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.

The CRIT framework for identifying cross patterns in systems biology and application to chemogenomics.

Abstract: Biological data is often tabular but finding statistically valid connections between entities in a sequence of tables can be problematic - for example, connecting particular entities in a drug property table to gene properties in a second table, using a third table associating genes with drugs. Here we present an approach (CRIT) to find connections such as these and show how it can be applied in a variety of genomic contexts including chemogenomics data.

DiNeR: a Differential Graphical Model for analysis of co-regulation Network Rewiring

Abstract: Background: During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. "co-binding changes") can affect the co-regulating associations between TFs (i.e. "rewiring the co-regulator network"). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. Methods: To address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. Results: We assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. Conclusions: Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.

Tools needed to navigate landscape of the genome

Abstract: SIR — Your Special Report “The costs of global warming” (Nature 439, 374–375; 2006) gives an unbalanced picture of the emissions scenarios developed by the Intergovernmental Panel on Climate Change (IPCC). In contrast to the claim that these scenarios are outdated, a recent peer-reviewed assessment has concluded that, with a few notable exceptions, they compare reasonably well to recent data and projections for gross domestic product, population and emissions (D.v.V. and B.O’N. Clim. Change, in the press. doi: 10.1007/s10584-005-9031-0; see www. iiasa.ac.at/Research/PCC/pubs/vanVuuren& ONeill2006_CC_uncorproof.pdf). Although we believe that progress on scenario development can and will be made, the elements of ‘up-to-date’ economic theory identified as overlooked — “how future societies will operate, how fast the population will grow, and how technological progress will change things” — are either too vague to be meaningful, or are issues the community has been dealing with for years. The Energy Modeling Forum has a 30-year history of model comparisons, exploring the implications for climate policy of a range of rates of economic growth and technological change (D. W. Gaskins and J. P. Weyant Am. Econ. Rev. 83, 318–323; 1993, and J. P. Weyant Energy Econ. 26, 501–515; 2004). It is not correct to imply that the scenarios only use market exchange rates, or that they all assume that “the economies of poor countries will quickly catch up with those of rich nations”. Some scenarios are also reported in terms of purchasing-power parity exchange rates in the original 2000 IPCC Special Report. The debate on the emissions impacts of alternative exchange rates in economic modelling is not conclusive, but such impacts are likely to be small compared with the influence of technology, lifestyle and climate policies. And in no scenario do developing countries become as affluent as industrialized ones. The assumed degree of catching up in the scenarios covers a wide range of possibilities. Focusing on a small number of most-likely futures ignores lessons from history: if the world always worked according to best-guess projections, we would now be living with nuclear power too cheap to meter and no ozone hole. Arnulf Grubler*†, Brian O’Neill*‡, Detlef van Vuuren§ *International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria †School of Forestry & Environmental Studies, Yale University, New Haven, Connecticut 06511, USA ‡Watson Institute for International Studies, Brown University, Providence, Rhode Island 02912, USA §Netherlands Environmental Assessment Agency, PO Box 303, 3720 BA Bilthoven, The Netherlands

A XML-Based Approach to Integrating Heterogeneous Yeast Genome Data.

Abstract: While there are an increasing number of genomes (including the human genome) whose sequences have been fully or nearly completed, the budding yeast Saccharomyces cerevisiae was the first fully sequenced eukaryotic genome. Given its ease of genetic manipulation and the fact that many of its genes are strikingly similar to human genes, the yeast genome has been studied extensively through a wide range of biological experiments (e.g., microarray experiments). As a result, a large variety of types of yeast genome data have been generated and made accessible through many resources (e.g., SGD, MIPS, and YPD). While these resources serve many specific needs of individual researchers, we can reap more benefits by integrating these disparate datasets to facilitate larger-context data mining. However, such integrated analysis is hampered by the heterogeneous formats that are used for data distribution. With the increasing use of eXtensible Mark Language (XML) in the bioinformatics domain, we demonstrate how to use XML to standardize the exchange of a variety of types of yeast data between different resources. In particular, we propose a standard XML format called “Yeast Hub XML” (YHX). This format consists of: i) metadata and ii) data. While the former describes the resource and data structure, the latter is used to represent the data. In addition, we apply various XML-related technologies including XPath and XSLT to query, integrate, and transform multiple XML datasets. We have implemented a prototype yeast hub server that allows sharing, querying, and integration of different types and formats of yeast genome data that are located in disparate sources.

SigLASSO: a LASSO approach jointly optimizing sampling likelihood and cancer mutation signatures

Abstract: Multiple mutational processes drive carcinogenesis, leaving characteristic signatures on tumor genomes. Determining the active signatures from the full repertoire of potential ones can help elucidate the mechanisms underlying cancer initiation and development. This involves decomposing the frequency of cancer mutations categorized according to their trinucleotide context into a linear combination of known mutational signatures. We formulate this task as an optimization problem with L1 regularization and develop a software tool, sigLASSO, to carry it out efficiently. First, by explicitly adding multinomial sampling into the overall objective function, we jointly optimize the likelihood of sampling and signature fitting. This is especially important when mutation counts are low and sampling variance, high, such as the case in whole exome sequencing. sigLASSO uses L1 regularization to parsimoniously assign signatures to mutation profiles, leading to sparse and more biologically interpretable solutions. Additionally, instead of hard thresholding and choosing a priori, a discrete subset of active signatures, sigLASSO fine-tunes model complexity parameters, informed by the scale of the data and prior knowledge. Finally, it is challenging to evaluate sigLASSO signature assignments. To do this, we construct a set of criteria, which we can apply consistently across assignments.

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

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.

The Protein Data Bank

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.

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。