The Bioinformatics CRO Podcast

Transcript of Episode 88: Ben Langmead

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO Podcast. Today, we’re joined by Dr. Ben Langmead. Ben is a professor of computer science at Johns Hopkins University, where he directs the Langmead Lab with a joint appointment in biostatistics at the Bloomberg School of Public Health. His group works at the intersection of computational genomics, sequence alignment, text indexing, statistics, and high-performance computing, with a focus on open source tools and resources that help life scientists use high throughput sequencing data. Many listeners will know tools and resources associated with Ben and his group, including Bowtie, Bowtie 2, recount3, and Snaptron. He is also founder and principal of InOrder Labs and has made a large collection of teaching materials freely available online. Ben, welcome to the podcast

Ben Langmead: Thank you for having me. It’s an honor, Grant

Grant Belgard: So for listeners who know your name but not your day-to-day, what are the biggest problems taking up most of your attention right now?

Ben Langmead: We always like to work on problems where there is a hole in genomics everyday practice that can be filled with something from computer science. So in the last, six, seven years, I would say that the main thing that we’ve been working on is pangenomics. So we’re in this new era where long read sequencing is much less expensive than it used to be, the reads are higher quality than they used to be.

Ben Langmead: And so we can basically make our dreams come true in terms of assembling many new genomes. And we have our new telomere to telomere human assembly, and we have the Human Pangenome Consortium producing high quality human genome assemblies in bundles of hundreds at this point. And so this is just one of the many ways in which in genomics, we would love to move in this direction of more should be better, right?

Ben Langmead: More is better, right? But the truth is usually the more comes first and then the better maybe comes later if we really work at it. And when it comes to pangenomics, I think we’re still working at trying to make a typical everyday bioinformatics workflow truly benefit from the existence of all these very high quality reference genomes.

Ben Langmead: And to make that happen, there’s been a really superb, fun interplay between computer science and genomics, right? We needed more when it came to we need better indexing approaches, we need better algorithms, and we need it to not cost much more than it used to cost us to analyze data with respect to a single reference genome.

Ben Langmead: And computer science has been providing those missing pieces, and in fact, we now have multiple choices for where in the computer science theory literature we can turn to find new solutions to these problems. And so we’ve had a lot of fun since roughly 2019 going down multiple roads, but mainly down this road of compressed full text indexing.

Ben Langmead: How do we fit pangenomes in efficient compressed full text indexes? And then how do we let that be the basis for the next generation of tools, whatever’s coming after the current generation, but that can allow people to work seamlessly with huge pangenome collections.

Grant Belgard: How do you explain your work differently to a computer scientist, a biologist, and a clinician?

Ben Langmead: Yeah. So it’s funny because a lot of the work I do, it’s definitely more applied than what a typical theoretical computer scientist would work on. And in fact, theoretical computer scientists look at me and they see someone that’s almost like a biologist.

Ben Langmead: And which a biologist would chuckle at, right? I’m not even remotely a biologist. But to people doing very theoretical computer science, the fact that I understand the domain and understand the application of these things makes me a little bit more foreign.

Ben Langmead: And so I do, when I’m speaking to computer scientists, I tend to speak in terms of these are the open problems, and I try to frame it like it is a computational problem. The open problem is we need to be able to build a full text index, but it has to be not much bigger than the full text index for a single genome, blah, blah, blah.

Ben Langmead: And then for for people all the way on the other side in biomedicine, what I really want them to appreciate about the work we’re doing is that it should be freeing for them. It should make their lives easier. It should reduce friction. It should address the things that are bothering them about the computational work they’ve done in the past, where they say, “Oh my gosh, we’re doing all of this with respect to a single linear reference.

Ben Langmead: I know that I missed the– I know that there are entire genes that are not present in that reference. I know that there are rare alleles that are present in that reference, and yet I’m making all my arguments based on that reference. What, this is bothering me.” And so what I want the people in biomedicine to understand is that all the ways in which you feel like there’s a closed door it could be opened if only, we had an interplay and talked about what really would you prefer to do?

Ben Langmead: You mentioned at the top, our work on I’ve been talking about pangenomics, but you mentioned recount3 and Snaptron. That’s one of my favorite stories because recount3 and Snaptron, those are large scale summaries and indexes built over public RNA sequencing data sets. So these data sets, the raw data is already out there in the public.

Ben Langmead: It’s in the sequence read archive and in other resources like that. And we made summaries of that data available because it’s much easier to use than the raw data. And then we built indexes over it in the hope that we’re producing something like Google, but for archived RNA sequencing data.

Ben Langmead: And when we started publishing papers I heard from one of my colleagues at Hopkins Jonathan Ling, who wanted a meeting, and when we met, he said, “We love your paper, but it’s not what we wanted. What we wanted was this.” And so Jonathan did me the favor of making the link. He saw what we were doing, but he also saw it wasn’t what he wanted.

