Publications by authors named "Vladimir Jojic"

23 Publications

  • Page 1 of 1

Fully Phased Sequence of a Diploid Human Genome Determined from the DNA of a Single Individual.

G3 (Bethesda) 2020 09 2;10(9):2911-2925. Epub 2020 Sep 2.

Calico Life Sciences LLC, South San Francisco, CA 94080

In recent years, improved sequencing technology and computational tools have made genome assembly more accessible. Many approaches, however, generate either an unphased or only partially resolved representation of a diploid genome, in which polymorphisms are detected but not assigned to one or the other of the homologous chromosomes. Yet chromosomal phase information is invaluable for the understanding of phenotypic trait inheritance in the cases of compound heterozygosity, allele-specific expression or -acting variants. Here we use a combination of tools and sequencing technologies to generate a diploid assembly of the human primary cell line WI-38. First, data from PacBio single molecule sequencing and Bionano Genomics optical mapping were combined to generate an unphased assembly. Next, 10x Genomics linked reads were combined with the hybrid assembly to generate a partially phased assembly. Lastly, we developed and optimized methods to use short-read (Illumina) sequencing of flow cytometry-sorted metaphase chromosomes to provide phase information. The final genome assembly was almost fully (94%) phased with the addition of approximately 2.5-fold coverage of Illumina data from the sequenced metaphase chromosomes. The diploid nature of the final genome assembly improved the resolution of structural variants between the WI-38 genome and the human reference genome. The phased WI-38 sequence data are available for browsing and download at wi38.research.calicolabs.com. Our work shows that assembling a completely phased diploid genome from the DNA of a single individual is now readily achievable.
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http://dx.doi.org/10.1534/g3.119.400995DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7466960PMC
September 2020

Modeling Multiplexed Images with Reveals Novel Tissue Microenvironments.

J Comput Biol 2020 08 3;27(8):1204-1218. Epub 2020 Apr 3.

Calico Life Sciences LLC, South San Francisco, California, USA.

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http://dx.doi.org/10.1089/cmb.2019.0340DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7415889PMC
August 2020

Single-cell transcriptomics of the naked mole-rat reveals unexpected features of mammalian immunity.

PLoS Biol 2019 11 21;17(11):e3000528. Epub 2019 Nov 21.

Calico Life Sciences LLC, South San Francisco, California, United States of America.

The immune system comprises a complex network of specialized cells that protects against infection, eliminates cancerous cells, and regulates tissue repair, thus serving a critical role in homeostasis, health span, and life span. The subterranean-dwelling naked mole-rat (NM-R; Heterocephalus glaber) exhibits prolonged life span relative to its body size, is unusually cancer resistant, and manifests few physiological or molecular changes with advancing age. We therefore hypothesized that the immune system of NM-Rs evolved unique features that confer enhanced cancer immunosurveillance and prevent the age-associated decline in homeostasis. Using single-cell RNA-sequencing (scRNA-seq) we mapped the immune system of the NM-R and compared it to that of the short-lived, cancer-prone mouse. In contrast to the mouse, we find that the NM-R immune system is characterized by a high myeloid-to-lymphoid cell ratio that includes a novel, lipopolysaccharide (LPS)-responsive, granulocyte cell subset. Surprisingly, we also find that NM-Rs lack canonical natural killer (NK) cells. Our comparative genomics analyses support this finding, showing that the NM-R genome lacks an expanded gene family that controls NK cell function in several other species. Furthermore, we reconstructed the evolutionary history that likely led to this genomic state. The NM-R thus challenges our current understanding of mammalian immunity, favoring an atypical, myeloid-biased mode of innate immunosurveillance, which may contribute to its remarkable health span.
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http://dx.doi.org/10.1371/journal.pbio.3000528DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894886PMC
November 2019

HLA-B57 micropolymorphism defines the sequence and conformational breadth of the immunopeptidome.

Nat Commun 2018 11 8;9(1):4693. Epub 2018 Nov 8.

Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia.

