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.

Mismatch oligonucleotides in human and yeast: guidelines for probe design on tiling microarrays

Abstract: Background Mismatched oligonucleotides are widely used on microarrays to differentiate specific from nonspecific hybridization. While many experiments rely on such oligos, the hybridization behavior of various degrees of mismatch (MM) structure has not been extensively studied. Here, we present the results of two large-scale microarray experiments on S. cerevisiae and H. sapiens genomic DNA, to explore MM oligonucleotide behavior with real sample mixtures under tiling-array conditions.

Dual Platform Long-Read RNA-Sequencing Dataset of the Human Cytomegalovirus Lytic Transcriptome.

Abstract: RNA-sequencing has revolutionized transcriptomics and the way we measure gene expression (Wang et al., 2009). As of today, short-read RNA sequencing is more widely used, and due to its low price and high throughput, is the preferred tool for the quantitative analysis of gene expression. However, the annotation of transcript isoforms is rather difficult using only short-read sequencing data, because the reads are shorter than most transcripts (Steijger et al., 2013). Long-read sequencing, on the other hand, can provide full contig information about transcripts, including exon-connectivity, and its merits in transcriptome profiling are being increasingly acknowledged (Sharon et al., 2013; Abdel-Ghany et al., 2016; Wang et al., 2016; Kuo et al., 2017). Due to the relatively low throughput of current long-read sequencing technologies, they can only characterize smaller transcriptomes in high-depth (Weirather et al., 2017). The Human cytomegalovirus (HCMV) is a ubiquitous betaherpesvirus, which can cause mononucleosis-like symptoms in adults (Cohen and Corey, 1985), and severe life-threatening infections in newborns (Wen et al., 2002). Latent HCMV infection has recently been implicated to affect cancer formation (Dziurzynski et al., 2012; Jin et al., 2014). Examining the transcriptome of the virus can go a long way in helping understand its molecular biology. Short-read RNA sequencing studies have discovered splice junctions and non-coding transcripts (Gatherer et al., 2011) and have shown that the most abundant HCMV transcripts are similarly expressed in different cell types (Cheng et al., 2017). Our long-read RNA sequencing experiments using the Pacific Biosciences (PacBio) RSII platform revealed a great number of transcript isoforms, polycistronic RNAs and transcriptional overlaps (Balázs et al., 2017a).

Serine biosynthesis as a novel therapeutic target for dilated cardiomyopathy.

Abstract: AIMS Genetic dilated cardiomyopathy (DCM) is a leading cause of heart failure. Despite significant progress in understanding the genetic aetiologies of DCM, the molecular mechanisms underlying the pathogenesis of familial DCM remain unknown, translating to a lack of disease-specific therapies. The discovery of novel targets for the treatment of DCM was sought using phenotypic sceening assays in induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) that recapitulate the disease phenotypes in vitro. METHODS AND RESULTS Using patient-specific iPSCs carrying a pathogenic TNNT2 gene mutation (p.R183W) and CRISPR-based genome editing, a faithful DCM model in vitro was developed. An unbiased phenotypic screening in TNNT2 mutant iPSC-derived cardiomyocytes (iPSC-CMs) with small molecule kinase inhibitors (SMKIs) was performed to identify novel therapeutic targets. Two SMKIs, Gö 6976 and SB 203580, were discovered whose combinatorial treatment rescued contractile dysfunction in DCM iPSC-CMs carrying gene mutations of various ontologies (TNNT2, TTN, LMNA, PLN, TPM1, LAMA2). The combinatorial SMKI treatment upregulated the expression of genes that encode serine, glycine, and one-carbon metabolism enzymes and significantly increased the intracellular levels of glucose-derived serine and glycine in DCM iPSC-CMs. Furthermore, the treatment rescued the mitochondrial respiration defects and increased the levels of the tricarboxylic acid cycle metabolites and ATP in DCM iPSC-CMs. Finally, the rescue of the DCM phenotypes was mediated by the activating transcription factor 4 (ATF4) and its downstream effector genes, phosphoglycerate dehydrogenase (PHGDH), which encodes a critical enzyme of the serine biosynthesis pathway, and Tribbles 3 (TRIB3), a pseudokinase with pleiotropic cellular functions. CONCLUSIONS A phenotypic screening platform using DCM iPSC-CMs was established for therapeutic target discovery. A combination of SMKIs ameliorated contractile and metabolic dysfunction in DCM iPSC-CMs mediated via the ATF4-dependent serine biosynthesis pathway. Together, these findings suggest that modulation of serine biosynthesis signalling may represent a novel genotype-agnostic therapeutic strategy for genetic DCM.

Discovery of Novel Human Gene Regulatory Modules from Gene Co-expression and Promoter Motif Analysis

Abstract: Deciphering gene regulatory networks requires identification of gene expression modules. We describe a novel bottom-up approach to identify gene modules regulated by cis-regulatory motifs from a human gene co-expression network. Target genes of a cis-regulatory motif were identified from the network via the motif's enrichment or biased distribution towards transcription start sites in the promoters of co-expressed genes. A gene sub-network containing the target genes was extracted and used to derive gene modules. The analysis revealed known and novel gene modules regulated by the NF-Y motif. The binding of NF-Y proteins to these modules' gene promoters were verified using ENCODE ChIP-Seq data. The analyses also identified 8,048 Sp1 motif target genes, interestingly many of which were not detected by ENCODE ChIP-Seq. These target genes assemble into house-keeping, tissues-specific developmental, and immune response modules. Integration of Sp1 modules with genomic and epigenomic data indicates epigenetic control of Sp1 targets' expression in a cell/tissue specific manner. Finally, known and novel target genes and modules regulated by the YY1, RFX1, IRF1, and 34 other motifs were also identified. The study described here provides a valuable resource to understand transcriptional regulation of various human developmental, disease, or immunity pathways.

Atlas of transcriptionally active transposable elements in human adult tissues

Abstract: Approximately half of the human genome consists of mobile repetitive DNA sequences known as transposable elements (TEs). They are usually silenced by epigenetic mechanisms, but a few are known to escape silencing at embryonic stages, affecting early human development by regulating nearby protein-coding genes. To investigate transcriptional activity in human adult tissues we systematically investigate the expression landscape of about 4.2 million non-coding TEs in 8,051 RNA-Seq datasets from up to 49 adult tissues and 540 individuals. We show that approximately 79,558 individual TEs (2%). belonging to 856 subfamilies escape epigenetic silencing in adult tissues and become transcriptionally active, often in a very tissue-specific manner. Supporting a role for TEs in the regulation of expression of nearby genes, we found the expression of TEs often correlated with the expression of nearby genes, and significantly stronger when the TEs include eQTLs for the genes. We identified thousands of tissue-elevated, sex-associated TEs in the breast, ethnicity-associated in the skin and age-associated in the tibial artery, where we found a potential implication of two TE subfamilies in atherosclerosis. Our results suggest a functional role of TEs in the regulation of gene expression, support their implication in human phenotypes, and also serve as a comprehensive resource of transcriptionally active TEs in human adult tissues.

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.