Ben Langmead: So he got in touch and told us what would be the missing pieces that would take our work all the way to where he was working. And so I think when the system works well there are people in each of these silos, right? The super theoretical computer science, the in-between computational genomicists like me who are maybe like glorified brokers, and then people on the biomedical research side, all understanding just enough of the other people’s language that they can phrase their problem.

Ben Langmead: And so though when I interact with people in biomedicine, I feel like my job is to try to make them understand which are the doors they thought were closed or like which are the problems they thought were too hard that if only we could solve a problem in computational genomics, they would suddenly be open and that can be more creative, right?

Ben Langmead: You can go back, ” tell me more about how you even framed your problem in the first place. Did you do, did you look at the public data right away or did you wait to look at the public data until the end to validate what you were trying to do?” So absolutely we all have different languages and we all talk to each other.

Ben Langmead: We all talk to each other in our own language, but we do especially well when we learn a little bit of each other’s language. And right now I would say those three pieces are talking to each other pretty well and I think better than when I first started in the field, which was when second generation sequencing was still young.

Grant Belgard: What has changed most about the kinds of questions that people ask of sequencing data since you started in the field?

Ben Langmead: Yeah. When I first started, second gen was really– we were really just starting to see data. So when I started grad school in 2007, and I think some of the people I was working with at University of Maryland, they had just seen for the first time a FASTQ file from Solexa or a FASTA file or something like that full of reads.

Ben Langmead: And it was just starting to sink in that they truly could not make progress with this new data type unless there were new advances in computer science. There was no hope of putting these things in BLAST. It just wasn’t gonna work. And so the problem at that point was it was almost existential.

Ben Langmead: It was how do we even make progress with this data type? But there was very fast progress. Computer scientists and others did snap to attention, and suddenly we had MAQ and we had Eland, and then there was BWA and Bowtie, and suddenly we were off to the races and the problems moved downstream, right?

Ben Langmead: So the next question was, “Oh maybe we can use sequencers to study other things. We can sequence RNAs.” And so the “what do we even do with sequencing data” sequence of questions have been pretty well answered, and we do have pretty good algorithms. We even have, what you might call like best practices and best practice workflows, and we have benchmarks, and we have comparison papers and bake-offs.

Ben Langmead: And so there’s been a lot of, I think, flourishing of improved tooling and an improved understanding of what it is you’re supposed to do when you first start with a sequencing data set. But now I’d say we’re changing our attitudes in a couple of different ways. So first of all, I think that we could change our mind about what we even use sequencing to do versus what we use sequencing data that’s already out there to do, right?

Ben Langmead: In the same way that if we start a new project today, or if not even a science project, if we’re doing a renovation to our house or we’re doing s- we’re doing something difficult today, we probably need to start to investigate things like who are the best vendors and what are the tax implications, right?

Ben Langmead: And we Google a thousand things or we ask ChatGPT a thousand things. Similarly in science, there is all this archived data, we could start there. It’s not typical, but we could, right? So in other words, even upstream of deciding what it is you want to sequence, you could decide I want to narrow my– I want to form my hypothesis or I want to narrow my hypothesis by looking out in the public data world, and we have, many petabases of of public data in the sequence, Sequence Read Archive, for example, and letting that be a a wellspring for new hypotheses or narrowing our hypotheses.

Ben Langmead: That is, I think, still a little bit of an undiscovered country, right? We, w- it’s still the case that the Sequence Read Archive is a little bit un– not, I won’t say unused, but it’s a quiet place, right? It’s not some-something that people are querying every day and learning from every day, even though we’re paying good money to have all that data out there.

Ben Langmead: Then the other way in which I think attention is shifting is that in order to build those pipelines, we made some bargains. We decided we were okay with certain trades, and I think we’re revisiting some of those now. And so all the work in pangenomics, in a sense, is really about addressing one of those maybe not so good bargains we made at the outset, which was to use a single linear reference, and that gets us very far.

Ben Langmead: The single linear reference is really, has been good to us in many ways, right? It defines a very easy to understand coordinate system. Anything you might wanna refer to is a nice, simple 1D interval. You and your friend can refer to the same interval of the genome in exactly the same language and no one’s confused.

Ben Langmead: But on the other hand, it brought us reference bias, which we’ve been living with for, a decade and a half now, where when we use a single linear reference, there are places on that single linear reference that are not a good reference for the individual we’re studying. They’re just not similar enough to the individual we’re studying, and it creates confusion from then on, and there’s often not really a way to recover from that confusion later.

Ben Langmead: There can be in some cases, but… And so I think that’s another thing that we’re turning our attention to. We built wonderful tooling, but it was built on some trades, and now that we wanna address those trades, it does require a bit of uprooting of how we did things before and a bit of disturbance so that we can adapt.

