Massively parallel tag sequencing reveals the complexity of anaerobic marine protistan communities

Background  

Recent advances in sequencing strategies make possible unprecedented depth and scale of sampling for molecular detection of microbial diversity. Two major paradigm-shifting discoveries include the detection of bacterial diversity that is one to two orders of magnitude greater than previous estimates, and the discovery of an exciting 'rare biosphere' of molecular signatures ('species') of poorly understood ecological significance. We applied a high-throughput parallel tag sequencing (454 sequencing) protocol adopted for eukaryotes to investigate protistan community complexity in two contrasting anoxic marine ecosystems (Framvaren Fjord, Norway; Cariaco deep-sea basin, Venezuela). Both sampling sites have previously been scrutinized for protistan diversity by traditional clone library construction and Sanger sequencing. By comparing these clone library data with 454 amplicon library data, we assess the efficiency of high-throughput tag sequencing strategies. We here present a novel, highly conservative bioinformatic analysis pipeline for the processing of large tag sequence data sets.     

Massively parallel sequencing of the mouse exome to accurately identify rare, induced mutations: an immediate source for thousands of new mouse models

Accurate identification of sparse heterozygous single-nucleotide variants (SNVs) is a critical challenge for identifying the causative mutations in mouse genetic screens, human genetic diseases and cancer. When seeking to identify causal DNA variants that occur at such low rates, they are overwhelmed by false-positive calls that arise from a range of technical and biological sources. We describe a strategy using whole-exome capture, massively parallel DNA sequencing and computational analysis, which identifies with a low false-positive rate the majority of heterozygous and homozygous SNVs arising de novo with a frequency of one nucleotide substitution per megabase in progeny of N-ethyl-N-nitrosourea (ENU)-mutated C57BL/6j mice. We found that by applying a strategy of filtering raw SNV calls against known and platform-specific variants we could call true SNVs with a false-positive rate of 19.4 per cent and an estimated false-negative rate of 21.3 per cent. These error rates are small enough to enable calling a causative mutation from both homozygous and heterozygous candidate mutation lists with little or no further experimental validation. The efficacy of this approach is demonstrated by identifying the causative mutation in the Ptprc gene in a lymphocyte-deficient strain and in 11 other strains with immune disorders or obesity, without the need for meiotic mapping. Exome sequencing of first-generation mutant mice revealed hundreds of unphenotyped protein-changing mutations, 52 per cent of which are predicted to be deleterious, which now become available for breeding and experimental analysis. We show that exome sequencing data alone are sufficient to identify induced mutations. This approach transforms genetic screens in mice, establishes a general strategy for analysing rare DNA variants and opens up a large new source for experimental models of human disease.     

Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers

Many microbial communities are characterized by high genetic diversity. 16S ribosomal RNA sequencing can determine community members, and metagenomics can determine the functional diversity, but resolving the functional role of individual cells in high throughput remains an unsolved challenge. Here, we describe epicPCR (Emulsion, Paired Isolation and Concatenation PCR), a new technique that links functional genes and phylogenetic markers in uncultured single cells, providing a throughput of hundreds of thousands of cells with costs comparable to one genomic library preparation. We demonstrate the utility of our technique in a natural environment by profiling a sulfate-reducing community in a freshwater lake, revealing both known sulfate reducers and discovering new putative sulfate reducers. Our method is adaptable to any conserved genetic trait and translates genetic associations from diverse microbial samples into a sequencing library that answers targeted ecological questions. Potential applications include identifying functional community members, tracing horizontal gene transfer networks and mapping ecological interactions between microbial cells.     

ReadDepth: a parallel R package for detecting copy number alterations from short sequencing reads

Copy number alterations are important contributors to many genetic diseases, including cancer. We present the readDepth package for R, which can detect these aberrations by measuring the depth of coverage obtained by massively parallel sequencing of the genome. In addition to achieving higher accuracy than existing packages, our tool runs much faster by utilizing multi-core architectures to parallelize the processing of these large data sets. In contrast to other published methods, readDepth does not require the sequencing of a reference sample, and uses a robust statistical model that accounts for overdispersed data. It includes a method for effectively increasing the resolution obtained from low-coverage experiments by utilizing breakpoint information from paired end sequencing to do positional refinement. We also demonstrate a method for inferring copy number using reads generated by whole-genome bisulfite sequencing, thus enabling integrative study of epigenomic and copy number alterations. Finally, we apply this tool to two genomes, showing that it performs well on genomes sequenced to both low and high coverage. The readDepth package runs on Linux and MacOSX, is released under the Apache 2.0 license, and is available at http://code.google.com/p/readdepth/.     

