Okay, so I realize some people are not back on lunch yet, but that should be okay It's just a very quick introduction to 16 years at the beginning and now Talk a little bit about the What we're going to do for the last so For those of you who Didn't hear my very brief introduction at the back of this morning. My name is William. I'm from the BC Public Health Lab and also in clinical assistant professor at UBC, so the dual affiliation sort of allows me to Apply both genomics and metagenomics to a more Public health diagnostic lab setting So if you have any interesting doubt any question about that feel free to come talk to me about that so the learning objective for this This this afternoon is to focus on the the marker genes as you already know and At the end of this module including the lab We're hoping that you'll be able to understand and perform marker gene based microbiome analysis You'll be able to analyze 16s RNA market genes. So that's the that's what we'll be focusing on but the same protocols can be applied to other types of of marker genes and What you 16s RNA market genes to profile and to compare microbiomes and select suitable parameters and sort of hopefully demonstrate Some in some cases what are the parametral choice and the algorithm of choice when you do market gene analysis and We'll also explain the advantage and disadvantage of market gene based microbiome analysis The last part I'll keep it fairly brief Morgan tomorrow will go into a little bit more on using PyCross and actually compare Market gene based analysis versus predicted functional gene based based analysis You saw this this morning. So My lecture essentially follow this flow. We'll talk a little bit about the wet lab component attracting DNA amplifying target primers target using primers and then filtered out the errors and building an OTU cluster Using two of the most popular tools for market gene analysis and then we'll Talk a little bit about diversity We won't have too much time to go into that and again This is sort of to give you a brief introduction and I know about half of you have experience with 16s already So the hope is that for those who don't have experience you would Learn something new for those who have some experience. You'll share your experience and you'll You know Rob mentioned this morning your sort of real life experience will be very valuable for other people And also this is like recording. So if I say something wrong or something Incorrect, it's better that you catch me right now then to you know To let us live and it will be wrong perpetually. So let's hope that doesn't happen So feel free to ask questions. Feel free to stop me anytime if you if anything's unclear So Rob already talked about it this morning that represent RNA genes is the most popular universal phylogenetic markers and they have certain attributes that make it a very popular choice for For your community profiling analysis the these include that Revital RNA is present in all living organisms it is concerned because it plays such a critical role in protein translation and As a result of that the Revital RNA genes are also relatively It's relatively rare that it's acquired horizontally. There are some documented cases actually Rob and I sort of both studied horizontal gene transfer for our PHD and postdoc respectively. That's when we actually first met I guess over 10 years ago about 10 years ago so So then so it's horizontal gene transfer also holds for a special place in my heart, although I haven't been keeping up as much as Rob has and Because it's Fairly conserved it does behave like a molecular clock So it's useful for phylogenetic analysis and useful for building a universal tree of life Allowing you to place Organisms onto a single tree and of these 16s RNA RNA is the most commonly used For the reason that it But Rob mentioned this morning and it has a good database. It's Just about the right size to give you the right phylogenetic to give you sufficient phylogenetic signals Okay, so given that RNA is well studied The Attributes of the of RNA to not only make it a good tool for phylogenetic analysis It's also a useful tool for understanding composition. So microbial community So we'll talk a little bit about alpha diversity how you can The different measurements that you can use to profile a community And extrapolating from there. You can also use the same marker for relating one microbial community to another and And Typically this type of comparison is it's called using the beta diversity index to to compare your communities Lastly as we gain more understanding of how the microbiome interact with the host or with an environment You can actually then use the profile the microbiome profile as a readout was a biomarker for diseases or for a Condition in the environment. So you can actually develop Classifiers are used using microbiome information to tell you whether this sample comes from a particular It comes from a disease individual or disease Tree for example or this sample comes from a healthy sample. So It allows We can use the entire profile microbiome profile to characterize condition so 16 s of course, it's not the only marker gene used and 16 s of course only applied for For periodic organisms if you have eukaryotic organisms such as protus or fungi You would need either the 18 s or ITS or some other types of markers It both 18 as an ITS have been developed and have reasonably Comprehensive database and they're typically the choice for eukaryotic organisms ITS is especially useful for fungi not so much for protus For bacteria you can besides 16 s you can also have used a chaperone Gene cpn-60 you can there have been ITS developed also for bacteria Genomes and while back Groups like John John's nice Isen's lab Proposed using rec A genes as a universal marker For the reason that record is a single copy gene and there are other single copy genes Where 16 s as Rob mentioned is has multiple copies and sometimes That sort of confuses the the analysis when you have multiple copies of 16 s in a given organism For viruses, of course It's the case where it's very hard to come up But it's impossible to come up with a universal marker But they are specific bio markers for specific families of viruses such as GP 23 For T4 like bacterial phage or rdrp for corona viruses Anyone else here work with viruses or protus and do you use any other types of marker genes for? Not yet developed. Okay. Yeah, so this I got you got this list from one of the studies that I'm participating in which look at the the watersheds in in BC and we essentially profile the The water from both contaminated and and pristine sites and using the different biomarkers to characterize the different fractions of organisms And so we have some experience with all these biomarkers in from our study So if you have any questions regarding these biomarkers, feel free to come talk to me afterwards I wanted to mention that some of these marker genes are more fast fast Evolved faster, so they're useful for strain level differentiation. We're 16 s is typically not good for a strain level differentiation Okay, so I'm going to go into a bit about the wet lab component About nutrition as well. So my knowledge of the wet lab component is it's sort of By proxy I guess by talking to the Technicians the postdocs and so on but I have spent quite a bit of time talking to them So I'm aware sort of of the challenges and differences. So try to highlight some of these for you So the DNA extraction it's very early on in the human microbiome study It was known that the different protocols DNA extraction protocols that you give you fairly different profile Microbiome profile and as a result of that There Some standardizations were made for large-scale projects such as the HMP or the earth microbiome project And there I listed an example from the earth microbiome project, which is the same protocol used by HMP for their DNA extraction and And HMP had done the study if you go to their website There's links to studies. I will show you, you know the different Extraction kits how they end up Giving you a different sets of organisms preferentially Emphasis on different sets of organisms so And as I mentioned DNA extraction can also be done after you fractionate your samples to separate out the different Organisms based on their cellular size and other characteristics Miguel from our institute had produced a video on this online journal of videos highlighting some of the highlighting the protocols and some of the challenges associated with This particular approach Unfortunately, it's it's it's not an open-access site So you might have to you know go through your institute to access the website If it if if your institute happened to subscribe to it or come talk to me if you're interested in Okay, so During DNA extraction process, it's very common and it's almost impossible to avoid contamination from the lab This can come from the reagents can come from the environment the lab We can come from the sequencers from the previous run that you did and you didn't watch the sequencer properly then All these can result in contamination in other words DNA is out That that's not part of your sample end up in your sequence readout so Usually the level of DNA Contamination is quite low compared to your sample, especially if you're extracting from fecal material or material that have high yield but if you are extracting from Low yielding low yield material that generates very little DNA to begin with then Contamination can become a significant issue And in those cases it's recommended that you actually include extraction negative control. So run through DNA extraction process with no input. So just use the pure, you know use Molecular gray water the best water that you can possibly get and run through the process And you'll be surprised that you still end up with something in the native control But then bioinformatically we can subtract all those Organisms from our analysis. So it's very useful to have a extraction negative control in your lab process pipeline The other source of contamination is Contaminations are already in your sample this include a host or environment the DNA that come DNA that comes from the host what comes from the the environment and These type of sample this these type of Contaminations in your sample are usually not easily removed in the web lab You can try doing subtractive hybridization where you can try some kind of fractionation process to remove some of these Contaminants, but at the end of the day you probably do need to rely on bioinformatics Computational tools to remove these sequences from your from your samples So here I listed one that's Recommended by the HMP project for removing human contaminants One thing that may be worth noting is that I'll get to that Okay, so once you have your DNA the next step is to amplify the Biomarkers that you're interested in so for dark target and Affecation, I just wanted to highlight some terminologies on this In this picture here, so we're all on the same page in terms of terminology. So when I say adapter, I mean the Sequence that's Complementary to what's on say the the sequencing chip. So that's the part that hybridized to your sequencing chip So it's called the the sequence adapter and Immediate downstream of the sequence adapter is the the primer that you use to amplify your target So in the case here, it will be the 16 s primer that you used to amplify the v4 region of the 16 s represent what DNA You can In between these you cannot introduce a unique index or unique barcode to Allow you to pull your to pull multiple samples into the same run and then later on Using this unique barcode and you can pull out the individual samples from your pool to run So I think earlier someone mentioned about your indexing so in that case the index is just present in both the 5 prime and the 3 prime