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 tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks

Abstract: Summary: Biological processes involve complex networks of interactions between molecules. Various large-scale experiments and curation efforts have led to preliminary versions of complete cellular networks for a number of organisms. To grapple with these networks, we developed TopNet-like Yale Network Analyzer (tYNA), a Web system for managing, comparing and mining multiple networks, both directed and undirected. tYNA efficiently implements methods that have proven useful in network analysis, including identifying defective cliques, finding small network motifs (such as feed-forward loops), calculating global statistics (such as the clustering coefficient and eccentricity), and identifying hubs and bottlenecks. It also allows one to manage a large number of private and public networks using a flexible tagging system, to filter them based on a variety of criteria, and to visualize them through an interactive graphical interface. A number of commonly used biological datasets have been pre-loaded into tYNA, standardized and grouped into different categories. Availability: The tYNA system can be accessed at http://networks.gersteinlab.org/tyna. The source code, JavaDoc API and WSDL can also be downloaded from the website. tYNA can also be accessed from the Cytoscape software using a plugin. Contact: mark.gerstein@yale.edu Supplementary information: Additional figures and tables can be found at http://networks.gersteinlab.org/tyna/supp

MUSIC: identification of enriched regions in ChIP-Seq experiments using a mappability-corrected multiscale signal processing framework

Abstract: We present MUSIC, a signal processing approach for identification of enriched regions in ChIP-Seq data, available at http://www.music.gersteinlab.org . MUSIC first filters the ChIP-Seq read-depth signal for systematic noise from non-uniform mappability, which fragments enriched regions. Then it performs a multiscale decomposition, using median filtering, identifying enriched regions at multiple length scales. This is useful given the wide range of scales probed in ChIP-Seq assays. MUSIC performs favorably in terms of accuracy and reproducibility compared with other methods. In particular, analysis of RNA polymerase II data reveals a clear distinction between the stalled and elongating forms of the polymerase.

Integrated assessment of genomic correlates of protein evolutionary rate.

Abstract: Rates of evolution differ widely among proteins, but the causes and consequences of such differences remain under debate. With the advent of high-throughput functional genomics, it is now possible to rigorously assess the genomic correlates of protein evolutionary rate. However, dissecting the correlations among evolutionary rate and these genomic features remains a major challenge. Here, we use an integrated probabilistic modeling approach to study genomic correlates of protein evolutionary rate in Saccharomyces cerevisiae. We measure and rank degrees of association between (i) an approximate measure of protein evolutionary rate with high genome coverage, and (ii) a diverse list of protein properties (sequence, structural, functional, network, and phenotypic). We observe, among many statistically significant correlations, that slowly evolving proteins tend to be regulated by more transcription factors, deficient in predicted structural disorder, involved in characteristic biological functions (such as translation), biased in amino acid composition, and are generally more abundant, more essential, and enriched for interaction partners. Many of these results are in agreement with recent studies. In addition, we assess information contribution of different subsets of these protein properties in the task of predicting slowly evolving proteins. We employ a logistic regression model on binned data that is able to account for intercorrelation, non-linearity, and heterogeneity within features. Our model considers features both individually and in natural ensembles (“meta-features”) in order to assess joint information contribution and degree of contribution independence. Meta-features based on protein abundance and amino acid composition make strong, partially independent contributions to the task of predicting slowly evolving proteins; other meta-features make additional minor contributions. The combination of all meta-features yields predictions comparable to those based on paired species comparisons, and approaching the predictive limit of optimal lineage-insensitive features. Our integrated assessment framework can be readily extended to other correlational analyses at the genome scale.

VarSim: a high-fidelity simulation and validation framework for high-throughput genome sequencing with cancer applications

Abstract: Summary: VarSim is a framework for assessing alignment and variant calling accuracy in high-throughput genome sequencing through simulation or real data. In contrast to simulating a random mutation spectrum, it synthesizes diploid genomes with germline and somatic mutations based on a realistic model. This model leverages information such as previously reported mutations to make the synthetic genomes biologically relevant. VarSim simulates and validates a wide range of variants, including single nucleotide variants, small indels and large structural variants. It is an automated, comprehensive compute framework supporting parallel computation and multiple read simulators. Furthermore, we developed a novel map data structure to validate read alignments, a strategy to compare variants binned in size ranges and a lightweight, interactive, graphical report to visualize validation results with detailed statistics. Thus far, it is the most comprehensive validation tool for secondary analysis in next generation sequencing. Availability and implementation: Code in Java and Python along with instructions to download the reads and variants is at http://bioinform.github.io/varsim. Contact: moc.anib@dr Supplementary information: Supplementary data are available at Bioinformatics online.

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。