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Diffantom is a whole-brain digital phantom generated from a dataset from the Human Connectome Project. Diffantom is presented here to be openly and freely distributed along with the diffantomizer workflow to generate new diffantoms. We encourage the neuroimage community to contribute with their own diffantoms and share them openly.
DNA assembly is a core methodological step in metagenomic pipelines used to study the structure and function within microbial communities. Here we investigate the utility of Pacific Biosciences long and high accuracy circular consensus sequencing (CCS) reads for metagenomics projects. We compared the application and performance of both PacBio CCS and Illumina HiSeq data with assembly and taxonomic binning algorithms using metagenomic samples representing a complex microbial community. Eight SMRT cells produced approximately 94 Mb of CCS reads from a biogas reactor microbiome sample, which averaged 1319 nt in length and 99.7 % accuracy. CCS data assembly generated a comparative number of large contigs greater than 1 kb, to those assembled from a [~]190x larger HiSeq dataset ([~]18 Gb) produced from the same sample (i.e approximately 62 % of total contigs). Hybrid assemblies using PacBio CCS and HiSeq contigs produced improvements in assembly statistics, including an increase in the average contig length and number of large contigs. The incorporation of CCS data produced significant enhancements in taxonomic binning and genome reconstruction of two dominant phylotypes, which assembled and binned poorly using HiSeq data alone. Collectively these results illustrate the value of PacBio CCS reads in certain metagenomics applications.
BackgroundCharacterization of genetic variations in maize has been challenging, mainly due to deterioration of collinearity between individual genomes in the species. An international consortium of maize research groups combined resources to develop the maize haplotype version 3 (HapMap 3), built from whole genome sequencing data from 1,218 maize lines, covering pre-domestication and domesticated Zea mays varieties across the world.\n\nResultsA new computational pipeline was set up to process over 12 trillion bp of sequencing data, and a set of population genetics filters were applied to identify over 83 million variant sites.\n\nConclusionsWe identified polymorphisms in regions where collinearity is largely preserved in the maize species. However, the fact that the B73 genome used as the reference only represents a fraction of all haplotypes is still an important limiting factor.
Spatial organization of the genome is critical for condition-specific gene expression. Previous studies have shown that functionally related genes tend to be spatially proximal. However, these studies have not been extended to multiple human cell types, and the extent to which context-specific spatial proximity of a pathway is related to its context-specific activity is not known. We report the first pathway-centric analyses of spatial proximity in six human cell lines. We find that spatial proximity of genes in a pathway tends to be context-specific, in a manner consistent with the pathways context-specific expression and function; housekeeping genes are ubiquitously proximal to each other, and cancer-related pathways such as p53 signaling are uniquely proximal in hESC. Intriguingly, we find a correlation between the spatial proximity of genes and interactions of their protein products, even after accounting for the propensity of co-pathway proteins to interact. Related pathways are also often spatially proximal to one another, and housekeeping genes tend to be proximal to several other pathways suggesting their coordinating role. Further, the spatially proximal genes in a pathway tend to be the drivers of the pathway activity and are enriched for transcription, splicing and transport functions. Overall, our analyses reveal a pathway-centric organization of the 3D nucleome whereby functionally related and interacting genes, particularly the initial drivers of pathway activity, but also genes across multiple related pathways, are in spatial proximity in a context-specific way. Our results provide further insights into the role of differential spatial organization in cell type-specific pathway activity.
Advances in RNA-seq technologies provide unprecedented insight into the variability and heterogeneity of gene expression at the single-cell level. However, such data offers only a snapshot of the transcriptome, whereas it is often the progression of cells through dynamic biological processes that is of interest. As a result, one outstanding challenge is to infer such progressions by ordering gene expression from single cell data alone, known as the cell ordering problem. Here, we introduce a new method that constructs a low-dimensional non-linear embedding of the data using laplacian eigenmaps before assigning each cell a pseudotime using principal curves. We characterise why on a theoretical level our method is more robust to the high levels of noise typical of single-cell RNA-seq data before demonstrating its utility on two existing datasets of differentiating cells.
