Research Area:	Bioinformatics & Stats (inc. Modelling and Computational Biology)
Technology Exchange:	Bioinformatics, Computational biology and Statistical genetics
Scientific Themes:	Genetics & Genomics
Keywords:	Population genetics, Genome evolution, Statistical modeling, Transposable element evolution and Next-gen sequencing technologies

My group is interested in investigating the processes of evolution and biology using computational methods.  Our focus is on sequencing data, and are developing methods from early data analysis (read mapping, variant calling) to inference methods to investigate evolutionary questions in population genetics.

Name	Department	Institution	Country
Prof Nazneen Rahman FMedSci FRCP	Division of Genetics and Epidemiology	Institute for Cancer Research	UK
Dr Tom Hart	Zoology	University of Oxford	UK
Prof Dominic Kelly	NDM	Universit of Oxford	UK
Prof Gil McVean	Wellcome Trust Centre for Human Genetics	Oxford University	UK

Westesson O, Lunter G, Paten B, Holmes I. 2012. Accurate reconstruction of insertion-deletion histories by statistical phylogenetics. PLoS One, 7 (4), pp. e34572. Read abstract | Read more

The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes. Hide abstract

Mailund T, Dutheil JY, Hobolth A, Lunter G, Schierup MH. 2011. Estimating divergence time and ancestral effective population size of Bornean and Sumatran orangutan subspecies using a coalescent hidden Markov model. PLoS Genet, 7 (3), pp. e1001319. Read abstract | Read more

Due to genetic variation in the ancestor of two populations or two species, the divergence time for DNA sequences from two populations is variable along the genome. Within genomic segments all bases will share the same divergence-because they share a most recent common ancestor-when no recombination event has occurred to split them apart. The size of these segments of constant divergence depends on the recombination rate, but also on the speciation time, the effective population size of the ancestral population, as well as demographic effects and selection. Thus, inference of these parameters may be possible if we can decode the divergence times along a genomic alignment. Here, we present a new hidden Markov model that infers the changing divergence (coalescence) times along the genome alignment using a coalescent framework, in order to estimate the speciation time, the recombination rate, and the ancestral effective population size. The model is efficient enough to allow inference on whole-genome data sets. We first investigate the power and consistency of the model with coalescent simulations and then apply it to the whole-genome sequences of the two orangutan sub-species, Bornean (P. p. pygmaeus) and Sumatran (P. p. abelii) orangutans from the Orangutan Genome Project. We estimate the speciation time between the two sub-species to be thousand years ago and the effective population size of the ancestral orangutan species to be , consistent with recent results based on smaller data sets. We also report a negative correlation between chromosome size and ancestral effective population size, which we interpret as a signature of recombination increasing the efficacy of selection. Hide abstract

1000 Genomes Project Consortium, Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. 2010. A map of human genome variation from population-scale sequencing. Nature, 467 (7319), pp. 1061-1073. Read abstract | Read more

The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10(-8) per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research. Hide abstract

Lunter G, Goodson M. 2011. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res, 21 (6), pp. 936-939. Read abstract | Read more

High-volume sequencing of DNA and RNA is now within reach of any research laboratory and is quickly becoming established as a key research tool. In many workflows, each of the short sequences ("reads") resulting from a sequencing run are first "mapped" (aligned) to a reference sequence to infer the read from which the genomic location derived, a challenging task because of the high data volumes and often large genomes. Existing read mapping software excel in either speed (e.g., BWA, Bowtie, ELAND) or sensitivity (e.g., Novoalign), but not in both. In addition, performance often deteriorates in the presence of sequence variation, particularly so for short insertions and deletions (indels). Here, we present a read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation. This results in a higher useable sequence yield and improved accuracy compared to that of existing software. Hide abstract

Meader S, Ponting CP, Lunter G. 2010. Massive turnover of functional sequence in human and other mammalian genomes. Genome Res, 20 (10), pp. 1335-1343. Read abstract | Read more

Despite the availability of dozens of animal genome sequences, two key questions remain unanswered: First, what fraction of any species' genome confers biological function, and second, are apparent differences in organismal complexity reflected in an objective measure of genomic complexity? Here, we address both questions by applying, across the mammalian phylogeny, an evolutionary model that estimates the amount of functional DNA that is shared between two species' genomes. Our main findings are, first, that as the divergence between mammalian species increases, the predicted amount of pairwise shared functional sequence drops off dramatically. We show by simulations that this is not an artifact of the method, but rather indicates that functional (and mostly noncoding) sequence is turning over at a very high rate. We estimate that between 200 and 300 Mb (∼6.5%-10%) of the human genome is under functional constraint, which includes five to eight times as many constrained noncoding bases than bases that code for protein. In contrast, in D. melanogaster we estimate only 56-66 Mb to be constrained, implying a ratio of noncoding to coding constrained bases of about 2. This suggests that, rather than genome size or protein-coding gene complement, it is the number of functional bases that might best mirror our naïve preconceptions of organismal complexity. Hide abstract

Chaix R, Somel M, Kreil DP, Khaitovich P, Lunter GA. 2008. Evolution of primate gene expression: drift and corrective sweeps? Genetics, 180 (3), pp. 1379-1389. Read abstract | Read more