Primer sequence Okay, so the PCR primer are designed to amplify specific regions of the genome It's worth noting that sometimes when you get a dirty sample there could be a lot of Implication inhibitors in your sample in that case usually need to dilute your samples before you Can successfully amplify the target region In other more complex samples, you can actually end up with non-specific implication. In other words, you have multiple bands On your gel after you you run your PCR implication One way we have dealt with that issue is to Use gel size selection to only to cut out the bands out that correspond to the marker genes that were interested in It's if you're a wet lab person, you know, that's a very tedious process to do by hand. So we actually collaborate with a company that use That robotics to do gel to do gel extraction Kate so Again, I think Rob mentioned this earlier on but the different So for 16 s RNA, there are not hyper variable regions And if you target target different hyper if you amplify different Hypervariable regions you can actually get slightly different microbial profile And OTU based on the Region that you selected there are a few papers and I think I listed one of them here But I can I certainly can send you more About target bias issues with target bias and In our study we mostly focus on the V4 region using the of 16 s using the Illumina protocol Because as you might have noticed in the in this earlier slide the V4 region is just the right size About 250 base pairs that would give you a decent overlap to allow you to correct for Sequencing errors that happen typically in the three prime end of your sequences I'm sure most of you know that sequence quality degrades as you move from five prime to three prime So the ability to correct some correction in the five in the three prime is Desirable so and we'll see how that can be done in the in the lab session. Okay, so Even on the desktop sequencer like my seek you have You have you generate significantly more sequences than you need for to characterize a given sample So the strategy is typically to multiple to put multiple samples Into the same into a single run and so I mentioned already that the way you do it is by Tagging each of your samples with a unique barcode that you can then later use to separate out the The the reads into into the different samples The Number of reads that you need to characterize a sample of course the different Really depends on the sample itself Rob Knife famously say it only takes about a thousand sequins to separate your elbow from your ass So it's when the environments are very different Hopefully between your elbow and your ass You don't need a lot of reads to sub to differentiate the environment that the samples But when the samples are much more similar than you potentially need more reads to differentiate your samples It's sort of the the guiding principle When we first started metagenomics studies were aiming for you know 10 to a hundred thousand Reads per sample we now realize that's probably an overkill for for several reasons One is typically you don't need that many to differentiate One sample from one type of sample from another type and second because a lot of the Samples environment are highly uneven so you have very abundant organisms and very Rare organisms if even if you sequence to a hundred thousand or ten thousand reads you're still not capturing those rare Organisms and we have done studies where we actually spike in known amount of DNA from a known organism And and see if we can detect it in sequencing and we realize you know if you speak Spike low amount You know it's there, but you're not going to even see it in your sequence So for those reasons, you know unless you have specific reasons to sequence to a high depth It's typically advised that you sequence more samples rather than sequence to a much to to High depth for a given sample Oh And the last point is that the way that these Parallel sequencers so so my seek is an example works is that it takes a picture of the the sequencing the Sequencing by synthesis, so it takes pictures snapshots of the DNA molecule as it grows and each time it adds a New base it emits a signal So you can imagine if that On your slide if all the reta Homogeneous are the same then you for each cycle of your sequencing you're going to get Either a lot of spots lit up or no spot lit up so because of that if you get you just imagine like You know looking at a picture if there's all y to all block you get very little information, right? So For that reason you usually need to when you don't be sequencing one type of ampere con you need to spike in Some additional sequences are different from your ampere con. So Phi X is a control read is the default People spike into their Illumina runs in order to sort of make the sample more had the More heterogeneous to allow better resolution. So you might think that if I don't spike in any Other sequences I get more reads, but it's actually counterproductive because your quality of your readout will be much lower Then if you spike in a different sequence Hopefully that's clear Okay, so the last Point that I want to carry is that you can actually do one step versus two-step PCI amplification and the Protocol that's described by by Rob nice group Uses the one-step in amplification and as shown in the The diagram earlier you can see the adapter the barcode and the primers are all in one construct and And you only need to do one PCI Step to amplify the target genes and to have the sequencing adopters already on your molecule ready for for sequencing That type of approach is great in the sense that it's much quicker protocol But it's not Suitable if you have degraded sorry degenerate primer that you're using or if You're trying to amplify many different types of empathons because these long primers typically can the The adapter and the mark and the barcodes cannot you interfere with the PCR The target primers that you're using so you if you do the one-step application you actually have to test out each of your Long primers in order to to make sure that they they do amplify your Target properly and there's no marked Bias due to the the presence of a barcode For 16 as it has been tested quite nicely before other markers There's very little Existing one step and put on one step primers available for amplification So for that reason when we did our study where we have multiple target sites that we're trying to amplify We end up using a two-step process which we use the published Target primer so will test the target primer to amplify the target and then we Then like gate the the adapter and the barcodes to the the empathons after the PCR step this approach is it's you do lose biomaterial using this approach because You know that the ligation is certainly not hundred percent efficient But it is compatible with rendering and generate primer amplification steps and this approach is supported by most of the Primer vendors such as Illumina we use a company called bio before New England bio lab also have a set of primers That you can use for for these type of approach Okay, so I'm moving into the more balance make analysis component going to introduce you to two different tools One is chime And the other is called mother So chime stands for quantitative insight into microbial ecology It only takes geniuses like Rob nice to come up with these hard to react to them that we mean something Mother on the other hand doesn't stand as far as I know for any acronyms, but the developer Patrick Patshlaw Have a series of tools names son daughter and other so I guess the next logical step is For his naming scheme is his mother. So that's what that's where the name other came from Okay, but so these two tools started off from very different Emphasis one They but the overtime they serve So initially they have very different functions and you kind of have to pick one with the other when you need to do certain Type of analysis, but over time they they start to converge and now a day You can pretty much use either one to to accomplish your analysis There are some subtle differences both in sort of philosophical in the philosophy of their design, but also in some sort of practical Advises so Chime is actually a Python interface that glue together many different programs And mother on the other hand is a single program with minimal external dependency So because of that It's much easier to install and set up mother and it's much easier to learn how to use mother on the other hand Chime is Has a large number of dependencies It could easily take you a day or two just to install all the dependencies required for chime but they do make a virtual machine available that you can download and launch on your own computer the downside of virtual machine of course is quite limited by the number of Quite limited by the size of your machine So if you load it on your laptop, then it's it's not a very powerful machine to do analysis And if you then very rarely can you actually deploy it on the on the server or powerful machine? So the other Did I miss anything? Yeah, so the other point is that because chime is actually a glue that brings together many different programs It's more scalable in the sense that you can use different programs for different Purposes and some of the programs are highly paralyzable mother on the other hand is best suited to run on a single machine and For some of the analysis that machine hopefully is is as powerful as you can can get we have run into situations we where we have these servers with you know 32 CPUs and or 32 cores and 1 terabyte of memory and we still run into trouble when running mother on a large Data set and that's because some of the steps that it requires loading the entire data set into the memory and And it's very resource in intensive Since I mean it has since come up with better ways to break down the Large data sets into smaller ones, but still It's not it's not a scale go as I'm going to highlight two links here one is a publication pulled out by the chime group sort of telling Giving you a walkthrough of how to use chime and a lot of my actually Material that I'm showing here came directly from this paper The other one to highlight is the my seek SOP from the mother Website and again a lot of my tutorial is coming From this material here, and it's well worth your time if you want to do biomarker analysis to to be familiar with both of these resources Okay, so here's an overview of the the balance of magic workflow I'll go through these steps Individually, but just to show you that you typically start off with your sequence data as input and with that sequence data You will have some Additional information about your your samples so that those are the metadata so the sequence data need to be Processed in order to be ready for for For OTU picking for for clustering and once you have to pick the OT once you have picked the OTU's you can either directly assigned There were the OTU's to different taxa or you can do an alignment and from this aligned sequence Then you can do a phylogenetic analysis which will give you a phylogenetic tree when you do Taxonomic assignment what you end up is an OTU table. So a list of OTU's And which OTU's in which sample and from the OTU table or the phylogenetic tree Then you can perform downstream analysis looking at the diversity of your samples or to visualize the The composition of your of your samples Okay, so we what we'll do is My slides will refer to these numbers Here as we go through them so you can sort of flip back and forth just to know which Stage we're at for the analysis and I did the same for the lab as well So you can refer back to this diagram to see which stage of the analysis you are in when doing the lab okay, so I mentioned contamination already and I also mentioned that the Contam the Contam the contamination When you