Monthly Archives: June 2014

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

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Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

Anand Bhaskar, Y.X. Rachel Wang, Yun S. Song

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal which is difficult to pick up with small sample sizes. Lastly, we apply our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing dataset of tens of thousands of individuals assayed at a few hundred genic regions.

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PhyloPythiaS+: A self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes

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PhyloPythiaS+: A self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes
I. Gregor, J. Dröge, M. Schirmer, C. Quince, A. C. McHardy
Subjects: Quantitative Methods (q-bio.QM)

Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. For communities of up to medium diversity, e.g. excluding environments such as soil, this is often achieved by a combination of sequence assembly and binning, where sequences are grouped into ‘bins’ representing taxa of the underlying microbial community from which they originate. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for the recovery of species bins from an individual metagenome sample is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in a composition-based taxonomic metagenome classifier and identifies the ‘training’ sequences using marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area may not have. With these challenges in mind, we have developed PhyloPythiaS+, a successor to our previously described method PhyloPythia(S). The newly developed + component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated k-mer counting 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion.

Conservation and losses of avian non-coding RNA loci

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Conservation and losses of avian non-coding RNA loci
Paul P. Gardner, Mario Fasold, Sarah W. Burge, Maria Ninova, Jana Hertel, Stephanie Kehr, Tammy E. Steeves, Sam Griffiths-Jones, Peter F. Stadler
Comments: 17 pages, 1 figure
Subjects: Genomics (q-bio.GN)

Here we present the results of a large-scale bioinformatic annotation of non-coding RNA loci in 48 avian genomes. Our approach uses probabilistic models of hand-curated families from the Rfam database to infer conserved RNA families within each avian genome. We supplement these annotations with predictions from the tRNA annotation tool, tRNAscan-SE and microRNAs from miRBase. We show that a number of lncRNA-associated loci are conserved between birds and mammals, including several intriguing cases where the reported mammalian lncRNA function is not conserved in birds. We also demonstrate extensive conservation of classical ncRNAs (e.g., tRNAs) and more recently discovered ncRNAs (e.g., snoRNAs and miRNAs) in birds. Furthermore, we describe numerous “losses” of several RNA families, and attribute these to genuine loss, divergence or missing data. In particular, we show that many of these losses are due to the challenges associated with assembling Avian microchromosomes. These combined results illustrate the utility of applying homology-based methods for annotating novel vertebrate genomes.

Multi-locus analysis of genomic time series data from experimental evolution

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Multi-locus analysis of genomic time series data from experimental evolution
Jonathan Terhorst, Yun S. Song

Genomic time series data generated by evolve-and-resequence (E&R) experiments offer a powerful window into the mechanisms that drive evolution. However, standard population genetic inference procedures do not account for sampling serially over time, and new methods are needed to make full use of modern experimental evolution data. To address this problem, we develop a Gaussian process approximation to the multi-locus Wright-Fisher process with selection over a time course of tens of generations. The mean and covariance structure of the Gaussian process are obtained by computing the corresponding moments in discrete-time Wright-Fisher models conditioned on the presence of a linked selected site. This enables our method to account for the effects of linkage and selection, both along the genome and across sampled time points, in an approximate but principled manner. Using simulated data, we demonstrate the power of our method to correctly detect, locate and estimate the fitness of a selected allele from among several linked sites. We also study how this power changes for different values of selection strength, initial haplotypic diversity, population size, sampling frequency, experimental duration, number of replicates, and sequencing coverage depth. In addition to providing quantitative estimates of selection parameters from experimental evolution data, our model can be used by practitioners to design E&R experiments with requisite power. Finally, we explore how our likelihood-based approach can be used to infer other model parameters, including effective population size and recombination rate, and discuss extensions to more complex models.

Long-term balancing selection in LAD1 maintains a missense trans-species polymorphism in humans, chimpanzees and bonobos

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Long-term balancing selection in LAD1 maintains a missense trans-species polymorphism in humans, chimpanzees and bonobos
João C. Teixeira, Cesare de Filippo, Antje Weihmann, Juan R. Meneu, Fernando Racimo, Michael Dannemann, Birgit Nickel, Anne Fischer, Michel Halbwax, Claudine Andre, Rebeca Atencia, Matthias Meyer, Genís Parra, Svante Pääbo, Aida M. Andrés