Changes in gene expression play an important role in species' evolution. Earlier studies uncovered evidence that the effect of mutations on expression levels within the primate order is skewed, with many small downregulations balanced by fewer but larger upregulations. In addition, brain-expressed genes appeared to show an increased rate of evolution on the branch leading to human. However, the lack of a mathematical model adequately describing the evolution of gene expression precluded the rigorous establishment of these observations. Here, we develop mathematical tools that allow us to revisit these earlier observations in a model-testing and inference framework. We introduce a model for skewed gene-expression evolution within a phylogenetic tree and use a separate model to account for biological or experimental outliers. A Bayesian Markov chain Monte Carlo inference procedure allows us to infer the phylogeny and other evolutionary parameters, while quantifying the confidence in these inferences. Our results support previous observations; in particular, we find strong evidence for a sustained positive skew in the distribution of gene-expression changes in primate evolution. We propose a "corrective sweep" scenario to explain this phenomenon. Hide abstract

Lunter G, Rocco A, Mimouni N, Heger A, Caldeira A, Hein J. 2008. Uncertainty in homology inferences: assessing and improving genomic sequence alignment. Genome Res, 18 (2), pp. 298-309. Read abstract | Read more

Sequence alignment underpins all of comparative genomics, yet it remains an incompletely solved problem. In particular, the statistical uncertainty within inferred alignments is often disregarded, while parametric or phylogenetic inferences are considered meaningless without confidence estimates. Here, we report on a theoretical and simulation study of pairwise alignments of genomic DNA at human-mouse divergence. We find that >15% of aligned bases are incorrect in existing whole-genome alignments, and we identify three types of alignment error, each leading to systematic biases in all algorithms considered. Careful modeling of the evolutionary process improves alignment quality; however, these improvements are modest compared with the remaining alignment errors, even with exact knowledge of the evolutionary model, emphasizing the need for statistical approaches to account for uncertainty. We develop a new algorithm, Marginalized Posterior Decoding (MPD), which explicitly accounts for uncertainties, is less biased and more accurate than other algorithms we consider, and reduces the proportion of misaligned bases by a third compared with the best existing algorithm. To our knowledge, this is the first nonheuristic algorithm for DNA sequence alignment to show robust improvements over the classic Needleman-Wunsch algorithm. Despite this, considerable uncertainty remains even in the improved alignments. We conclude that a probabilistic treatment is essential, both to improve alignment quality and to quantify the remaining uncertainty. This is becoming increasingly relevant with the growing appreciation of the importance of noncoding DNA, whose study relies heavily on alignments. Alignment errors are inevitable, and should be considered when drawing conclusions from alignments. Software and alignments to assist researchers in doing this are provided at http://genserv.anat.ox.ac.uk/grape/. Hide abstract

Ponjavic J, Ponting CP, Lunter G. 2007. Functionality or transcriptional noise? Evidence for selection within long noncoding RNAs. Genome Res, 17 (5), pp. 556-565. Read abstract | Read more

Long transcripts that do not encode protein have only rarely been the subject of experimental scrutiny. Presumably, this is owing to the current lack of evidence of their functionality, thereby leaving an impression that, instead, they represent "transcriptional noise." Here, we describe an analysis of 3122 long and full-length, noncoding RNAs ("macroRNAs") from the mouse, and compare their sequences and their promoters with orthologous sequence from human and from rat. We considered three independent signatures of purifying selection related to substitutions, sequence insertions and deletions, and splicing. We find that the evolution of the set of noncoding RNAs is not consistent with neutralist explanations. Rather, our results indicate that purifying selection has acted on the macroRNAs' promoters, primary sequence, and consensus splice site motifs. Promoters have experienced the greatest elimination of nucleotide substitutions, insertions, and deletions. The proportion of conserved sequence (4.1%-5.5%) in these macroRNAs is comparable to the density of exons within protein-coding transcripts (5.2%). These macroRNAs, taken together, thus possess the imprint of purifying selection, thereby indicating their functionality. Our findings should now provide an incentive for the experimental investigation of these macroRNAs' functions. Hide abstract

Lunter G, Ponting CP, Hein J. 2006. Genome-wide identification of human functional DNA using a neutral indel model. PLoS Comput Biol, 2 (1), pp. e5. Read abstract | Read more

It has become clear that a large proportion of functional DNA in the human genome does not code for protein. Identification of this non-coding functional sequence using comparative approaches is proving difficult and has previously been thought to require deep sequencing of multiple vertebrates. Here we introduce a new model and comparative method that, instead of nucleotide substitutions, uses the evolutionary imprint of insertions and deletions (indels) to infer the past consequences of selection. The model predicts the distribution of indels under neutrality, and shows an excellent fit to human-mouse ancestral repeat data. Across the genome, many unusually long ungapped regions are detected that are unaccounted for by the neutral model, and which we predict to be highly enriched in functional DNA that has been subject to purifying selection with respect to indels. We use the model to determine the proportion under indel-purifying selection to be between 2.56% and 3.25% of human euchromatin. Since annotated protein-coding genes comprise only 1.2% of euchromatin, these results lend further weight to the proposition that more than half the functional complement of the human genome is non-protein-coding. The method is surprisingly powerful at identifying selected sequence using only two or three mammalian genomes. Applying the method to the human, mouse, and dog genomes, we identify 90 Mb of human sequence under indel-purifying selection, at a predicted 10% false-discovery rate and 75% sensitivity. As expected, most of the identified sequence represents unannotated material, while the recovered proportions of known protein-coding and microRNA genes closely match the predicted sensitivity of the method. The method's high sensitivity to functional sequence such as microRNAs suggest that as yet unannotated microRNA genes are enriched among the sequences identified. Furthermore, its independence of substitutions allowed us to identify sequence that has been subject to heterogeneous selection, that is, sequence subject to both positive selection with respect to substitutions and purifying selection with respect to indels. The ability to identify elements under heterogeneous selection enables, for the first time, the genome-wide investigation of positive selection on functional elements other than protein-coding genes. Hide abstract