MICROBIOME SOFTWARE: END OF BEGINNING.

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1 MICROBIOME SOFTWARE: END OF BEGINNING. DR. CHARLES ROBERTSON DIVISION OF INFECTIOUS DISEASES, UNIVERSITY OF COLORADO SCHOOL OF MEDICINE DR. DANIEL N. FRANK, DIVISION OF INFECTIOUS DISEASES, SCHOOL OF MEDICINE DR. J. KIRK HARRIS, DEPT. OF PEDIATRICS, SCHOOL OF MEDICINE & CHILDREN S HOSPITAL CO

2 OVERVIEW Sequence Data Microbiome Sequence Analysis Tools Results Today: Look at three items in the Black Box

The culture independent method Member NAS, election year 1991 Nucleic acid

3 OUR MENTOR: NORMAN PACE NORM S MENTOR: CARL WOESE Nucleic acid biochemist Extensive ribozyme work: RNase P Invented the basis of all microbiome studies: The culture independent method Member NAS, election year 1991 Nucleic acid biochemist Discovered the Archaea Put forth the RNA world hypothesis Member NAS, election year 1988

4 MY BACKGROUND Extensive use of computers starting in 1968 Itinerant programmer in the US and Europe BS Electrical Engineering & Computer Science, 1982 School of Engineering, University of Colorado, Boulder 2 years spent doing logic design at a supercomputer company 25 years in the Electronic Design Automation business Building commercial software to solve NP complete problems Hardware description languages, Circuit Simulation, Logic Simulation, Placement, Routing, PCB s & IC s Last position in the EDA industry: CEO PhD, 2008 Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder Current: Hardware/Software/Sequence Analysis Have processed >200 MiSeq runs in the last 5 years (> 2 billion sequences) ~90% medical ~10% environmental (Customers: primarily CU Boulder School of Engineering)

5 THE MICROBIOME PROCESS Sample Extract DNA Amplify One Molecule Sequence Identify & Count Each Sequence Type Community Composition wet bench work computer informatics The primary topic of this presentation

chromosomes A heavily constrained portion of the information processing system The ribosome is a

6 The culture independent method use DNA sequences to identify microbes Woese selected the ribosome Ribosome: complex machine that assembles proteins from amino acids per information encoded in chromosomes A heavily constrained portion of the information processing system The ribosome is a ribozyme Shape is everything Precise positioning of reactants and charged ions to get enzymatic activity

7 Small SubUnit rrna 16S

Desire uniform amplification of all kinds Ever a compromise between length (cost per sequence) and primer locations

8 Information rich molecule Primary sequence Secondary Structure Information content is non-uniformly distributed across the entire molecule The Tree of Life cannot be reproduced with short sequences Amplicon access via universal primers Desire uniform amplification of all kinds Ever a compromise between length (cost per sequence) and primer locations Small SubUnit rrna 16S Easy to identify phylum by short sequence No simple/consistent way to get to species with short sequences

9 Ordination The Human Microbiome Consortium Nature 486:

10 IN THE BEGINNING, WITH SANGER SEQUENCING Only use full length sequences for analysis The full length sequences had to be ALIGNED (NP complete) Why align? To assure comparison of homologous nucleotides G-CGTAATCGAAGGCCATTACGCTTGCGTAATGGCCCGATTACG-C GCC-TAATCG--GGCCATTACGCTTGCGTAATGGCCCGATTA-GGC GCCGT-ATCGAAGGCCATTAC-CTTG-GTAATGGCCCGAT-ACGGC GCCGTAATC---GGC-ATTACGCTTGCGTAAT-GCC-GATTACGGC GCCGTAATCGAAGGCCATTA-GCTTGC-TAATGGCCCGATTACGGC Build phylogenetic trees (NP complete) Iteratively make informed guesses as to the shapes of trees & measure their probabilities Informed trial and error! C T T G G C C G A T T A T A A T C G C G G C A G C A G C C G T A A T A T T A G C C G G C

11 1997, Sanger 1990, Pre and early Sanger Woese, et al. PNAS, 1990 June; 87 (12): Pace, NR. Science,1997 May 2;276(5313):

12 ADVENT OF NEXT GENERATION SEQUENCING Induced very rapid change due to very large decrease in price per sequence Sequences/Sample: Sanger/454/MiSeq: 96/8,000/100,000 Other scientific disciplines suddenly very motivated to explore the microbiomes of their knowledge sub-domains Ecologists Geologists Physicians A big problem arose: Alignment & Tree building s/w of that time did not scale well Existing analysis approaches (computers/software tools) could not cope with the onslaught of the large number of sequences in NGS datasets