Grant Belgard: How would you explain sequence alignment to someone who uses aligners but has never thought about how they work?

Ben Langmead: Yeah that’s funny. I remember after Bowtie was out, I went to a conference, and at the conference was my colleague from Penn State, Anton Nekrutenko, who was one of the founders of the Galaxy Project. And he was saying, he basically asked me that same question because he said, “People are asking me what is the tool that is the fastq to SAM converter?

Ben Langmead: And I keep telling them it’s Bowtie, but it’s not really a converter, right? It actually has an algorithm in it.” Yeah. So I do explain this in the classroom once a year every year. And we start with the basics of, why it is that the sequencer emits fragments.

Ben Langmead: Why wouldn’t the sequencer just give us what we want, which is the complete picture from beginning to end with no mistakes? And, my analogy is it can’t do that. It’d be like you can read, but if I gave you a book and told you to read the whole book aloud from beginning to end without ever stopping, you wouldn’t be able to do it, right?

Ben Langmead: You would have a coughing fit, or you would have to go to the bathroom or something like that, right? So similarly, sequencers can’t do that, which means we’re inevitably faced with this problem of having fragments that have to be pieced together into the completed picture. And we don’t have to do this with alignment.

Ben Langmead: We can also do it with assembly, and so there’s… So I need to explain the difference between those two things, and then explaining alignment is it’s a bit like putting together a jigsaw puzzle, but where you can see a picture of the completed puzzle. And so you can hold the pieces up to the puzzle, and you can figure out how to piece it together that way.

Ben Langmead: And all of that is all well and good, but it doesn’t tell you what any of the algorithms are. So then we move into how do you do exact matching? And that’s, many decades of computer science worth of stuff we can talk about, and including some fantastic algorithms that we don’t generally use in genomics but that are used elsewhere on your computer in your day-to-day computer use.

Ben Langmead: And then we say, “Ah, but there can be differences because of sequencing errors and genetic differences.” And so then we go into approximate matching and then all our, beloved bio sequence analysis tools like Smith-Waterman and Needleman-Wunsch and such. So I guess I usually explain it through the storytelling of what are the problems we need to solve, and then why do we need this new kind of algorithm to solve these different problems?

Grant Belgard: Why does indexing matter so much in genomics?

Ben Langmead: Yeah. So indexing was in some sense the answer to the question earlier of how do we even… So like when the Solexa data was coming over the wall and people could see how voluminous it was a lot of the existing methods BLAST included, were all about scanning the reference database looking for good matches for the query, right?

Ben Langmead: You paste your query sequence into the box, and BLAST will then go and it does this in a fast and distributed way, but it’s actually literally scanning reference sequences in order to answer your question. And I think once Solexa came along, we outgrew that strategy because we had so many queries, it didn’t make sense to, for each of them, go scan the reference.

Ben Langmead: The analogy I like to use is your search engine, Google. When you put a query into Google, it is not literally scanning all those web pages while you wait and then giving you an answer because that would not be as instantaneous as you need it to be. It’s using an index, right? So indexing comes into play when you have one reference that you know you’re gonna use again and again.

Ben Langmead: I’m gonna go searching for a needle in this particular haystack over and over again. And not only that, but I’m gonna search for many needles in that haystack. When you get to that point, then it becomes a, a question of amortizing. In computer science we like to amortize effort.

Ben Langmead: So if we’re gonna put in effort ahead of time to build a data structure to help us solve this problem, it makes much more sense to build it over the haystack. And so building that data structure to help us search inside a very large collection of text that’s what we call text indexing.

Ben Langmead: And the name index is a good one because it’s a good analogy. The index of a book is a perfectly good analogy for what it is we’re doing, right? We’re building something that has pointers into the huge the vast amorphous data set that we’re trying to index. It has pointers into it that help us jump to exactly where we need to go at query time.

Ben Langmead: And so text indexing has, in computer science, has been around for a very long time. There’s a wonderful sort of parade of data structures that came out over time, and genomics has absolutely been on the bandwagon of following and adopting each of these as they became popular. So suffix trees and suffix arrays were already very popular when I started graduate school, and then through the work of others and through my work in graduate school, these more, I won’t say modern but very frequent, very frequently used today this very frequently used idea of the Burrows-Wheeler transform became the one that really was useful because it was the one that let everything fit in memory on a typical computer at least at the time.

Ben Langmead: And these days now the trick is trying to use some of those same tools to get entire pangenome indexes to fit in memory on a typical computer.

Grant Belgard: How has your thinking about alignment changed as sequencing technologies have changed?

