Older blog entries for titus (starting at number 456)

Laurie Dillon just posted the SIGPLAN eduction board article on Why Undergraduates Should Learn the Principles of Programming Languages to our faculty mailing list at the MSU Computer Science department. One question that came up in the ensuing conversation was: what functional programming language(s) would/should we teach?

I mentioned OCaml, Haskell, and Erlang as reasonably pure but still pragmatic FP languages. Anything else that's both "truly" functional and used somewhat broadly in the real world?

thanks!

--titus

Our sequencing analysis course ended last Friday, with an overwhelmingly positive response from the students. The few negative comments that I got were largely about organizational issues, and could be reshaped as suggestions for next time rather than as condemnations of this year's course.

The 23 students -- most with no prior command-line experience -- spent two weeks experiencing at first hand the challenges of dealing with dozens of gigabytes of sequencing data. Each of the students went through genome-scale mapping, genome assembly, mRNAseq analysis on an "emerging model organism" (a.k.a "one with a crappy genome", lamprey), resequencing analysis on E. coli, and ChIP-seq analysis on Myxococcus xanthus. By the beginning of the second week, many students were working with their own data -- a real victory. Python programming competency may take a bit longer, but many of them seem motivated.

If you had told me three weeks ago that we could pull this off, I would have told you that you were crazy. This does beg the question of what I was thinking when I proposed the course -- but don't dwell on that, please...

The locale was great, as you can see:

One of the most important lessons of the course for me is that cloud computing works well to backstop this kind of course. I was very worried about the suitabiliy and reliability and ease of use, but AWS did a great job, providing an easy-to-use Web interface and a good range of machine images. I have little doubt that this course would have been nearly impossible (and either completely ineffective or much more expensive) without it.

In the end, we spent more on beer than on computational power. That says something important to me :)

The course notes are available under a CC license although they need to be reworked to use publicly available data sets before they become truly useful. At that point I expect them to become awesomely useful, though.

From the scientific perspective, the students derived a number of significant benefits from the course. One that I had not really expected was that some students had no idea what went in to computational "sausage", and were kind of shocked to see what kinds of assumptions us comp bio people made on their behalf. This was especially true in the case of students from companies, who have pipelines that are run on their data. One student lamented that "we used to look at the raw traces... now all we get are spreadsheet summaries!" Another student came to me in a panic because they didn't realize that there was no one true answer -- that that was in fact part of the "fun" of all biology, not just experimental biology. These reactions alone made teaching the course worthwhile.

Of course, the main point is that many of the students seem to be capable of at least starting their own analyses now. I was surprised at the practical power of our cut-and-paste approach -- for example, if you look at the Short-read assembly with ABySS tutorial, it turns out to be relatively straightforward to adapt this to doing assemblies of your own genomic or transcriptomic data. I based our approach on Greg Wilson's post on the failure of inquiry-based teaching and so far I like it.

I am particularly amused that we have now documented, in replicable detail, the Kroos Lab MrpC ChIP analysis. We also have the best documentation for Jeff Barrick's breseq software, I think; this is what is used to analyze the Long Term Evolution Experiment lines -- and I can't wait for the anti-evolutionists to pounce on that... "Titus Brown -- making evolution experiments accessible to creationists." Yay?

There were a number of problems and mistakes that we had to steamroller through. In particular, more background and more advanced tutorials would have be great, but we just didn't have time to write them. Some 454, Helicos, and SOLiD data sets (and next year, PacBio?) would be a good addition. We had a general lack of multiplexing data, which is becoming a Big Thing now that sequencing is so ridiculously deep. I would also like to introduce additional real data analyses next year, reprising things like the Cufflinks analysis and whole-vertebrate-genome ChIP-seq/mRNAseq a la the Wold Lab. I'm weighing adding metagenomics data analysis in for a day, although it's a pretty separate field of inquiry (and frankly much harder in terms of "unknown unknowns"). We also desperately need some plant genomics expertise, because frankly I know nothing about plant genomes; my last-minute plant genomics TA fell through due to lack of planning on my part. (Conveniently, plant genomics is something MSU is particularly good at, so I'm sure I can find someone next year.)

