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.

Abnormal Differentiation and Proliferation of Coronary Arterial Endothelium in Hypoplastic Left Heart Syndrome

Abstract: Hypoplastic left heart syndrome (HLHS) is a severe form of single ventricle congenital heart disease characterized by an underdevelopment of the left ventricle. Early serial postmortem examinations revealed high rate of coronary artery abnormalities in HLHS fetal hearts, which may impact ventricular development and intra-cardiac hemodynamics, leading to a poor prognosis after surgical palliations. Previous study reported that endothelial cells (ECs) lining the coronary vessels showed DNA damage in the left ventricle of human fetal heart with HLHS, indicating that EC dysfunction may contribute to the coronary abnormalities in HLHS. To investigate the underlying mechanism of HLHS coronary artery abnormalities, we profiled both human fetal heart with an underdeveloped left ventricle (ULV) and ECs differentiated from induced pluripotent stem cells (iPSCs) derived from HLHS patients at single cell resolution. CD144+/NPR3- vascular ECs were selected and further classified as venous EC (NR2F2high), arterial EC (EFNB2high) and late arterial EC (GJA5high) subclusters based on previously reported marker genes. To study the arterial phenotype, we specifically generated iPSC-arterial ECs (AECs, CD34+CDH5+CXCR4+NT5E-/low) derived from three HLHS patients and three age-matched healthy controls to further dissect the phenotype of HLHS-AECs. As compared to normal human heart and control iPSC-ECs respectively, ULV late arterial EC subcluster and HLHS iPSC-EC arterial clusters showed significantly reduced expression of arterial genes GJA5, DLL4, and HEY1. Pathway enrichment analysis based on differentially expressed genes revealed several defects in late AEC cluster from ULV compared to normal human heart, such as impaired endothelial proliferation, development and Notch signaling. HLHS iPSCs exhibited impaired AEC differentiation as evidenced by the significantly reduced CXCR4+NT5E-/low AEC progenitor population. Consistent with human heart transcriptomic data, matured HLHS iPSC-AECs also showed a lower expression of the arterial genes such as GJA5, DLL4, HEY1 which are also downstream of NOTCH signaling. Additionally, matured HLHS iPSC-AECs showed significantly decreased expression of cell proliferation marker Ki67 and G1/S transition genes (CCND1, CCND2) compared with controls. Interestingly, NOTCH ligand Jag1 treatment significantly rescued this cell proliferation defect in HLHS AECs, accompanied by upregulation of various G1/S transition genes. In summary, we found that coronary AECs from HLHS showed impaired arterial development and proliferation downstream of NOTCH signaling. These functional defects in HLHS coronary AECs could contribute to the vascular structure malformation and impaired ventricular development.

Fast Optimal Genome Tiling with Applications to Microarray Design and Homology Search

Abstract: In this paper we consider several variations of the following basic tiling problem: given a sequence of real numbers with two size bound parameters, we want to find a set of tiles such that they satisfy the size bounds and the total weight of the tiles is maximized This solution to this problem is important to a number of computational biology applications, such as selecting genomic DNA fragments for amplicon microarrays, or performing homology searches with long sequence queries Our goal is to design efficient algorithms with linear or near-linear time and space in the normal range of parameter values for these problems For this purpose, we discuss the solution of a basic online interval maximum problem via a sliding window approach and show how to use this solution in a nontrivial manner for many of our tiling problems We also discuss NPhardness and approximation algorithms for generalization of our basic tiling problem to higher dimensions

AdaTiSS: A Novel Data-Adaptive Robust Method for Quantifying Tissue Specificity Scores

Abstract: Motivation Accurately detecting tissue specificity (TS) in genes helps researchers understand tissue functions at the molecular level, and further identify disease mechanisms and discover tissue-specific therapeutic targets. The Genotype-Tissue Expression (GTEx) project (Consortium, 2015), and the Human Protein Atlas (HPA) project (Uhlen, et al., 2015) are two publicly available data resources, providing large-scale gene expressions across multiple tissue types. Multiple tissue comparisons, technical background noise and unknown variation factors make it challenging to accurately identify tissue specific gene expressions. Several methods worked on measuring the overall TS in gene expressions and classifying genes into tissue-enrichment categories. There still lacks a robust method to provide quantitative TS scores for each tissue. Methods We recognized that the key to quantify tissue specific gene expressions is to properly define a concept of expression population. We considered that inside the population, the sample expressions from various tissues are more or less balanced, and the outlier expressions outside the population may indicate tissue specificity. We then formulated the question to robustly estimate the population distribution. In a linear regression problem, we developed a novel data-adaptive robust estimation based on density-power-weight under unknown outlier distribution and non-vanishing outlier proportion (Wang, et al., 2019). In the question of quantifying TS, we focused on the Gaussian-population mixture model. We took into account gene heterogeneities and applied the robust data-adaptive procedure to estimate the population. With the robustly estimated population parameters, we constructed the AdaTiSS algorithm to obtain data-adaptive quantitative TS scores. Results Our TS scores from the AdaTiSS algorithm achieve the goal that the TS scores are comparable across tissues and also across genes, which standardize gene expressions in terms of TS. Compared to the categorical TS method such as the HPA criterion, our method provides more information on the population fitting, and shows advantages in quantitatively analyzing tissue specific functions, making the biology functional analysis more precise. We also discuss some limitations and possible future work. Contact mpsnyder@stanford.edu

Virus protein microarray and uses therefor

Abstract: A virus protein micorarray that can serve as a rapid, sensitive and simple tool for identification of viral specific antibodies in sera, such as a SARS coronavirus protein microarray and methods of using the protein microarray.

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.