do the Contam the contamination in in a Banff-Morgan analysis This typically involves mapping your reads to a database containing the suspected contamination So if you want to remove the human sequences, you will search against the human genome database if you want to remove same Mouse or rat you'll search again their respective genomes one So Typically this type of search is you're getting millions of sequences if you use BLAST search, which I'm sure most of you are familiar with It's a fairly slow Algorithm so it could take days just to search through your your data set So typically what we do is use a shory aligner To do this type of search I Won't go into this at this point, but the the HMP The contamination protocol would give you a sense of how that that can be done And if you want more information, I'd be happy to talk to you more about, you know The shory aligners within what they are Well, we in our own analysis However, we found that most of the shory aligners give you comparable results So it really doesn't matter which one you pick to use What does make a difference is that if your database can have the it can include the host variant So as you know the human genome when the sequence, it's a it's a compass like It's a composite of multiple genomes and then later on they add the the different variants to the to To the database and you can choose download just a core genome with the download also a list of variants And what we found is a few included host variant In other words snip or whatever type of variants that's available In your search, it will help to improve the matching and therefore remove more host sequences from your From your set from your from your sequences for Downstream analysis any question so far any mistakes So most of the shory aligners actually have fairly stringent threshold typically it's about two 23% could probably go up to about 5% but anything above that then it's much less efficient at At the matching so you usually do two to three percent as a cutoff But that's why I said to include the variants because some of the very some of the more variable regions could easily Exceed a two two percent threshold Yeah, so Nick Lohman And so you just Google Nick Lohman or he's on Twitter like no one's business So if you want to find on Twitter or find him on on Google you can he did he and others Publish a paper looking at common Contaminants found in reagents, and I don't know if there's a specific database associated with what they found But certainly you can pull out the organisms from NCBI that correspond to to what they found to be in those reagents If you're not doing the novel Do you still need to Less so yeah, you can probably get away without doing if you if you're not doing the novel Picking which we'll talk about in a bit. Yeah, you can probably skip this step and certainly for MPCOM based analysis, this is less critical because your M your Your PC amplification. Hopefully is specific enough. You have very few contaminants to begin with It's more of an issue for sure for metagenomics analysis Any other questions? Okay, so I Think I covered this already, but basically the first step of Pre-processing is to remove the barcodes and the PCR primers. Those are artifacts of your Amplification so you need to remove those from your actual amplicons before Before your analysis These step is fairly well worked out. So I'm not going to drill too on it too much, but But if you again any questions feel free to Talk to me. Okay, so the next step is that Once you remove the obvious Adopter and primer sequences There you leave with hopefully what hopefully what you're truly trying to amplify But some of these amplicons will have low quality. So the next step is to remove Reads that have low quality both chime and mother have fairly good Protocols worked out for removing low quality Sequences and some of the common parameters used are Minimum lens of consecutive high quality base in other words after you cut out the low quality region how much of your Original amplicon is left if it's shorter than a certain Certain proportion of your original amplicon that it might be discarded entirely rather than trying to keep it short To mimic Yes, so I guess the question is if you have four five four data, how do you Okay, so if you have the Lumina Gotcha Right so In that case, I actually will recommend using mother as you will see mother has the my seek SOP that's designed to remove to to do the filtering and do the The The assembly for you So I'll show that in a bit, but if you want to use chime, I'm pretty sure chime now has a Lumina based protocol to allow you to remove to allow you to merge your Sequences and actually in our lab I'll show you one of the tools that can be fit into chime to do that The both chime and mother were developed when four five four was the Platform of choice. So both of them have since adopted to to alumina for sure Okay, so besides the length of the read you will also Look for maximum number of low quality bases. So you have a long stretch of low quality basis That's another sign that the entire read might not be very good The next is the number of ambiguous bases and an indication of a low quality and you can also set a quality threshold to To scan through your entire sequence and if there's again regions out of low quality you can discard the entire sequence Here I'm just listing some of the tools that can be used So both as I say mother and chime have built-in filters that you can use But there are other tools that you can use independently outside of these tools to do quality filtering and and to do pair and assembly show some of that later and fast QC is a tool that's very popular for Summarizing your quality sequence quality. So all you have to do is feed it A fast Q file and it will give you a summary report of what How how good your sequence quality is? Okay, moving on to your OTU picking Okay, so OTU's essentially arbitrarily Formed clusters based on sequence identity the 97% sequence similarity is The default cutoff for most of the programs and that roughly Correspond to the species level although I'll show later. There's publications indicating that the 97% It's really just a very rough guideline and you really should be Tailoring the threshold based on the the organism that you're interested in targeting So I'll highlight three different approaches de novo clustering Close reference and open reference and there's more detail about you picking at this link here Okay, so I'll start with de novo clustering This is sort of conceptually probably the most straightforward to understand essentially you have a group of sequences that you want to Cluster based on their sequence identity So what you start with is is just pair-wise comparison of all your sequences to establish how Similar each pair is and then you can use hierarchical clustering basically starting from the most similar pairs of sequences and build up your clustering that way and Then you let's say have a 90% 97% cutoff that means once you build your hierarchical clustering any sequences that are Beyond and beyond your false outside of your your similarity cutoff Forms its own Cluster, so you end up with multiple clusters each roughly about 97 Each roughly about 3% different So This process requires a lot of memories because you can imagine if you have a 10th Let's say a thousand sequence. That's a thousand times a thousand pairs of comparison that you need to do So if you have a million that's a million times a million, which is I don't even know what that one I think it's hundred billion or so or one trillion that sequences I see Pairs of sequences that you're comparing so that could easily overwhelm even the largest Machine that you have and this is typically the step where we run out of memory on our Computers So here's So besides hierarchical clustering there's a faster algorithm developed by Call you class or later on it's also part of the you search tool that will Show in in the class in the in a lot later So this type of approach instead of doing pairwise comparison what it does is to try to find the centroid For your sequence So then instead of doing pairwise comparison everything that falls within a certain distance from the centroid Grouped into the same cluster. So So that for example, it's an OTU represented by this particular sequence in the middle so that's what we call the the representative sequence and all these are the Equivalent sequences For that OTU. So when you call an OTU you have to keep in mind that What you're essentially saying is that every sequence is that fall within the OTU you're going to treat it the same For as far as your downstream analysis is concerned So that's why picking the threshold is quite important because you don't want to lump together sequences That shouldn't be treated as the same Organ that this shouldn't be treated as the same organism or the same text In this case, oh So how is centroid chosen in this case is typically you rank your sequence from the most abundant to the least abundant and then you pick your centroid by picking the longest sequences and you just go from top to bottom and and essentially take the the most abundant sequence scan through your rest of your sequence to see which one falls 97% from the from the From the sequence you pick and everything that's why it's called greedy because depending on which one you choose as your first It if it affects your down here your OTU clustering And then there's different Protocols, I'm just describing the most sort of common way of doing it But different tweaks and different protocols cannot really improve your OTU picking Yes, that's why you define as a user definable. So the T the threshold value is defined by you Right, so are you saying is there rules for okay Yes, and no, I mean that the 97% is the rule found people use for species level comparison and tools like you You search actually recommend you don't go below 97% because if we go below 97% the The algorithm becomes unreliable. I mean we can talk more about why it becomes unreliable, but 97% is the short answer Typically 97 people say species but but we in reality that range is actually from anywhere between 90 to 95% so so it's really all over the place and I have some stats for you later on The other point I should mention is that if you look at this diagram, let's say the T is three here that means 3% is the minimum distance Between your two sequences in other words if you if you look at this sequence here and this sequence there Obviously, that's not 3% right. That's more like 6% So so when you pick 97% what end up in your OTU could actually be as little as 94 93% So that's something to be careful about and another way to get around this issue is that you do the the OTU Clustering twice you first cluster at high stringency 99 98% and then you cluster again at the lower stringency that helped to cut out some of the issues associated with Just using a very relaxed threshold to begin with Okay Okay, so moving on to close reference this one Simply means you take your sequences and you match to an existing database of reference sequences So it's shown in the picture here essentially your list of sequences is compared to an existing database and Anything that hit the database that has a match in the database is kept in your OTU table and what's Not matched into the database is simply discarded So this type of approach is fast and it could be paralyzed because you know The matching could be done independently You can take the first 10 to do the matching and then the next 10 on a different machine And you can submit the next 10 to a different machine to do the matching So you can then afterwards combine the results since the matching is just between your query sequence and the database It really doesn't It the the other sequences in your data set has no no bearing no influence on what the