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Mark Gerstein

Bio: Mark Gerstein is an academic researcher from Yale University. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 168, co-authored 751 publications receiving 149578 citations. Previous affiliations of Mark Gerstein include Rutgers University & Structural Genomics Consortium.
Topics: Genome, Gene, Human genome, Genomics, Pseudogene


Papers
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Posted ContentDOI
07 May 2016-bioRxiv
TL;DR: In this article, the authors apply frustration to study the impacts of a large number of SNVs available throughout a number of next-generation sequencing datasets, and observe that disease-associated SNVs have a strong proclivity to induce strong changes in localized frustration, and rare variants tend to disrupt local interactions to a larger extent than do common variants.
Abstract: The rapidly declining costs of sequencing human genomes and exomes are providing deeper insights into genomic variation than previously possible. Growing sequence datasets are uncovering large numbers of rare single-nucleotide variants (SNVs) in coding regions, many of which may even be unique to single individuals. The rarity of such variants makes it difficult to use conventional variant-phenotype associations as a means of predicting their potential impacts. As such, protein structures may help to provide the needed means for inferring otherwise difficult-to-discern rare SNV-phenotype associations. Previous efforts have sought to quantify the effects of SNVs on structures by evaluating their impacts on global stability. However, local perturbations can severely impact functionality (such as catalysis,allosteric regulation, interactions and specificity) without strongly disrupting global stability.Here, we describe a workflow in which localized frustration (which quantifies unfavorable residue-residue interactions) is employed as a metric to investigate such effects. We apply frustration to study the impacts of a large number of SNVs available throughout a number of next-generation sequencing datasets. Most of our observations are intuitively consistent: we observe that disease-associated SNVs have a strong proclivity to induce strong changes in localized frustration, and rare variants tend to disrupt local interactions to a larger extent than do common variants. Furthermore, we observe that somatic SNVs associated with oncogenes induce stronger perturbations at the surface, whereas those associated with tumor suppressor genes (TSGs) induce stronger perturbations in the interior. These findings are consistent with the notion that gain-of-function (for oncogenes) and loss-of-function events (for TSGs) may act through changes in regulatory interactions and basic functionality, respectively.

2 citations

Journal ArticleDOI
TL;DR: The iTAR web server provides a user-friendly interface and supports target gene identification in seven species, ranging from yeast to human, and is a useful tool in identifying TF target genes from ChIP-seq/ChIP-chip data and discovering biological insights.
Abstract: Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) or microarray hybridization (ChIP-chip) has been widely used to determine the genomic occupation of transcription factors (TFs). We have previously developed a probabilistic method, called TIP (Target Identification from Profiles), to identify TF target genes using ChIP-seq/ChIP-chip data. To achieve high specificity, TIP applies a conservative method to estimate significance of target genes, with the trade-off being a relatively low sensitivity of target gene identification compared to other methods. Additionally, TIP’s output does not render binding-peak locations or intensity, information highly useful for visualization and general experimental biological use, while the variability of ChIP-seq/ChIP-chip file formats has made input into TIP more difficult than desired. To improve upon these facets, here we present are fined TIP with key extensions. First, it implements a Gaussian mixture model for p-value estimation, increasing target gene identification sensitivity and more accurately capturing the shape of TF binding profile distributions. Second, it enables the incorporation of TF binding-peak data by identifying their locations in significant target gene promoter regions and quantifies their strengths. Finally, for full ease of implementation we have incorporated it into a web server ( http://syslab3.nchu.edu.tw/iTAR/ ) that enables flexibility of input file format, can be used across multiple species and genome assembly versions, and is freely available for public use. The web server additionally performs GO enrichment analysis for the identified target genes to reveal the potential function of the corresponding TF. The iTAR web server provides a user-friendly interface and supports target gene identification in seven species, ranging from yeast to human. To facilitate investigating the quality of ChIP-seq/ChIP-chip data, the web server generates the chart of the characteristic binding profiles and the density plot of normalized regulatory scores. The iTAR web server is a useful tool in identifying TF target genes from ChIP-seq/ChIP-chip data and discovering biological insights.

