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.

Characterization of Enhancer Function from Genome-Wide Analyses

Abstract: There has been a recent surge in the use of genome-wide methodologies to identify and annotate the transcriptional regulatory elements in the human genome. Here we review some of these methodologies and the conceptual insights about transcription regulation that have been gained from the use of genome-wide studies. It has become clear that the binding of transcription factors is itself a highly regulated process, and binding does not always appear to have functional consequences. Numerous properties have now been associated with regulatory elements that may be useful in their identification. Several aspects of enhancer function have been shown to be more widespread than was previously appreciated, including the highly combinatorial nature of transcription factor binding, the postinitiation regulation of many target genes, and the binding of enhancers at early stages to maintain their competence during development. Going forward, the integration of multiple genome-wide data sets should become a standard approach to elucidate higher-order regulatory interactions.

SeqFold: Genome-scale reconstruction of RNA secondary structure integrating high-throughput sequencing data

Abstract: We present an integrative approach, SeqFold, that combines high-throughput RNA structure profiling data with computational prediction for genome-scale reconstruction of RNA secondary structures. SeqFold transforms experimental RNA structure information into a structure preference profile (SPP) and uses it to select stable RNA structure candidates representing the structure ensemble. Under a high-dimensional classification framework, SeqFold efficiently matches a given SPP to the most likely cluster of structures sampled from the Boltzmann-weighted ensemble. SeqFold is able to incorporate diverse types of RNA structure profiling data, including parallel analysis of RNA structure (PARS), selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq), fragmentation sequencing (FragSeq) data generated by deep sequencing, and conventional SHAPE data. Using the known structures of a wide range of mRNAs and noncoding RNAs as benchmarks, we demonstrate that SeqFold outperforms or matches existing approaches in accuracy and is more robust to noise in experimental data. Application of SeqFold to reconstruct the secondary structures of the yeast transcriptome reveals the diverse impact of RNA secondary structure on gene regulation, including translation efficiency, transcription initiation, and protein-RNA interactions. SeqFold can be easily adapted to incorporate any new types of high-throughput RNA structure profiling data and is widely applicable to analyze RNA structures in any transcriptome.

Measuring the evolutionary rewiring of biological networks.

Abstract: We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or "rewire", at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of "commonplace" networks such as family trees, co-authorships and linux-kernel function dependencies.

Substrate specificity analysis of protein kinase complex Dbf2-Mob1 by peptide library and proteome array screening

Abstract: The mitotic exit network (MEN) is a group of proteins that form a signaling cascade that is essential for cells to exit mitosis in Saccharomyces cerevisiae. The MEN has also been implicated in playing a role in cytokinesis. Two components of this signaling pathway are the protein kinase Dbf2 and its binding partner essential for its kinase activity, Mob1. The components of MEN that act upstream of Dbf2-Mob1 have been characterized, but physiological substrates for Dbf2-Mob1 have yet to be identified. Using a combination of peptide library selection, phosphorylation of opitmal peptide variants, and screening of a phosphosite array, we found that Dbf2-Mob1 preferentially phosphorylated serine over threonine and required an arginine three residues upstream of the phosphorylated serine in its substrate. This requirement for arginine in peptide substrates could not be substituted with the similarly charged lysine. This specificity determined for peptide substrates was also evident in many of the proteins phosphorylated by Dbf2-Mob1 in a proteome chip analysis. We have determined by peptide library selection and phosphosite array screening that the protein kinase Dbf2-Mob1 preferentially phosphorylated substrates that contain an RXXS motif. A subsequent proteome microarray screen revealed proteins that can be phosphorylated by Dbf2-Mob1 in vitro. These proteins are enriched for RXXS motifs, and may include substrates that mediate the function of Dbf2-Mob1 in mitotic exit and cytokinesis. The relatively low degree of sequence restriction at the site of phosphorylation suggests that Dbf2 achieves specificity by docking its substrates at a site that is distinct from the phosphorylation site

An integrated approach for finding overlooked genes in yeast

Abstract: We report here the discovery of 137 previously unappreciated genes in yeast through a widely applicable and highly scalable approach integrating methods of gene-trapping, microarray-based expression analysis, and genome-wide homology searching. Our approach is a multistep process in which expressed sequences are first trapped using a modified transposon that produces protein fusions to β-galactosidase (β-gal); nonannotated open reading frames (ORFs) translated as β-gal chimeras are selected as a candidate pool of potential genes. To verify expression of these sequences, labeled RNA is hybridized against a microarray of oligonucleotides designed to detect gene transcripts in a strand-specific manner. In complement to this experimental method, novel genes are also identified in silico by homology to previously annotated proteins. As these methods are capable of identifying both short ORFs and antisense ORFs, our approach provides an effective supplement to current gene-finding schemes. In total, the genes discovered using this approach constitute 2% of the yeast genome and represent a wealth of overlooked biology.

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.