Ben Langmead: Well, sequencing technology, the way it has changed it didn’t have to do this, but it did in fact reveal new problems to solve as it went. I should say yes and no, because sometimes it reveals new problems and sometimes old problems become current again. So for example, long-read sequencing produced reads that were more amenable to the kinds of alignment algorithms that had already been developed for Sanger sequencing.

Ben Langmead: And therefore part of the need from the computational genomics community was let’s rediscover and dust off some of those older algorithms that we had for longer reads. So sometimes it points us to something we’ve already done, but sometimes it shows us that something entirely new is required.

Ben Langmead: And so these days, in a way, the advent of long reads has changed everything we want to do, whether it’s with short reads or with long reads when it comes to alignment, because the long reads gave us this abundance of new references. And the trick is: how do we use them? How do we use them in a way that truly benefits us, but for not much more cost?

Ben Langmead: And so that is a truly new… It’s in one sense, a truly new way of viewing the problem that we have to solve. But the good news is it actually calls on some of the exact same methods that we were already using, right? So like the Burrows-Wheeler transform, which I mentioned before, this is like the core.

Ben Langmead: If you’ve used BWA or Bowtie, the Burrows-Wheeler transform is at the core of those tools. It’s at the core of some other genomics tools as well. It has many variants like the positional BWT and things like this. But the BWT was originally invented not for indexing, it was invented for compression. So it was invented to basically compress files on your computer, like Zip does.

Ben Langmead: Which is a fortunate thing because this problem of indexing pangenomes, it requires compression, right? One thing that won’t work is just building the same old indexes over each of the sequences in your pangenome. Let’s say your pangenome consists of hundreds of human haplotypes, because that’s just gonna grow proportionally to the length of all those haplotypes, and you will very quickly exhaust the memory of the computer you’re using.

Ben Langmead: So it turns out, happily, that the exact tool that we’ve been using since, 2009-ish for our, to solve alignment problems, the Burrows-Wheeler transform, was invented for something like this in the first place. Compressed representations. So the latest revolution is really about compressed indexing and being able to efficiently query compressed indexes, so where the index itself is not much bigger than a fully de-duplicated version of the text that it’s indexing

Grant Belgard: Where do pangenome approaches feel mature and where are they still experimental?

Ben Langmead: That’s a tough question to answer. So I think that there are some things that people are getting used to doing that involve using multiple references. But whether people are yet used to fully pangenome approaches, I don’t quite know, right? I think people still wonder this, right?

Ben Langmead: They still wonder “how do I do this in my day-to-day?” They know there’s such a thing as pangenomes and graph pangenomes, and there’s graph aligners, which are probably the most mature single thing we can point to. So things like VG and Giraffe, these are quite mature, so people can use these tools.

Ben Langmead: But what people do day-to-day still looks to me like thinking of genomes as individual things, but trying to use more of them. So instead of embracing the whole forest, it’s like just swinging from vine to vine or something like that. So let’s try to use this reference genome, and if it looks like the situation is breaking down in some situations, like it looks like some of my reads failed to align, but they did align to this other reference genome, that means that other reference genome is the one I need to study this particular thing.

Ben Langmead: Maybe therefore I’m gonna hop from this reference to that reference and, but, and do everything again, but with respect to that other reference. Which isn’t even a very bad way of doing things because you, it doesn’t take a large number of reference genomes before one of them is probably a pretty good reference for whatever it is you’re trying to study.

Ben Langmead: But it’s definitely not yet the case that we train people to use pangenomes instead of linear reference genomes. So that’s, I think, one thing that eventually needs to change, is it has to be part of our training. We have to learn what they are and what reference bias is, how to detect when we might be falling prey to reference bias, and then understanding how to use the pangenome to cure that.

Ben Langmead: But also there’s a lot of unanswered questions, right? So there’s actually just plain still more work to do in pangenomics. And, I think what a lot of biologists would say is, “It’s great that you have the pangenome for me to use, but how do I explain it? If I use it in my paper, how do I explain what I found?”

Ben Langmead: There’s a anxiety about the notion of pangenome coordinates. Like, how do I even describe where I am in a pangenome? And is it gonna make sense to somebody who reads my paper? Compounded with that is the fact that pangenome coordinates themselves are a little bit mythical, right?

Ben Langmead: It’s not necessarily true that a pangenome does or should have a global coordinate system because it’s not necessarily globally collinear. If you insist on understanding a pangenome in a global way, the global landmarks are gonna fall away more and more the more genomes you add, right?

Ben Langmead: Because some genome is gonna disagree about what’s next to what in the genome, and as soon as you add that one, you’ve knocked out part of the common coordinate system. So part of what we need to address is maybe we don’t need to insist on the pangenome coordinate system being truly global.

Ben Langmead: Maybe it’s something that we navigate in a way where it would be awesome if we could answer our questions in a fully zoomed out global way. But when that’s not possible, we should zoom in and say, “Okay I can provide a good coordinate system for what it is you’re studying, but only if I limit my attention from here to here and discard these sequences from the database.