Massively parallel implementations of theories for apparent motion

Two solutions for the correspondence problem for long-range motion are investigated. The first is a modification of the Minimal Mapping Theory (S. Ullman: The Interpretation of Visual Motion, MIT Press, Cambridge, 1979) that is implemented by a massively parallel network. In this network, every two units are interconnected, and thus, its convergence is fast and relatively independent of the number of image features. Computer simulations show that our method accounts as well as the Minimal Mapping Theory for apparent-motion phenomena, although some differences exist. Mathematical proofs provide conditions for the convergence of the network. The second 'solution' for the correspondence problem is called the Structural Theory. This theory assumes that the three-dimensional structure of viewed objects does not change fast in time. Then, the theory looks for the correspondence and three-dimensional structure that best fulfill this assumption. A massively parallel network implementation of this theory is also possible. However, its performance is poor due to the high complexity of its solution space. This supports Ullman's (1979) suggestion that the visual system separates the structure-from-motion process into two stages. First, a stage for motion measurement, and then a stage for structure recovery.     

Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms.

Massively parallel signature sequencing (MPSS) is one of the newest tools available for conducting in-depth expression profiling. MPSS is an open-ended platform that analyses the level of expression of virtually all genes in a sample by counting the number of individual mRNA molecules produced from each gene. There is no requirement that genes be identified and characterised prior to conducting an experiment. MPSS has a routine sensitivity at a level of a few molecules of mRNA per cell, and the datasets are in a digital format that simplifies the management and analysis of the data. Therefore, of the various microarray and non-microarray technologies currently available, MPSS provides many advantages for generating the type of complete datasets that will help to facilitate hypothesis-driven experiments in the era of digital biology.     

Massively parallel acceleration methods for image handling operations

In the image handling and image processing areas, most operations can be executed in a pixel-by-pixel or cluster-by-cluster manner. These parallel and simultaneous executions have many benefits, and many researchers showed remarkable improvements. In this paper, we started from a specific and practical image handling and feature extraction sequences. We focused on the detailed design and robust implementation on the modern massively parallel architecture of CUDA. We present the enhanced features of our implementation and their design details. Our final result shows 13 times faster execution speed, in comparison with its previous CPU-based implementation. These methods can be applied to the variety of image manipulation processes.     

Massively parallel rRNA gene sequencing exacerbates the potential for biased community diversity comparisons due to variable library sizes

Technologies for massively parallel sequencing are revolutionizing microbial ecology and are vastly increasing the scale of ribosomal RNA (rRNA) gene studies. Although pyrosequencing has increased the breadth and depth of possible rRNA gene sampling, one drawback is that the number of reads obtained per sample is difficult to control. Pyrosequencing libraries typically vary widely in the number of sequences per sample, even within individual studies, and there is a need to revisit the behaviour of richness estimators and diversity indices with variable gene sequence library sizes. Multiple reports and review papers have demonstrated the bias in non-parametric richness estimators (e.g. Chao1 and ACE) and diversity indices when using clone libraries. However, we found that biased community comparisons are accumulating in the literature. Here we demonstrate the effects of sample size on Chao1, ACE, CatchAll, Shannon, Chao-Shen and Simpson's estimations specifically using pyrosequencing libraries. The need to equalize the number of reads being compared across libraries is reiterated, and investigators are directed towards available tools for making unbiased diversity comparisons.     

Massively parallel sequencing of Chikso (Korean brindle cattle) to discover genome-wide SNPs and InDels

Since the completion of the bovine sequencing projects, a substantial number of genetic variations such as single nucleotide polymorphisms have become available across the cattle genome. Recently, cataloguing such genetic variations has been accelerated using massively parallel sequencing technology. However, most of the recent studies have been concentrated on European Bos taurus cattle breeds, resulting in a severe lack of knowledge for valuable native cattle genetic resources worldwide. Here, we present the first whole-genome sequencing results for an endangered Korean native cattle breed, Chikso, using the Illumina HiSeq 2,000 sequencing platform. The genome of a Chikso bull was sequenced to approximately 25.3-fold coverage with 98.8% of the bovine reference genome sequence (UMD 3.1) covered. In total, 5,874,026 single nucleotide polymorphisms and 551,363 insertion/deletions were identified across all 29 autosomes and the X-chromosome, of which 45% and 75% were previously unknown, respectively. Most of the variations (92.7% of single nucleotide polymorphisms and 92.9% of insertion/deletions) were located in intergenic and intron regions. A total of 16,273 single nucleotide polymorphisms causing missense mutations were detected in 7,111 genes throughout the genome, which could potentially contribute to variation in economically important traits in Chikso. This study provides a valuable resource for further investigations of the genetic mechanisms underlying traits of interest in cattle, and for the development of improved genomics-based breeding tools.     

