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arXiv2012

Improved haplotyping of rare variants using next-generation sequence data

Accurate identification of haplotypes in sequenced human genomes can provide invaluable information about population demography and fine-scale correlations along the genome, thus empowering both population genomic and medical association studies. Yet phasing unrelated individuals remains a challenging problem. Incorporating available data from high throughput sequencing into traditional statistical phasing approaches is a promising avenue to alleviate these issues. We present a novel statistical method that expands on an existing graphical haplotype reconstruction method (shapeIT) to incorporate phasing information from paired-end read data. The algorithm harnesses the haplotype graph information estimated by shapeIT from genotypes across the population and refines haplotype likelihoods for a given individual to be compatible with the sequencing data. Applying the method to HapMap individuals genotyped on the Affymetrix Axiom chip at 7,745,081 SNPs and on a trio sequenced by Complete Genomics, we found that the inclusion of paired end read data significantly improved phasing, with reductions in switch error on the order of 4-15% against shapeIT across all panels. As expected, the improvements were found to be most significant at sites harboring rare variants; furthermore, we found that longer read sizes and higher throughput translated to greater decreases in switching error, as did higher variance in the size of the insert separating the two reads--suggesting that multi-platform next generation sequencing may be exploited to yield particularly accurate haplotypes. Overall, the phasing improvements afforded by this new method highlight the power of integrating sequencing read information and population genotype data for reconstructing haplotypes in unrelated individuals.

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arXiv2012

Modelling Breakage-Fusion-Bridge Cycles as a Stochastic Paper Folding Process

Breakage-Fusion-Bridge cycles in cancer arise when a broken segment of DNA is duplicated and an end from each copy joined together. This structure then `unfolds' into a new piece of palindromic DNA. This is one mechanism responsible for the localised amplicons observed in cancer genome data. The process has parallels with paper folding sequences that arise when a piece of paper is folded several times and then unfolded. Here we adapt such methods to study the breakage-fusion-bridge structures in detail. We firstly consider discrete representations of this space with 2-d trees to demonstrate that there are 2^(n(n-1)/2) qualitatively distinct evolutions involving n breakage-fusion-bridge cycles. Secondly we consider the stochastic nature of the fold positions, to determine evolution likelihoods, and also describe how amplicons become localised. Finally we highlight these methods by inferring the evolution of breakage-fusion-bridge cycles with data from primary tissue cancer samples.

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arXiv2012

The genetic architecture of adaptations to high altitude in Ethiopia

Although hypoxia is a major stress on physiological processes, several human populations have survived for millennia at high altitudes, suggesting that they have adapted to hypoxic conditions. This hypothesis was recently corroborated by studies of Tibetan highlanders, which showed that polymorphisms in candidate genes show signatures of natural selection as well as well-replicated association signals for variation in hemoglobin levels. We extended genomic analysis to two Ethiopian ethnic groups: Amhara and Oromo. For each ethnic group, we sampled low and high altitude residents, thus allowing genetic and phenotypic comparisons across altitudes and across ethnic groups. Genome-wide SNP genotype data were collected in these samples by using Illumina arrays. We find that variants associated with hemoglobin variation among Tibetans or other variants at the same loci do not influence the trait in Ethiopians. However, in the Amhara, SNP rs10803083 is associated with hemoglobin levels at genome-wide levels of significance. No significant genotype association was observed for oxygen saturation levels in either ethnic group. Approaches based on allele frequency divergence did not detect outliers in candidate hypoxia genes, but the most differentiated variants between high- and lowlanders have a clear role in pathogen defense. Interestingly, a significant excess of allele frequency divergence was consistently detected for genes involved in cell cycle control, DNA damage and repair, thus pointing to new pathways for high altitude adaptations. Finally, a comparison of CpG methylation levels between high- and lowlanders found several significant signals at individual genes in the Oromo.

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arXiv2012

Genome and transcriptome studies of the protozoan parasites Trypanosoma cruzi and Giardia intestinalis

Trypanosoma cruzi and Giardia intestinalis are two human pathogens and protozoan parasites responsible for the diseases Chagas disease and giardiasis, respectively. Both diseases cause suffering and illness in several million individuals. The former disease occurs primarily in South America and Central America, and the latter disease occurs worldwide. Current therapeutics are toxic and lack efficacy, and potential vaccines are far from the market. Increased knowledge about the biology of these parasites is essential for drug and vaccine development, and new diagnostic tests. In this thesis, high-throughput sequencing was applied together with extensive bioinformatic analyses to yield insights into the biology and evolution of Trypanosoma cruzi and Giardia intestinalis. Bioinformatics analysis of DNA and RNA sequences was performed to identify features that may be of importance for parasite biology and functional characterization. This thesis is based on five papers (i-v). Paper i and ii describe comparative genome studies of three distinct genotypes of Giardia intestinalis (A, B and E). Paper iii describes a genome comparison of the human infecting Trypanosoma cruzi with the bat-restricted subspecies Trypanosoma cruzi marinkellei. Paper iv describes the repertoire of small non-coding RNAs in Trypanosoma cruzi epimastigotes. Paper v describes transcriptome analysis using paired-end RNA-Seq of three distinct genotypes of Giardia intestinalis (A, B and E).