Ben Langmead: Now I can give you a really clear, nice picture, maybe like a multiple alignment or something like that of that area.” But these tools are also missing, right? In fact, at this point, the pangenomes are getting to be so big it’s hard to even build a multiple alignment of them, right?

Ben Langmead: We already have pangenome data sets for which I doubt we’re ever gonna have a good, reliable multiple sequence alignment of those sequences, even though they are as related as humans, for example. So another obstacle is how do we both take advantage of the additional data that the pangenome’s giving us, but without going too far, right?

Ben Langmead: So more is better right up until the moment that it’s not, right? More is better right up until the pangenome stops being a good global coordinate system for what you’re trying to study. So we do need a way of saying, “More is better, but there are limits. And if you wanna, if you’re studying what you’re studying, we recommend this subset.

Ben Langmead: Or in this area, you should be using this subset. Or I see what you’re studying I’ve detected that these 80% of the references are perfectly good for what you’re trying to study, and these 20% are just gonna be confusing, so I’m gonna zoom in for you.” There’s still a lot of work, I think, to be done in that area.

Ben Langmead: So I don’t know if it’s even time yet to train people to learn about pangenomes. It’s certainly a good time to learn about reference bias and how you can avoid it by moving from linear reference to linear reference, and there are certainly some problems we can already solve with graph pangenomes and graph aligners.

Ben Langmead: But the future is still fairly uncharted, I would say, when it comes to how are people really going to switch to pangenome thinking, right? We’re still trying to wrap our heads around what pangenome thinking is, and therefore how we should, what tools we should build and how we should train people.

Grant Belgard: How do metagenomic data problems differ from human genome data problems computationally?

Ben Langmead: In some ways they’re quite similar. So for example, in metagenomics, maybe you’ve collected a sample from the human gut or from a soil sample or a surface that you’ve swabbed in a hospital or a waterway, and you are sequencing everything that is there. So you are getting sequencing reads derived from the DNA genomes of whatever was there.

Ben Langmead: And again your job is to put together a puzzle. So quite similar to if you had just sequenced one human. But the problem with this puzzle is you don’t necessarily have the picture of the completed puzzle for all these sequences, right? Because there’s a lot of organisms that you could collect in a metagenomic sample for which we just don’t have reference genomes and maybe don’t even have nearby.

Ben Langmead: Maybe we don’t even have cousins, we don’t have anything close enough in our reference databases. So that’s one immediate difference. Another difference is it’s quantitative in the sense that if a organism is there in a higher abundance, you’re gonna get more data from it that usually when we’re sequencing, say DNA from a human, we want to sequence equally from all parts of the genome and that’s quite possible.

Ben Langmead: But for a metagenomic sample, we usually have to deal with the fact that some organisms are much more abundant than others. That’s another additional problem But other than that, it actually looks similar, right? Because we usually analyze these metagenomic sequencing reads with respect to a reference database.

Ben Langmead: That reference database now is not just one species of reference genome, but it’s a whole collection of species of reference genomes. But again, one of our most valuable tools in this situation is indexing. So this again is a text indexing problem. It’s even, again, a compressed text indexing problem because if you think of what reference genomes we have for what clades in the tree of life, we have a huge abundance of reference genomes for some clades and then poorly sampled reference genomes for other clades.

Ben Langmead: But like things that are important to human health, things like COVID and SARS-CoV-2 and E. coli and things like this we have tons and tons of reference genomes in those clades. But then in other clades, maybe for organisms that are hard to culture or hard to isolate, we don’t have as many reference genomes.

Ben Langmead: So we have some parts of the tree of life where it is crucial that we do compressed indexing because we’re just not gonna be able to fit all those sequences in unless we do. And so indexing, compressed indexing, these are still the valid problems. We end up using, in practice, often an expedient here, which is to build a k-mer index.

Ben Langmead: So in other words, the sort of keys, if you think of the index of a book, right? The keys there are the key terms that you might wanna look up. In a k-mer index, the key terms are just substrings of length K, right? So those are the key terms that are held in our metagenomics index. So that’s, it has its pros and cons.

Ben Langmead: K- Once you do this, you have to pick the value of K, right? So you have to decide what is the true one and only substring length that’s gonna tell me what I need to know. And, almost everybody just picks a default, which is often 31. But it is far from obvious that’s what should be done.

Ben Langmead: It’s also far from obvious that’s what should be done everywhere in the tree of life, because again, some parts of the tree of life are very dense with sample genomes and some are very sparse. Some parts of the tree of life just have more divergence among the individuals in that clade, and others have less.