Oops, did I say next year? Well, yes. If I can find funding for my princely salary, then I will almost certainly run the course again next year. I can cover TAs and my own room/board and speakers with workshop fees, but if I'm going to keep room+board+fees under $1000/student -- a practical necessity for most -- there's no way I can pay myself, too. And while this year I relied on my lovely, patient, and frankly long-suffering wife to hold down the home fort while I was away for two weeks, I simply can't put her through that again, so I will need to pay for a nanny next year. So doing it for free is not an option.

In other words, if you are a sequencing company, or an NIH/NSF/USDA program director, interested in keeping this going, please get in touch. I plan to apply for this Initiative to Maximize Research Education in Genomics in September, but I am not confident of getting that on the first try, and in any case I will need letters of support from interested folks. So drop me a note at ctb@msu.edu.

Course development this year was sponsored by the MSU Gene Expression in Disease and Development, to whom I am truly grateful. The course would simply not have been possible without their support.

My overall conclusion is that it is possible to teach bench biologists with no prior computational experience to achieve at least minimal competency in real-world data analysis of next-generation sequencing data. I can't conclusively demonstrate this without doing a better job of course evaluation, and of course only time will tell if it sticks for any of the students, but right now I'm feeling pretty good about the course overall. Not to mention massively relieved.

--titus

p.s. Update from one student -- "It's not even 12 o'clock Monday morning and I'm already getting people asking me how to run assemblies and analyze data." Heh.

So, I've been teaching a course on next-generation sequence analysis for the last week, and one of the issues I had to deal with before I proposed the course was how to deal with the volume of data and the required computation.

You see, next-generation sequence analysis involves analyzing not just entire genomes (which are, after all, only 3gb or so in size) but data sets that are 100x or 1000x as big! We want to not just map these data sets (which is CPU-intensive), but also perform memory-intensive steps like assembly. If you have a class with 20+ students in it, you need to worry about a lot of things:

• computational power: how do you provide 24 "good" workstations
• memory
• disk space
• bandwidth
• "take home" ability

One strategy would be to simply provide some Linux or Mac workstations, with cut-down data sets. But then you wouldn't be teaching reality -- you'd be teaching a cut-down version of reality. This would make the course particularly irrelevant given that one of the extra-fun things about next-gen sequence analysis is how hard it is to deal with the volume of data. You also have to worry that the course would be made even more irrelevant because the students would leave the course and be unable to use the information without finding infrastructure and installing a bunch of software and then administering the machine.

While I enjoy setting up computers and installing software and managing users, I'm clearly masochistic. It's also entirely besides the point for bioinformaticians and biologists - they just want to analyze data!

The solution I came up with was to use Amazon Web Services and rent some EC2 machines. There's a large variety of hardware configurations available (see instance types) and they're not that expensive per hour (see pricing).

This has worked out really, really well.

It's hard to enumerate the benefits, because there have been so many of them ;). A few of the obvious ones --

We've been able to write tutorials (temporary home here: http://ged.msu.edu/angus/) that make use of specific images and should be as future-proof as they can be. We've given students cut and paste command lines that Just Work, and that they can tweak and modify as they want. If it borks, they always just throw it away and start from a clean install.

It's dirt cheap. We spent less than $50 the first week, for ~30 people using an average of 8 hours of CPU time. The second week will increase to an average of 8 hours of CPU time a day, and for larger instances -- so probably about $300 total, or maybe even $500 -- but that's ridiculously cheap, frankly, when you consider that there are no hardware issues or OS re-install problems to deal with!

Students can choose whatever machine specs they need in order to do their analysis. More memory? Easy. Faster CPU needed? No problem.