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arXiv2012

Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed

When dealing with large scale gene expression studies, observations are commonly contaminated by unwanted variation factors such as platforms or batches. Not taking this unwanted variation into account when analyzing the data can lead to spurious associations and to missing important signals. When the analysis is unsupervised, e.g., when the goal is to cluster the samples or to build a corrected version of the dataset - as opposed to the study of an observed factor of interest - taking unwanted variation into account can become a difficult task. The unwanted variation factors may be correlated with the unobserved factor of interest, so that correcting for the former can remove the latter if not done carefully. We show how negative control genes and replicate samples can be used to estimate unwanted variation in gene expression, and discuss how this information can be used to correct the expression data or build estimators for unsupervised problems. The proposed methods are then evaluated on three gene expression datasets. They generally manage to remove unwanted variation without losing the signal of interest and compare favorably to state of the art corrections.

stat.AP↗

arXiv2012

Utilizing RNA-Seq Data for Cancer Network Inference

An important challenge in cancer systems biology is to uncover the complex network of interactions between genes (tumor suppressor genes and oncogenes) implicated in cancer. Next generation sequencing provides unparalleled ability to probe the expression levels of the entire set of cancer genes and their transcript isoforms. However, there are onerous statistical and computational issues in interpreting high-dimensional sequencing data and inferring the underlying genetic network. In this study, we analyzed RNA-Seq data from lymphoblastoid cell lines derived from a population of 69 human individuals and implemented a probabilistic framework to construct biologically-relevant genetic networks. In particular, we employed a graphical lasso analysis, motivated by considerations of the maximum entropy formalism, to estimate the sparse inverse covariance matrix of RNA-Seq data. Gene ontology, pathway enrichment and protein-protein path length analysis were all carried out to validate the biological context of the predicted network of interacting cancer gene isoforms.

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arXiv2012

Membrane environment imposes unique selection pressures on transmembrane domains of G protein-coupled receptors

We have investigated the influence of the plasma membrane environment on the molecular evolution of G protein-coupled receptors (GPCRs), the largest receptor family in Metazoa. In particular, we have analyzed the site-specific rate variation across the two primary structural partitions, transmembrane (TM) and extramembrane (EM), of these membrane proteins. We find that transmembrane domains evolve more slowly than do extramembrane domains, though TM domains display increased rate heterogeneity relative to their EM counterparts. Although the majority of residues across GPCRs experience strong to weak purifying selection, many GPCRs experience positive selection at both TM and EM residues, albeit with a slight bias towards the EM. Further, a subset of GPCRs, chemosensory receptors (including olfactory and taste receptors), exhibit increased rates of evolution relative to other GPCRs, an effect which is more pronounced in their TM spans. Although it has been previously suggested that the TM's low evolutionary rate is caused by their high percentage of buried residues, we show that their attenuated rate seems to stem from the strong biophysical constraints of the membrane itself, or by functional requirements. In spite of the strong evolutionary constraints acting on the transmembrane spans of GPCRs, positive selection and high levels of evolutionary rate variability are common. Thus, biophysical constraints should not be presumed to preclude a protein's ability to evolve.

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arXiv2017

Brain Gene Expression Analysis: a MATLAB toolbox for the analysis of brain-wide gene-expression data

The Allen Brain Atlas project (ABA) generated a genome-scale collection of gene-expression profiles using in-situ hybridization. These profiles were co-registered to the three-dimensional Allen Reference Atlas (ARA) of the adult mouse brain. A set of more than 4,000 such volumetric data are available for the full brain, at a resolution of 200 microns. These data are presented in a voxel-by-gene matrix. The ARA comes with several systems of annotation, hierarchical (40 cortical regions, 209 sub-cortical regions in the whole brain), or non-hierarchical (12 regions in the left hemisphere, with refinement into 94 regions, and cortical layers). The high-dimensional nature of this dataset and the possible connection between anatomy and gene expression pose challenges to data analysis. We developed the Brain Gene Expression Analysis Toolbox, whose functionalities include: determination of marker genes for brain regions, statistical analysis of brain-wide co-expression patterns, and the computation of brain-wide correlation maps with cell-type specific microarray data.