Ben Langmead: So we use these expedients, and that’s because it’s hard. It’s harder than, for example, the human pangenome, because the different clades don’t have sequence similarity across the clades, right? So the compressed indexing is only going to be beneficial within a clade. It’s not particularly beneficial across clades.

Ben Langmead: So we have to rely on these expedients. We can’t quite do anything quite as comprehensive as a full text index. At least not usually, though some tools do this, and I think the future probably is for tools that do this. So it is in some ways a harder computational problem, but it is a similar one

Grant Belgard: What makes a bioinformatics tool trustworthy?

Ben Langmead: So the research world is appropriately a world where we like to let 1,000 flowers bloom and a lot of software gets put out there. And this is good, right? Students need to be able to have an open playing field to go ahead and make their contribution.

Ben Langmead: But not everything is a hit, right? And it’s impossible to predict what’s gonna be a hit. It’s slightly possible to predict what’s gonna be a hit, but generally it’s not possible to predict what’s gonna be a hit. So the truth of the matter is everything starts on kind of equal footing.

Ben Langmead: Maybe this is gonna have traction, maybe it’s not. And if something has traction, then hopefully the student and the advisor are seeing that happen and responding. And often the way you respond is you say, “Oh, wow, we should be thinking about releasing new versions and talking to some of the users, or at least trying to figure out what the users are thinking.”

Ben Langmead: And sometimes things get in the way of that happening. Maybe the student graduated and the PI is very busy and teaching, and so nobody notices. This is okay. But sometimes everything comes together and we can see, oh, this is a hit. Let’s put out more indexes, for example. Maybe this is an indexing-based tool and some people wanna use human indexes, but other people wanna use mouse indexes, so let’s put out more mouse indexes.

Ben Langmead: Or maybe people are requesting features, let’s work on those features. And you can tell from the outside, to your question, you can tell from the outside whether this is happening or not, right? There’s a lot of signals, right? Has the website been updated? Is there a recent release?

Ben Langmead: Are they, are there, is there anything going on the issue tracker, right? And I say this and I think, I hope people don’t check all my issue trackers on all my tools ’cause I’m not proud of what you’ll find in all cases. But we have ways of from the outside interrogating to figure out if this is having a long and healthy life in this lab, or if maybe the lab has turned their attention elsewhere and maybe this thing isn’t too usable.

Ben Langmead: Documentation is another very important thing. Has the team done something more like a reference implementation of an algorithm and then moved on? Or did they think through with empathy for the user? What will the user actually do and be confused by, and can we break it down into subcommands that the user understands what they are and how to sequence them in order to get to their, to their goal?

Grant Belgard: Your lab has always been really good at that, by the way.

Ben Langmead: I appreciate that and it’s ongoing communication, right? And sometimes it has to be face-to-face, right? There’s definitely tools where I think everything is going a certain way. I’m interacting with the issue tracker. I’m having weekly meetings with my software engineer.

Ben Langmead: Then I go out and give a talk and have a meeting with somebody, and I realize, oh, this person thinks about what they want from this tool in a totally different way than I expected. And then, next meeting I have with my software engineer, I’ll relay this and I’ll say “maybe this means that we should provide this additional command, or maybe this means that we need to take it in this direction.”

Ben Langmead: So it’s ever-evolving and it definitely requires nice high bandwidth communication with the community, which doesn’t always mean it can be limited to things like GitHub issues or emails. It sometimes has to be go out in the community and ask people questions and figure out what they’re confused about or what they’re worried about.

Ben Langmead: But yeah, we all know the difference and my take would just be it’s okay for software to go out there and not get traction and gently, sink back into the earth over time. That’s perfectly okay. I think that is important for a healthy research ecosystem.

Ben Langmead: PhD students need to have projects, and nobody knows which are the ones that are gonna get traction. And so if everything works out just right, then the ones that should get traction do, and the people who made it have the time to pay attention and do the documentation and have the empathy to make it truly easy to use and also commit to it long term and go out and talk to people about it when they’re at conferences and talks and such.

Grant Belgard: What do you decide when software is ready for other people to use?

Ben Langmead: I think it is true that some of what we put out is more of the form of a reference implementation. So in other words, that software is there because the idea is good, and in order to show that it was good, we had to implement some software. And that’s not always software where I’m gonna expect a white coat-wearing biologist to come along and download it and use it.

Ben Langmead: And again, that’s okay. There should be a notion of a reference implementation. And in the day of AI coding and AI rewrites, it could even be that reference implementations are the main thing that people like me put out. Because if it’s gonna get traction, perhaps it’ll get traction through an AI rewrite of my reference code rather than my code per se.

Ben Langmead: I like to think… first of all, when you write the documentation, that’s a pretty good stress test of whether you really did think through how users are gonna use it, right? ‘Cause if your documentation is reiterating your paper, that’s probably not a good sign.