All of the data analysis takes place off-site. As long as we can provide the data sets somewhere else (I've been using S3, of course) the students don't need to transfer multi-gigabyte files around.

The students can go home, rent EC2 machines, and do their own analyses -- without their labs buying any required infrastructure.

Home institution computer admins can use the EC2 tutorials as documentation to figure out what needs to be installed (and potentially, maintained) in order for their researchers to do next-gen sequence analysis.

The documentation should even serve as a general set of tutorials, once I go through and remove the dependence on private data sets! There won't be any need for students to do difficult or tricky configurations on their home machines in order to make use of the tutorial info.

So, truly awesome. I'm going to be using it for all my courses from now on, I think.

There have been only two minor hitches.

First, I'm using Consolidated Billing to pay for all of the students' computer use during the class, and Amazon has some rules in place to prevent abuse of this. They're limiting me to 20 consolidated billing accounts per AWS account, which means that I've needed to get a second AWS account in order to add all 30 students, TAs, and visiting instructors. I wouldn't even mention it as a serious issue but for the fact that they don't document it anywhere, so I ran into this on the first day of class and then had to wait for them to get back to me to explain what was going on and how to work around it. Grr.

Second, we had some trouble starting up enough Large instances simultaneously on the day we were doing assembly. Not sure what that was about.

Anyway, so I give a strong +1 on Amazon EC2 for large-ish style data analysis. Good stuff.

cheers, --titus

So, I'm running this summer course and I am trying to figure out how to organize the notes for students. I'd like to mix curriculum-specific notes ("here's what we're doing today, and here are some problems to work on") with tutorials (material independent of a single course, like "here's how to transfer files between computers" or "here's how to parse CSV files"), and allow students to search the documents, annotate them in their Web browser, search the annotations, and perhaps even do public or private bookmarking and tagging. The ability to edit the primary content in something other than a Web GUI would be really, really nice, too -- that way I can write in something like ReST and then upload into the system.

(This is a system I could write myself, but that's kind of silly, dontcha think?)

It should also be lightweight, reasonably mature, easy to set up, and (preferably) written in Python, although I'm willing to compromise on the last simply because I'm desperate.

Pointers, comments, suggestions welcome!

--titus

Inspired by the brilliant mind(s) behind python-commandments.org, here's a list of ideas you can use to help newbies learn Python!

1. Write Web pages with overly broad self-granted authority. That way they'll sound authoritative!
2. Make sure they're anonymous pages, too. It's important that people not recognize that "Commandment" means "My Opinion", especially in the presence of actual Python community-level commandments. You know, like "we're switching to Python 3".
3. Keep it short. Nobody wants to overwhelm people with, umm, information.
4. Have a favorite toolkit? Push it!
5. Oh, wait -- does your favorite toolkit only really address one particular problem? That's OK! Naive technical discussions are just fine -- opinions only, information just gets in the way!
6. Remember, keep that self-authority coming!

Seriously, I gather that these are the folk helping out on the #python channel on IRC. Gawd help us.

--titus

p.s. I stumbled across it when someone pointed it out to me as a reason to not prioritize Python 3 for a GSoC project.

I've been doing some more focused bioinformatics programming recently, and as I'm thinking about how to teach biologists about data analysis, I realize more and more how much backstory goes into even relatively simple programming.