2 citations

Posted ContentDOI
07 Aug 2018-bioRxiv
TL;DR: A comprehensive genome-wide annotation of the pseudogenes in the mouse reference genome and associated strains is presented, finding that the overall mouse pseudogene repertoire is similar to human in terms of overall size, biotype distribution and top family composition.
Abstract: Pseudogenes are ideal markers of genome remodeling. In turn, the mouse is an ideal platform for studying them, particularly with the availability of developmental transcriptional data and the sequencing of 18 strains. Here, we present a comprehensive genome-wide annotation of the pseudogenes in the mouse reference genome and associated strains. We compiled this by combining manual curation of over 10,000 pseudogenes with results from automatic annotation pipelines. Also, by comparing the human and mouse, we annotated 165 unitary pseudogenes in mouse, and 303 unitaries in human. We make all our annotation available through mouse.pseudogene.org. The overall mouse pseudogene repertoire (in the reference and strains) is similar to human in terms of overall size, biotype distribution (~80% processed/~20% duplicated) and top family composition (with many GAPDH and ribosomal pseudogenes). However, notable differences arise in the pseudogene age distribution, with multiple retro-transpositional bursts in mouse evolutionary history and only one in human. Furthermore, in each strain about a fifth of the pseudogenes are unique, reflecting strain-specific functions and evolution. Additionally, we find that ~15% of the pseudogenes are transcribed, a fraction similar to that for human, and that pseudogene transcription exhibits greater tissue and strain specificity compared to protein-coding genes. Finally, we show that highly transcribed parent genes tend to give rise to processed pseudogenes.

2 citations

Journal ArticleDOI
TL;DR: The impact of the present situation of relative anarchy on the scientific research front is highlighted, with two major impediments to achieving wide-scale interoperability: the state of database protection legislation and computer security issues.
Abstract: The significant growth of this annual over the past decade is testament to the importance of databases to scientific research in general and biomedical sciences in particular. In fact, the centrality of databases within our society at large is demonstrated by the continued debate in Congress regarding the legal protection of databases and the information they contain. Productive utilization of databases requires interoperability: that is, the precise yet flexible interrelating of information from one database to another. There are, at present, two major impediments to achieving wide-scale interoperability: the state of database protection legislation and computer security issues. While most non-commercial/academic databases may not be overly concerned with the protection of their intellectual property, they still put up barriers to entrance, and consequently interoperability, due to concerns regarding the security of their computing infrastructure. We succinctly outline these two issues below in an effort to raise awareness of the issues among scientists. The issues surrounding the legal protection of database are intricate and complex; this forum is not the place for complicated legal doctrine, suffice it to say that presently there is no intellectual property model that fully encompasses databases (1). But, what ought to be stressed here is the impact of the present situation of relative anarchy (i.e. no laws expressly guaranteeing the protection of databases here in the United States, in addition to the disparity of legal recourse between here and Europe) on the scientific research front. Bioinformatics research exemplifies the need to be able to integrate heterogeneous, diverse and distributed large-scale datasets. It attempts to efficiently process, curate, manage, and mine the deluge of biological data available. As it does not produce its own raw data, as is the case with many other fields, it instead must examine and integrate other researchers' data, relying on a culture of sharing to attain this information. This integration, while obviously leading to a better understanding of whatever subject is at hand, also tends to allow for the discovery of new and pertinent information regarding those biological systems: the sum is definitely greater than its parts. The basic requirements for this integration are uniformity and accessibility; data are ineffectual if scattered among incompatible resources. Unfortunately though, without any legal recourse to protect their databases, many providers have looked toward digital rights management schemes to defend their investments. The protections, digital locks that prevent easy access to data through passwords, complicated web …

2 citations

Journal ArticleDOI
TL;DR: Computer security in academia—a potential roadblock to distributed annotation of the human genome and how to overcome it is explored.
Abstract: Computer security in academia—a potential roadblock to distributed annotation of the human genome

2 citations


Cited by
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Journal ArticleDOI
TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Abstract: The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

70,111 citations

Journal ArticleDOI
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Abstract: The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.

34,239 citations

Journal ArticleDOI
TL;DR: 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 outperforms other aligners by a factor of >50 in mapping speed.
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/.

30,684 citations

Journal ArticleDOI
TL;DR: Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches and can be used simultaneously to achieve even greater alignment speeds.
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.

20,335 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组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�ly.
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。

18,940 citations