Massively parallel interrogation of aptamer sequence, structure and function

Background

Optimization of high affinity reagents is a significant bottleneck in medicine and the life sciences. The ability to synthetically create thousands of permutations of a lead high-affinity reagent and survey the properties of individual permutations in parallel could potentially relieve this bottleneck. Aptamers are single stranded oligonucleotides affinity reagents isolated by in vitro selection processes and as a class have been shown to bind a wide variety of target molecules.

Methodology/Principal Findings

High density DNA microarray technology was used to synthesize, in situ, arrays of approximately 3,900 aptamer sequence permutations in triplicate. These sequences were interrogated on-chip for their ability to bind the fluorescently-labeled cognate target, immunoglobulin E, resulting in the parallel execution of thousands of experiments. Fluorescence intensity at each array feature was well resolved and shown to be a function of the sequence present. The data demonstrated high intra- and inter-chip correlation between the same features as well as among the sequence triplicates within a single array. Consistent with aptamer mediated IgE binding, fluorescence intensity correlated strongly with specific aptamer sequences and the concentration of IgE applied to the array.

Conclusion and Significance

The massively parallel sequence-function analyses provided by this approach confirmed the importance of a consensus sequence found in all 21 of the original IgE aptamer sequences and support a common stem:loop structure as being the secondary structure underlying IgE binding. The microarray application, data and results presented illustrate an efficient, high information content approach to optimizing aptamer function. It also provides a foundation from which to better understand and manipulate this important class of high affinity biomolecules.     

A new method for detecting neural interconnectivity

We propose a class of counting process models for analyzing firing times of an ensemble of neurons. We allow the counting process intensities to be unspecified, unknown functions of the times passed since the most recent firings. Under this assumption we derive a class of statistics with their respective thresholds as well as graphical methods for detecting neural connectivity. We introduce a model under which detection is shown to be certain for long series of observations. We suggest ways to classify interactions as inhibition or excitation and to estimate their strengths. The power of the proposed methods is compared by simulating observations from artificial networks. By analyzing empirically obtained series we obtain results which are consistent with those obtained from cross-correlation-based methods but in addition obtain new insights on further aspects of the interactions. Received: 7 February 1996 / Accepted in revised form: 5 March 1997     

Network-based diffusion analysis: a new method for detecting social learning

Social learning has been documented in a wide diversity of animals. In free-living animals, however, it has been difficult to discern whether animals learn socially by observing other group members or asocially by acquiring a new behaviour independently. We addressed this challenge by developing network-based diffusion analysis (NBDA), which analyses the spread of traits through animal groups and takes into account that social network structure directs social learning opportunities. NBDA fits agent-based models of social and asocial learning to the observed data using maximum-likelihood estimation. The underlying learning mechanism can then be identified using model selection based on the Akaike information criterion. We tested our method with artificially created learning data that are based on a real-world co-feeding network of macaques. NBDA is better able to discriminate between social and asocial learning in comparison with diffusion curve analysis, the main method that was previously applied in this context. NBDA thus offers a new, more reliable statistical test of learning mechanisms. In addition, it can be used to address a wide range of questions related to social learning, such as identifying behavioural strategies used by animals when deciding whom to copy.     

A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE

We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as "noise" or "error") within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms.     

A new method for detecting endocytosed proteins.

A new reagent, DPSgt, is described which has been designed to label cell surface proteins at 0 degree C. The reagent is easily made; it is water soluble and contains a reactive impermeant ester at one end, a tyrosine which can be radioiodinated at the other, and a disulphide in-between. The label can be removed from cells by cleaving the disulphide linkage in it with glutathione at 0 degree C. When cells are warmed to 37 degrees C between labelling and reduction, labelled proteins which are endocytosed acquire resistance to reduction. This provides a simple way of measuring the endocytosis of surface proteins. The intracellular pools of transferrin and LDL receptors in K562 cells and fibroblasts have been estimated. The results indicate that intracellular receptors are in non-reducing compartments, and that uptake of average cell surface (by non-coated pit processes) in K562 cells is small.     

Cross-platform compatibility of Hi-Plex,a streamlined approach for targeted massively parallel sequencing

Although per-base sequencing costs have decreased during recent years, library preparation for targeted massively parallel sequencing remains constrained by high reagent cost, limited design flexibility, and protocol complexity. To address these limitations, we previously developed Hi-Plex, a polymerase chain reaction (PCR) massively parallel sequencing strategy for screening panels of genomic target regions. Here, we demonstrate that Hi-Plex applied with hybrid adapters can generate a library suitable for sequencing with both the Ion Torrent and the TruSeq chemistries and that adjusting primer concentrations improves coverage uniformity. These results expand Hi-Plex capabilities as an accurate, affordable, flexible, and rapid approach for various genetic screening applications.