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Author

Michael Snyder

Bio: Michael Snyder is an academic researcher from Stanford University. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 169, co-authored 840 publications receiving 130225 citations. Previous affiliations of Michael Snyder include Wyss Institute for Biologically Inspired Engineering & Public Health Research Institute.
Topics: Gene, Genome, Medicine, Chromatin, Human genome


Papers
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Journal ArticleDOI
TL;DR: The contribution of LRS to herpesvirus transcriptomics is reviewed and the complexity revealed by this technology is presented, while also discussing the functional significance of this phenomenon.
Abstract: Long-read sequencing (LRS) techniques are very recent advancements, but they have already been used for transcriptome research in all of the three subfamilies of herpesviruses. These techniques have multiplied the number of known transcripts in each of the examined viruses. Meanwhile, they have revealed a so far hidden complexity of the herpesvirus transcriptome with the discovery of a large number of novel RNA molecules, including coding and non-coding RNAs, as well as transcript isoforms, and polycistronic RNAs. Additionally, LRS techniques have uncovered an intricate meshwork of transcriptional overlaps between adjacent and distally located genes. Here, we review the contribution of LRS to herpesvirus transcriptomics and present the complexity revealed by this technology, while also discussing the functional significance of this phenomenon.

20 citations

Journal ArticleDOI
TL;DR: The Personal Health Dashboard (PHD) as mentioned in this paper is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step.
Abstract: The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19. The increasing scale and scope of biomedical data is generating tremendous opportunities for improving health outcomes, but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, the authors develop the Personal Health Dashboard, which provides an end-to-end solution for deep biomedical data analytics.

20 citations

Journal ArticleDOI
TL;DR: The date of October 2014 was chosen because no papers were shown on Kadmon’s website earlier than that date, which is five years after the company was founded, and the company has a responsibility to share its discoveries with the scientific community in peerreviewed journals.
Abstract: Understanding Cloud security for genomics data is challenging but a critical need. It is our observation that security requirements are often inconsistent not only across datasets but also between on-premise solutions and Cloud for the same dataset. We attempt to summarize these security requirements across a wide range of regulatory bodies from government and private sources. While we expect the implementation to be different between Clouds and evolution in implementation methodology in time, we expect these guidelines to be applicable in foreseeable future. We also note that security does not necessary provide privacy and significant effort is needed to address privacy for a research centric platform.

20 citations

Journal ArticleDOI
TL;DR: A simulation toolbox that will help us optimally combine different technologies for genome re-sequencing, especially in reconstructing large structural variants (SVs) and semi-realistic simulations show how this can be combined to optimally solve the assembly at low cost.
Abstract: The goal of human genome re-sequencing is obtaining an accurate assembly of an individual's genome. Recently, there has been great excitement in the development of many technologies for this (e.g. medium and short read sequencing from companies such as 454 and SOLiD, and high-density oligo-arrays from Affymetrix and NimbelGen), with even more expected to appear. The costs and sensitivities of these technologies differ considerably from each other. As an important goal of personal genomics is to reduce the cost of re-sequencing to an affordable point, it is worthwhile to consider optimally integrating technologies. Here, we build a simulation toolbox that will help us optimally combine different technologies for genome re-sequencing, especially in reconstructing large structural variants (SVs). SV reconstruction is considered the most challenging step in human genome re-sequencing. (It is sometimes even harder than de novo assembly of small genomes because of the duplications and repetitive sequences in the human genome.) To this end, we formulate canonical problems that are representative of issues in reconstruction and are of small enough scale to be computationally tractable and simulatable. Using semi-realistic simulations, we show how we can combine different technologies to optimally solve the assembly at low cost. With mapability maps, our simulations efficiently handle the inhomogeneous repeat-containing structure of the human genome and the computational complexity of practical assembly algorithms. They quantitatively show how combining different read lengths is more cost-effective than using one length, how an optimal mixed sequencing strategy for reconstructing large novel SVs usually also gives accurate detection of SNPs/indels, how paired-end reads can improve reconstruction efficiency, and how adding in arrays is more efficient than just sequencing for disentangling some complex SVs. Our strategy should facilitate the sequencing of human genomes at maximum accuracy and low cost.

20 citations

Journal ArticleDOI
TL;DR: Molecular and preclinical in vivo data supporting CD4LVFOXP3 cell clinical progression are presented and these cells have potent regulatory function, suggesting their potential as an innovative therapeutic.
Abstract: Objectives Genetic or acquired defects in FOXP3+ regulatory T cells (Tregs) play a key role in many immune-mediated diseases including immune dysregulation polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome. Previously, we demonstrated CD4+ T cells from healthy donors and IPEX patients can be converted into functional Treg-like cells by lentiviral transfer of FOXP3 (CD4LVFOXP3). These CD4LVFOXP3 cells have potent regulatory function, suggesting their potential as an innovative therapeutic. Here, we present molecular and preclinical in vivo data supporting CD4LVFOXP3 cell clinical progression. Methods The molecular characterisation of CD4LVFOXP3 cells included flow cytometry, qPCR, RNA-seq and TCR-seq. The in vivo suppressive function of CD4LVFOXP3 cells was assessed in xenograft-versus-host disease (xeno-GvHD) and FOXP3-deficient IPEX-like humanised mouse models. The safety of CD4LVFOXP3 cells was evaluated using peripheral blood (PB) humanised (hu)- mice testing their impact on immune response against pathogens, and immune surveillance against tumor antigens. Results We demonstrate that the conversion of CD4+ T cells to CD4LVFOXP3 cells leads to specific transcriptional changes as compared to CD4+ T-cell transduction in the absence of FOXP3, including upregulation of Treg-related genes. Furthermore, we observe specific preservation of a polyclonal TCR repertoire during in vitro cell production. Both allogeneic and autologous CD4LVFOXP3 cells protect from xeno-GvHD after two sequential infusions of effector T cells. CD4LVFOXP3 cells prevent hyper-proliferation of CD4+ memory T cells in the FOXP3-deficient IPEX-like hu-mice. CD4LVFOXP3 cells do not impede in vivo expansion of antigen-primed T cells or tumor clearance in the PB hu-mice. Conclusion These data support the clinical readiness of CD4LVFOXP3 cells to treat IPEX syndrome and other immune-mediated diseases caused by insufficient or dysfunctional FOXP3+ Tregs.

19 citations


Cited by
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Journal ArticleDOI
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

30,684 citations

Journal ArticleDOI
TL;DR: Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches and can be used simultaneously to achieve even greater alignment speeds.
Abstract: Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

20,335 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: It is shown that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads, and estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired- end reads, depending on the number of possible splice forms for each gene.
Abstract: RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

14,524 citations

Journal ArticleDOI
06 Sep 2012-Nature
TL;DR: The Encyclopedia of DNA Elements project provides new insights into the organization and regulation of the authors' genes and genome, and is an expansive resource of functional annotations for biomedical research.
Abstract: The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

13,548 citations