Ben Langmead: But if your documentation is saying, “If you have this problem, then run this command,” and you thought through what are the problems the user might have, then that’s better. So it becomes like a question of what are the nouns and the verbs? Do they map onto things that biologists actually already know?

Ben Langmead: Can we use standard file formats like when a biologist comes along to interact, are we gonna be introducing them to new file formats? That often is a point of friction. Or are we telling them, “Yeah, you can use the stuff you’re very used to using.” You can think of it as a converter if you want to.

Ben Langmead: It’s true, we put a lot of effort into the algorithm, but sometimes things really do fit into a pipeline and play a role in a pipeline, and we need to allow the user to think in terms of getting from point A to point B in their pipeline and not get too hung up on what’s our algorithm. So yeah, I think that if you can think in terms of the user’s problems and nouns and verbs, and if your documentation is written like that as opposed to, “Here’s everything we did,” ” here’s all the functions we implemented,” then I think you’re well on your way.

Grant Belgard: What first pulled you towards computer science?

Ben Langmead: Gosh, computer science I was into from roughly high school. Maybe even middle school. So I was at a middle school where they taught us Basic, the programming language, and I could tell that it was fun, and I could tell that I was good at it. And so when high school came along, I took all the computer science courses I could.

Ben Langmead: Also, my brother, who’s eight years older, he was taking computer science classes in college, and I was asking him to send me his homework so that I could work on them too, and, I was trying to learn C programming. And so by the time I got to college, I was very sold on the idea that I really like the ability to tell the computer what to do, and that it truly does what I told it to do.

Ben Langmead: And then when I got to college, I knew right away I wanted to major in computer science, and I took my computer science courses. And I think what I further fell in love with was computational thinking and thinking about software and hardware. You mentioned high-performance computing as one of my emphases at the top and that’s true.

Ben Langmead: In fact, before I went to graduate school, I basically only did high-performance computing. And my favorite thing about that is like un-un-black-boxifying the computer, right? Being able to think of the different pieces of the computer so that it’s not a mystery. “I ran this, I was sure it was gonna be fast, but it was slow.

Ben Langmead: Why did that happen?” Trying to demystify these things and say, “No, I get it. I see how that mapped in an unfortunate… It was a good idea, but it mapped in an unfortunate way onto the way the hardware works. I need to think about the way the hardware works and come up with a new idea.” So I was, I’d say, sold from an early age on the idea that programming is fun and it’s just a fantastic hobby, and I, and if I could do it as part of my job, that would be great.

Ben Langmead: And then in college, I think I fell even more in love with making the computer something other than a black box. And then the next step for me was graduate school, which is where I truly got to commune with the idea that computer science can be used for basic science, which is not necessarily something that computer scientists ever learn, right?

Ben Langmead: Like you can go through an entire computer science undergraduate curriculum and think the whole time that computer science equals big tech. Computer science equals Google, Microsoft, Facebook and of course I’m gonna move to San Francisco when I’m done, right? But going to grad school, at the time I did especially, taught me no.

Ben Langmead: People who are computer scientists can work shoulder to shoulder with basic scientists, and that was the next step in my evolution. And that piece I think is… like my story is a very common story, I’d say right up until grad school because that final piece of connecting computer science with another science is not something we do so well in our curricula.

Grant Belgard: Was there a moment when you realized that genomics had problems you wanted to spend your career on?

Ben Langmead: Totally. Yeah, when I got to graduate school, I was working with one professor at the time at University of Maryland, Bill Pugh, and I was not working on genomics. But at the orientation events, I went to an orientation event where Stephen Salzberg was speaking, and he was describing next-gen sequencing which at that time was brand new.

Ben Langmead: And like I say, he had probably only seen one or two, Solexa data files at that point. And he was talking about what a big deal it is that it’s so inexpensive now, and what a big problem it is that we can’t– we don’t think we currently have the tools to analyze these. And I was so compelled by that, that I went up afterwards and I asked him “look, my background is this and this.

Ben Langmead: Like I, I kinda do high-performance computing, and I’m very much a computer scientist, and I don’t know, last biology class I took was freshman year of high school. Is this an area where I can work?” And he said, “Absolutely.” He was like, “We need people like you.” And he told me to talk to two of his students at the time, Mike Schatz and Cole Trapnell.

Ben Langmead: And luckily for me, Mike and Cole were giving a seminar shortly after that on something they’d been working on called MUMmerGPU, which was a whole genome alignment program that used CUDA and GPUs. This was in, the year 2007 or 2008, so it was very early for that kind of thing. And when I went and saw their talk, they gave a tag team talk, I was at that point completely sold, right?

Ben Langmead: I was– I wanted to be like them. I would do anything to be like these guys. And so I started to talk more to Stephen and also to Mihai Pop, who was my master’s advisor along with Stephen Salzberg, and try to figure out, okay what are the handholds we can get on this alignment problem?