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arXiv2012

A Combinatorial Perspective of the Protein Inference Problem

In a shotgun proteomics experiment, proteins are the most biologically meaningful output. The success of proteomics studies depends on the ability to accurately and efficiently identify proteins. Many methods have been proposed to facilitate the identification of proteins from the results of peptide identification. However, the relationship between protein identification and peptide identification has not been thoroughly explained before. In this paper, we are devoted to a combinatorial perspective of the protein inference problem. We employ combinatorial mathematics to calculate the conditional protein probabilities (Protein probability means the probability that a protein is correctly identified) under three assumptions, which lead to a lower bound, an upper bound and an empirical estimation of protein probabilities, respectively. The combinatorial perspective enables us to obtain a closed-form formulation for protein inference. Based on our model, we study the impact of unique peptides and degenerate peptides on protein probabilities. Here, degenerate peptides are peptides shared by at least two proteins. Meanwhile, we also study the relationship of our model with other methods such as ProteinProphet. A probability confidence interval can be calculated and used together with probability to filter the protein identification result. Our method achieves competitive results with ProteinProphet in a more efficient manner in the experiment based on two datasets of standard protein mixtures and two datasets of real samples. We name our program ProteinInfer. Its Java source code is available at http://bioinformatics.ust.hk/proteininfer

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arXiv2012

Running PeptideProphet Separately on Replicates Improves Peptide Identification Results

Limited spectrum coverage is a problem in shotgun proteomics. Replicates are generated to improve the spectrum coverage. When integrating peptide identification results obtained from replicates, the state-of-the-art algorithm PeptideProphet combines Peptide-Spectrum Matches (PSMs) before building the statistical model to calculate peptide probabilities. In this paper, we find the connection between merging results of replicates and Bagging, which is a standard routine to improve the power of statistical methods. Following Bagging's philosophy, we propose to run PeptideProphet separately on each replicate and combine the outputs to obtain the final peptide probabilities. In our experiments, we show that the proposed routine can improve PeptideProphet consistently on a standard protein dataset, a Human dataset and a Yeast dataset.

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arXiv2012

Examining marginal sequence similarities between bacterial type III secretion system and Trypanosoma cruzi surface proteins: Horizontal gene transfer or convergent evolution?

The cell invasion mechanism of Trypanosoma cruzi has similarities with some intracellular bacterial taxa especially regarding calcium mobilization. This mechanism is not observed in other trypanosomatids, suggesting that the molecules involved in this type of cell invasion were a product of (1) acquired by horizontal gene transfer; (2) secondary loss in the other trypanosomatid lineages of the mechanism inherited since the bifurcation Bacteria-Neomura (1.9 billion to 900 million years ago) or (3) de novo evolution from non-homologous proteins via convergent evolution. Similar to T. cruzi, several bacterial genera require increased host cell cytosolic calcium for intracellular invasion. Among intracellular bacteria, the mechanism of host cell invasion of genus Salmonella is the most similar to T. cruzi. The invasion of Salmonella occurs by contact with the host's cell surface and is mediated by the type III secretion system (T3SS) that promotes the contact-dependent translocation of effector proteins directly into host's cell cytoplasm. Here we provide evidence of distant sequence similarities and structurally conserved domains between T. cruzi and Salmonella spp T3SS proteins. Exhaustive database searches were directed to a wide range of intracellular bacteria and trypanosomatids, exploring sequence patterns for comparison of structural similarities and Bayesian phylogenies. Based on our data we hypothesize that T. cruzi acquired genes for calcium mobilization mediated invasion by ancient horizontal gene transfer from ancestral Salmonella lineages.

q-bio.PE↗

arXiv2012

ZRT1 harbors an excess of nonsynonymous polymorphism and shows evidence of balancing selection in Saccharomyces cerevisiae

Estimates of the fraction of nucleotide substitutions driven by positive selection vary widely across different species. Accounting for different estimates of positive selection has been difficult, in part because selection on polymorphism within a species is known to obscure a signal of positive selection between species. While methods have been developed to control for the confounding effects of negative selection against deleterious polymorphism, the impact of balancing selection on estimates of positive selection has not been assessed. In Saccharomyces cerevisiae, there is no signal of positive selection within protein coding sequences as the ratio of nonsynonymous to synonymous polymorphism is higher than that of divergence. To investigate the impact of balancing selection on estimates of positive selection we examined five genes with high rates of nonsynonymous polymorphism in S. cerevisiae relative to divergence from S. paradoxus. One of the genes, a high affinity zinc transporter ZRT1, shows an elevated rate of synonymous polymorphism indicative of balancing selection. The high rate of synonymous polymorphism coincides with nonsynonymous divergence between three haplotype groups, which we find to be functionally indistinguishable. We conclude that balancing selection is not likely to be a common cause of genes harboring a large excess of nonsynonymous polymorphism in yeast.