matching Whether it will match or not match right that makes sense Whereas this particular approach you can paralyze it easily because that the algorithm has to scan the entire queries your entire queries your entire input data set to establish the clusters So that's why this close reference is much faster and it's suitable if you have a comprehensive and Properly annotated database such as the 16s database But it certainly doesn't work very well if your reference database is poorly annotated or it's very sparse So you're going to get a lot of sequences with no hits to your database in those cases You're much better off using either the novel or open reference Which we'll see next so open reference simply put is just a combination of close reference search followed by Processing the dismissed sequences using the novel Clustering so you simply take the rest of the sequence that don't match to your database and do the novel clustering Then at the end you merge the two OTU tables. There's different ways of implementing this particular approach suffice to say this is the Choice that if you have the time So in one the most robust data set this is the the approach to use Okay, and I already mentioned the represented sequences Typically the centroid of your OTU and there are several ways of picking that centroid one is based on abundance So most abundant sequences are likely to be most relevant for your analysis and The other way is just use a centroid that's used for the the novel OTU picking you can also pick the longest sequence available for From your set of of OT sequence in the given OTU pick the longest one is unlikely to be the representative The caveat for that of course is that if your longest one happened to be Chimera or some sort of hybrid sequence that's not real then then that could be an issue If you just pick simply by lens Basically, if you're if you know your ample count is 250 base pair long then any then you shouldn't be picking and Representing six representative sequence. That's 500 base pairs long And there's few others that are less Less Often used so most common is just the most abundant sequences used as a represent a sequence Okay, so as I mentioned Doing your PC our amplification process you can actually end up with a chimeric sequence That's artificially joined sequence from more than one template. So for example in this case here You have two template and it gives you a chimeric sequence. That's partly from this sequence X and second Paraphones sequence Y so We'll see how some we'll see some of the tools that can be used to Remove chimeric sequence and typically the detection is based on identification of a three-way alignment So, you know this sequence this part aligned to X this part aligned to Y That's a good indication of chimeric sequence All right moving on to taxonomy assignment It's important to differentiate Otu's from tax on so Otu's don't have names But we as human often like to give a name to an entity, right? So Instead of referring things as Otu one Otu two It's usually much more meaningful for us to refer to something as E. Coli or Samo Nila or Bactroides and so on so So as a result of that Most of time we end up assigning Otu's to known tax on In an attempt to to make to make sense of the data But bear in mind that the Otu or the sequences is not exactly the same as the tax on there's just And there's the issue of resolution. So imagine you It's a good example. So imagine you have different you know Different animals that you can call it animal A animal B and so on But if you once you assign it a given name, let's say elephant We know what you you know, we commonly know what you're referring to as an elephant But there are always things that look like mammoth for example that look kind of like an elephant So would you classify that as part of would you call it elephant where you call it something else? So the name one trying to get out is the name and the actual entity are not exactly equivalent So you keep that in mind it would help you to understand the difference between an Otu analysis and the taxonomic analysis and the the process of assigning an Otu to a Taxon is simply by similarity search So we match we map an Otu to a non taxon based on similarity and again the cutoff of what you use Can affect your your interpretation of the results? Okay, so Your Most of your said I get most of your Yeah Right so so that's where you know understanding the difference between Otu and taxon is is the key you can do Otu clustering at a given cutoff and then do the taxonomic mapping at a different cutoff So often an Otu that's not Assigned right away could still be forced into a taxon based on the cutoff you're using So so that's the key sort of the key message here is that The two are not exactly the same and important to keep that in mind Yeah, but yeah in the case where the Otu really cannot be assigned to a known taxon then you keep the Otu destination There's nothing not much you can do is you just call it Otu X and move on with your analysis Right so once you assign things to taxon let's say you use Cpn60 to define and your taxonomic mapping say to E. Coli and then you use 16s also to assign to two Differentials your sequences and assign things to E. Coli then at the taxonomic level you then can treat those as equivalent Whether that's ideal or not It's probably not but it's it's possible to do do that. So once you Assign things to taxon taxonomy again, you're treating everything within the taxon within the taxon the same All right, so So then you can do comparison that way It's for that will probably not be your primary comparison, but it will probably form a meta comparison So when you trying to compare across different environment, then you can use a different markers and hopefully they give you a complimentary signals that will allow you to to Characterize your environment better than just a single marker and that's sort of the approach we took for the watershed project And so just saying we look at 16s. We look at a whole bunch of biomarkers and we're trying to Compare in contrast what the different biomarkers report as the the important organisms and actually in a lot of cases we identified the same important Organisms or species Using different markers But the key is you have to assign it to a taxon in order to do that comparison Okay So here just to show quickly that you have a set of otus and it's the unique it the matching algorithm or the mapping algorithm is important as I mentioned otu and Tax on tax on a different thing So when you report out your analysis, you actually have to report the matching algorithm you use To go from otu to the specific taxonomy Assignments that you give So and the and also the different taxonomy databases could give you different results as we'll see in the lab section so Yeah, so just Keep that in mind I think Rob mentioned the different databases already so I'll skip over that. Oh Well, so as I mentioned here Green jeans is preferred by chime and selva is preferred by mother The key difference is green gene is a much shorter alignment and selva It's a much longer alignment So you're 16 s genes are the same size, right? But the different alignment method creates template that are short or long and the argument made by Patch loss is that the longer alignment actually is more accurate alignment than the shorter alignment Which forces base to align when they shouldn't shouldn't align so so if you believe had you use Selva if you believe The chime group then you you you can use green gene That Yeah, every six months or so so the database is updated every six months or so. Yeah What RDP is actually a little bit more frequent than that Because essentially it's an automated pipeline that pulls sequences from NCBI and convert it to their own taxonomy Okay Getting to the end so Bear with me for another 10 minutes or so Okay, so the one caveat I want to mention this came up a few times already only 11% of the 150 or so human associated bacteria Genera have species that fall within the 95 to 99 percent 16 s RNA Identity cut off so the 97 percent cut off that we typically use only really apply for about 10 11 percent of the Human associated Genera so that really give you a sense that the 97 percent is really just a rule of thumb and if possible You really will want to explore what the diversity within your species of interest is Before deciding what the cutoff to use is Okay, so once you do the taxonomy assignment typically get a bar graph that just shows you the proportion of each Text on in your sample So sure that's so pretty self-explanatory, but we'll see some of that in the bottom All right, so next few steps fairly quickly so Otu tables is actually just a sample by observation matrix so you have the Otu's in One X X is the samples on the other axis and you just list the number of occurrence for each Otu in a given sample So that's that's essentially what an Otu table is The Otu table then can be mapped to taxonomy information So you can have a separate file that says Otu one or it could be in the same file But anyway, so you could have another a bit bit of information that says Otu one is say Bactroides or or some other organism and so on so The Typically pipe the pipelines I will see that the extreme rare Otu's are removed Filter out and often these are attributed to sequencing errors and and they're just removed to to facilitate downstream analysis And This sort of echoes at my point earlier that these really rare sequences no matter how Deep you sequence you often not going to catch them So there's really not a whole lot of point sequence Asample to a to a huge depth unless you know for sure you're trying to capture some rare Otu's or rare organisms Okay And this Otu table can be converted to this common biome format. It's actually a binary format so So again, it speed up the analysis by compressing the Otu information into a biome file Okay, so sequence alignment We I think most of you hear a familiar with sequence alignment But it is the required step before you can do distance-based analysis or for example a phylogeny Tree phylogeny tree phylogeny analysis The traditional alignment programs that you might be familiar with such as cluster of W muscle and so on a way too slow to Align to do multiple alignments of thousands if not ten to thousand or millions of sequences so new type of method have been developed such as Pi NAS which will look at later on and inferno these are called template-based aligners essentially what you do is You can almost think of this as blaster to you search against a pre-aligned data set to establish The closest relative to your query sequence once that's found then instead of aligning your sequence against Thousands of sequences in the database or in your own data What in your own data set you're not only to align your sequence to a handful of what's called templates in your Data pulled out from the database based on similarity So in other words it cuts down the number of sequences you have to feed into an alignment process So dramatically speed up the the alignment process Okay, so once you have the alignment you can use any of the phylogeny analysis programs that you might be familiar with The exception is when your data set is really large, then you might need to use programs such as fast tree Which is a fax approximation Likelihood-based algorithm that's a fax approximation of maximum likelihood to to build your your phylogeny tree Okay, so I mentioned that we usually pull samples into the same run as a result of that it's very hard to have even number of reads from each sample so the But then the sampling depth can actually affect your richness and diversity Calculation so the common current practice now is to essentially do with Essentially, verify your samples to the low to the lowest or to a acceptable number of reads In your sample. So if you're you