1The accurate identification of low-frequency variants in tumors remains an unsolved problem. To support characterization of the issues in a realistic setting, we have developed software tools and a reference dataset for diagnosing variant calling pipelines. The dataset contains millions of variants at frequencies ranging from 0.05 to 1.0. To generate the dataset, we performed whole-genome sequencing of a mixture of two Corriel cell lines, NA19240 and NA12878, the mothers of YRI (Y) and CEU (C) HapMap trios, respectively. The cells were mixed in three different proportions, 10Y/90C, 50Y/50C and 90Y/10C, in an effort to simulate the heterogeneity found in tumor samples. We sequenced three biological replicates for each mixture, yielding approximately 1.4 billion reads per mixture for an average of 64X coverage. Using the published genotypes as our reference, we evaluate the performance of a general variant calling algorithm, constructed as a demonstration of our flexible toolset, and make comparisons to a standard GATK pipeline. We estimate the overall FDR to be 0.028 and the FNR (when coverage exceeds 20X) to be 0.019 in the 50Y/50C mixture. Interestingly, even with these relatively well studied individuals, we predict over 475,000 new variants, validating in well-behaved coding regions at a rate of 0.97, that were not included in the published genotypes.
The functional mechanisms underlying disease association identified by Genome-wide Association Studies remain unknown for susceptibility loci located outside gene coding regions. In addition to the regulation of gene expression, synthesis of effects from multiple surrounding functional variants has been suggested as an explanation of hard-to-interpret associations.\n\nHere, we define filter criteria based on linkage disequilibrium measures and allele frequencies which reflect expected properties of synthesizing variant sets. For eligible candidate sets we search for those haplotypes that are highly correlated with the risk alleles of a genome-wide associated variant.\n\nWe applied our methods to 1,000 Genomes reference data and confirmed Crohns Disease and Type 2 Diabetes susceptibility loci. Of these, a proportion of 32% allowed explanation by three-variant-haplotypes carrying at least two functional variants, as compared to a proportion of 16% for random variants (P = 2.92 {middle dot} 10-6). More importantly, we detected examples of known loci whose association can fully be explained by surrounding missense variants: three missense variants from MUC19 synthesize rs11564258 (L0C105369736/MUC19, intron; Crohns Disease). Next, rs2797685 (PER3, intron; Crohns Disease) is synthesized by a 57 kilobase haplotype defined by five missense variants from PER3 and three missense variants from UTS2. Finally, the association of rs7178572 (HMG20A, intron; Type 2 Diabetes) can be explained by the synthesis of eight haplotypes, each carrying at least one missense variant in either PEAK1, TBC1D2B, CHRNA5 or ADAMTS7.\n\nIn summary, application of our new methods highlights the potential of synthesis analysis to guide functional follow-up investigation of findings from association studies.
The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional - omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent works on \"selective advantage\" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications as it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations and progression model inference. We demonstrate PiCnIcs ability to reproduce much of the current knowledge on colorectal cancer progression, as well as to suggest novel experimentally verifiable hypotheses.\n\nSO_SCPLOWTATEMENTC_SCPLOW O_SCPLOWOFC_SCPLOW SO_SCPLOWIGNIFICANCEC_SCPLOW: A causality based new machine learning Pipeline for Cancer Inference (PicNic) is introduced to infer the underlying somatic evolution of ensembles of tumors from next generation sequencing data. PicNic combines techniques for sample stratification, driver selection and identification of fitness-equivalent exclusive alterations to exploit a novel algorithm based on Suppes probabilistic causation. The accuracy and translational significance of the results are studied in details, with an application to colorectal cancer. PicNic pipeline has been made publicly accessible for reproducibility, interoperability and for future enhancements.
Rawcopy is an R package for processing of Affymetrix CytoScan HD, CytoScan 750k and SNP 6.0 microarray raw intensities (CEL files). It uses data from a large number of reference samples to produce log ratio for total copy number analysis and B-allele frequency for allele-specific copy number and heterozygosity analysis. Rawcopy achieves higher signal-to-noise ratio than commonly used free and proprietary alternatives, leading to improved identification of copy number alterations. In addition, Rawcopy visualises each microarray sample for assessment of technical quality, patient identity and genome-wide absolute copy number states.
