Published on March 5, 2014
Christopher W. Beitel, Aaron Darling twitter: @koadman
Genomics revolutionized our understanding of bacteria Perna et al 2001 Nature, Welch et al 2002 PNAS Three genomes, same species only 40% genes in common
Phylogenetic diversity: the known unknowns ● Can amplify and sequence the 16S rRNA gene present in all bacteria and archaea ● Millions of 16S sequences obtained per run ● Align sequences, build evolutionary tree, analyze Rinke et al 2013, Nature Uncultured bacterial diversity greatly exceeds known isolate diversity
Is metagenomics the answer...?? Smash with bead beater Sample Purify DNA Analyze Shear DNA to 400nt frags Metagenomics loses long-range linkage At least two paths forward: 1. Physically “dissect” microbial communities Sequence (yay!) 2. Better inference methods Prep sequencer library 3. Nanopore long read sequencing?
What is Hi-C? High throughput sequencing of proximity-ligation products Landmark papers Chromosome conformation capture: sdfdf Hi-C: Lieberman-Aiden et al 2009 Single (human) cell Hi-C: Nagano et al 2013
We propose to use Hi-C for metagenomics Cell/Species A Cell/Species B Conjecture: DNA in the same cell will become crosslinked, ligated, and sequenced more frequently (green links) than DNA fragments from different cells (red links)
A little experiment to test Hi-C Selected 5 strains, 4 species: + E. coli BL21 (BL21) + E. coli K12 (K12) + Pediococcus pentosaceus (Ped) + Burkholderia thailandensis (2 chromosomes: Bur1, Bur2) + Lactobacillus brevis (chromosome + 2 plasmids: Lac0, Lac1, Lac2) Grew up cultures, mixed in equal parts based on OD600 Applied standard Hi-C protocol, sequenced on Illumina MiSeq ~22M read pairs, 160nt reads
Reconstructing genomes from within metagenomes A classic problem in metagenomics Can't de novo assemble Hi-C data due to presence of ligation junctions, other issues To test reconstruction we instead simulated reads with BioGrinder and assembled with soapdenovo. Assembly stats: Cov. Contigs (scaff.) Bp assem. N50 (scaff.) N90 (scaf) Max. len. Contig. error Scaff. error Pct. assem. 100 7687 (557) 15,577,164 87634 8552 379623 0 0 77.37
A Hi-C scaffold graph ● Nodes are assembly contigs ● node size ∝ contig size ● ● edge weights ∝ normalized Hi-C read counts linking contigs Fruchterman-Reingold layout
A Hi-C scaffold graph ● Nodes are assembly contigs ● node size ∝ contig size ● edge weights ∝ normalized Hi-C read counts linking contigs ● Fruchterman Reingold layout ● Nodes colored by SPECIES ● Can we actually compute clusters? ● Markov clustering, I=1.1: 4 clusters, >97% of genome in each bin
Contig clustering: why it works Rate at which Hi-C reads associate within and across species Hi− read pair insert distributions C L. brevis ATCC367 P. pentosaceus ATCC25745 E. coli K12 DH10B E. coli BL21(DE3) B. thailandensis E264 chrI B. thailandensis E264 chrII 0 500000 1000000 1500000 2000000 2500000 Insert distance in nucleotides ● 99% of Hi-C read pairs associate within-species ● Hi-C read pairs associate throughout the chromosome ● Spans distances that no existing or imaginary “long read” technology can cover
Resolving strain differences: Single nucleotide variants AA TT AA AA G G C C G G C C - SNPs between E. coli K12 and BL21 are nodes - Edges link SNPs observed in same read pair Are Hi-C graphs are better connected than mate-pair?? 5k MP Largest 6.2% connected component 10k MP 20k MP Hi-C 16.6% 32.4% 97.8% Mate pair Hi-C
Resolving strain differences: Hi-C versus mate-pair Distance on genome versus path length in variant graph Mate pair graph distances scale linearly. Hi-C graphs are scale-free. Estimation error in probability of variant linkage grows with path length!
Opportunities to collaborate 1. Species clustering of assemblies 2. Resolving strain haplotypes 3. Application to microbial communities! 4. Improvement of the Hi-C protocol 5. Population metagenomics – track gene flow of, e.g. antibiotic resistance plasmids 6. Your idea (and some grant funding) here
Thanks to… Lutz Froenicke Richard Michelmore Ian Korf Matthew DeMaere Christopher Beitel Jonathan Eisen UC Davis UC Davis Jenna Lang UC Davis Catherine Burke Manuscript preprint online at https://peerj.com/preprints/260/ twitter: @koadman
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