The problem: given a reference genome, and a very large set of short, error-prone, random sequences from an individual (with a slightly different genome) aligned to that genome, find all differences and provide enough summary information that probably-real differences can be distinguished from differences due only to error. More explicitly, if I have a sequence to which a bunch of shorter sequences have been aligned, like so:

GAGGGCTACAAAAGGAGGCATAGGACCGATAGGACAT (reference genome)

   GGCTAC          -HWI-EAS216_0019:6:44:8736:13789#0/1[0:6]
   GGCTAAA         HWI-EAS216_0019:6:11:12331:10539#0/1[28:35]
   GGCTAAAAA       HWI-EAS216_0019:6:54:3745:18270#0/1[26:35]
   GGCTAAAAAA      HWI-EAS216_0019:6:75:4175:16939#0/1[25:35]
   GGATACAACAG     -HWI-EAS216_0019:6:96:16613:3276#0/1[0:11]
   GGCTAAAAAAA     HWI-EAS216_0019:6:11:8510:9363#0/1[22:33]
   GGCTAAAAAAA     HWI-EAS216_0019:6:72:14343:19416#0/1[21:32]
   GGCTACAACAG     -HWI-EAS216_0019:6:116:10526:5940#0/1[4:15]
   GGCTAAAACAA     HWI-EAS216_0019:6:103:9198:1208#0/1[18:29]
   GGCTACAACAG     -HWI-EAS216_0019:6:50:14928:13905#0/1[7:18]
   GGCTACAACAG     -HWI-EAS216_0019:6:109:18796:4787#0/1[14:25]
   GGCTACAACAG     -HWI-EAS216_0019:6:102:14050:8429#0/1[18:29]
   GGCTACAACAG     -HWI-EAS216_0019:6:22:14814:10742#0/1[23:34]
    GCTCCACCAG     -HWI-EAS216_0019:6:106:8241:6484#0/1[25:35]
      TACAACAG     HWI-EAS216_0019:6:4:8095:15474#0/1[0:8]
        CAACAG     HWI-EAS216_0019:6:115:15766:4844#0/1[0:6]
     * **  * *
     1 23  4 5

how do you gather all of the locations with differences, and then figure out which differences are significant? (Variations in 1 and 2 are probably errors, 4 and 5 may be real, and 3 is pretty clearly real, I would say.)

The numbers here aren't trivial: you're looking at ~1 million-3 billion bases for the reference genome, and 20-120 million or more shorter resequencing reads.

The first thing to point out is that it's not enough to simply look at all of the aligned sequences individually; you need to know how many total sequences are aligned to each spot, and what each nucleotide is. This is because the new sequences are error-prone: observing a single variation isn't enough, you need to know that it's systematic variation. So it's a total-information situation, and hence not just pleasantly parallel: you need to integrate the information afterwards.

I came up with two basic approaches: you can first figure out where there's any variation, and then iterate again over the short sequences, figure out which ones overlap detected variation, and record those counts; or you can figure out where there's variation, index the short sequences by position, and then query them by position. Both are easy to code.

For the first, approach A:

for seq, location in aligned_short_sequences:
   record_variation(genome[location])

for seq, location in aligned_short_sequences:
   if has_variation(location):
      record_nucleotide(genome[location], seq)

For the second, approach B:

for seq, location in aligned_short_sequences:
   record_variation(genome[location])

for location in recorded_variation:
   aligned_subset = get_aligned_short_sequences(location)
   for seq, _ in aligned_subset:
      record_nucleotide(genome[location], seq)

It's readily apparent that barring stupidity, approach A is generally going to be faster: there's one less loop, for one thing. Also, if you think about implementation details, approach B is potentially going to involve retrieving aligned short sequences multiple times, while approach A looks at each aligned short sequence exactly once.

However, there are tradeoffs, especially if you're building tools to support either approach. The most obvious one is, what if you need to look at variation on less than a whole-genome level? For example, for visualization, or gene overlap calculations, you're probably not going to care about an immense region of the genome; you may want to zoom in on a few hundred bases. In that case, you'd want to use some of the tools from approach B: given a position, what aligns there?

if variation_at(genome[location]):
    aligned_subset = get_aligned_short_sequences(location)
    for seq, _ in aligned_subset:
       record_nucleotide(genome[location], seq)

Another tradeoff is simply whether or not to store everything in memory (fast) or on disk (slower, but scales to bigger data sets) -- I first wrote this code using an in-memory set, to look at low-variation chicken genome resequencing. Then, when I tried to apply it to a high variation Drosophila data set and a deeply sequenced Campylobacter data set, it consumed a lot of RAM. So you have to think about whether or not you want a single tool that works across all these situations, and if so, how you want it to behave for large genomes, with lots of real variation, and extremely deep sequencing -- plan for the worst, in other words.

