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Six ways to Sunday: approaches to computational reproducibility in non-model system sequence analysis.

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Six ways to Sunday: approaches to computational reproducibility in non-model system sequence analysis. C. Titus Brown ctb@msu.edu May 21, 2014


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Hello! Assistant Professor; Microbiology; Computer Science; etc. More information at: ged.msu.edu/ github.com/ged-lab/ ivory.idyll.org/blog/ @ctitusbrown


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The challenges of non-model sequencing Missing or low quality genome reference. Evolutionarily distant. Most extant computational tools focus on model organisms – Assume low polymorphism (internal variation) Assume reference genome Assume somewhat reliable functional annotation More significant compute infrastructure …and cannot easily or directly be used on critters of interest.


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Shotgun sequencing & assembly http://eofdreams.com/library.html; http://www.theshreddingservices.com/2011/11/paper-shredding-services-small-business/; http://schoolworkhelper.net/charles-dickens%E2%80%99-tale-of-two-cities-summary-analysis/


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Shotgun sequencing analysis goals: Assembly (what is the text?) Produces new genomes & transcriptomes. Gene discovery for enzymes, drug targets, etc. Counting (how many copies of each book?) Measure gene expression levels, protein-DNA interactions Variant calling (how does each edition vary?) Discover genetic variation: genotyping, linkage studies… Allele-specific expression analysis.


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Assembly It was the best of times, it was the wor , it was the worst of times, it was the isdom, it was the age of foolishness mes, it was the age of wisdom, it was th It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness …but for lots and lots of fragments!


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Shared low-level fragments may not reach the threshold for assembly. Lamprey mRNAseq: Pooling all your data is important


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Introducing k-mers CCGATTGCACTGGACCGA (<- read) CCGATTGCAC CGATTGCACT GATTGCACTG ATTGCACTGG TTGCACTGGA TGCACTGGAC GCACTGGACC ACTGGACCGA


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K-mers give you an implicit alignment CCGATTGCACTGGACCGATGCACGGTACCGTATAGCC CATGGACCGATTGCACTGGACCGATGCACGGTACCG


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K-mers give you an implicit alignment CCGATTGCACTGGACCGATGCACGGTACCGTATAGCC CATGGACCGATTGCACTGGACCGATGCACGGTACCG CATGGACCGATTGCACTGGACCGATGCACGGACCG (with no accounting for mismatches or indels)


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De Bruijn graphs – assemble on overlaps J.R. Miller et al. / Genomics (2010)


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The problem with k-mers CCGATTGCACTGGACCGATGCACGGTACCGTATAGCC CATGGACCGATTGCACTCGACCGATGCACGGTACCG Each sequencing error results in k novel k-mers!


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Conway T C , Bromage A J Bioinformatics 2011;27:479-486 © The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com Assembly graphs scale with data size, not information.


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Practical memory measurements (soil) Velvet measurements (Adina Howe)


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Data set size and cost $1000 gets you ~100m “reads”, or about 10-40 GB of data, in ~week. > 1000 labs doing this regularly. Each data set analysis is ~custom. Analyses are data intensive and memory intensive.


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Efficient data structures & algorithms


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Shotgun sequencing is massively redundant; can we eliminate redundancy while retaining information? Analog: JPEG lossy compression


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Sparse collections of k-mers can be stored efficiently in Bloom filters Pell et al., 2012, PNAS; doi: 10.1073/pnas.1121464109


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Data structures & algorithms papers “These are not the k-mers you are looking for…”, Zhang et al., arXiv 1309.2975, in review. “Scaling metagenome sequence assembly with probabilistic de Bruijn graphs”, Pell et al., PNAS 2012. “A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data”, Brown et al., arXiv 1203.4802, under revision.


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Data analysis papers “Tackling soil diversity with the assembly of large, complex metagenomes”, Howe et al., PNAS, 2014. Assembling novel ascidian genomes & transcriptomes, Lowe et al., in prep. A de novo lamprey transcriptome from large scale multi-tissue mRNAseq, Scott et al., in prep.


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Lab approach – not intentional, but working out.


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This leads to good things. (khmer software)


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Current research (khmer software)


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Testing & version control – the not so secret sauce High test coverage - grown over time. Stupidity driven testing – we write tests for bugs after we find them and before we fix them. Pull requests & continuous integration – does your proposed merge break tests? Pull requests & code review – does new code meet our minimal coding etc requirements? Note: spellchecking!!!


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On the “novel research” side: Novel data structures and algorithms; Permit low(er) memory data analysis; Liberate analyses from specialized hardware.


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Running entirely w/in cloud Complete data; AWS m1.xlarge ~40 hours (See PyCon 2014 talk; video and blog post.) MEMORY


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On the “novel research” side: Novel data structures and algorithms; Permit low(er) memory data analysis; Liberate analyses from specialized hardware. This last bit? => reproducibility.


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Reproducibility! Scientific progress relies on reproducibility of analysis. (Aristotle, Nature, 322 BCE.) “There is no such thing as ‘reproducible science’. There is only ‘science’, and ‘not science.’” – someone on Twitter (Fernando Perez?)


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Disclaimer Not a researcher of reproducibility! Merely a practitioner. Please take my points below as an argument and not as research conclusions. (But I’m right.)


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My usual intro: We practice open science! Everything discussed here: Code: github.com/ged-lab/ ; BSD license Blog: http://ivory.idyll.org/blog (‘titus brown blog’) Twitter: @ctitusbrown Grants on Lab Web site: http://ged.msu.edu/research.html Preprints available. Everything is > 80% reproducible.


