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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: This Review concentrates on the technology behind the third- and fourth-generation sequencing methods: their challenges, current limitations, and tantalizing promise.
Abstract: DNA sequencing is in the throes of an enormous technological shift marked by dramatic throughput increases, a precipitously dropping per-base cost of raw sequence, and an accompanying requirement for substantial investment in large capital equipment in order to utilize the technology. Investigations that were, for most, unreachable luxuries just a few years ago (individual genome sequencing, metagenomics studies, and the sequencing of myriad organisms of interest) are being increasingly enabled, at a rapid pace. This Review concentrates on the technology behind the third- and fourth-generation sequencing methods: their challenges, current limitations, and tantalizing promise. First-generation sequencing encompasses the chain termination method pioneered by Sanger and Coulson1 in 1975 or the chemical method of Maxam and Gilbert in 1976–1977.2 In 1977, Sanger sequenced the first genome, bacteriophage ΦX 174, which is 5375 bases in length.3 These methods and their early history4 have been reviewed in detail previously.5 Four-color fluorescent Sanger sequencing, where each color corresponds to one of the four DNA bases, is the method used by the automated capillary electrophoresis (CE) systems marketed by Applied Biosystems Inc., now integrated into Life Technologies, and by Beckman Coulter Inc. (Table 1).6 The first composite human genome sequence, reported in 2001, was obtained largely using CE, at great cost and with intense human effort over more than a decade.7,8 While the genome reported in 2001 was a work in progress, the availability of an ever-improving “reference” genome is the basis of an ongoing transformation of biological science and remains fundamental to investigations of genotype–phenotype relationships. Considering reports that have appeared (and not appeared) in the literature to date, it could well be that medically meaningful (actionable) insights into complex diseases will require additional types of “personal” genomic data, for instance, tissue-specific mRNA expression profiling and mRNA sequencing, individualized analysis of gene regulatory regions, epigenetic profiling, and high-quality, long-range chromosome mapping to catalog significant deletions, insertions, rearrangements, etc. Correlation of such integrated genomic data sets with comprehensive medical histories for hundreds or thousands of individuals may be what it takes to reach an era of personalized medicine.9–11 Large-scale sequencing centers are now completing the conversion to next-generation sequencers; the Joint Genome Institute (JGI) has retired all of their Sanger sequencing instruments.12 At the other extreme, until small-scale next-generation sequencers can outperform CE on a cost per accurate base called as well as read length, CE systems will likely remain in heavy use for benchtop-scale, targeted sequencing for directed investigations such as quantitative gene expression, biomarker identification, and pathway analysis. Table 1 First- and Second-Generation Sequencing Technologies

346 citations

Journal ArticleDOI
TL;DR: This unit describes protocols for performing RNA‐Seq using the Illumina sequencing platform, and has been used successfully to precisely quantify transcript levels, confirm or revise previously annotated 5′ and 3′ ends of genes, and map exon/intron boundaries.
Abstract: A recently developed technique called RNA Sequencing (RNA-Seq) uses massively parallel sequencing to allow transcriptome analyses of genomes at a far higher resolution than is available with Sanger sequencing- and microarray-based methods. In the RNA-Seq method, complementary DNAs (cDNAs) generated from the RNA of interest are directly sequenced using next-generation sequencing technologies. The reads obtained from this can then be aligned to a reference genome in order to construct a whole-genome transcriptome map. RNA-Seq has been used successfully to precisely quantify transcript levels, confirm or revise previously annotated 5' and 3' ends of genes, and map exon/intron boundaries. This unit describes protocols for performing RNA-Seq using the Illumina sequencing platform.

332 citations

Journal ArticleDOI
TL;DR: A computational pipeline is developed that identifies allele‐specific events with significant differences in the number of mapped reads between maternal and paternal alleles, and investigates the coordination between ASE and ASB from multiple transcription factors events using a regulatory network framework.
Abstract: To study allele-specific expression (ASE) and binding (ASB), that is, differences between the maternally and paternally derived alleles, we have developed a computational pipeline (AlleleSeq). Our pipeline initially constructs a diploid personal genome sequence (and corresponding personalized gene annotation) using genomic sequence variants (SNPs, indels, and structural variants), and then identifies allele-specific events with significant differences in the number of mapped reads between maternal and paternal alleles. There are many technical challenges in the construction and alignment of reads to a personal diploid genome sequence that we address, for example, bias of reads mapping to the reference allele. We have applied AlleleSeq to variation data for NA12878 from the 1000 Genomes Project as well as matched, deeply sequenced RNA-Seq and ChIP-Seq data sets generated for this purpose. In addition to observing fairly widespread allele-specific behavior within individual functional genomic data sets (including results consistent with X-chromosome inactivation), we can study the interaction between ASE and ASB. Furthermore, we investigate the coordination between ASE and ASB from multiple transcription factors events using a regulatory network framework. Correlation analyses and network motifs show mostly coordinated ASB and ASE.

330 citations

Journal ArticleDOI
TL;DR: Wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses and developed a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection, and wearables distinguish physiological differences between insulin-sensitive and -resistant individuals.
Abstract: A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO2] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.

325 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