I also tend to worry about correctness. Is the code giving you the right results, and how easy is it to understand? Each approach has different drawbacks, and relies on different bodies of code that make certain operations easy and other operations hard.

The upshot is that I wrote several programs implementing different approaches, and tried them out on different data sets, and chose the fastest one for one purpose (overall statistics) and a slower one for visualization. And most programmers I know would have done the same: tried a few different approaches, benchmarked them, and chosen the right tool for the current job.

Where does this leave people who don't intuitively understand the difference between the algorithms, and don't really understand what the tools are doing underneath, and why one tool might return different results (based on the parameters given) than another? How do you explain that sort of thing in a short amount of time?

I don't know, but it's going to be interesting to find out...

BTW, the proper way to do this is with pygr will someday soon be:

for seq, location in aligned_short_sequences:
   record_variation(genome[location])

mapping = index_short_sequences()

results = intersect_mappings(variations, mapping)

where 'intersect_mappings' uses internal knowlege of the data structures to do everything quickly and correctly. That day is still a few months off...

--titus

These days, molecular biologists are dealing with lots and lots of sequences, largely due to next-gen sequencing technologies. For example, the Illumina GA2 is producing 100-200 million DNA sequences, each of 75-125 bases, per run; that works out to 20 gb of sequence data per run, not counting metadata such as names and quality scores.

Storing, referencing, and retrieving these sequences is kind of annoying. It's not so much that the files themselves are huge, but that they're in a flat file format called 'FASTA' (or FASTQ), with no way to quickly retrieve a particular sequence short of scanning through the file. This is not something you want to do on-demand or keep in-memory, no matter what BioPython says ;).

So everyone working with these sequences has evolved one way or another to deal with it. A number of different approaches have been used, but they mostly boil down to using some sort of database to either index or store the sequences and associated metadata. It won't surprise you to learn that performance varies widely; that benchmarks are few and far between; and that many people just use MySQL, even though you can predict that using something like MySQL (or really any full-blown r/w database) is going to be suboptimal. This is because FASTA and FASTQ files don't change, and the overhead associated with being able to rearrange your indices to accomodate new data is unnecessary.

A while ago -- nearly 2 years ago!? -- we started looking at various approaches for dealing with the problem of "too much sequence", because the approach we used in pygr was optimized for retrieving subsequences from a relatively small number of relatively large sequences (think human chromosomes). Alex Nolley, a CSE undergrad working in the lab, looked at a number of approaches, and settled on a simple three-level write-once-read-many scheme involving a hash table (mapping names to record IDs), another table mapping fixed-size record IDs to offsets within a database file, and a third randomly-indexed database file. He implemented this in C++ and wrote a Pyrex wrapper and a Python API, and we called it screed.

Imagine our surprise when we found out that even absent the hashing, sqlite was faster than our screed implementation! sqlite is an embedded SQL database that is quite popular and (among other things) included with Python as of Python 2.5. Yet because it's a general purpose database, it seemed unlikely to us that it would be faster than a straightforward 2-level indexed flat file scheme. And yet... it was! A cautionary tale about reimplementing something that really smart/good programmers have spent a lot of time thinking about, eh? (Thanks to James Casbon, in particular, for whacking us over the head on this issue; he wins, I lose.)

