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.

Distribution of NF-κB-binding sites across human chromosome 22

Abstract: We have mapped the chromosomal binding site distribution of a transcription factor in human cells. The NF-κB family of transcription factors plays an essential role in regulating the induction of genes involved in several physiological processes, including apoptosis, immunity, and inflammation. The binding sites of the NF-κB family member p65 were determined by using chromatin immunoprecipitation and a genomic microarray of human chromosome 22 DNA. Sites of binding were observed along the entire chromosome in both coding and noncoding regions, with an enrichment at the 5′ end of genes. Strikingly, a significant proportion of binding was seen in intronic regions, demonstrating that transcription factor binding is not restricted to promoter regions. NF-κB binding was also found at genes whose expression was regulated by tumor necrosis factor α, a known inducer of NF-κB-dependent gene expression, as well as adjacent to genes whose expression is not affected by tumor necrosis factor α. Many of these latter genes are either known to be activated by NF-κB under other conditions or are consistent with NF-κB's role in the immune and apoptotic responses. Our results suggest that binding is not restricted to promoter regions and that NF-κB binding occurs at a significant number of genes whose expression is not altered, thereby suggesting that binding alone is not sufficient for gene activation.

Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

Abstract: Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings.

The transcriptional activity of human Chromosome 22

Abstract: A DNA microarray representing nearly all of the unique sequences of human Chromosome 22 was constructed and used to measure global-transcriptional activity in placental poly(A) + RNA. We found that many of the known, related and predicted genes are expressed. More importantly, our study reveals twice as many transcribed bases as have been reported previously. Many of the newly discovered expressed fragments were verified by RNA blot analysis and a novel technique called differential hybridization mapping (DHM). Interestingly, a significant fraction of these novel fragments are expressed antisense to previously annotated introns. The coding potential of these novel expressed regions is supported by their sequence conservation in the mouse genome. This study has greatly increased our understanding of the biological information encoded on a human chromosome. To facilitate the dissemination of these results to the scientific community, we have developed a comprehensive Web resource to present the findings of this study and other features of human Chromosome 22 at http://array.mbb.yale.edu/chr22.

The human body at cellular resolution: the NIH Human Biomolecular Atlas Program

Abstract: Author(s): Snyder, Michael P; Lin, Shin; Posgai, Amanda; Atkinson, Mark; Regev, Aviv; Rood, Jennifer; Rozenblatt-Rosen, Orit; Gaffney, Leslie; Hupalowska, Anna; Satija, Rahul; Gehlenborg, Nils; Shendure, Jay; Laskin, Julia; Harbury, Pehr; Nystrom, Nicholas A; Silverstein, Jonathan C; Bar-Joseph, Ziv; Zhang, Kun; Borner, Katy; Lin, Yiing; Conroy, Richard; Procaccini, Dena; Roy, Ananda L; Pillai, Ajay; Brown, Marishka; Galis, Zorina S; Cai, Long; Shendure, Jay; Trapnell, Cole; Lin, Shin; Jackson, Dana; Snyder, Michael P; Nolan, Garry; Greenleaf, William James; Lin, Yiing; Plevritis, Sylvia; Ahadi, Sara; Nevins, Stephanie A; Lee, Hayan; Schuerch, Christian Martijn; Black, Sarah; Venkataraaman, Vishal Gautham; Esplin, Ed; Horning, Aaron; Bahmani, Amir; Zhang, Kun; Sun, Xin; Jain, Sanjay; Hagood, James; Pryhuber, Gloria; Kharchenko, Peter; Atkinson, Mark; Bodenmiller, Bernd; Brusko, Todd; Clare-Salzler, Michael; Nick, Harry; Otto, Kevin; Posgai, Amanda; Wasserfall, Clive; Jorgensen, Marda; Brusko, Maigan; Maffioletti, Sergio; Caprioli, Richard M; Spraggins, Jeffrey M; Gutierrez, Danielle; Patterson, Nathan Heath; Neumann, Elizabeth K; Harris, Raymond; deCaestecker, Mark; Fogo, Agnes B; van de Plas, Raf; Lau, Ken; Cai, Long; Yuan, Guo-Cheng; Zhu, Qian; Dries, Ruben; Yin, Peng; Saka, Sinem K; Kishi, Jocelyn Y; Wang, Yu; Goldaracena, Isabel; Laskin, Julia; Ye, DongHye; Burnum-Johnson, Kristin E; Piehowski, Paul D | Abstract: Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings.

STAR: ultrafast universal RNA-seq aligner

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/.

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

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.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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。

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

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.

An integrated encyclopedia of DNA elements in the human genome

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.