have ten samples the lowest Read for a given sample is a thousand and the highest is ten thousand then you are actually losing 9,000 sequences by verifying to the lowest common the lowest common The lowest number of reads for the four sample It's been shown that this type of approach at least statistically speaking is not very sound Because you're not only losing a lot of your reads that diversity Information you're you get from the artificial truncation of your read depth. It's actually There was it actually affected that the answering analysis as well So the the new approach that people are recommending is covariance stabilizing transformation Essentially control them if you have higher depths in your sampling usually that results in higher variance So by controlling the variance in your samples That's a one way of squishing down your high depth reads to a more to a more manageable I should say more comparable Value as the rest of your samples so there are a couple of references there that you can and look into for that type of approaches Okay, so quickly Rob already talked about alpha diversity So and and richness is just a number of species observed or estimated in your sample Evenness is a relative abundance of each samples diversity takes both evenness and richness into account and Suffice to say there are a lot of different diversity measures both implement in chime and mother and As to which one to choose I don't know if anyone has really a good explanation But the rule of thumb is typically try a few popular ones and hopefully they all give you the same stories and contradicting each other This is sort of a rare faction plot showing when you have close reference It's hard to read here. You your OTU count is much lower around 20 compared to when you have open reference with the novel Was a same sample OTU count is much higher when you have to know for open reference And the rare fraction curve essentially plots the Estimate number of OTU's other currents at a given sequencing depth So as your sequencing depth goes up, you would expect that the curve Would go up as well and what you're hoping to see is that the curve essentially? Evens out with plateaus out that means you reach the saturation of your diversity Okay, beta diversity is comparison across samples or across environments and again multiple Different measure measures for those the key differences is probably once that measured the membership of your Samples so looking at OTU abundance looking at the OTU presence or absence only versus once I look at the structure of your Community so that looked at the relative abundance of OTU's and these are some of the common measures used Okay, so for beta diversity analysis you first need to do pairwise comparison across your samples and establish a distance matrix for all your samples so the distance of course will be The similarity will be the highest say one between your samples or the distance will be zero It's typically a score between zero and one So here I'm showing the similarity So one simple one against itself has the highest similarity Simple two against simple one has lower similarity and so on so forth. So by establishing a matrix like this then you can transform it to Pico a plot or other types of plot to look at the relationship of your of your samples Okay, so unifrack is one of the popular beta diversity measures the way it works essentially you map all your OTU's to a phylogenetic to the same phylogenetic tree and You sum up the branch lengths that are unique to each samples. So for example in this case the purple ones shows the The branches are unique to the red samples versus the green samples and when you have a very different community then the You'll see a much longer unique branch So the the the larger the unique fraud score the the more that the more different the communities Against each other the two versions weighted versus unweighted again correspond to so the weighted one Correspond to the abundance measure and the unweighted one correspond to the presence or absence of OTU's Okay, so the way I like to describe these principle coordinate or component analysis and Rob sort of put me on spot to try to explain I had to ask him what the huge what the differences are and I'll get into that a little bit but conceptually what we want to do is that when you have a multi Variable high dimension Sample it's very hard to visualize that On a graph we're much more familiar with two-dimensional visualization or three-dimensional visualization so in order to view multi-dimensional data the distance with the the distance in terms of the p-co-a principle coordinate analysis or the Covariance in terms of the the principle component analysis essentially it's projected into a lower dimension And the most common of course is two or three dimensions so you can actually view it in in a graph I don't know if anyone tried to plot four-dimensional graphs But if you figure out how to do that, let me know probably some sort of time-serial analysis But anyway anything five-dimension. I don't think it's it's imaginable, but just imagine you have 3d product 3d object You want to project it such that you can still tell the salient feature of that object Right, so this is the chair if you project it this way you can still tell it's a chair But imagine if you project it this way Now all a sudden that could be a ladder or could be something else right that gives much less information than this particular projection so the goal of these projection is to maximize the the To maximize the variations with the salient features that can explain your data set In the lower dimension so clear. Okay, so Okay, so here's a plot of principle coordinate analysis So it's actually a two-dimensional plot showing you that these samples are different from these set of samples So in the first dimension and in the second dimension further separate out the blue ones from the yellow yellow ones So that's how you would read P code a principle coordinate plot Basically dimension by dimension All right, hi echo clustering can also be done on the same data set Yeah The biggest difference was so PC a or P the PC a essentially it's is well, let me say the The coordinate analysis so when you have distance information You need to project that onto a fixed coordinate in order to interpret the end result so what so the difference between PC a and P co a is that when you have distance information that need to be projected into a fixed corner and from there You can you do the same PC a analysis principle coordinate I'll try to Identify the covariance in your in your data and max and projected such what such that the maximum Variance is is the small salient features is shown in your graph So that's sort of the So peace P co a essentially is projection into a of your distance matrix Into a fixed coordinate then from there a principle component analysis and a principle component analysis I don't know if anyone have a better explanation of What that is but it's it's a decomposition of your covariance in your data with with the basic idea of trying to maximize the Features that separate out your data sets the best To get Yes, eventually The P co a is Distant projecting the distance into a fixed coordinate then then you do principle component analysis so you still end up with the you know the Components of your of your you know your vectors Okay, so hierarchical clustering You can apply this you can use the same you can input the same data set and get hierarchical clustering the key difference here is that Now your samples are forced into bifurcation trees and a lot of time the samples are not related in the bifurcating Manner right so the clustering approach look a lot more natural than the bifurcating method You can still see the yellow ones and the blue ones sort of separate out in the tree But as a human we tend to over interpret a youth the the relationship in a in a bifurcating tree so I think fewer people are using hierarchical clustering to show show the Show the the Coordination results Okay, okay, so marker gene versus shotgun sequencing as I mentioned Morgan would talk more about this shotgun metagenomics tomorrow, but suffice to say Marker gene is still cheaper computationally less demanding It provides taxonomic profiling rather than both taxonomy and functional profiling and you need sulfur such as PyCrafts to map the taxonomy to predict functions, which you'll see tomorrow The 16 s genes if you 16 s majority of genes can be assigned Whereas you will end up with a lot of unassigned gene fragments when you do shotgun metagenomics and again contamination more issue in shotgun and marker gene based What's worth noting is that? When you look at the function, sorry the taxonomic classification The samples can look quite different But when you look at the functional at the functional level the same samples now look much more even So this is to say that One way to interpret it is that the different bacterial Different bacteria actually in co-similar functions and fill in similar niche So sometimes it it's a bit advantageous to look at the functions because you probably get a more consistent Output then to look and certain more consistent pattern than looking at the taxa alone Morgan will cover PyCrafts tomorrow, so skip that and Okay, so we'll take a quick break and then move into the lab any questions Okay Yes, you mean within a genome no it doesn't So you need you can correct for that if you wish


And welcome back to DEI Matters, conversations with Margaret Kretel-Thomas. And today we are here with Jill Harvey, who is the DEI Director for Arlington Town. Hi Jill! Hi. How are you? Good. Good. Thank you for coming back. You know, we always do this every year. This is our annual kind of inauguration into the 2023-2024 school year. Yeah. So how you been? Good. Good. It's been a busy time. It's been a busy time. Yeah. Because you know, most people think that when the school year ends, like, we're done. Summer is a break. Well, it's not. And we're actually working even harder in the summer, right, to just get prepared for the next coming year. Yeah. So one of the questions that I have for both of us, and we always ask this question, right? So let's reflect on our roles as DEI Directors, and what are some of the key milestones and accomplishments for you, because you've been in your role for four years. I'm starting my third year. So what are some of the key accomplishments that or milestones that you have gotten to in the last four years? Yeah. The last year was definitely a big one. Lots of changes. You know, my DEI division went from a division of one to three, which is great. We added Theresa Marzilli, our outreach and engagement coordinator, and Tim Ross, our full-time ADA coordinator. So that has been a real, just huge difference, and something that a lot of other municipalities don't have. So it's still a singular DEI person. Also in the last year, we completed the community equity audit, which was a long process, but also a great accomplishment. And then we also started, and I'm working on what the next steps will be, doing employee training. So we did workshops. We did DEI and racial equity workshops for all of the town employees. So there was something that had never been done before. We did it over the course of, I want to say, five months, and we wrapped that up by the end of January. But yeah, those are the really big things that happened in the last year, and just, also, there's so much more. We did our first town-wide Lunar New Year celebration, which was amazing. And now we're really gearing up the language access project that we have going on. So we'll be testing out some translator tools, really investing in translation services, because we know that that's really the number one need that we've heard from our departments, but also from community members. So it was a busy year, and