Anyway, after a frustrating few weeks trying to think of why screed would be slower than sqlite and trying some naive I/O optimizations, we abandoned our implementation and Alex reimplemented everything on top of sqlite. That is now available as the new screed.

screed can import both common forms of sequence flat file (FASTA and FASTQ), and provides a unified interface for retrieving records by the sequence name. It stores all records as single rows in a table, along with another table to describe what the rows are and what special features they have. Because all of the data is stored in a single database, screed databases are portable and you can regenerate the original file from the index.

screed is designed to be easy to install and use. Conveniently, screed is pure Python: sqlite comes with Python 2.5, and because screed stays away from sqlite extensions, no C/C++ compiler is needed. As a result installation is really easy, and since screed behaves just like a read-only dictionary, anyone -- not just pygr users -- can use it trivially. For example,

>>> import screed
>>> db = screed.read_fasta_sequences('tests/test.fa')

or

>>> db = screed.read_fastq_sequences('tests/test.fq')

and then

>>> for name in db:
...   print name, db[name].sequence

will index the given database and print out all the associated sequences.

Now, because we are actually considering publishing screed (along with another new tool, which I'll post about next), I asked Alex to benchmark screed against MySQL and PostgreSQL. While sqlite is far more convenient for our purposes -- embeddable, included with Python so no installation needed, and not client-server based -- "convenience" isn't as defensible as "way faster" ;). Alex dutifully did so, and the results for accessing sequences, once they are indexed, are below:

That is, sqlite/screed seems to achieve a roughly 1.8x speedup over postgresql, and 2.2x speedup over mysql, with a roughly constant retrieval time for up to a million sequences. (We've seen constant retrieval times out to 10m or so, but past that it becomes annoying to repeat benchmarks, so we'd like to get our benchmark code correct before running them.)

The benchmark code is here: screed, mysql, and postgresql. We're still working on making it easy to run for other people but you should get the basic sense of it from the code I linked to.

screed's performance is likely to improve as we streamline some of the internal screed code, although the improvements should be marginal because I/O and hashing should dominate. So, these benchmarks can be read as "underperforming Python interface wrapped around sqlite vs mysql and postgresql".

There is one disappointment with sqlite. sqlite doesn't come with any sort of compression system built-in, so even though DNA sequence is eminently compressible, we're stuck with a storage and indexing system that requires about 120% of the space of the original file.

--titus

Analyzing Next-Generation Sequencing Data

May 31 - June 11th, 2010

Kellogg Biological Station, Michigan State University

CSE 891 s431 / MMG 890 s433, 2 cr

Applications are due by midnight EST, April 9th, 2010.

Course sponsor: Gene Expression in Disease and Development Focus Group at Michigan State University.

Instructors: Dr. C. Titus Brown and Dr. Gregory V. Wilson.

Course Description

This intensive two week summer course will introduce students with a strong biology background to the practice of analyzing short-read sequencing data from the Illumina GA2 and other next-gen platforms. The first week will introduce students to computational thinking and large-scale data analysis on UNIX platforms. The second week will focus on mapping, assembly, and analysis of short-read data for resequencing, ChIP-seq, and RNAseq.

No prior programming experience is required, although familiarity with some programming concepts is suggested, and bravery in the face of the unknown is necessary. 2 years or more of graduate school in a biological science is strongly suggested.

See the course Web site for more information.

A new meme was born at PyCon 2010: The Testing Goat.

Or, "Be Stubborn. Obey the Goat."

The goat actually emerged from the Testing In Python Birds of a Feather session at PyCon, where Terry Peppers used slides full of goat in his introduction. This was apparently an overreaction to lolcat, but the testing goat is now being held up in opposition to the Django pony.

sigh.

--titus

On the heels of my aggressive competence post, about (among other things) my failure to outline my expectations for students, I've started putting together a page to help manage student expectations for the pony-build project, which is participating in the Undergraduate Capstone Open-Source Projects (UCOSP) course this term.

(Please comment over at the Wordpress blog for UCOSP:

http://ucosp.wordpress.com/2010/01/02/managing-student-expectations/

so the students can see your words of wisdom!)

--titus