Genome Alignment

Alignment Methods Needleman-Wunsch (global) and Smith- Waterman (local) use dynamic programming Guaranteed to find an optimal alignment given a particular scoring function Too computationally intensive for genome alignment, especially multiple genomes

Genome Alignment Depending on level of similarity, genome alignments may need to contend with rearrangements and large-scale duplications and deletions Draft or partial genomes can both benefit from and confound alignment Need to visualize results in summary form

Genome Alignment Pair-wise –Align two genomes –Example: MUMmer Multiple or complex samples and a reference genome –All of one genome plus whatever parts match from the other genome(s) –Example: PIPs Multiple alignment –All of all the genomes –Example: Mauve

Some aligners

MUMmer (Maximal Unique Match) Fast pair-wise comparison of draft or complete genomes using nucleotide or 6- frame translated sequences MUMmer 3.0 can find all 20-basepair or longer exact matches between a pair of 5- megabase genomes in 13.7 seconds, using 78 MB of memory, on a 2.4 GHz Linux desktop computer

Suffix Tree Delcher et al. Fast algorithms for large-scale genome alignment and comparison. Nucleic Acids Res Jun 1;30(11):

MUMMER plot Genome 1 Genome 2

5 Campylobacter PROmer analysis Fouts et al. Major structural differences and novel potential virulence mechanisms from the genomes of multiple campylobacter species. PLoS Biol Jan;3(1):e15. One genome is used as the x-axis for all four pair-wise comparisons X-shape characteristic of collinearity interrupted by inversions around the origin or terminus of replication Loss of collinearity in more distant comparisons

Human Gut metagenome Percent Identity Plot (PIP) of random shotgun reads to a complete Bifidobacterium genome and a good quality draft Methanobrevibacter genome Gill et al. Metagenomic analysis of the human distal gut microbiome. Science Jun 2; 312(5778):

Mauve Multiple Genome Aligner Able to identify and align collinear regions of multiple genomes even in the presence of rearrangements Find and extend seed matches Group into locally collinear blocks Align intervening regions Darling et al. Genome Res Jul;14(7):

Progressive Mauve alignment of 12 E. coli genome Aaron Darling 2006 Ph.D. thesis, darling_thesis.pdf darling_thesis.pdf

Figure 1. The difference between positional homology alignment and glocal alignment. Three example linear genomes are broken into genes labeled A,B,C,D, and R. R is a multi- copy (repetitive) gene, with different copies labeled using numeric subscripts. Each copy of R is assumed to be identical in sequence, so that orthology/paralogy is unknowable from nucleotide substitution (as is often the case with mobile DNA repeat elements). Genes shifted downward in a given genome are inverted (reverse complement) relative to the reference genome. The positional homology alignment would ideally create two local alignment blocks where each block has exactly one alignment row for each genome. Only positionally- conserved copies of the repetitive gene family R become aligned to each other. The glocal alignment would ideally create four local alignment blocks wherein all copies of the repetitive gene family become aligned to each other.

Progressive Genome Alignment similar to CLUSTAL (next week) with integrated synteny mapping and positional homology and anchored alignment

Performance Metrics actual \ predicted negativepositive Negative TNFP Positive FNTP Accuracy – Proportion correct TN+TP/total TPR (Recall) – Proportion of predicted positives that are correct TP/FP+TP Sensitivity – Proportion of positives correctly predicted TP/FN+TP Specificity – Proportion of negatives correctly predicted TN/TN+FP

Sensitivity Positive Predictive Value (PPV) For nucleotide pairs, a TP is a pair aligned in both the calculated and correct alignments. A FP is a nucleotide pair in the calculated alignment that is absent from the correct alignment. Likewise, a FN is a pair in the correct alignment not present in the calculated alignment. We do not quantify True Negative (TN) alignments as the number of TN possibilities is extremely large, growing with the product of sequence lengths.

ENCODE project Goal = to identify all functional elements in the human genome Margulies et al reports results of the pilot project to analyze 1% of the genome using genome alignment to detect which regions of the sequence are evolutionarily constrained.

4 aligners –MAVID –MLAGAN –TBA –PECAN 23 mammalian species 30 Mb; 44 regions

Alignment Breakpoints

Alignment Coverage For example, vs. armadillo: MAVID27.4% MLAGAN42.4% TBA41.2% PECAN40.1% 17.4% covered by all 4 aligners Of which 66.1% are aligned identically

Performance Metrics Sensitivity – coverage of protein coding regions and ancestral repeats Specificity – primate specific repeats (Alu) and periodicity of substitutions in protein coding regions