Immunophenotypic differences between closely related human leukocyte antigen (HLA) alleles have been associated with divergent clinical outcomes in infection, autoimmunity, transplantation and drug hypersensitivity. Here we explore the impact of micropolymorphism on peptide antigen presentation by three closely related HLA molecules, HLA-B*57:01, HLA-B*57:03 and HLA-B*58:01, that are differentially associated with the HIV elite controller phenotype and adverse drug reactions. For each allotype, we mine HLA ligand data sets derived from the same parental cell proteome to define qualitative differences in peptide presentation using classical peptide binding motifs and an unbiased statistical approach. The peptide repertoires show marked qualitative overlap, with 982 peptides presented by all allomorphs. However, differences in peptide abundance, HLA-peptide stability, and HLA-bound conformation demonstrate that HLA micropolymorphism impacts more than simply the range of peptide ligands. These differences provide grounds for distinct immune reactivity and insights into the capacity of micropolymorphism to diversify immune outcomes.
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http://dx.doi.org/10.1038/s41467-018-07109-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6224591PMC
November 2018

Design of synthetic bacterial communities for predictable plant phenotypes.

PLoS Biol 2018 02 20;16(2):e2003962. Epub 2018 Feb 20.

Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.

Specific members of complex microbiota can influence host phenotypes, depending on both the abiotic environment and the presence of other microorganisms. Therefore, it is challenging to define bacterial combinations that have predictable host phenotypic outputs. We demonstrate that plant-bacterium binary-association assays inform the design of small synthetic communities with predictable phenotypes in the host. Specifically, we constructed synthetic communities that modified phosphate accumulation in the shoot and induced phosphate starvation-responsive genes in a predictable fashion. We found that bacterial colonization of the plant is not a predictor of the plant phenotypes we analyzed. Finally, we demonstrated that characterizing a subset of all possible bacterial synthetic communities is sufficient to predict the outcome of untested bacterial consortia. Our results demonstrate that it is possible to infer causal relationships between microbiota membership and host phenotypes and to use these inferences to rationally design novel communities.
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http://dx.doi.org/10.1371/journal.pbio.2003962DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819758PMC
February 2018

Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes.

Nat Commun 2017 05 5;8:15309. Epub 2017 May 5.

Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, UK.

Transcript levels are a critical determinant of the proteome and hence cellular function. Because the transcriptome is an outcome of the interactions between genes and their products, it may be accurately represented by a subset of transcript abundances. We develop a method, Tradict (transcriptome predict), capable of learning and using the expression measurements of a small subset of 100 marker genes to predict transcriptome-wide gene abundances and the expression of a comprehensive, but interpretable list of transcriptional programs that represent the major biological processes and pathways of the cell. By analyzing over 23,000 publicly available RNA-Seq data sets, we show that Tradict is robust to noise and accurate. Coupled with targeted RNA sequencing, Tradict may therefore enable simultaneous transcriptome-wide screening and mechanistic investigation at large scales.
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http://dx.doi.org/10.1038/ncomms15309DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424156PMC
May 2017

Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states.

Nat Med 2017 Feb 16;23(2):174-184. Epub 2017 Jan 16.

CIRID, UMR CNRS 5164, Université Bordeaux 2, Bordeaux Cedex, France.

Low-grade, chronic inflammation has been associated with many diseases of aging, but the mechanisms responsible for producing this inflammation remain unclear. Inflammasomes can drive chronic inflammation in the context of an infectious disease or cellular stress, and they trigger the maturation of interleukin-1β (IL-1β). Here we find that the expression of specific inflammasome gene modules stratifies older individuals into two extremes: those with constitutive expression of IL-1β, nucleotide metabolism dysfunction, elevated oxidative stress, high rates of hypertension and arterial stiffness; and those without constitutive expression of IL-1β, who lack these characteristics. Adenine and N-acetylcytidine, nucleotide-derived metabolites that are detectable in the blood of the former group, prime and activate the NLRC4 inflammasome, induce the production of IL-1β, activate platelets and neutrophils and elevate blood pressure in mice. In individuals over 85 years of age, the elevated expression of inflammasome gene modules was associated with all-cause mortality. Thus, targeting inflammasome components may ameliorate chronic inflammation and various other age-associated conditions.
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http://dx.doi.org/10.1038/nm.4267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320935PMC
February 2017

Learning Microbial Interaction Networks from Metagenomic Count Data.

J Comput Biol 2016 06;23(6):526-35

5 Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.