MotivationWe introduce TRONCO (TRanslational ONCOlogy), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g., retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g., multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints in uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy.\n\nAvailabilityTRONCO is released under the GPL license, it is hosted in the Software section at http://bimib.disco.unimib.it/ and archived also at bioconductor.org.\n\nContacttronco@disco.unimib.it
Models of cancer progression provide insights on the order of accumulation of genetic alterations during cancer development. Algorithms to infer such models from the currently available mutational profiles collected from different cancer patiens (cross-sectional data) have been defined in the literature since late 90s. These algorithms differ in the way they extract a graphical model of the events modelling the progression, e.g., somatic mutations or copy-number alterations.\n\nTRONCO is an R package for TRanslational ONcology which provides a serie of functions to assist the user in the analysis of cross-sectional genomic data and, in particular, it implements algorithms that aim to model cancer progression by means of the notion of selective advantage. These algorithms are proved to outperform the current state-of-the-art in the inference of cancer progression models. TRONCO also provides functionalities to load input cross-sectional data, set up the execution of the algorithms, assess the statistical confidence in the results and visualize the models.\n\nAvailability. Freely available at http://www.bioconductor.org/ under GPL license; project hosted at http://bimib.disco.unimib.it/ and https://github.com/BIMIB-DISCo/TRONCO.\n\nContact. tronco@disco.unimib.it
High-throughput sequencing (HTS) is increasingly being used for broad applications of microbial characterization, such as microbial ecology, clinical diagnosis, and outbreak epidemiology. However, the analytical task of comparing short sequence reads against the known diversity of microbial life has proved to be computationally challenging. The One Codex data platform was created with the dual goals of analyzing microbial data against the largest possible collection of microbial reference genomes, as well as presenting those results in a format that is consumable by applied end-users. One Codex identifies microbial sequences using a \"k-mer based\" taxonomic classification algorithm through a web-based data platform, using a reference database that currently includes approximately 40,000 bacterial, viral, fungal, and protozoan genomes. In order to evaluate whether this classification method and associated database provided quantitatively different performance for microbial identification, we created a large and diverse evaluation dataset containing 50 million reads from 10,639 genomes, as well as sequences from six organisms novel species not be included in the reference databases of any of the tested classifiers. Quantitative evaluation of several published microbial detection methods shows that One Codex has the highest degree of sensitivity and specificity (AUC = 0.97, compared to 0.82-0.88 for other methods), both when detecting well-characterized species as well as newly sequenced, \"taxonomically novel\" organisms.
As part of the Cambrian explosion of omics data, metagenomics brings to the table a specific, defining trait: its social essence. The meta prefix exerts its influence, with multitudes manifesting themselves everywhere; from samples to data analysis, from actors involved to (present and future) applications. Of these dimensions, data analysis is where needs lay further from what current tools provide. Key features are, among others, scalability, reproducibility, data provenance and distribution, process identity and versioning. These are the goals guiding our work in MG7, a 16S metagenomics data analysis system. The basic principle is a new approach to data analysis, where configuration, processes, or data locations are static, type-checked and subject to the standard evolution of a well-maintained software project. Cloud computing, in its Amazon Web Services incarnation, when coupled with these ideas, produces a robust, safely configurable, scalable tool. Processes, data, machine behaviors and their dependencies are expressed using a set of libraries which bring as much as possible checking and validation to the type level, without sacrificing expressiveness. Together they form a toolkit for defining scalable cloud-based workflows composed of stateless computations, with a static reproducible specification of dependencies, behavior and wiring of all steps. The modeling of taxonomy data is done using Bio4j, where the new paradigm of graph databases allows for both a simple expression of taxonomic assignment tasks and the calculation of taxa abundance values considering the hierarchic structure of the taxonomy tree. MG7 includes a new 16S reference database, 16S-DB7, built with a flexible and sustainable update system, and the possibility of project-driven personalization.
One of the major properties of genes is their expression pattern. Notably, genes are often classified as tissue-specific or housekeeping. This property is of interest to molecular evolution as an explanatory factor of, e.g., evolutionary rate, as well as a functional feature which may in itself evolve. While many different methods of measuring tissue specificity have been proposed and used for such studies, there has been no comparison or benchmarking of these methods to our knowledge, and little justification of their use. In this study we compare nine measures of tissue-specificity. Most methods were established for ESTs and microarrays, and several were later adapted to RNA-seq. We analyze their capacity to distinguish gene categories, their robustness to the choice and number of tissues used, and their capture of evolutionary conservation signal.
