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.

10 Transposon Tagging I: A Novel System for Monitoring Protein Production, Function and Localization

Abstract: Publisher Summary The use of transposons allows the rapid construction of a large number of the alleles of a gene of interest. Transposon insertion libraries can be used for both the mutagenesis and identification of genes regulated by particular growth conditions and strain backgrounds. This chapter presents an overview of a newly developed multipurpose transposon mutagenesis system that allows the monitoring of protein production, function, and localization in yeast. The system uses two basic types of transposon, designated as mTn-3×HA/lacZ and mTn 3×HA/GFP. The transposon system was tested by the mutagenesis of several individual yeast genes. The HAT tag was successfully used to analyze the localization of the Spa2, Arp100, and Sao1 proteins. The new transposons expand the repertoire of insertions that may be generated to include GFT fusions and epitope tags. It is expected that shuttle mutagenesis will continue to be an important tool for the characterization of individual genes and their products and for systematic analysis of the entire yeast genome.

Specific Plasma Autoantibody Reactivity in Myelodysplastic Syndromes

Abstract: Increased autoantibody reactivity in plasma from Myelodysplastic Syndromes (MDS) patients may provide novel disease signatures, and possible early detection. In a two-stage study we investigated Immunoglobulin G reactivity in plasma from MDS, Acute Myeloid Leukemia post MDS patients, and a healthy cohort. In exploratory Stage I we utilized high-throughput protein arrays to identify 35 high-interest proteins showing increased reactivity in patient subgroups compared to healthy controls. In validation Stage II we designed new arrays focusing on 25 of the proteins identified in Stage I and expanded the initial cohort. We validated increased antibody reactivity against AKT3, FCGR3A and ARL8B in patients, which enabled sample classification into stable MDS and healthy individuals. We also detected elevated AKT3 protein levels in MDS patient plasma. The discovery of increased specific autoantibody reactivity in MDS patients, provides molecular signatures for classification, supplementing existing risk categorizations, and may enhance diagnostic and prognostic capabilities for MDS.

Fast Metagenomic Binning via Hashing and Bayesian Clustering.

Abstract: We introduce GATTACA, a framework for fast unsupervised binning of metagenomic contigs. Similar to recent approaches, GATTACA clusters contigs based on their coverage profiles across a large cohort of metagenomic samples; however, unlike previous methods that rely on read mapping, GATTACA quickly estimates these profiles from kmer counts stored in a compact index. This approach can result in over an order of magnitude speedup, while matching the accuracy of earlier methods on synthetic and real data benchmarks. It also provides a way to index metagenomic samples (e.g., from public repositories such as the Human Microbiome Project) offline once and reuse them across experiments; furthermore, the small size of the sample indices allows them to be easily transferred and stored. Leveraging the MinHash technique, GATTACA also provides an efficient way to identify publicly available metagenomic data that can be incorporated into the set of reference metagenomes to further improve binning accuracy. Thus, enabling easy indexing and reuse of publicly available metagenomic data sets, GATTACA makes accurate metagenomic analyses accessible to a much wider range of researchers.

Master lineage transcription factors anchor trans mega transcriptional complexes at highly accessible enhancer sites to promote long-range chromatin clustering and transcription of distal target genes

Abstract: The term 'super enhancers' (SE) has been widely used to describe stretches of closely localized enhancers that are occupied collectively by large numbers of transcription factors (TFs) and co-factors, and control the transcription of highly-expressed genes. Through integrated analysis of >600 DNase-seq, ChIP-seq, GRO-seq, STARR-seq, RNA-seq, Hi-C and ChIA-PET data in five human cancer cell lines, we identified a new class of autonomous SEs (aSEs) that are excluded from classic SE calls by the widely used Rank Ordering of Super-Enhancers (ROSE) method. TF footprint analysis revealed that compared to classic SEs and regular enhancers, aSEs are tightly bound by a dense array of master lineage TFs, which serve as anchors to recruit additional TFs and co-factors in trans. In addition, aSEs are preferentially enriched for Cohesins, which likely involve in stabilizing long-distance interactions between aSEs and their distal target genes. Finally, we showed that aSEs can be reliably predicted using a single DNase-seq data or combined with Mediator and/or P300 ChIP-seq. Overall, our study demonstrates that aSEs represent a unique class of functionally important enhancer elements that distally regulate the transcription of highly expressed genes.

PRC2 clock: a universal epigenetic biomarker of aging and rejuvenation

Abstract: DNA methylation (DNAm) is one of the most reliable biomarkers for aging across many mammalian tissues. While the age-dependent global loss of DNAm has been well characterized, age-dependent DNAm gain is less specified. Multiple studies have demonstrated that polycomb repressive complex 2 (PRC2) targets are enriched among the CpG sites which gain methylation with age. However, a systematic whole-genome examination of all PRC2 targets in the context of aging methylome as well as whether these associations are pan-tissue or tissue-specific is lacking. Here, by analyzing DNAm data from different assays and from multiple young and old human and mouse tissues, we found that low-methylated regions (LMRs) which are highly bound by PRC2 in embryonic stem cells gain methylation with age in all examined somatic mitotic cells. We also estimated that this epigenetic change represents around 90% of the age-dependent DNAm gain genome-wide. Therefore, we propose the “PRC2 clock,” defined as the average DNAm in PRC2 LMRs, as a universal biomarker of cellular aging in somatic cells. In addition, we demonstrate the application of this biomarker in the evaluation of different anti-aging interventions, including dietary restriction and partial epigenetic reprogramming.

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.