Mostafa Ronaghi

Multiplexed genotyping with sequence-tagged molecular inversion probes

We report on the development of molecular inversion probe (MIP) genotyping, an efficient technology for large-scale single nucleotide polymorphism (SNP) analysis. This technique uses MIPs to produce inverted sequences, which undergo a unimolecular rearrangement and are then amplified by PCR using common primers and analyzed using universal sequence tag DNA microarrays, resulting in highly specific genotyping. With this technology, multiplex analysis of more than 1,000 probes in a single tube can be done using standard laboratory equipment. Genotypes are generated with a high call rate (95%) and high accuracy (>99%) as determined by independent sequencing.

A pyrosequencing-tailored nucleotide barcode design unveils opportunities for large-scale sample multiplexing

Multiplexed high-throughput pyrosequencing is currently limited in complexity (number of samples sequenced in parallel), and in capacity (number of sequences obtained per sample). Physical-space segregation of the sequencing platform into a fixed number of channels allows limited multiplexing, but obscures available sequencing space. To overcome these limitations, we have devised a novel barcoding approach to allow for pooling and sequencing of DNA from independent samples, and to facilitate subsequent segregation of sequencing capacity. Forty-eight forward–reverse barcode pairs are described: each forward and each reverse barcode unique with respect to at least 4 nt positions. With improved read lengths of pyrosequencers, combinations of forward and reverse barcodes may be used to sequence from as many as n2 independent libraries for each set of ‘n’ forward and ‘n’ reverse barcodes, for each defined set of cloning-linkers. In two pilot series of barcoded sequencing using the GS20 Sequencer (454/Roche), we found that over 99.8% of obtained sequences could be assigned to 25 independent, uniquely barcoded libraries based on the presence of either a perfect forward or a perfect reverse barcode. The false-discovery rate, as measured by the percentage of sequences with unexpected perfect pairings of unmatched forward and reverse barcodes, was estimated to be <0.005%.

Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data

Background The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. Methodology/Results We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. Conclusions Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

Viral Population Estimation Using Pyrosequencing

The diversity of virus populations within single infected hosts presents a major difficulty for the natural immune response as well as for vaccine design and antiviral drug therapy. Recently developed pyrophosphate-based sequencing technologies (pyrosequencing) can be used for quantifying this diversity by ultra-deep sequencing of virus samples. We present computational methods for the analysis of such sequence data and apply these techniques to pyrosequencing data obtained from HIV populations within patients harboring drug-resistant virus strains. Our main result is the estimation of the population structure of the sample from the pyrosequencing reads. This inference is based on a statistical approach to error correction, followed by a combinatorial algorithm for constructing a minimal set of haplotypes that explain the data. Using this set of explaining haplotypes, we apply a statistical model to infer the frequencies of the haplotypes in the population via an expectation–maximization (EM) algorithm. We demonstrate that pyrosequencing reads allow for effective population reconstruction by extensive simulations and by comparison to 165 sequences obtained directly from clonal sequencing of four independent, diverse HIV populations. Thus, pyrosequencing can be used for cost-effective estimation of the structure of virus populations, promising new insights into viral evolutionary dynamics and disease control strategies.

Six RNA viruses and forty-one hosts: Viral small RNAs and modulation of small RNA repertoires in vertebrate and invertebrate systems

We have used multiplexed high-throughput sequencing to characterize changes in small RNA populations that occur during viral infection in animal cells. Small RNA-based mechanisms such as RNA interference (RNAi) have been shown in plant and invertebrate systems to play a key role in host responses to viral infection. Although homologs of the key RNAi effector pathways are present in mammalian cells, and can launch an RNAi-mediated degradation of experimentally targeted mRNAs, any role for such responses in mammalian host-virus interactions remains to be characterized. Six different viruses were examined in 41 experimentally susceptible and resistant host systems. We identified virus-derived small RNAs (vsRNAs) from all six viruses, with total abundance varying from “vanishingly rare” (less than 0.1% of cellular small RNA) to highly abundant (comparable to abundant micro-RNAs “miRNAs”). In addition to the appearance of vsRNAs during infection, we saw a number of specific changes in host miRNA profiles. For several infection models investigated in more detail, the RNAi and Interferon pathways modulated the abundance of vsRNAs. We also found evidence for populations of vsRNAs that exist as duplexed siRNAs with zero to three nucleotide 3′ overhangs. Using populations of cells carrying a Hepatitis C replicon, we observed strand-selective loading of siRNAs onto Argonaute complexes. These experiments define vsRNAs as one possible component of the interplay between animal viruses and their hosts.