we're excited to keep plugging away at the things we need to do this year. Yeah. It's interesting that you added onto your department, because I added onto my department. So I'm so excited that I have a new DEI-BJ specialist. I was like, oh, this is what it feels like to talk to someone in a department. And then the other thing that we did last year, we did Narrative 4. Yeah. So we've been doing a lot of partnering with Chief Flattery and the Arlington Police Department. And so that was one of our events that, in bridging our relationships with the APD and the town and Arlington Public Schools. And do we want to share a little bit more about that experience? Yeah. It was a great day. We had students that we worked with, like you said, Narrative 4, they do story exchanges. So we worked with some Meku students, and then a few other students as well that participated. But then, I think, five or six officers and Chief Flattery as well from the Police Department. But we did a full day of just learning about listening, really building some empathy skills and talking about compassion and just getting to know one another and share stories so that you can really start to get an understanding of what someone else is going through. Not necessarily taking on their experience, but trying to put yourself in their shoes so that you can have a little glimpse of what they were going through or feeling in a certain moment by sharing a story. Yeah. It was really great. Yeah. It was a really, really amazing day. One of the things that you mentioned, we did our equity audit now, I think it was almost two years ago, and you just completed the town equity audit last year. What are some of, what were some of the key findings from that audit that, and I think you're starting to do some of those implementations from those key findings. But what were some of the key findings and what were some of the recommendations? Yeah. So we had, I want to say there were 12 findings, either 12 or 14. And then, I want to say 10 total recommendations. We've been probably since February working on parsing out what's feasible short term and what we really need to plan for long term, because with all of this comes our price tag, you can't make systemic changes without providing resources. So with that, the idea is also that we'd be using some of the continued ARPA funds that were appropriated for that. So again, the number one thing that came out of it was language access, making sure that we are serving our community members, but also our employees too. We're not doing our jobs. We're not feeling fulfilled if we also can't communicate with folks. That's absolutely true. Yeah. So that was really the highlight that we really are focusing on now and looking to invest in. In addition to that, I mean, the recommendations were things that I think you and I kind of know, but now we have the data and everything to back it up. But some of the other topics that we're trying to get to are around communications, both internally and externally, that there's some type of breakdown in which you might not know what the department next to you is doing, because their work is so siloed, so figuring out ways to increase better communication means, but then also for our community members too. We're going to be looking at hopefully a way to better diversify boards and commissions. That's something that came up, but again, that goes back to kind of a larger education around how the town works, letting folks know that boards and commissions do exist. What is it? What is it? How is it important? Why should you get involved? So really, a lot of what came out of it was that there's a gap in knowledge about the town structure, how things work, and how to get involved, and so we need to figure out the best ways to do that outreach. I think we provide a lot of opportunities. There's so many, and we have so many services and programs, but we can't expect people to come to us. We also have to figure out how to bring those things to the community, and the different parts of the community that we know are in higher need. You mentioned that you're doing some professional development with employees of the town. I know we do professional development with our educators in Arlington Public Schools, and so let's kind of unpack a little bit of why is there an importance of doing professional development with our stakeholders who work for the town and work for the schools? Yeah. I mean, for me, it was a mix of things, because I know that we on the town side don't have a set standard of training that everyone goes through. It's definitely different than schools, and much like you legally have to do certain things, we don't have that. Certain departments do, but for the most part, we don't. And so in knowing that we would be conducting the equity audit, I also wanted to make sure that all of our employees understood what that meant, that whatever comes out in this report isn't a reflection of you, but it's a reflection of the structure we're working within and how we all contribute to it and how we need to understand really the baseline, just terms and how our history as well to know that we might not have created this mess, but we know who has and we need to fix it. So with our employees, like I said, it was the first time. It was a logistical challenge to get a little over 400 people to be in the same place over the course of a few days, back to back to back, but we made it work. The commitment of leadership was key because it came down from the top that everyone will be doing this. We worked with Strategy Matters, a consulting firm out of Boston who was really great in helping us craft something that wasn't your cookie cutter training. And we really didn't even use the word training. We used workshop more so because a lot of what we were doing was engaging with one another and talking and yes, some learning off of a projector, but also doing more activities to kind of start to unpack some of the things we're feeling or biases we have, et cetera. It laid the framework. There's a lot more to do. And now that we kind of have that baseline, my goal is to then start to look ahead and how to do some more work by department. So with all those trainings that we did do, everyone was mixed. We intentionally put people at certain tables so that they would have to engage with folks that they've probably never talked to, but they've both been here for 20 years.  And yes, there were days that were difficult, but then there were also days that we really had some great breakthroughs and by the end of it, it was we came up with five goals as a whole organization. Oh, that's great. That everyone came to consensus on. So what we're doing now is with those goals, we are tying them back to each recommendation of the audit to make sure that the voices of the employees are also incorporated. The things that they've said they want to see is also a part of what we're choosing to prioritize. Wow, that's amazing. One of the things that I am thinking about with our work, you've been in your position for four. I'm just starting my third year and we know that this work is ever evolving, ever changing one minute. It's this policy the next minute. It's not that policy. It's this policy. And I think I want to talk a little bit about how what are some of the strategies or techniques that you're using with your team. You know, I'm thinking about it to continue just to evolve in doing the I B J work. Because I feel like people think it's like a destination. So every time I do professional development, I always feel like people are asking me like, no, Margaret, it just needs you to give me this. And I'm just like, it's not that simple just to give you this, right? Because I just feel like the work is ever adaptive. And so I always tell people it's not a destination. It's a journey, right? So like, what are some of the things that you feel like you're using with your team to continue to evolve and grow and not become stagnant? We, well, I'll speak for myself. I like to say that, like, we're not the experts. Like, yes, we know what we're doing, but because it's always changing, you need to stay up to date on whatever it is in the moment. So for myself and my team, I'm consistently looking for opportunities again, because we don't have structured PD and PD is professional development. Do we say that? Oh, yeah, you know, here we go. Use an acronym. Thank you. But I'm consistently looking for opportunities for my team to continue learning. So I know we're sending our outreach coordinator to a conference. It's not September yet, but in September, mid-September. Tomorrow is September. That's like a national-wide, esteemed community engagement. Like, it is the thing that if you are in this field, you should be doing it. You should be doing it, yeah. Same with our ADA coordinator, you know, different symposiums, trainings. He's probably been training once a month because, again, things, laws, best practices. Best practices don't always reflect the laws. Usually, laws are the bare minimum. So we're looking for ways to make sure that we're doing the best that we can. Anything for me, if it's talking to other folks in the field within municipalities, but also in other organizations, just because, like, it's a little different in government because there is a lot of red tape, but I get creative. You get very creative. I figure things out and, you know, you get very creative in this position. Yeah, so just constantly talking to people and just staying in conversation because you don't have all the answers you learn from others. So that's what we're doing. We're just constantly reading, looking up articles, making sure we're double-checking things before we share it and always open to feedback, advice, suggestions because we don't have the answers. And the other biggest thing I think now that we have someone who's leading community engagement is doing more of that engagement work because, again, we're providing services and programs and what have you to our community, but if we're not talking to them or hearing from them, then what are we doing it for? Yeah, the family engagement and community engagement is really important because now we have a new family engagement and communications director, which is really important for us to have. One of the things that I wanted to kind of go back to and for us to talk about is you and I, we meet regularly. Let's talk about why. Why what? Why do we meet? Why do we meet? Yes, I know why we meet. You were kind of like, wait, what's going on, right? No, let's tell the audience why we feel like it's important for you and I to meet. So you and I meet every other week, just you and I. And then we meet also with Chief Flattery once a month. So let's really talk about why. Because some people are like, you're meeting with her? Yeah. And I think it's more like, do I have to give you an understanding of collaboration, right? So I work for Arlington Public Schools, but Arlington Public Schools is part of the town. So yeah, we're working essentially for the same people, the same community, your students, their parents, their parents, most of them are probably on my boards and commissions. We're working with the same individuals and so we have to make sure that the work is aligned, that we're sending the same messages if there's an issue that you're dealing with or a success that we know and we can share that with you and vice versa, whatever's happening on, I guess, the government side, schools should know it's, we're all, I think used to not working in unison, but that needs to change just for everyone's benefit. But yeah, checking in and we do a lot of programming together as well. So that makes sense in terms of why we have to meet to plan. Yeah, we have to meet to plan. And then the other thing that we work closely with is the Human Rights Commission, right? So if anything happens within the schools, we do have a protocol to ensure that you all get that information and then we also talk about how we work together to do some professional development for, so we talked about this, we do education for our students and we need to do some education around our families because we also had this conversation about to have that kind of work go hand in hand with one another. So yeah, so those are some of the reasons why we meet. We've done the equity audit, we've shared equity audits, we shared our experiences with that and yeah, so I just wanted to say that because I think sometimes they're like, okay, you all meet because for what, okay. What have been some of the difficulties that you have faced within the last four years of being in this position? Boundaries and time management. This job, I feel like I've probably said it before, but this job just doesn't get left at the office. It doesn't. It's constant. You mean you don't have a nine to five?  