The significance of single-cell transcription resides not only in the cumulative expression strength of the cell population but also in its heterogeneity. We propose a new model that improves the detection of changes in the transcriptional heterogeneity pattern of RNA-Seq data using two heterogeneity parameters: burst proportion and burst magnitude, whose changes are validated using RNA-FISH. Transcriptional co-bursting - governed by distinct mechanisms during myoblast proliferation and differentiation - is described here.
SummaryTargeted enrichment of conserved and ultraconserved genomic elements allows universal collection of phylogenomic data from hundreds of species at multiple time scales (< 5 Ma to > 300 Ma). Prior to downstream inference, data from these types of targeted enrichment studies must undergo pre-processing to assemble contigs from sequence data; identify targeted, enriched loci from the off-target background data; align enriched contigs representing conserved loci to one another; and prepare and manipulate these alignments for subsequent phylogenomic inference. PHYLUCE is an efficient and easy-to-install software package that accomplishes these tasks across hundreds of taxa and thousands of enriched loci.\n\nAvailability and ImplementationPHYLUCE is written for Python 2.7. PHYLUCE is supported on OSX and Linux (RedHat/CentOS) operating systems. PHYLUCE source code is distributed under a BSD-style license from https://www.github.com/fairclothUlab/phyluce/. PHYLUCE is also available as a package (https://binstar.org/fairclothUlab/phyluce) for the Anaconda Python distribution that installs all dependencies, and users can request a PHYLUCE instance on iPlant Atmosphere (tag: phyluce). The software manual and a tutorial are available from http://phyluce.readthedocs.org/en/latest/ and test data are available from doi: 10.6084/m9.figshare.1284521.\n\nContactbrant@fairclothUlab.org\n\nSupplementary informationSupplementary Figure 1.\n\nO_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC=\"FIGDIR/small/027904_figS1.gif\" ALT=\"Figure 1\">\nView larger version (40K):\norg.highwire.dtl.DTLVardef@1f6fb2aorg.highwire.dtl.DTLVardef@1e39afforg.highwire.dtl.DTLVardef@1d51cf1org.highwire.dtl.DTLVardef@5f2eda_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOSupplementary Figure 1.C_FLOATNO PHYLUCE workflow for phylogenomic analyses of data collected from conserved genomic loci using targeted enrichment.\n\nC_FIG
As the second most common type of variations in the human genome, insertions and deletions (indels) have been linked to many diseases, but indels of more than a few bases are still challenging to discover from short-read sequencing data. Scalpel (http://scalpel.sourceforge.net) is open-source software for reliable indel detection based on the micro-assembly technique. To date, it has been successfully used to discover mutations in novel candidate genes for autism, and is extensively used in other large-scale studies of human diseases. This protocol gives an overview of the algorithm and describes how to use Scalpel to perform highly accurate indel calling from whole genome and exome sequencing data. We provide detailed instructions for an exemplary family-based de novo study, but we also characterize the other two supported modes of operation for single sample and somatic analysis. Indel normalization, visualization, and annotation of the mutations are also illustrated. Using a standard server, indel discovery and characterization in the exonic regions of the example sequencing data can be finished in ~6 hours after read mapping.
The availability of genomes across the tree of life is highly biased toward vertebrates, pathogens, human disease models, and organisms with relatively small and simple genomes. Recent progress in genomics has enabled the de novo decoding of the genome of virtually any organism, greatly expanding its potential for understanding the biology and evolution of the full spectrum of biodiversity. The increasing diversity of sequencing technologies, assays, and de novo assembly algorithms have augmented the complexity of de novo genome sequencing projects in non-model organisms. To reduce the costs and challenges in de novo genome sequencing projects and streamline their experimental design and analysis, we developed iWGS (in silico Whole Genome Sequencer and Analyzer), an automated pipeline for guiding the choice of appropriate sequencing strategy and assembly protocols. iWGS seamlessly integrates the four key steps of a de novo genome sequencing project: data generation (through simulation), data quality control, de novo assembly, and assembly evaluation and validation. The last three steps can also be applied to the analysis of real data. iWGS is designed to enable the user to have great flexibility in testing the range of experimental designs available for genome sequencing projects, and supports all major sequencing technologies and popular assembly tools. Three case studies illustrate how iWGS can guide the design of de novo genome sequencing projects and evaluate the performance of a wide variety of user-specified sequencing strategies and assembly protocols on genomes of differing architectures. iWGS, along with a detailed documentation, is freely available at https://github.com/zhouxiaofan1983/iWGS.