Many microbes associate with higher eukaryotes and impact their vitality. To engineer microbiomes for host benefit, we must understand the rules of community assembly and maintenance that, in large part, demand an understanding of the direct interactions among community members. Toward this end, we have developed a Poisson-multivariate normal hierarchical model to learn direct interactions from the count-based output of standard metagenomics sequencing experiments. Our model controls for confounding predictors at the Poisson layer and captures direct taxon-taxon interactions at the multivariate normal layer using an ℓ1 penalized precision matrix. We show in a synthetic experiment that our method handily outperforms state-of-the-art methods such as SparCC and the graphical lasso (glasso). In a real in planta perturbation experiment of a nine-member bacterial community, we show our model, but not SparCC or glasso, correctly resolves a direct interaction structure among three community members that associates with Arabidopsis thaliana roots. We conclude that our method provides a structured, accurate, and distributionally reasonable way of modeling correlated count-based random variables and capturing direct interactions among them.
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http://dx.doi.org/10.1089/cmb.2016.0061DOI Listing
June 2016

Cytomegalovirus infection enhances the immune response to influenza.

Sci Transl Med 2015 Apr;7(281):281ra43

Department of Microbiology and Immunology, School of Medicine, Stanford University, Stanford, CA 94305, USA. Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA. The Howard Hughes Medical Institute.

Cytomegalovirus (CMV) is a β-herpesvirus present in a latent form in most people worldwide. In immunosuppressed individuals, CMV can reactivate and cause serious clinical complications, but the effect of the latent state on healthy people remains elusive. We undertook a systems approach to understand the differences between seropositive and negative subjects and measured hundreds of immune system components from blood samples including cytokines and chemokines, immune cell phenotyping, gene expression, ex vivo cell responses to cytokine stimuli, and the antibody response to seasonal influenza vaccination. As expected, we found decreased responses to vaccination and an overall down-regulation of immune components in aged individuals regardless of CMV status. In contrast, CMV-seropositive young adults exhibited enhanced antibody responses to influenza vaccination, increased CD8(+) T cell sensitivity, and elevated levels of circulating interferon-γ compared to seronegative individuals. Experiments with young mice infected with murine CMV also showed significant protection from an influenza virus challenge compared with uninfected animals, although this effect declined with time. These data show that CMV and its murine equivalent can have a beneficial effect on the immune response of young, healthy individuals, which may explain the ubiquity of CMV infection in humans and many other species.
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http://dx.doi.org/10.1126/scitranslmed.aaa2293DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4505610PMC
April 2015

Variation in the human immune system is largely driven by non-heritable influences.

Cell 2015 Jan;160(1-2):37-47

Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94304, USA; Institute of Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94304, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94304, USA. Electronic address:

There is considerable heterogeneity in immunological parameters between individuals, but its sources are largely unknown. To assess the relative contribution of heritable versus non-heritable factors, we have performed a systems-level analysis of 210 healthy twins between 8 and 82 years of age. We measured 204 different parameters, including cell population frequencies, cytokine responses, and serum proteins, and found that 77% of these are dominated (>50% of variance) and 58% almost completely determined (>80% of variance) by non-heritable influences. In addition, some of these parameters become more variable with age, suggesting the cumulative influence of environmental exposure. Similarly, the serological responses to seasonal influenza vaccination are also determined largely by non-heritable factors, likely due to repeated exposure to different strains. Lastly, in MZ twins discordant for cytomegalovirus infection, more than half of all parameters are affected. These results highlight the largely reactive and adaptive nature of the immune system in healthy individuals.
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http://dx.doi.org/10.1016/j.cell.2014.12.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302727PMC
January 2015

Drug-induced mRNA signatures are enriched for the minority of genes that are highly heritable.

Pac Symp Biocomput 2015 :395-406

Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

The blood gene expression signatures are used as biomarkers for immunological and non- immunological diseases. Therefore, it is important to understand the variation in blood gene expression patterns and the factors (heritable/non-heritable) that underlie this variation. In this paper, we study the relationship between drug effects on the one hand, and heritable and non-heritable factors influencing gene expression on the other. Understanding of this relationship can help select appropriate targets for drugs aimed at reverting disease phenotypes to healthy states. In order to estimate heritable and non-heritable effects on gene expression, we use Twin-ACE model on a gene expression dataset MuTHER, measured in blood samples from monozygotic and dizygotic twins. In order to associate gene expression with drug effects, we use CMap database. We show that, even though the expressions of most genes are driven by non-heritable factors, drugs are more likely to influence expression of genes, driven by heritable rather than non-heritable factors. We further study this finding in the context of a gene regulatory network. We investigate the relationship between the drug effects on gene expression and propagation of heritable and non-heritable factors through regulatory networks. We find that the decisive factor in determining whether a gene will be influenced by a drug is the flow of heritable effects supplied to the gene through regulatory network.
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April 2016

Robust multimodal dictionary learning.