I mean, just in terms of even scheduling, like again, working with commissions, those are things that are after hours into the night, but the work is also personal. Like you have your identity and when things happen in the world or the next town over or anywhere really, it impacts pretty much everything that we do. So even though you're not on, I'm always on. That's probably been the biggest challenge, just I think the timing also of when I started. Because you started before the pandemics hit, right? Yeah, a few months before. And so with that and then with just 2020 in general, with the racial reawakening. Yep, it was reawakening. Yeah. I think we went back to sleep, but. Right. That part. I think that was the expectation, because everyone was on. And then as things settled, folks kind of got back to maybe their norm. But there's unfortunately something not great happening every day that has some type of impact. So yeah, I'd say that's been the biggest challenges that I don't really get to separate myself from the job. Right. Yeah. As I told you, I have a new specialist and she's like, so what do you do lunch? Lunch? I don't even know how to answer this question. I have to say to her, so you just have to be like, Margaret, we need to possibly be like, okay. But I think people don't understand it's like, especially once you get in, right?  There's a challenge and so the neat schedule, we don't have a neat schedule at all, right? And so you and I did the civics project with the eighth graders last year and remember they acted. They were typical day. It was a typical day. And remember you and I looked at each other and started laughing because we were like, typical day. I wish I knew.  It's everyone else's. Yeah. Yeah. That's really the sad part about it. What? I feel like that just got really depressing. Sorry.  Still. Right. And I'm part of Association Amcell. It's the Association of School Equity Leaders, which are all DEI directors. Can we talk a little bit about why those organizations or associations are so important for us? Yeah. I think people need to hear that piece. Yeah, for sure. So yeah, I started with, at the time, the DEI director for Beverly, Abu Tapin, who left. But we started the Mast EI Coalition. So just a space for folks who have our job, essentially, for our town or city. That was February 2021. I've gone through two and a half co-chairs, so I'm still the chair because that turn over for these roles is usually 12 to 18 months, so I'm sticking it out. But we started just meeting once a month, and then very quickly it was realized that it needed to be more frequent. So we meet still every first and third Friday at 12, which is after hours because town. And at 12. And at 12. So. We need to state that. So yeah, I mean, we meet virtually, but we've been building in quarterly, in-person sessions as well that are usually longer. They're usually like three or four hours because it's an opportunity. So roll it back. These roles, again, are typically singular. Usually someone gets hired, unfortunately, in response to something unless there's been a lot of thought and care put behind it, which I get. I'm very grateful. Oops. Sorry, Mike. I'm very grateful that I have the leadership and support of the folks here in town because that's made a huge difference in the success of my role and division, whereas other places, it's really, it's not that. But because these roles are usually a one person show, there's no guidance. There's no department at the state level that is giving you things you need to follow, procedures, policies, anything like that. So we don't have anywhere to look. You can look to other industries like higher ed and the private sector, but things obviously function very different for government. It's just, it's different. So yeah, we started the group basically to be a place for support, for shared learning, dealing with challenges that come up. And most recently, I'd say we've been really looking at how to share what we do with municipalities as a whole. So a year ago, I think it was a year ago, a year and a half maybe, I don't remember. We completed a municipal guide, the DEI municipal guide, which we, a small subgroup of us created to help guide municipalities. If they're thinking about hiring someone, if they have someone, within that guide or tips to check on them, make sure that you're providing them with the right things to be successful, explaining that the work isn't just one person, but it needs to be baked into everyone's responsibility. So we created the guide, we're actually starting to do an updated version, but yeah, we meet because it's really the support network that's needed that doesn't exist elsewhere. Most, not most, but a number of other fields have that. I know there's like the planners of color group, there's different public health groups. So and that's a piece I don't think I stated that many of the people who are in these roles are usually people of color and typically also in predominantly white spaces. So it's just another space where you can comfortably be in community with folks who are experiencing something similar and help you work through some of the challenges and kind of celebrate the successes. Yeah, that's exactly the same for us on the Association Amcel, which got their official launch in June. It's the same exact thing, a space for us, a space of learning, a space of we always bring a problem, somebody can bring a problem practice. You have a lot of thought partners in a room when you're like, like you said, sometimes you are the only one and everybody comes to you. So it's really great to be able to have a space. We meet once a month, but it's great to have that space and we meet all day. So to just kind of be like there are other people in the room that understand what I do and I don't have to explain it. So Joe, we're almost out of time. Can you and you know, I always love talking to you. Can you give me a maybe one word or a song, a song, like a song that you're listening to one word or a song that that is something that is really your anchor, your like your encouragement or, you know, or is it a picture? Is it a favorite book? I just don't want to limit limited to what I said is one word or a song. But what does that go to for you that encourages you? It brings you joy. It re-centers you. That's a deep question. And I have like different categories in my head. So it's not one thing. OK. Can I just say them all? Yeah. OK. Pretty much almost every day, even if it's just in the background, I usually have the office or parks and rec on. OK. And it just like gives me life because it's so absurd.  So it's just funny. And it reminds me that, like, even though it feels like we're kind of always dealing with like chaos or what not, that it's just life. Another thing which just recently, actually, yesterday.   Live every day like it's your last. And it just really hit me. So that's what I'm sticking with. Yeah. Yeah. It's interesting because I shared we were in. We, you know, our convocation was yesterday. We did a flash mob. I heard. Oh, it was amazing. And so it was in a department meeting yesterday. And I was just sharing that I just want to be more in the moment and intentional, like just like how you and I are right here. I just want to be in this moment and not think like, what's the next moment? But just pause and be right here. And I don't think we do a lot of that. And so I'm going to try really, really hard to just be in each moment that I'm in. And then the other thing is, and when I said what word is, I'm really trying to be intentional of giving people grace. Because I just don't, you know, you just don't know what's going on in the background of someone's life. And so sometimes we need to pause and just give and just think about you're not just Jill D. I. Director. You all you have other intersectionalities. You're somebody's daughter, right?  And I just think about when I touched you the other day and you and I have a good relationship and you were just like, yeah, no. That was like, wait, what's going on? And it was that pause moment. That we both had to do and kind of be like, and I was like, what do you need? I'm going to step in and we've done that for each other. And I think we need to like share that out of you see us as, you know, in our roles. But remember, there's some background noise that's going on for us, whether that's family, friends or whatever. And to remember that if we do drop a ball, if we do not get it right to remember to give us grace because there are some background noise for us also, as for everybody else. Yep, absolutely. Jill, thank you so much for being back here. Thanks for having me. DEI Matters Conversations with Margaret Credo-Thomas. We are thankful that you all could come back again and join us for these conversations, and we hope that you will come back.


Today, we're eating 100 years of McDonald's, 100 years of prison food, and 100 years of airplane food. Be sure to watch until the end because you won't believe some of the food we're about to try, and some exclusive food from the future that hasn't come out yet. Starting with airplane food. The first ever meal served on a plane was in 1921, and it included a cold ham and cheese sandwich, a hard-boiled egg, fruit, and water. Also, a plane ticket in the 1920s cost only $5. It's time to taste it. How is it? How is it? Wow. It's good, but why is everything cold? Because this is before they served warm foods since there was no working outlets on planes, and no way to use a microwave. There were also no seat belts back then, so if you hit turbulence, this is probably what happened. Let's just move on to the 1940s, where flight attendants were first introduced to make flying more comfortable. Here you go. A sky mill in the 1940s was a filet mignon, green beans, brownies, and sparkling cider.  And not only was the service better, planes themselves became way more comfortable, and you could even nap on them, too. Oh, Shawn, here comes the airplane. You're so funny. What do I have to do to join the mouth? Just follow me to my cockpit. Sir, the flight landed. Is everything okay? In the 1960s, airline food got even fancier. That's because flying was more and more common. A typical meal was lobster, poached salmon, fruit pie, salad, and red wine. I call it a salmon. What? You need to eat the salad, not the salmon. Mmm. Whoa. It's tasty. You know what else is tasty? You guys.  