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.

Differential regulation of the Kar3p kinesin-related protein by two associated proteins, Cik1p and Vik1p.

Abstract: The mechanisms by which kinesin-related proteins interact with other proteins to carry out specific cellular processes is poorly understood. The kinesin-related protein, Kar3p, has been implicated in many microtubule functions in yeast. Some of these functions require interaction with the Cik1 protein (Page, B.D., L.L. Satterwhite, M.D. Rose, and M. Snyder. 1994. J. Cell Biol. 124:507–519). We have identified a Saccharomyces cerevisiae gene, named VIK1, encoding a protein with sequence and structural similarity to Cik1p. The Vik1 protein is detected in vegetatively growing cells but not in mating pheromone-treated cells. Vik1p physically associates with Kar3p in a complex separate from that of the Kar3p-Cik1p complex. Vik1p localizes to the spindle-pole body region in a Kar3p-dependent manner. Reciprocally, concentration of Kar3p at the spindle poles during vegetative growth requires the presence of Vik1p, but not Cik1p. Phenotypic analysis suggests that Cik1p and Vik1p are involved in different Kar3p functions. Disruption of VIK1 causes increased resistance to the microtubule depolymerizing drug benomyl and partially suppresses growth defects of cik1Δ mutants. The vik1Δ and kar3Δ mutations, but not cik1Δ, partially suppresses the temperature-sensitive growth defect of strains lacking the function of two other yeast kinesin-related proteins, Cin8p and Kip1p. Our results indicate that Kar3p forms functionally distinct complexes with Cik1p and Vik1p to participate in different microtubule-mediated events within the same cell.

Global changes in STAT target selection and transcription regulation upon interferon treatments

Abstract: The STAT (signal transducer and activator of transcription) proteins play a crucial role in the regulation of gene expression, but their targets and the manner in which they select them remain largely unknown. Using chromatin immunoprecipitation and DNA microarray analysis (ChIP-chip), we have identified the regions of human chromosome 22 bound by STAT1 and STAT2 in interferon-treated cells. Analysis of the genomic loci proximal to these binding sites introduced new candidate STAT1 and STAT2 target genes, several of which are affiliated with proliferation and apoptosis. The genes on chromosome 22 that exhibited interferon-induced up- or down-regulated expression were determined and correlated with the STAT-binding site information, revealing the potential regulatory effects of STAT1 and STAT2 on their target genes. Importantly, the comparison of STAT1-binding sites upon interferon (IFN)-γ and IFN-α treatments revealed dramatic changes in binding locations between the two treatments. The IFN-α induction revealed nonconserved STAT1 occupancy at IFN-γ-induced sites, as well as novel sites of STAT1 binding not evident in IFN-γ-treated cells. Many of these correlated with binding by STAT2, but others were STAT2 independent, suggesting that multiple mechanisms direct STAT1 binding to its targets under different activation conditions. Overall, our results reveal a wealth of new information regarding IFN/STAT-binding targets and also fundamental insights into mechanisms of regulation of gene expression in different cell states.

Detecting and annotating genetic variations using the HugeSeq pipeline

Abstract: volume 30 number 3 march 2012 nature biotechnology Liege, Belgium. 31The Babraham Institute, Cambridge, UK. 32Genomatix Software GmbH, Munich, Germany. 33Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. 34Christian-Albrechts-Universitaet Zu Kiel, Kiel, Germany. 35Cellzome AG, Heidelberg, Germany. 36Institut National de la Sante et de la Recherche Medicale, Marseille, France. 37Weizmann Institute of Science, Rehovot, Israel. 38Barcelona Supercomputing Center, Barcelona, Spain. 39Centro Nacional de Investigaciones Oncologicas, Madrid, Spain. 40University Medical Centre Groningen, Groningen, The Netherlands. 41University of Saarland, Saarbruecken, Germany. 42Oxford Nanopore Technologies Ltd., Oxford, UK. e-mail: h.stunnenberg@ncmls.ru.nl

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

Abstract: Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.

Regulatory analysis of the C. elegans genome with spatiotemporal resolution

Abstract: Discovering the structure and dynamics of transcriptional regulatory events in the genome with cellular and temporal resolution is crucial to understanding the regulatory underpinnings of development and disease. We determined the genomic distribution of binding sites for 92 transcription factors and regulatory proteins across multiple stages of Caenorhabditis elegans development by performing 241 ChIP-seq (chromatin immunoprecipitation followed by sequencing) experiments. Integration of regulatory binding and cellular-resolution expression data produced a spatiotemporally resolved metazoan transcription factor binding map. Using this map, we explore developmental regulatory circuits that encode combinatorial logic at the levels of co-binding and co-expression of transcription factors, characterizing the genomic coverage and clustering of regulatory binding, the binding preferences of, and biological processes regulated by, transcription factors, the global transcription factor co-associations and genomic subdomains that suggest shared patterns of regulation, and identifying key transcription factors and transcription factor co-associations for fate specification of individual lineages and cell types.

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.