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.

Protein substrates of the arginine methyltransferase Hmt1 identified by proteome arrays.

Abstract: Arginine methylation on nonhistone proteins is associated with a number of cellular processes including RNA splicing, protein localization, and the formation of protein complexes. In this manuscript, Saccharomyces cerevisiae proteome arrays carrying 4228 proteins were used with an antimethylarginine antibody to first identify 88 putatively arginine-methylated proteins. By treating the arrays with recombinant arginine methyltransferase Hmt1, 42 proteins were found to be possible substrates of this enzyme. Analysis of the putative arginine-methylated proteins revealed that they were predominantly nuclear or nucleolar in localization, consistent with the localization of Hmt1. Many are involved in known methylarginine-associated functions, such as RNA processing and ribonucleoprotein complex biogenesis, yet others are of newer classes, namely RNA/DNA helicases and tRNA-associated proteins. Using ex vivo methylation and MS/MS, a set of 12 proteins (Brr1, Dia4, Hts1, Mpp10, Mrd1, Nug1, Prp43, Rpa43, Rrp43, Spp381, Utp4, and Npl3), including the RNA helicase Prp43 and tRNA ligases Dia4 and Hts1, were all validated as Hmt1 substrates. Interestingly, the majority of these also had human orthologs, or family members, that have been documented elsewhere to carry arginine methylation. These results confirm arginine methylation as a widespread modification and Hmt1 as the major arginine methyltransferase in the S. cerevisiae cell.

Decoding personal biotic and abiotic airborne exposome

Abstract: The complexity and dynamics of human diseases are driven by the interactions between internal molecular activities and external environmental exposures. Although advances in omics technology have dramatically broadened the understanding of internal molecular and cellular mechanisms, understanding of the external environmental exposures, especially at the personal level, is still rudimentary in comparison. This is largely owing to our limited ability to efficiently collect the personal environmental exposome (PEE) and extract the nucleic acids and chemicals from PEE. Here we describe a protocol that integrates hardware and experimental pipelines to collect and decode biotic and abiotic external exposome at the individual level. The described protocol has several advantages over conventional approaches, such as exposome monitoring at the personal level, decontamination steps to increase sensitivity and simultaneous capture and high-throughput profiling of biotic and abiotic exposures. The protocol takes ~18 h of bench time over 2–3 d to prepare samples for high-throughput profiling and up to a couple of weeks of instrumental time to analyze, depending on the number of samples. Hundreds to thousands of species and organic compounds could be detected in the airborne particulate samples using this protocol. The composition and complexity of the biotic and abiotic substances are heavily influenced by the sampling spatiotemporal factors. Basic skillsets in molecular biology and analytical chemistry are required to carry out this protocol. This protocol could be modified to decode biotic and abiotic substances in other types of low or ultra-low input samples. The authors describe hardware setup and experimental workflows for collecting and analyzing the biotic and abiotic environmental exposome at the individual level.

Annotation of Spatially Resolved Single-cell Data with STELLAR

Abstract: Spatial protein and RNA imaging technologies have been gaining rapid attention but current computational methods for annotating cells are based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method that utilizes spatial and molecular cell information to automatically assign cell types from an annotated reference set as well as discover new cell types and cell states. STELLAR transfers annotations across different dissection regions, tissues, and donors and detects higher-order tissue structures with dramatic time savings.

Real-time Alerting System for COVID-19 Using Wearable Data

Abstract: Early detection of infectious disease is crucial for reducing transmission and facilitating early intervention. We built a real-time smartwatch-based alerting system for the detection of aberrant physiological and activity signals (e.g. resting heart rate, steps) associated with early infection onset at the individual level. Upon applying this system to a cohort of 3,246 participants, we found that alerts were generated for pre-symptomatic and asymptomatic COVID-19 infections in 78% of cases, and pre-symptomatic signals were observed a median of three days prior to symptom onset. Furthermore, by examining over 100,000 survey annotations, we found that other respiratory infections as well as events not associated with COVID-19 (e.g. stress, alcohol consumption, travel) could trigger alerts, albeit at a lower mean period (1.9 days) than those observed in the COVID-19 cases (4.3 days). Thus this system has potential both for advanced warning of COVID-19 as well as a general system for measuring health via detection of physiological shifts from personal baselines. The system is open-source and scalable to millions of users, offering a personal health monitoring system that can operate in real time on a global scale.

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.