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.

Association of Immune-Related Adverse Events, Hospitalization, and Therapy Resumption With Survival Among Patients With Metastatic Melanoma Receiving Single-Agent or Combination Immunotherapy

Abstract: Key Points Question Are immune-related adverse events associated with longer overall survival among patients with metastatic melanoma treated with combination immune checkpoint blockade? Findings In this cohort study of 492 patients, those who experienced clinically significant immune-related adverse events had significantly longer median overall survival (56.3 months vs 18.5 months). This trend was maintained with combination immune checkpoint blockade and unaltered by hospitalization, while resumption of immunotherapy after the adverse event was associated with longer survival. Meaning This study suggests that despite increased hospitalization, immune-related adverse events are associated with longer survival among patients receiving combination immune checkpoint blockade, while resuming immunotherapy may benefit selected patients.

Rare Variant Burden Analysis within Enhancers Identifies CAV1 as a New ALS Risk Gene

Abstract: Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease CAV1 and CAV2 organise membrane lipid rafts (MLR) important for cell signalling and neuronal survival, and overexpression of CAV1 ameliorates ALS phenotypes in vivo Genome-wide association studies localise a large proportion of ALS-risk variants within the non-coding genome, but further characterisation has been limited by lack of appropriate tools Applying a new pipeline to identify pathogenic genetic variation within enhancer-elements responsible for regulating gene expression, we have discovered disease-associated variation within CAV1/CAV2 enhancers, which replicated in an independent cohort Discovered enhancer mutations reduce CAV1/CAV2 expression and disrupt MLR in patient-derived cells; and CRISPR/Cas9 perturbation proximate to a patient-mutation is sufficient to reduce CAV1/CAV2 expression in neurons Additional enrichment of ALS-associated mutations within CAV1 exons positions CAV1 as a new ALS gene We propose CAV1/CAV2 overexpression as a personalised medicine target for ALS

An integrated web interface for large-scale characterization of sequence data

Abstract: Large-scale genome projects require the analysis of large amounts of raw data. This analysis often involves the application of a chain of biology-based programs. Many of these programs are difficult to operate because they are non-integrated, command-line driven, and platform-dependent. The problem is compounded when the number of data files involved is large, making navigation and status-tracking difficult. To demonstrate how this problem can be addressed, we have created a platform-independent Web front end that integrates a set of programs used in a genomic project analyzing gene function by transposon mutagenesis in Saccharomyces cerevisiae. In particular, these programs help define a large number of transposon insertion events within the yeast genome, identifying both the precise site of transposon insertion as well as potential open reading frames disrupted by this insertion event. Our Web interface facilitates this analysis by performing the following tasks. Firstly, it allows each of the analysis programs to be launched against multiple directories of data files. Secondly, it allows the user to view, download, and upload files generated by the programs. Thirdly, it indicates which sets of data directories have been processed by each program. Although designed specifically to aid in this project, our interface exemplifies a general approach by which independent software programs may be integrated into an efficient protocol for large-scale genomic data processing.

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.