Ben Langmead: And by the first summer after I started grad school, I was fully only working in that area.

Grant Belgard: For a student who likes both biology and computing, how should they choose what to learn deeply?

Ben Langmead: That’s a great question and I can only step on toes by answering your question, so I’m just gonna go ahead and do it. So what I always tell students who approach me and ask that is, I think the computational toolbox is the one to learn earlier. If you’re gonna learn– If you’re gonna sequence these things I’m gonna do one and then I’m gonna do the other, or I’m gonna do one and I’m gonna apply it to the other.

Ben Langmead: I think the thing that’s important to get early is the computational thinking. It is possible to then pick up more of the biological thinking. Okay? That’s my opinion, right? This is not what I would say necessarily to my biology colleague, but this is what I say to students. So I feel like if you’re, if you have that computer science training and toolbox, there’s not really a door that’s closed to you.

Ben Langmead: I don’t think there’s too many professional opportunities where they’ll say, “But you’re a computer scientist. We’re, we do XYZ.” As long as you can demonstrate that you are, that you have the computer science, but you know how to apply it and you know how biological people think, I think it’s perfectly fine to have that computer science degree.

Ben Langmead: So I’m often talking to students who are trying to weigh, “Do I wanna major in computer science and then minor in something?” Or maybe they’re looking at grad school and they’re saying, “Do I wanna do a computer science PhD program or maybe a, a biomedicine or a computational biology or some other…”

Ben Langmead: I usually tell them, “I think that computational toolbox is the crucial one. It’s important to learn it early, and so it’s good to put your academic emphasis on that and grow into the other areas.” I think, like one of my experiences early in my career was before I got my PhD, I worked as a research associate at Johns Hopkins in biostatistics with Rafat Irizarry, a biostatistician, and he was collaborating with a epigenomicist, Andy Feinberg.

Ben Langmead: And I would go to Andy Feinberg’s lab meetings, and at first, I understood basically none of what I heard. It was like being immersed in a foreign language. But if you keep going, and maybe also if the students are willing to answer your questions after the lab meeting if you didn’t understand something, you start to see s- things, certain things repeating, you start to see certain themes, and then you do pick it up after a while.

Ben Langmead: And so I feel like that is a possible thing to do, whereas I’m not sure quite how possible that is with computer science. You have to bang your head against, “My program is not compiling. My program is not working. My algorithm is taking 10 times longer than I thought it would.” Those are things you have to commit to long periods of time of banging your head against it before you can get to the end.

Ben Langmead: So I usually advise that the toolbox to get early and to have represented primarily in your credentials is the computer science one. Now, I’m obviously biased and I always tell students that. So listeners should know that was a biased opinion.

Grant Belgard: To close this out what advice do you find yourself giving again and again?

Ben Langmead: Okay, so I interact with a lot of students at all levels, undergrad and graduate students. And so I think when I’m talking to undergrads, it’s usually because they’re trying to figure out how to map out their college experience and what do you do? And they’re trying to figure out, should I cram in as many classes as possible, or should I triple major?

Ben Langmead: And what I’m usually telling them is something like it’s actually quite difficult to predict what it is you did as an undergrad that’s gonna unlock that next door in your career. It might seem like you want to take as many classes as possible in the thing you’re most interested in, but my story is when I finished undergrad, I applied to several jobs, and I ended up getting a job at a small consulting company called Reservoir Labs.

Ben Langmead: And after working there for a couple years, I was talking to my boss, and he revealed, “Basically, the reason we hired you, the reason we picked your resume out of the pile, is I really liked your writing sample.” And this was, like, a essay about Japanese history from one of my East Asian studies classes, right?

Ben Langmead: So could I have predicted that my first job out of undergrad as a computer science major would have been based on my writing sample? No. And so I think that as important as it is to craft the right undergrad curriculum, it’s also important to explore and make sure that you’re developing that wider skill set: communication, writing presentation skills, as well as whatever it is you’re doing.

Ben Langmead: And then for grad students, I would say one of my, I have lots of advice for grad students, what does it mean to be a PhD student? But I think one of the messages that’s hard to take but important is failure is an option, right? This is science.

Ben Langmead: Your most beloved hypothesis the underlying science doesn’t care how much you love it, right? It’s you have to go with what the data is telling you. And unfortunately, that means that you can’t point at the calendar and say, “This thesis chapter is gonna be done by this date,” or at least not not without being frustrated at the end of the day.

Ben Langmead: So I think people who go to grad school are committing themselves to allowing science and the data to tell them when they’re done and when they’re not done, and that’s a tough commitment, but it’s the right one.

Grant Belgard: Ben, it’s been a great conversation. Thank you so much for joining us

Ben Langmead: Thank you. Pleasure to be here