Med Image Comput Comput Assist Interv 2013 ;16(Pt 1):259-66

University of North Carolina at Chapel Hill, NC, USA.

We propose a robust multimodal dictionary learning method for multimodal images. Joint dictionary learning for both modalities may be impaired by lack of correspondence between image modalities in training data, for example due to areas of low quality in one of the modalities. Dictionaries learned with such non-corresponding data will induce uncertainty about image representation. In this paper, we propose a probabilistic model that accounts for image areas that are poorly corresponding between the image modalities. We cast the problem of learning a dictionary in presence of problematic image patches as a likelihood maximization problem and solve it with a variant of the EM algorithm. Our algorithm iterates identification of poorly corresponding patches and refinements of the dictionary. We tested our method on synthetic and real data. We show improvements in image prediction quality and alignment accuracy when using the method for multimodal image registration.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4339052PMC
http://dx.doi.org/10.1007/978-3-642-40811-3_33DOI Listing
February 2014

Systems analysis of sex differences reveals an immunosuppressive role for testosterone in the response to influenza vaccination.

Proc Natl Acad Sci U S A 2014 Jan 23;111(2):869-74. Epub 2013 Dec 23.

Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305-5323.

Females have generally more robust immune responses than males for reasons that are not well-understood. Here we used a systems analysis to investigate these differences by analyzing the neutralizing antibody response to a trivalent inactivated seasonal influenza vaccine (TIV) and a large number of immune system components, including serum cytokines and chemokines, blood cell subset frequencies, genome-wide gene expression, and cellular responses to diverse in vitro stimuli, in 53 females and 34 males of different ages. We found elevated antibody responses to TIV and expression of inflammatory cytokines in the serum of females compared with males regardless of age. This inflammatory profile correlated with the levels of phosphorylated STAT3 proteins in monocytes but not with the serological response to the vaccine. In contrast, using a machine learning approach, we identified a cluster of genes involved in lipid biosynthesis and previously shown to be up-regulated by testosterone that correlated with poor virus-neutralizing activity in men. Moreover, men with elevated serum testosterone levels and associated gene signatures exhibited the lowest antibody responses to TIV. These results demonstrate a strong association between androgens and genes involved in lipid metabolism, suggesting that these could be important drivers of the differences in immune responses between males and females.
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http://dx.doi.org/10.1073/pnas.1321060111DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896147PMC
January 2014

Identification of transcriptional regulators in the mouse immune system.

Nat Immunol 2013 Jun 28;14(6):633-43. Epub 2013 Apr 28.

Computer Science Department, Stanford University, Stanford, California, USA.

The differentiation of hematopoietic stem cells into cells of the immune system has been studied extensively in mammals, but the transcriptional circuitry that controls it is still only partially understood. Here, the Immunological Genome Project gene-expression profiles across mouse immune lineages allowed us to systematically analyze these circuits. To analyze this data set we developed Ontogenet, an algorithm for reconstructing lineage-specific regulation from gene-expression profiles across lineages. Using Ontogenet, we found differentiation stage-specific regulators of mouse hematopoiesis and identified many known hematopoietic regulators and 175 previously unknown candidate regulators, as well as their target genes and the cell types in which they act. Among the previously unknown regulators, we emphasize the role of ETV5 in the differentiation of γδ T cells. As the transcriptional programs of human and mouse cells are highly conserved, it is likely that many lessons learned from the mouse model apply to humans.
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http://dx.doi.org/10.1038/ni.2587DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3690947PMC
June 2013

Apoptosis and other immune biomarkers predict influenza vaccine responsiveness.

Mol Syst Biol 2013 Apr 16;9:659. Epub 2013 Apr 16.

Department of Microbiology and Immunology, School of Medicine, Stanford University, Palo Alto, CA 94305, USA.

Despite the importance of the immune system in many diseases, there are currently no objective benchmarks of immunological health. In an effort to identifying such markers, we used influenza vaccination in 30 young (20-30 years) and 59 older subjects (60 to >89 years) as models for strong and weak immune responses, respectively, and assayed their serological responses to influenza strains as well as a wide variety of other parameters, including gene expression, antibodies to hemagglutinin peptides, serum cytokines, cell subset phenotypes and in vitro cytokine stimulation. Using machine learning, we identified nine variables that predict the antibody response with 84% accuracy. Two of these variables are involved in apoptosis, which positively associated with the response to vaccination and was confirmed to be a contributor to vaccine responsiveness in mice. The identification of these biomarkers provides new insights into what immune features may be most important for immune health.
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http://dx.doi.org/10.1038/msb.2013.15DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3658270PMC
April 2013

Conservation and divergence in the transcriptional programs of the human and mouse immune systems.