13 SOLUTION: NEW TOOLS THE PROGRAMMERS ARRIVE Adopt new languages and rapid prototyping software creation processes Eg, the Python programming language Abandon NP complete processes Vigorously assert all of the following Full length sequences not always needed Local (or no) alignment good enough Just stop building phylogenetic trees (for the most part)

14 ITEM ONE IN THE BLACK BOX: NUMERICAL OTUS Per SOP s of Qiime and Mothur: Create numerical OTUs Generate enumerated clusters of sequences that are sort of close ( close enough, say 3%) Pick a single sequence as a representative of each cluster Classify only the representative sequence which is then attributed to all sequences in the cluster Less classification means faster dataset processing

15 CREATING CLUSTERS Intuitive example, that has issues similar to sequence clustering. Let the radius of a circle represent the size of an OTU, eg 3% OTU Picking with fixed radius clusters: Numerical OTUs are NOT canonical: Completely dependent on selection rules: Order & packing heuristic We don t have a theoretical framework guided by biochemistry, biology, etc, to inform how the clusters are to be created everyone is as correct as anyone else, but they are NOT DIRECTLY COMPARABLE.

16 PICKING ONE REPRESENTATIVE FOR THE CLUSTER Which dot is the single best representative for this OTU cluster? Why? Again: no theoretical biochemical/etc. framework to inform the selection of representatives of clusters Arguments can be made for various approaches, but the arguments are NOT based on biochemistry or biology they are based on statistics or computer science (which means programmer convenience)

17 CLUSTERING YIELDS BIASED RESULTS Numerical OTU s do a great job of enumerating differences between sets of sequences Great insights via ordination However: Clustering usually superposes a model (3% species bins) that does not fit current observations based on the Big Tree For medical analyses different organisms often appear in a single cluster Clustering adds a bias to the results The representative sequence does not appropriately match all of the sequences in the clusters The true positions of individual sequences become fuzzier

18 ITEM TWO FROM THE BLACK BOX: CLASSIFICATION The RDP Classifier: Naïve Bayesian Classification Eliminated the need for 2 computationally intensive activities: alignment & tree building How does it work? Start with unaligned sequence data and associated taxonomy lines (aka, The Training Set) Use Bayes Theorem to generate probability coefficients that allows very fast classification of unknown sequences RDP Classifier Unknown sequence Bacteria/Proteobacteria/ /E. coli Probabilistic Binning Cloud Norm Pace Bayes Theorem:

19 TRAINING SETS: UNALIGNED Makes use of unaligned reference sequences G-CGTAATCGAAGGCCATTACGCTTGCGTAATGGCCCGATTACG-C GCC-TAATCG--GGCCATTACGCTTGCGTAATGGCCCGATTA-GGC GCCGT-ATCGAAGGCCATTAC-CTTG-GTAATGGCCCGAT-ACGGC GCCGTAATC---GGC-ATTACGCTTGCGTAAT-GCC-GATTACGGC GCCGTAATCGAAGGCCATTA-GCTTGC-TAATGGCCCGATTACGGC Divide into groups of 8 columns: 8-mers GCGTAATCGAAGGCCATTACGCTTGCGTAATGGCCCGATTACGC.. GCCTAATCGGGCCATTACGCTTGCGTAATGGCCCGATTAGGC... GCCGTATCGAAGGCCATTACCTTGGTAATGGCCCGATACGGC... GCCGTAATCGGCATTACGCTTGCGTAATGCCGATTACGGC... GCCGTAATCGAAGGCCATTAGCTTGCTAATGGCCCGATTACGGC.. Classic alignment Retains correlation with the secondary structure! RDP classifier training set Loses correlation with the secondary structure! C T T G G C C G A T T A T A A T C G C G G C A G C A G C C G T A A T A T T A G C C G G C Using unaligned training sets changes precise boundaries into vague boundaries: Noise.

20 NAÏVE BAYES CLASSIFICATION: PROS/CONS Good Very fast (Computer: just multiplications and additions) Ubiquitous (Qiime/Mothur/RDP website) Bad (from our perspective) Training sets are often unstable don t get out what you put in Creating a stable training set is a black art To get the results you want, often have to add/delete apparently completely unrelated sequences The result provides no clues whatsoever as to how the classifier came up with the answer 100% oracle, 0% insight Which sequence in the reference training set was closest to an unknown? For very similar sequences, which few nucleotides were different? Does not provide an AUDIT TRAIL: critical for clinical medicine & epidemiology! which known species in the database was the basis for an unknown called as that species?

