The Genomic Geography and Evolution of Clusters of Tandemly Duplicated Genes in the Human and Mammal Genomes

Clusters of duplicated genes (CTDGs) are nearly ubiquitous in life's genomes, and are associated with several well-known gene families, such as olfactory receptors, zinc fingers, and immunity-related genes, as well as with several highly variable traits, including olfaction, body plan architecture, and pathogen resistance. However, these observations are usually anecdotal, restricted to specific cases, and lacking evolutionary context. In this study, we use a robust statistical approach to characterize the CTDG repertoire and analyze the distribution of CTDGs across 18 mammal genomes, including human. We found that, on average, 18% of the genes in each species are parts of CTDGs. Although genes in CTDGs are enriched for several biological processes, these tend to be involved in the interactions between the organism and its environment. We further found that mammalian CTDGs are not uniformly distributed across chromosomes and that orthologs of the human chromosome 19 are among the most clustered chromosomes in nearly all mammalian genomes analyzed. We also found evidence that the human chromosome 19 was formed by a fusion event that occurred before the diversification of the rodent and primate lineages and maintained its high density of CTDGs during its subsequent evolution. Finally, using chromosome-level alignments across mammalian genomes, we show how the syntenic regions of the human chromosome 19 have been shrinking, increasing their gene density and possibly increasing the compactness of its CTDGs. These results suggest that CTDGs are a major feature of mammalian genomes and provide novel insights into the origin and evolution of regions with unusually high densities of CTDGs.

Prefix-Free Parsing for Building Big BWTs

High-throughput sequencing technologies have led to explosive growth of genomic databases; one of which will soon reach hundreds of terabytes. For many applications we want to build and store indexes of these databases but constructing such indexes is a challenge. Fortunately, many of these genomic databases are highly-repetitive—a characteristic that can be exploited to ease the computation of the Burrows-Wheeler Transform (BWT), which underlies many popular indexes. In this paper, we introduce a preprocessing algorithm, referred to as prefix-free parsing, that takes a text T as input, and in one-pass generates a dictionary D and a parse P of T with the property that the BWT of T can be constructed from D and P using workspace proportional to their total size and O(|T|)-time. Our experiments show that D and P are significantly smaller than T in practice, and thus, can fit in a reasonable internal memory even when T is very large. In particular, we show that with prefix-free parsing we can build an 131-megabyte run-length compressed FM-index (restricted to support only counting and not locating) for 1000 copies of human chromosome 19 in 2 hours using 21 gigabytes of memory suggesting that we can build a 6.73 gigabyte index for 1000 complete human-genome haplotypes in approximately 102 hours using about 1 terabyte of memory