Proc Natl Acad Sci U S A 2013 Feb 4;110(8):2946-51. Epub 2013 Feb 4.

Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142,USA.

Much of the knowledge about cell differentiation and function in the immune system has come from studies in mice, but the relevance to human immunology, diseases, and therapy has been challenged, perhaps more from anecdotal than comprehensive evidence. To this end, we compare two large compendia of transcriptional profiles of human and mouse immune cell types. Global transcription profiles are conserved between corresponding cell lineages. The expression patterns of most orthologous genes are conserved, particularly for lineage-specific genes. However, several hundred genes show clearly divergent expression across the examined cell lineages, and among them, 169 genes did so even with highly stringent criteria. Finally, regulatory mechanisms--reflected by regulators' differential expression or enriched cis-elements--are conserved between the species but to a lower degree, suggesting that distinct regulation may underlie some of the conserved transcriptional responses.
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http://dx.doi.org/10.1073/pnas.1222738110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3581886PMC
February 2013

Deciphering the transcriptional network of the dendritic cell lineage.

Nat Immunol 2012 Sep 15;13(9):888-99. Epub 2012 Jul 15.

Immunology Institute, Mount Sinai School of Medicine, New York, New York, USA.

Although much progress has been made in the understanding of the ontogeny and function of dendritic cells (DCs), the transcriptional regulation of the lineage commitment and functional specialization of DCs in vivo remains poorly understood. We made a comprehensive comparative analysis of CD8(+), CD103(+), CD11b(+) and plasmacytoid DC subsets, as well as macrophage DC precursors and common DC precursors, across the entire immune system. Here we characterized candidate transcriptional activators involved in the commitment of myeloid progenitor cells to the DC lineage and predicted regulators of DC functional diversity in tissues. We identified a molecular signature that distinguished tissue DCs from macrophages. We also identified a transcriptional program expressed specifically during the steady-state migration of tissue DCs to the draining lymph nodes that may control tolerance to self tissue antigens.
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http://dx.doi.org/10.1038/ni.2370DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3985403PMC
September 2012

Genovo: de novo assembly for metagenomes.

J Comput Biol 2011 Mar;18(3):429-43

Department of Computer Science, Stanford University, Stanford, California, USA.

Next-generation sequencing technologies produce a large number of noisy reads from the DNA in a sample. Metagenomics and population sequencing aim to recover the genomic sequences of the species in the sample, which could be of high diversity. Methods geared towards single sequence reconstruction are not sensitive enough when applied in this setting. We introduce a generative probabilistic model of read generation from environmental samples and present Genovo, a novel de novo sequence assembler that discovers likely sequence reconstructions under the model. A nonparametric prior accounts for the unknown number of genomes in the sample. Inference is performed by applying a series of hill-climbing steps iteratively until convergence. We compare the performance of Genovo to three other short read assembly programs in a series of synthetic experiments and across nine metagenomic datasets created using the 454 platform, the largest of which has 311k reads. Genovo's reconstructions cover more bases and recover more genes than the other methods, even for low-abundance sequences, and yield a higher assembly score. Supplementary Material is available at www.liebertoinline.com/cmb .
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http://dx.doi.org/10.1089/cmb.2010.0244DOI Listing
March 2011

Population sequencing using short reads: HIV as a case study.

Pac Symp Biocomput 2008 :114-25

Microsoft Research, Redmond, WA 98052, USA.

Despite many drawbacks, traditional sequencing technologies have proven to be invaluable in modern medical research, even when the targeted genomes are highly variable. While it is often known in such cases that multiple slightly different sequences are present in the analyzed sample in concentrations that vary dramatically, the traditional techniques typically allow only the most dominant strain to be extracted from a single chromatogram. These limitations made some research directions rather difficult to pursue. For example, the analysis of HIV evolution (including the emergence of drug resistance) in a single patient is expected to benefit from a comprehensive catalog of the patient's HIV population. In this paper, we show how the new generation of sequencing technologies, based on high throughput of short reads, can be used to link site variants and reconstruct multiple full strains of the targeted gene, including those of low concentration in the sample. Our algorithm is based on a generative model of the sequencing process, and uses a tailored probabilistic inference and learning procedure to fit the model to the obtained reads.
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March 2008

Recognition of HIV-1 peptides by host CTL is related to HIV-1 similarity to human proteins.