Fruit pie, lobster. Wow. You know, before this, flying was only for the rich. However, this did cause problems because the rich didn't like flying with the poor. This is literally the best airplane food I've ever had. We'll get ready for the 1980s because food quality significantly dropped. The average meal served was spaghetti, mixed veggies, a cookie, and coke. Mmm. Okay. The spaghetti's bad. Well, that's because the airline cut costs and the food got a lot worse. This was also the decade that in-flight entertainment became introduced. Did somebody say entertainment? This is the entertainment we're talking about. Oh. Yeah, I was. Wait, don't stop. You wanted Caleb to continue stripping? Hey. Oh. Also, during this decade, at the end of every flight, you could visit the cockpit and meet the pilot. Hey, captain. So what made you want to become a pilot? To overcome my fear. A pirate? No. Dying alone. Let's move on to the next decade. In the 2000s, they served nothing. But most people would bring their own food on board, or they had to be okay with the snacks that the airline served, which was pretzels. Hey. Hey, you forgot our drinks. Oh, thank God. Yeah. Can we get something to drink? Yeah. Coffee or tea? Coffee. Wrong. It's tea.  Ladies, hit my line. Baby, I've been thinking a lot lately. Can't let it go to waste, right? Whoa. That's hot.  Actually, the pilot requested to see you. Really? Yeah, he insisted. I feel honored. Thanks, Captain. This is so cool. Is this the joystick you used to fly the plane? Nah. But I'm going to use my joints to fly you. Yeah. Let's get out of here before the plane goes down. In the 2020s, planes became even more accessible to everyone thanks to ultra-budget airlines, which then they served you chicken pasta, salad, and bread. We're not going to eat that. We're going to eat the meal you get when you buy a first-class ticket, which would be golden flake steak, golden brownies, salad, pastries, and a cheesecake. Oh. Here we go. You know what?  This is so delicate. Golden flake steak. You're eating gold? Oh. I feel like I'm about to poop out dollar bills after this.  Wow. Wow. Mm-hmm.  Honestly, this is the best food I've ever had. You've had a lot of food. And after trying all the meals airplanes have to offer, it was finally time to try 100 years of prison food.  So cute. Alex, they got you, too. We always said we were going to finish each other's sentences when we were younger. Hey, this looks good. Let's dig in. You guys got a visitor. Al Capone? What'd you get arrested for? Staying up too late. But that's not illegal. They said I was resisting arrest. No, but seriously, first-degree murder. You know, in the 1920s, they considered lobsters bonds of the sea and only fed them to prisoners.  Time to eat. Ah. You know what? I actually don't want to eat this. I don't. We're nasty. That's it.  You're going to come with me. Wait. Where are you taking us? To the 1940s, where things in prison got a lot worse. So in the 1940s, an average prison meal was corned beef, chowder, salad, and milk. Eat up. It tastes like soft beef jerky. I'll be the judge of that. Oh, wait. It does taste a little bit like beef jerky. You guys are eating good. We're about to eat good. This is my favorite so far. That was bad.  You know, that's almost as bad as prisoners were treated in the 1940s. Yeah. A lot of people at the time became prison guards just so they could beat the prisoners. Which is why sometimes we did this. What's going on here? Get out. Get out. Get out. Now it's time for a taste of your own medicine. Our next prison meal is from the 1960s and it consisted of boiled chicken drumsticks with no seasoning, plain white rice, mashed potatoes, some juice, and overcooked vegetables. And before we dig in, we have Charles Manson here who's going to eat this meal with us. Wait, what did you go to jail for? I'm a lady killer. Oh. No, like literally. Shall we feast? You know what? If I'm eating chicken, it has to be fried. You sound like Alex. Wait, where is Alex? You're going nowhere. I got you for three minutes. Three minutes of me time. And now we're joined by Jeffrey Dahmer. Hey guys. In the 1990s, they served Nutrilo, canned fruits, beans, and toast. You want to try the fruit?  It tastes like a granola bar with meat inside.  But you're probably more tasty. Hey, I'm choking. I'm actually choking. Bring some of this fruit juice. I think I'm just going to move on to the next decade. Yeah, me too. Where am I? Relax. We're just going to hang out, watch a movie, and take some pictures. What? I'm just kidding. So now we're in 2024. Yeah. Anyways, for modern day prison meals, inmates eat hamburgers, salad, water, and a baked potato.  Bonk? I thought you'd be out of here by now. Prison is not so bad when you find your soulmate. Get over here, honey. Tana? Is that you?  Hey, hey. Guess what?  No, you're not. Yes!  Honey, I'm sorry. Honey, I'm sorry. You're so clearly forcing Tana into this relationship. Tana, am I forcing you to do anything? Yes. Shit. That's the good. What are you waiting for? Oh my god, your teeth could fall out eating that. That's how hard you can get in prison. If you know what I mean. And what about for dessert? For dessert, you had honey bun. Well, you got to pay for the honey bun. With your honey bun. And look back today, it's your honey bun. After barely escaping jail, it was finally time to move on to 100 years of McDonald's. Behind me is the world's oldest McDonald's that still serves the original menu from the 1950s. The first meal ever served in the 1950s was the original deluxe burger, shoe shrink french fries and a classic vanilla shake. Can't wait to see how this tastes. I'm going to try the hamburger. How is it? Wow. And this is actually how the menu looked at the time. And the burger only cost 25 cents and only 50 cents for the entire meal. But did you know that in the 1950s, Ronald McDonald was at the mascot. It was actually this guy, Speedy. They called it Speedy to symbolize how fast and efficient the food service was. It's finally time to try the milkshake. How is it? Dude, this is good. But let's see how the food looked like in the 1960s. This was also the decade when Ronald McDonald was introduced as the mascot. So in the 1960s they served hula burger, filet-o-fish, Big Mac and cherry pie. That's a lot of food. By the way, this was also the year when the Big Mac was first introduced. Now we're going to try the hula burger and the filet-o-fish. It's just pineapples inside. Wait, what? There's no meat? Why the hell would they put just pineapples and sell that? Of course that would get discontinued. Are you going to finish that? No, you can actually have it. And the reason why we don't have our drinks yet is because Sonic was introduced in the 1960s and McDonald's had their workers also deliver their drinks and rollerblades to compete with Sonic. Oh, there's our drinks right now! I'll get you another drink right now. I'm so sorry. And that's why they stopped doing that. But now it's time to move on to the 1970s. I'm a Uber driver. Get in. I didn't order an Uber. We've got one now. I'm good, bro. Thanks. Oh, my.   Oh, wait, wait, wait. Want to help me look for my dog? I need your help. In the 1970s, the McPizza was first introduced. The chopped beef steak sandwich with fries, fried and beef oil, and the triple ripple ice cream. Ooh, I never knew McDonald's served pizza. All of this was only a dollar and 25 cents, by the way. Oh, no way. Mmm. It got discontinued because the pizza took way too long to make and it didn't fit the fast food theme. Yeah, because I would want my food now. But also during this decade, they introduced the first half-email. All right, we're going to show you guys what's inside. The first toy that McDonald's introduced. Wait, is that grimace from the Grimace Shake? Yes. Wait, why did it get discontinued? Because it would say inappropriate things like this. Whoa! Wait. Whoa, hey, hey. Okay, that was too much. Yeah. Let's see what was on the menu in the 1980s. In the 1980s, McDonald's introduced the McSpaghetti, McDLT, onion nuggets, and coffee all for the first time.  I think it's a burger you're supposed to put together yourself. Ooh, that crunch. All of this was only $2.50. Wow, that's a pretty good deal. I'm going to try the McSpaghetti. And I'm going to try the onion bites. Oh my God, it's spaghetti from McDonald's. It's the best thing ever. Mine wasn't bad, but I just feel like these things shouldn't belong on a McDonald's menu. Yeah, I agree. But did you guys know that McDonald's actually has a toy that's permanently banned? It's a McDonald's brass knuckle. Ooh, I know what to do with this. This is what I call a McBeatdown. A McBeatdown with an S tier. Ladies, there's plenty more where that came from. In the 1990s, McDonald's introduced a seafood menu for the first time. The McLobster, McSoup, and the McLean lettuce wrap to reduce fat.  Get to know my face. I'll just eat it. Okay. Ooh. This is good. Now it's time to try the McLobster. Ooh, is it healthy? Yeah. I'll get that out of my face now. This continued to make lobster because it was actually too expensive. Wait, where's our drink? The McSoup. No, it's not.  Oh, wait. Oh, my God. It tastes like carrot soup. I'm not trying that. Let's go ahead and move on to the next decade. In the 2000s, McDonald's introduced super-sized options for the first time. You can super-size anything you ordered to make it extra, extra big, like these fries, this burger, and this sprite. They also serve wings for the first time as well. Oh, no wings. Oh, this super-sized burger looks McTasty. Whoa, you're already super-sized. Unless you want to get McFatter. You don't have much room to talk. McTanner. All right, we're going to try the McWings. Oh, I remember this. Do you like it? Good. It is good. They should bring this back. I'm going to take this to go. Oh, okay. Mine ever. I feel like we're missing something for the fries. Super-sized ketchup. That is the biggest bottle of ketchup I've ever seen. Oh, hey. Dude, you're missing some food. A little bit more. It's still my more at the bottom usually. Okay, let's move on to the next decade. In 2010s, the McRib became very popular, along with Fish McBites, McNuggets, and the McFlurry. What's the Fish McBite? If I bite into a chicken nugget and there's fish in it, someone's about to catch these McHands. And that's a McPromise. It's not too bad. It's like biting into a chocolate chip cookie and there's raisins inside instead. Now we're going to try the McRib. Oh, my mom. Dude, that's the best thing I'll taste it ever. Really? Yeah. Let me have a bite. Calm down. Calm down. Yeah, we got to stop doing these food videos. Let's move on to the next decade. And now for the McDonald's meal from the future. Wait, where is it? It's being delivered right now. How? Look up. It's a McDonald's delivery drum that they're going to have in the future. No way! Oh, and in case you guys are wondering what happened to Tanner, this is him in the future. Yeah, I really let myself go. You guys are not going to believe what we have. Check it out. None of these have even been released yet, but we have the shrimp burger, the teriyaki burger, the spicy McRisby sandwich, the black angus bean, and the grimace ice cream. Let's try the teriyaki burger first. Look at that meat. All right, let's take a bite at the same time. Oh, my God. That's so good. Probably my favorite. I'm not going to lie. Now we're going to try the spicy McRisby sandwich. That was a good chicken. Now let's try the black angus bean. Holy crap! Amazing! This is wokoo beef. All right, now I'm going to try the shrimp burger. And now we're going to try the grimace ice cream. That's good. What is it? What flavor is that? What is this? Oh, no. Oh, my God. Not only does it taste good, but it also tastes healthy. Now we have to decide which decade was our favorite. Definitely this one. Yeah, 100% is a lot. 100% this. Subscribe if this was your favorite meal, but comment down below if you liked a different decade. Turner. Turner, you with? No.


We are going to make these multinational companies invest the millions of dollars that are necessary to repair your and every other community across Kentucky. Again, I'm not looking at punishment. I'm seeking responsibility, but I'm going to suit every single one of them until they take that responsibility.