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arXiv2012

Illumina Sequencing Artifacts Revealed by Connectivity Analysis of Metagenomic Datasets

Sequencing errors and biases in metagenomic datasets affect coverage-based assemblies and are often ignored during analysis. Here, we analyze read connectivity in metagenomes and identify the presence of problematic and likely a-biological connectivity within metagenome assembly graphs. Specifically, we identify highly connected sequences which join a large proportion of reads within each real metagenome. These sequences show position-specific bias in shotgun reads, suggestive of sequencing artifacts, and are only minimally incorporated into contigs by assembly. The removal of these sequences prior to assembly results in similar assembly content for most metagenomes and enables the use of graph partitioning to decrease assembly memory and time requirements.

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arXiv2012

Biological Database of Images and Genomes: tools for community annotations linking image and genomic information

Genomic data and biomedical imaging data are undergoing exponential growth. However, our understanding of the phenotype-genotype connection linking the two types of data is lagging behind. While there are many types of software that enable the manipulation and analysis of image data and genomic data as separate entities, there is no framework established for linking the two. We present a generic set of software tools, BioDIG, that allows linking of image data to genomic data. BioDIG tools can be applied to a wide range of research problems that require linking images to genomes. BioDIG features the following: rapid construction of web-based workbenches, community-based annotation, user management, and web-services. By using BioDIG to create websites, researchers and curators can rapidly annotate large number of images with genomic information. Here we present the BioDIG software tools that include an image module, a genome module and a user management module. We also introduce a BioDIG-based website, MyDIG, which is being used to annotate images of Mycoplasma.

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arXiv2012

GWAPP: A Web Application for Genome-wide Association Mapping in A. thaliana

Arabidopsis thaliana is an important model organism for understanding the genetics and molecular biology of plants. Its highly selfing nature, together with other important features, such as small size, short generation time, small genome size, and wide geographic distribution, make it an ideal model organism for understanding natural variation. Genome-wide association studies (GWAS) have proven a useful technique for identifying genetic loci responsible for natural variation in A. thaliana. Previously genotyped accessions (natural inbred lines) can be grown in replicate under different conditions, and phenotyped for different traits. These important features greatly simplify association mapping of traits and allow for systematic dissection of the genetics of natural variation by the entire Arabidopsis community. To facilitate this, we present GWAPP, an interactive web-based application for conducting GWAS in A. thaliana. Using an efficient Python implementation of a linear mixed model, traits measured for a subset of 1386 publicly available ecotypes can be uploaded and mapped with an efficient mixed model and other methods in just a couple of minutes. GWAPP features an extensive, interactive, and a user-friendly interface that includes interactive manhattan plots and interactive local and genome-wide LD plots. It facilitates exploratory data analysis by implementing features such as the inclusion of candidate SNPs in the model as cofactors.

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arXiv2012

Quantitative transcriptional analysis of aging C. elegans

My analysis uses methods developed for data mining microarray experiments, adapted for ageing research. Methods bridge knowledge of statistical mechanics with data mining methods developed in statistical mathematics. Analyses can reveal how the transcriptional regulation of genes might coincide, thereby implicating proteins as parts of networks acting together towards a common biological function. Such experiments are most useful for complex biological traits that result from the presumed functioning of several molecular pathways. Ageing is one such biological phenomenon that apparently incorporates numerous molecular mechanisms underlying environmental stimulus sensing, metabolic regulation, stress responses, reproductive signalling, hibernation, and transcriptional regulation.

q-bio.QM↗

arXiv2012

In the search of molecular signature of sarcopenia in C. elegans

Age-related muscle decline, a condition referred to as sarcopenia and defined as loss in muscle mass and muscle strength over time, is one of the most pervasive problems of the elderly, such that significant declines in strength and mobility affects essentially every old person We have found that ageing C. elegans body wall muscle undergoes a process remarkably reminiscent of human sarcopenia. Both have mid-life onset and are characterized by progressive loss of sarcomeres and cytoplasmic volume; both are associated with locomotory decline. To extend understanding of this fundamental problem, I surveyed expression of all known muscle related genes to describe a profile of transcriptional changes in muscle that transpires during adult life and ageing. Importantly, the intersection of this dataset with that from ageing flies and some human studies can suggest conserved genes that might impact the process most strongly. Hypotheses I formulate will be used to drive experiments at the bench and perhaps to focus attention for human therapies.

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arXiv2012

Assembling large, complex environmental metagenomes

The large volumes of sequencing data required to sample complex environments deeply pose new challenges to sequence analysis approaches. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires significant computational resources. We apply two pre-assembly filtering approaches, digital normalization and partitioning, to make large metagenome assemblies more comput\ ationaly tractable. Using a human gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes from matched Iowa corn and native prairie soils. The predicted functional content and phylogenetic origin of the assembled contigs indicate significant taxonomic differences despite similar function. The assembly strategies presented are generic and can be extended to any metagenome; full source code is freely available under a BSD license.

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