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My usual intro: We practice open science! Everything discussed here: Code: github.com/ged-lab/ ; BSD license Blog: http://ivory.idyll.org/blog (‘titus brown blog’) Twitter: @ctitusbrown Grants on Lab Web site: http://ged.msu.edu/research.html Preprints available. Everything is > 80% reproducible.


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My lab & the diginorm paper. All our code was already on github; Much of our data analysis was already in the cloud; Our figures were already made in IPython Notebook Our paper was already in LaTeX


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IPython Notebook: data + code =>


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My lab & the diginorm paper. All our code was already on github; Much of our data analysis was already in the cloud; Our figures were already made in IPython Notebook Our paper was already in LaTeX …why not push a bit more and make it easily reproducible? This involved writing a tutorial. And that’s it.


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To reproduce our paper: git clone <khmer> && python setup.py install git clone <pipeline> cd pipeline wget <data> && tar xzf <data> make && cd ../notebook && make cd ../ && make


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Now standard in lab -- All our papers now have: Source hosted on github; Data hosted there or on AWS; Long running data analysis => ‘make’ Graphing and data digestion => IPython Notebook (also in github) Qingpeng Zhang


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Research process


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Literate graphing & interactive exploration


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The process We start with pipeline reproducibility Baked into lab culture; default “use git; write scripts” Community of practice! Use standard open source approaches, so OSS developers learn it easily. Enables easy collaboration w/in lab Valuable learning tool!


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Growing & refining the process Now moving to Ubuntu Long-Term Support + install instructions. Everything is as automated as is convenient. Students expected to communicate with me in IPython Notebooks. Trying to avoid building (or even using) new tools. Avoid maintenance burden as much as possible.


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1. Use standard OS; provide install instructions Providing install, execute for Ubuntu Long-Term Support release 14.04: supported through 2017 and beyond. Avoid pre-configured virtual machines! Locks you into specific cloud homes. Challenges remixability and extensibility.


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2. Automate Literate graphing now easy with knitr and IPython Notebook. Build automation with make, or whatever. To first order, it does not matter what tools you use. Explicit is better than implicit. Make it easy to understand what you’re doing and how to extend it.


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Myths of reproducible research (Opinions from personal experience.)


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Myth 1: Partial reproducibility is hard. “Here’s my script.” => Methods More generally, Many scientists cannot replicate any part of their analysis without a lot of manual work. Automating this is a win for reasons that have nothing to do with reproducibility… efficiency! See: Software Carpentry.


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Myth 2: Incomplete reproducibility is useless Paraphrase: “We can’t possibly reproduce the experimental data exactly, so we shouldn’t bother with anything else, either.” (Analogous arg re software testing & code coverage.) …I really have a hard time arguing the paraphrase honestly… Being able to reanalyze your raw data? Interesting. Knowing how you made your figures? Really useful.


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Myth 3: We need new platforms Techies always want to build something (which is fun!) but don’t want to do science (which is hard!) We probably do need new platforms, but stop thinking that building them does a service. Platforms need to be use driven. Seriously. If you write good software for scientific inquiry and make it easy to use reproducibly, that will drive virtuousity.


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Myth 4. Virtual Machine reproducibility is an end solution. Good start! Better than nothing! But: Limits understanding & reuse. Limits remixing: often cannot install other software! “Chinese Room” argument: could be just a lookup table.


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Myth 5: We can use GUIs for reproducible research (OK, this is partly just to make people think ;) Almost all data analysis takes place within a larger pipeline; the GUI must consume entire pipeline in order to be reproducible. IFF GUI wraps command line, that’s a decent compromise (e.g. Galaxy) but handicaps researchers using novel approaches. By the time it’s in a GUI, it’s no longer research.


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Our current efforts? Semantic versioning of our own code: stable command-line interface. Writing easy-to-teach tutorials and protocols for common analysis pipelines. Automate ‘em for testing purposes. Encourage their use, inclusion, and adaptation by others.


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khmer-protocols


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khmer-protocols: Provide standard “cheap” assembly protocols for the cloud. Entirely copy/paste; ~2-6 days from raw reads to assembly, annotations, and differential expression analysis. ~$150 per data set (on Amazon rental computers) Open, versioned, forkable, citable….


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Literate testing Our shell-command tutorials for bioinformatics can now be executed in an automated fashion – commands are extracted automatically into shell scripts. See: github.com/ged-lab/literate-resting/. Tremendously improves peace of mind and confidence moving forward! Leigh Sheneman


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Doing things right => #awesomesauce


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Concluding thoughts We are not doing anything particularly neat on the computational side... No “magic sauce.” Much of our effort is now driven by sheer utility: Automation reduces our maintenance burden. Extensibility makes revisions much easier! Explicit instructions are good for training. Some effort needed at the beginning, but once practices are established, “virtuous cycle” takes over.


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What bits should people adopt? Version control! Literate graphing! Automated “build” from data => results! Make available data as early in your pipeline as possible.


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More concluding thoughts Nobody would care that we were doing things reproducibly if our science wasn’t decent. Make sure students realize that faffing about on infrastructure isn’t science. Research is about doing science. Reproducibility (like other good practices) is much easier to proselytize if you can link it to progress in science.


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Biology & sequence analysis is in a perfect place for reproducibility We are lucky! A good opportunity! Big Data: laptops are too small; Excel doesn’t scale any more; Few tools in use; most of them are $$ or UNIX; Little in the way of entrenched research practice;


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Thanks! Talk is on slideshare: slideshare.net/c.titus.brown E-mail or tweet me: ctb@msu.edu @ctitusbrown


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