to put myself in a position where I can make it work without the loan and that's why we're being a little specific with the property so just thought I'd give you guys that update you know there are a bunch of other properties that are fairly close to the one that is perfect it's just there further away you know they're four four and a half hours into Pennsylvania into upstate New York and I have a feeling if I get a property that's too far away I'm going to regret it down the line and I'm not gonna want to actually live there myself update number three for you guys as my hair gets crazier and crazier so that property that I just put the offer and on was listed at like 840 the buyer accepted an offer for 740 and I bid at 800 and I told him I would put more cash down so I'll probably know within two or three weeks if that property is definitely sold or not I know I said that there were a couple of properties that are close to this ideal property but are a bit further away I actually just checked on antenna search comm if there were any Wi-Fi or cell towers near those properties and about four out of five of them actually had a new tower built within a mile of the property and you guys have to understand that if you can visibly see one of these new towers you're getting radiated like you're standing next to a microwave that's turned on like you have a cell phone on right next to your head and they can turn the levels up of these towers so there's no compromise on getting a property near these towers near the cell infrastructure so are there you know 10 15 20 properties that I would buy right now yeah if there weren't cell towers nearby and the only two properties that are feasible right now one is like seven hours away and the other one is like it's a bit far away and the facilities aren't ideal I want to be able to get into a place and be producing milk and eggs within two or three months and that's very realistic if I find the right property but again if I don't have the facilities that I need if the property isn't set up I have to wait for a loan which might take two or three or four months then I have to build the structures I need which might take two three or four months and you guys aren't gonna have dairy for six seven eight months a year who knows how long so I might as well wait till next year anyway if I don't find the ideal property right now so if you guys have any ideas let me know if you guys you know want to help support us as I said earlier there's a bunch of stuff down in the description below