Balancing selection maintains advantageous genetic and phenotypic diversity in populations. When selection acts for long evolutionary periods selected polymorphisms may survive species splits and segregate in present-day populations of different species. Here, we investigated the role of long-term balancing selection in the evolution of protein-coding sequences in the Pan-Homo clade. We sequenced the exome of 20 humans, 20 chimpanzees and 20 bonobos and detected eight coding trans-species polymorphisms (trSNPs) that are shared among the three species and have segregated for approximately 14 million years of independent evolution. While the majority of these trSNPs were found in three genes of the MHC cluster, we also uncovered one coding trSNP (rs12088790) in the gene LAD1. All these trSNPs show clustering of sequences by allele rather than by species and also exhibit other signatures of long-term balancing selection, such as segregating at intermediate frequency and lying in a locus with high genetic diversity. Here we focus on the trSNP in LAD1, a gene that encodes for Ladinin-1, a collagenous anchoring filament protein of basement membrane that is responsible for maintaining cohesion at the dermal-epidermal junction; the gene is also an autoantigen responsible for linear IgA disease. This trSNP results in a missense change (Leucine257Proline) and, besides altering the protein sequence, is associated with changes in gene expression of LAD1.

The effective founder effect in a spatially expanding population

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The effective founder effect in a spatially expanding population
Benjamin Marco Peter, Montgomery Slatkin

The gradual loss of diversity associated with range expansions is a well known pattern observed in many species, and can be explained with a serial founder model. We show that under a branching process approximation, this loss in diversity is due to the difference in offspring variance between individuals at and away from the expansion front, which allows us to measure the strength of the founder effect, dependant on an effective founder size. We demonstrate that the predictions from the branching process model fit very well with Wright-Fisher forward simulations and backwards simulations under a modified Kingman coalescent, and further show that estimates of the effective founder size are robust to possibly confounding factors such as migration between subpopulations. We apply our method to a data set of Arabidopsis thaliana, where we find that the founder effect is about three times stronger in the Americas than in Europe, which may be attributed to the more recent, faster expansion.

Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling

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Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling
Stinus Lindgreen, Sinan Ugur Umu, Alicia Sook-Wei Lai, Hisham Eldai, Wenting Liu, Stephanie McGimpsey, Nicole Wheeler, Patrick J. Biggs, Nick R. Thomson, Lars Barquist, Anthony M. Poole, Paul P. Gardner
Comments: 16 pages, 4 figures
Subjects: Genomics (q-bio.GN)

Noncoding RNAs are increasingly recognized as integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.

SRST2: Rapid genomic surveillance for public health and hospital microbiology labs

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SRST2: Rapid genomic surveillance for public health and hospital microbiology labs

Michael Inouye, Harriet Dashnow, Lesley Raven, Mark B Schultz, Bernard J Pope, Takehiro Tomita, Justin Zobel, Kathryn E Holt

Rapid molecular typing of bacterial pathogens is critical for public health epidemiology, surveillance and infection control, yet routine use of whole genome sequencing (WGS) for these purposes poses significant challenges. Here we present SRST2, a tool for fast and accurate detection of genes, alleles and multi-locus sequence types from WGS data, which outperforms assembly-based methods. Using >900 genomes from common pathogens, we demonstrate SRST2’s utility for rapid genome surveillance in public health laboratory and hospital infection control settings.

svaseq: removing batch effects and other unwanted noise from sequencing data

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svaseq: removing batch effects and other unwanted noise from sequencing data

Jeffrey Leek

It is now well known that unwanted noise and unmodeled artifacts such as batch effects can dramatically reduce the accuracy of statistical inference in genomic experiments. We introduced surrogate variable analysis for estimating these artifacts by (1) identifying the part of the genomic data only affected by artifacts and (2) estimating the artifacts with principal components or singular vectors of the subset of the data matrix. The resulting estimates of artifacts can be used in subsequent analyses as adjustment factors. Here I describe an update to the sva approach that can be applied to analyze count data or FPKMs from sequencing experiments. I also describe the addition of supervised sva (ssva) for using control probes to identify the part of the genomic data only affected by artifacts. These updates are available through the surrogate variable analysis (sva) Bioconductor package.

Redefining Genomic Privacy: Trust and Empowerment

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Redefining Genomic Privacy: Trust and Empowerment

Arvind Narayanan, Kenneth Yocum, David Glazer, Nita Farahany, Maynard Olson, Lincoln D. Stein, James B. Williams, Jan A. Witkowski, Robert C. Kain, Yaniv Erlich

Fulfilling the promise of the genetic revolution requires the analysis of large datasets containing information from thousands to millions of participants. However, sharing human genomic data requires protecting subjects from potential harm. Current models rely on de-identification techniques that treat privacy versus data utility as a zero-sum game. Instead we propose using trust-enabling techniques to create a solution where researchers and participants both win. To do so we introduce three principles that facilitate trust in genetic research and outline one possible framework built upon those principles. Our hope is that such trust-centric frameworks provide a sustainable solution that reconciles genetic privacy with data sharing and facilitates genetic research.