PLoS One 2007 Sep 5;2(9):e823. Epub 2007 Sep 5.

Department of Microbiology, University of Washington, Seattle, Washington, United States of America.

Background: While human immunodeficiency virus type 1 (HIV-1)-specific cytotoxic T lymphocytes preferentially target specific regions of the viral proteome, HIV-1 features that contribute to immune recognition are not well understood. One hypothesis is that similarities between HIV and human proteins influence the host immune response, i.e., resemblance between viral and host peptides could preclude reactivity against certain HIV epitopes.

Methodology/principal Findings: We analyzed the extent of similarity between HIV-1 and the human proteome. Proteins from the HIV-1 B consensus sequence from 2001 were dissected into overlapping k-mers, which were then probed against a non-redundant database of the human proteome in order to identify segments of high similarity. We tested the relationship between HIV-1 similarity to host encoded peptides and immune recognition in HIV-infected individuals, and found that HIV immunogenicity could be partially modulated by the sequence similarity to the host proteome. ELISpot responses to peptides spanning the entire viral proteome evaluated in 314 individuals showed a trend indicating an inverse relationship between the similarity to the host proteome and the frequency of recognition. In addition, analysis of responses by a group of 30 HIV-infected individuals against 944 overlapping peptides representing a broad range of individual HIV-1B Nef variants, affirmed that the degree of similarity to the host was significantly lower for peptides with reactive epitopes than for those that were not recognized.

Conclusions/significance: Our results suggest that antigenic motifs that are scarcely represented in human proteins might represent more immunogenic CTL targets not selected against in the host. This observation could provide guidance in the design of more effective HIV immunogens, as sequences devoid of host-like features might afford superior immune reactivity.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000823PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1952107PMC
September 2007

Efficient approximations for learning phylogenetic HMM models from data.

Bioinformatics 2004 Aug;20 Suppl 1:i161-8

Microsoft Research, Redmond, WA 98052, USA.

Motivation: We consider models useful for learning an evolutionary or phylogenetic tree from data consisting of DNA sequences corresponding to the leaves of the tree. In particular, we consider a general probabilistic model described in Siepel and Haussler that we call the phylogenetic-HMM model which generalizes the classical probabilistic models of Neyman and Felsenstein. Unfortunately, computing the likelihood of phylogenetic-HMM models is intractable. We consider several approximations for computing the likelihood of such models including an approximation introduced in Siepel and Haussler, loopy belief propagation and several variational methods.

Results: We demonstrate that, unlike the other approximations, variational methods are accurate and are guaranteed to lower bound the likelihood. In addition, we identify a particular variational approximation to be best-one in which the posterior distribution is variationally approximated using the classic Neyman-Felsenstein model. The application of our best approximation to data from the cystic fibrosis transmembrane conductance regulator gene region across nine eutherian mammals reveals a CpG effect.
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http://dx.doi.org/10.1093/bioinformatics/bth917DOI Listing
August 2004

A panoramic view of yeast noncoding RNA processing.

Cell 2003 Jun;113(7):919-33

Banting and Best Department of Medical Research, University of Toronto, 112 College Street, M5G 1L6, Toronto, Ontario, Canada.

Predictive analysis using publicly available yeast functional genomics and proteomics data suggests that many more proteins may be involved in biogenesis of ribonucleoproteins than are currently known. Using a microarray that monitors abundance and processing of noncoding RNAs, we analyzed 468 yeast strains carrying mutations in protein-coding genes, most of which have not previously been associated with RNA or RNP synthesis. Many strains mutated in uncharacterized genes displayed aberrant noncoding RNA profiles. Ten factors involved in noncoding RNA biogenesis were verified by further experimentation, including a protein required for 20S pre-rRNA processing (Tsr2p), a protein associated with the nuclear exosome (Lrp1p), and a factor required for box C/D snoRNA accumulation (Bcd1p). These data present a global view of yeast noncoding RNA processing and confirm that many currently uncharacterized yeast proteins are involved in biogenesis of noncoding RNA.
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http://dx.doi.org/10.1016/s0092-8674(03)00466-5DOI Listing
June 2003
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