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.

A method to assess compositional bias in biological sequences and its application to prion-like glutamine/asparagine-rich domains in eukaryotic proteomes.

Abstract: We have derived a novel method to assess compositional biases in biological sequences, which is based on finding the lowest-probability subsequences for a given residue-type set. As a case study, the distribution of prion-like glutamine/asparagine-rich ((Q+N)-rich) domains (which are linked to amyloidogenesis) was assessed for budding and fission yeasts and four other eukaryotes. We find more than 170 prion-like (Q+N)-rich regions in budding yeast, and, strikingly, many fewer in fission yeast. Also, some residues, such as tryptophan or isoleucine, are unlikely to form biased regions in any eukaryotic proteome.

Predicting essential genes in fungal genomes.

Abstract: Essential genes are required for an organism’s viability, and the ability to identify these genes in pathogens is crucial to directed drug development. Predicting essential genes through computational methods is appealing because it circumvents expensive and difficult experimental screens. Most such prediction is based on homology mapping to experimentally verified essential genes in model organisms. We present here a different approach, one that relies exclusively on sequence features of a gene to estimate essentiality and offers a promising way to identify essential genes in unstudied or uncultured organisms. We identified 14 characteristic sequence features potentially associated with essentiality, such as localization signals, codon adaptation, GC content, and overall hydrophobicity. Using the well-characterized baker’s yeast Saccharomyces cerevisiae, we employed a simple Bayesian framework to measure the correlation of each of these features with essentiality. We then employed the 14 features to learn the parameters of a machine learning classifier capable of predicting essential genes. We trained our classifier on known essential genes in S. cerevisiae and applied it to the closely related and relatively unstudied yeast Saccharomyces mikatae. We assessed predictive success in two ways: First, we compared all of our predictions with those generated by homology mapping between these two species. Second, we verified a subset of our predictions with eight in vivo knockouts in S. mikatae, and we present here the first experimentally confirmed essential genes in this species.

Analysis of the yeast transcriptome with structural and functional categories: characterizing highly expressed proteins

Abstract: We analyzed 10 genome expression data sets by large-scale cross-referencing against broad structural and functional categories. The data sets, generated by different techniques (e.g. SAGE and gene chips), provide various representations of the yeast transcriptome (the set of all yeast genes, weighted by transcript abundance). Our analysis enabled us to determine features more prevalent in the transcriptome than the genome: i.e. those that are common to highly expressed proteins. Starting with simplest categories, we find that, relative to the genome, the transcriptome is enriched in Ala and Gly and depleted in Asn and very long proteins. We find, furthermore, that protein length and maximum expression level have a roughly inverse relationship. To relate expression level and protein structure, we assigned transmembrane helices and known folds (using PSI-blast) to each protein in the genome; this allowed us to determine that the transcriptome is enriched in mixed α–β structures and depleted in membrane proteins relative to the genome. In particular, some enzymatic folds, such as the TIM barrel and the G3P dehydrogenase fold, are much more prevalent in the transcriptome than the genome, whereas others, such as the protein-kinase and leucine-zipper folds, are depleted. The TIM barrel, in fact, is overwhelmingly the ‘top fold’ in the transcriptome, while it only ranks fifth in the genome. The most highly enriched functional categories in the transcriptome (based on the MIPS system) are energy production and protein synthesis, while categories such as transcription, transport and signaling are depleted. Furthermore, for a given functional category, transcriptome enrichment varies quite substantially between the different expression data sets, with a variation an order of magnitude larger than for the other categories cross-referenced (e.g. amino acids). One can readily see how the enrichment and depletion of the various functional categories relates directly to that of particular folds. Further information can be found at http://bioinfo.mbb.yale.edu/genome/expression

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。