21 NO UNIVERSAL CONSTANTS IN BIOLOGY Biology is an intrinsically observational activity The process: collect and assemble anecdotes Insight arises when a critical mass of anecdotes is accumulated No predictive mathematical formulations have been forthcoming, eg: No speed of light, No E = MC 2, Not even an Ohm s law equivalent How many kinds of microbes exist on the planet FOR CERTAIN? How much sequence distance exists within ALL species level clades in the Big Tree FOR CERTAIN? In retrospect 3% species should NOT have been enshrined in microbiome tools For new organisms: it must always come back to a pairwise comparison As it was for Linnaeus, so it is still for us. The new organism must be compared to the most similar organism that has already been documented The lack of a numeric predictive theoretical framework is at odds with Bioinformatics Software demands very specific answers to questions like: what means nearby between two sequences?

22 SO WHAT? There are limits to the precision we can get with numerical OTUs and Bayesian classification Where in the world are the error bars on these processes? Effective software solutions exist that are not based on numerical OTUs & Bayesian classification We are at the end of the beginning of microbiome analysis It is time to re-evaluate all of the fundamental assumptions to get to the future Next: The biggest bleeding sore : The libraries of reference sequences we all use.

23 CURATED DATABASES OF FULL LENGTH 16S SEQUENCES Why not just NCBI/EMBL? no attempt at all to place sequences in a phylogenetic context. Submitted sequences not unambiguously derived from cultivated sources are assigned taxonomy Environmental/Uncultivated The two most commonly used curated 16S phylogenetic databases: Greengenes & Silva Greengenes from the Pace Lab via Phil Hugenholtz to JGI. Qiime default Greengenes Database Consortium/2 nd Genome: but current status unclear. No updates since May, 2013 Silva Microbial Genomics Group at the Max Planck Institute for Marine Microbiology, Bremen and the Department of Microbiology at the Technical University Munich. Mothur default Well documented releases at somewhat irregular intervals; releases locked to EMBL versions. Latest: Silva 128, Sept 28, Silva >>> Greengenes

24 THE PRECISION LIMIT: REFERENCE SEQUENCES The most significant microbiome tools limit: database content All microbiome tools are vetted against, or make intrinsic use of these curated 16S databases How do we know issues exist? Recent availability of many microbial GENOMES rrna s of microbial genomes are relatively clean : uniform, consistent, little variation within By comparison with genomes rrnas, many database sequences have non-subtle defects Missing pieces, added pieces, perturbed secondary structures Database sequence defects source? the mishmash of sequencing technologies over the ages: Sanger, 454, Illumina We did not know what we did not know best efforts at the time Sequence databases are the ultimate Hotel California Sequences check into databases but they never leave. Infinite academic collegiality is in force No non-confrontational means to resolve issues.

25 DATABASE CURATION IS HARD, EXPENSIVE, UNDERFUNDED Even the best rrna database inadequate for calls to species level These databases are the equivalent of the literature and museums that Linnaeus used to deduce relationships: if we get them wrong, uncertainty propagates. Are genomes the silver bullet for high precision reference sequences? Evaluation of rrnas from genomes in Silva 128 finds some with defects: case-by-case scrutiny required! Defects: Missing pieces, added pieces, perturbed secondary structures, protein content Errors likely due to assembly process errors (software as oracle, again!) Most genomicists do not go to extra effort to verify structure of rrnas (focus is on proteins) But: genomes are clearly consistently better Current databases need to be re-evaluated in the light of the genomes rrna sequences! Fundamental career limiting disincentive for database work: The work is considered to be significant but NOT INNOVATIVE therefore, NO FUNDING

26 REFLECTIONS ON THE JOURNEY Phylogenetic analysis of full length sequences is still the gold standard High volume analysis techniques must be characterized in light of phylogenetics Taxonomic error bar characterization needed for microbiome analysis Numerical OTUs have and will continue to provide utility BUT: to maximize biological, biochemical, and evolutionary insight we need the most precise taxonomy calls that can be attained Let go of universal numeric constants! Reference Sequence Databases New Focus, Means (IEEE style?), and Funding mechanism required change the quid pro quo for this work! Perhaps just a wee bit of rebalance of focus back toward biochemistry instead of software?

27 OUR SOFTWARE SPECIFIC FUNDING NIH R21HG (Frank) CIHR Genome Canada (Parkinson) NIH UH2DK (Li)

28 THE END

Carl Woese. Used 16S rrna to develop a method to Identify any bacterium, and discovered a novel domain of life

Carl Woese. Used 16S rrna to develop a method to Identify any bacterium, and discovered a novel domain of life METAGENOMICS Carl Woese Used 16S rrna to develop a method to Identify any bacterium, and discovered a novel domain of life His amazing discovery, coupled with his solitary behaviour, made many contemporary