Inter- and intra-specific pan-genomes of Borrelia burgdorferi sensu lato: genome stability and adaptive radiation

Background Lyme disease is caused by spirochete bacteria from the Borrelia burgdorferi sensu lato (B. burgdorferi s.l.) species complex. To reconstruct the evolution of B. burgdorferi s.l. and identify the genomic basis of its human virulence, we compared the genomes of 23 B. burgdorferi s.l. isolates from Europe and the United States, including B. burgdorferi sensu stricto (B. burgdorferi s.s., 14 isolates), B. afzelii (2), B. garinii (2), B. “bavariensis” (1), B. spielmanii (1), B. valaisiana (1), B. bissettii (1), and B. “finlandensis” (1). Results Robust B. burgdorferi s.s. and B. burgdorferi s.l. phylogenies were obtained using genome-wide single-nucleotide polymorphisms, despite recombination. Phylogeny-based pan-genome analysis showed that the rate of gene acquisition was higher between species than within species, suggesting adaptive speciation. Strong positive natural selection drives the sequence evolution of lipoproteins, including chromosomally-encoded genes 0102 and 0404, cp26-encoded ospC and b08, and lp54-encoded dbpA, a07, a22, a33, a53, a65. Computer simulations predicted rapid adaptive radiation of genomic groups as population size increases. Conclusions Intra- and inter-specific pan-genome sizes of B. burgdorferi s.l. expand linearly with phylogenetic diversity. Yet gene-acquisition rates in B. burgdorferi s.l. are among the lowest in bacterial pathogens, resulting in high genome stability and few lineage-specific genes. Genome adaptation of B. burgdorferi s.l. is driven predominantly by copy-number and sequence variations of lipoprotein genes. New genomic groups are likely to emerge if the current trend of B. burgdorferi s.l. population expansion continues

Models for Inuktitut-English Word Alignment

This paper presents a set of techniques for bitext word alignment, optimized for a language pair with the characteristics of Inuktitut-English. The resulting systems exploit cross-lingual affinities at the sublexical level of syllables and substrings, as well as regular patterns of transliteration and the tendency towards monotonicity of alignment. Our most successful systems were based on classifier combination, and we found different combination methods performed best under the target evaluation metrics of F-measure and alignment error rate

Using Co-occurrence Statistics as an Information Source for Partial Parsing of Chinese

Our partial parser for Chinese uses a learned classifier to guide a bottom-up parsing process. We describe improvements'in performance obtained by expanding the information available to the classifier, from POS sequences only, to include measures of word association derived from co-occmrence statistics. We compare performance using different measures of association, and find that Yule's coefficient of colligation Y gives somewhat better results over other measures