Bacterial flora-typing with targeted, chip-based Pyrosequencing

BackgroundThe metagenomic analysis of microbial communities holds the potential to improve our understanding of the role of microbes in clinical conditions. Recent, dramatic improvements in DNA sequencing throughput and cost will enable such analyses on individuals. However, such advances in throughput generally come at the cost of shorter read-lengths, limiting the discriminatory power of each read. In particular, classifying the microbial content of samples by sequencing the < 1,600 bp 16S rRNA gene will be affected by such limitations.ResultsWe describe a method for identifying the phylogenetic content of bacterial samples using high-throughput Pyrosequencing targeted at the 16S rRNA gene. Our analysis is adapted to the shorter read-lengths of such technology and uses a database of 16S rDNA to determine the most specific phylogenetic classification for reads, resulting in a weighted phylogenetic tree characterizing the content of the sample. We present results for six samples obtained from the human vagina during pregnancy that corroborates previous studies using conventional techniques.Next, we analyze the power of our method to classify reads at each level of the phylogeny using simulation experiments. We assess the impacts of read-length and database completeness on our method, and predict how we do as technology improves and more bacteria are sequenced. Finally, we study the utility of targeting specific 16S variable regions and show that such an approach considerably improves results for certain types of microbial samples. Using simulation, our method can be used to determine the most informative variable region.ConclusionThis study provides positive validation of the effectiveness of targeting 16S metagenomes using short-read sequencing technology. Our methodology allows us to infer the most specific assignment of the sequence reads within the phylogeny, and to identify the most discriminative variable region to target. The analysis of high-throughput Pyrosequencing on human flora samples will accelerate the study of the relationship between the microbial world and ourselves.

Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain

Single-nucleus gene expression Identifying the genes expressed at the level of a single cell nucleus can better help us understand the human brain. Blue et al. developed a single-nuclei sequencing technique, which they applied to cells in classically defined Brodmann areas from a postmortem brain. Clustering of gene expression showed concordance with the area of origin and defining 16 neuronal subtypes. Both excitatory and inhibitory neuronal subtypes show regional variations that define distinct cortical areas and exhibit how gene expression clusters may distinguish between distinct cortical areas. This method opens the door to widespread sampling of the genes expressed in a diseased brain and other tissues of interest. Science, this issue p. 1586 Individual neurons have specific transcriptomic signatures and transcriptomic heterogeneity. The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.

Genome-wide Linkage Analysis of a Parkinsonian-Pyramidal Syndrome Pedigree by 500 K SNP Arrays

Robust SNP genotyping technologies and data analysis programs have encouraged researchers in recent years to use SNPs for linkage studies. Platforms used to date have been 10 K chip arrays, but the possible value of interrogating SNPs at higher densities has been considered. Here, we present a genome-wide linkage analysis by means of a 500 K SNP platform. The analysis was done on a large pedigree affected with Parkinsonian-pyramidal syndrome (PPS), and the results showed linkage to chromosome 22. Sequencing of candidate genes revealed a disease-associated homozygous variation (R378G) in FBXO7. FBXO7 codes for a member of the F-box family of proteins, all of which may have a role in the ubiquitin-proteosome protein-degradation pathway. This pathway has been implicated in various neurodegenerative diseases, and identification of FBXO7 as the causative gene of PPS is expected to shed new light on its role. The performance of the array was assessed and systematic analysis of effects of SNP density reduction was performed with the real experimental data. Our results suggest that linkage in our pedigree may have been missed had we used chips containing less than 100,000 SNPs across the genome.

Whole-Genome Sequencing and Assembly with High-Throughput, Short-Read Technologies

While recently developed short-read sequencing technologies may dramatically reduce the sequencing cost and eventually achieve the

mRNA-Seq of Single Prostate Cancer Circulating Tumor Cells Reveals Recapitulation of Gene Expression and Pathways Found in Prostate Cancer

1000 goal for re-sequencing, their limitations prevent the de novo sequencing of eukaryotic genomes with the standard shotgun sequencing protocol. We present SHRAP (SHort Read Assembly Protocol), a sequencing protocol and assembly methodology that utilizes high-throughput short-read technologies. We describe a variation on hierarchical sequencing with two crucial differences: (1) we select a clone library from the genome randomly rather than as a tiling path and (2) we sample clones from the genome at high coverage and reads from the clones at low coverage. We assume that 200 bp read lengths with a 1% error rate and inexpensive random fragment cloning on whole mammalian genomes is feasible. Our assembly methodology is based on first ordering the clones and subsequently performing read assembly in three stages: (1) local assemblies of regions significantly smaller than a clone size, (2) clone-sized assemblies of the results of stage 1, and (3) chromosome-sized assemblies. By aggressively localizing the assembly problem during the first stage, our method succeeds in assembling short, unpaired reads sampled from repetitive genomes. We tested our assembler using simulated reads from D. melanogaster and human chromosomes 1, 11, and 21, and produced assemblies with large sets of contiguous sequence and a misassembly rate comparable to other draft assemblies. Tested on D. melanogaster and the entire human genome, our clone-ordering method produces accurate maps, thereby localizing fragment assembly and enabling the parallelization of the subsequent steps of our pipeline. Thus, we have demonstrated that truly inexpensive de novo sequencing of mammalian genomes will soon be possible with high-throughput, short-read technologies using our methodology.