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.

An analysis of the present system of scientific publishing: what's wrong and where to go from here

Abstract: The internet has produced an unprecedented opportunity to provide free and unhindered access to the wealth of scientific information, the volume of which continues to grow at a furious pace. The current balkanised system of individual journals limits possibilities for powerful search tools and for an integrated repository of the whole body of scientific literature. This paper reviews the current publishing environment, commenting on its strong and weak points (for example peer review, which is both a strength and a weakness). It attempts to find a viable solution to the current issues that plague STM (scientific, technical, and medical) publishing in the introduction of a centralised repository of scientific literature. Related issues such as the question of long term archiving and the justified fears of STM publishers of becoming obsolete are also discussed.

Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys

Abstract: Background Individuals with later bedtimes have an increased risk of difficulties with mood and substances. To investigate the causes and consequences of late bedtimes and other sleep patterns, researchers are exploring social media as a data source. Pioneering studies inferred sleep patterns directly from social media data. While innovative, these efforts are variously unscalable, context dependent, confined to specific sleep parameters, or rest on untested assumptions, and none of the reviewed studies apply to the popular Reddit platform or release software to the research community. Objective This study builds on this prior work. We estimate the bedtimes of Reddit users from the times tamps of their posts, test inference validity against survey data, and release our model as an R package (The R Foundation). Methods We included 159 sufficiently active Reddit users with known time zones and known, nonanomalous bedtimes, together with the time stamps of their 2.1 million posts. The model’s form was chosen by visualizing the aggregate distribution of the timing of users’ posts relative to their reported bedtimes. The chosen model represents a user’s frequency of Reddit posting by time of day, with a flat portion before bedtime and a quadratic depletion that begins near the user’s bedtime, with parameters fitted to the data. This model estimates the bedtimes of individual Reddit users from the time stamps of their posts. Model performance is assessed through k-fold cross-validation. We then apply the model to estimate the bedtimes of 51,372 sufficiently active, nonbot Reddit users with known time zones from the time stamps of their 140 million posts. Results The Pearson correlation between expected and observed Reddit posting frequencies in our model was 0.997 on aggregate data. On average, posting starts declining 45 minutes before bedtime, reaches a nadir 4.75 hours after bedtime that is 87% lower than the daytime rate, and returns to baseline 10.25 hours after bedtime. The Pearson correlation between inferred and reported bedtimes for individual users was 0.61 (P<.001). In 90 of 159 cases (56.6%), our estimate was within 1 hour of the reported bedtime; 128 cases (80.5%) were within 2 hours. There was equivalent accuracy in hold-out sets versus training sets of k-fold cross-validation, arguing against overfitting. The model was more accurate than a random forest approach. Conclusions We uncovered a simple, reproducible relationship between Reddit users’ reported bedtimes and the time of day when high daytime posting rates transition to low nighttime posting rates. We captured this relationship in a model that estimates users’ bedtimes from the time stamps of their posts. Limitations include applicability only to users who post frequently, the requirement for time zone data, and limits on generalizability. Nonetheless, it is a step forward for inferring the sleep parameters of social media users passively at scale. Our model and precomputed estimated bedtimes of 50,000 Reddit users are freely available.

Cancer relevance of human genes

Abstract: Background: It is unclear how many of genes contribute to the biology of cancer We hypothesize that genes that interact with core cancer gene (CCG) in a protein-protein interaction network (PPI) may have functional importance Methods: We categorized genes into 1- (n=6791), 2- (n=7724), 3- (n=1587), and >3-steps (n=362) removed from the nearest CCG in the STRING PPI and demonstrate that the cancer-biology related functional contribution of the genes in these different neighborhood categories decreases as their distance from the CCGs increases Results: Genes closer to cancer genes manifest greater connectedness in the network, show greater importance in maintaining cell viability in a broad range of cancer cells in vitro, are also under greater negative germline selection pressure in the healthy populations, and have higher somatic mutation frequency and cancer effect Conclusions: Approximately 70% of human genes are 1 or 2 steps removed from cancer genes in protein network and show functional importance in cancer-biology These results suggest that the universe of cancer-relevant genes extends to thousands of genes that can contribute functional effects when dysregulated

Proteomics data interoperation with applications to integrated data mining and enhanced information retrieval

Abstract: This thesis addresses the problem of data integration and interoperation of large-scale, widely distributed and independently maintained data, focusing on biological proteomics data which exemplifies the problem and has a practical need for better interoperation, and shows how such integrated data can be leveraged for important applications such as detailed cross-database queries in support of scientific exploratory data analysis and enhanced information retrieval. Semantic web RDF and RDF databases, which fit the problem well, are used to build two biological data integration systems called YeastHub and LinkHub. YeastHub is a lightweight semantic web data warehouse of joined RDF-formatted biological (yeast) data and allows RDF query access to it. LinkHub focuses on a high-level structuring principal or "scaffold" for biological data, storing biological identifiers (e.g. for proteins, genes, etc.) and the complex relationships among them as a large RDF directed labeled graph; LinkHub is used through web interactive and query interfaces and also complements YeastHub. Through several nontrivial RDF queries of the joined YeastHub and LinkHub data, we demonstrate that practical integrated biological data analysis can be achieved by basic, lightweight methods which don't attempt to solve the complete integration problem. A key focus of the LinkHub system is support for enhanced information retrieval of web documents and articles from the biomedical scientific literature (PubMed). We attach documents to identifier nodes in the LinkHub RDF graph and provide for the flexible retrieval of the documents through queries of the RDF graph's relational structure. In addition, we use the LinkHub RDF relational data and attached documents as training sets to construct classifiers for document relevance ranking in support of enhanced automated information retrieval of web or biomedical scientific literature documents related to biological identifiers. The results of experiments done to empirically measure the performance of this enhanced automated information retrieval for proteomics (UniProt) identifier-related documents through the use of a manually curated bibliography of yeast protein-specific literature citations are presented.

Systematic control of protein interactions for systems biology.

Abstract: Since its birth, systems biology has gained a great deal from the protocols devised to study phenomena at the level of single proteins and nucleic acids. Such protocols find broad markets and utility at higher levels of biological organization, from next-generation sequencing, which uses modified nucloeotides and fluorescent identifiers (1), to ChIP-seq analysis, which identifies histone modifications and binding sites in protein–DNA interactions (2). In PNAS, Sivaramakrishnan and Spudich introduce a system for interrogating interactions between pairs of proteins, domains, and peptides (3), and it is very possible that their invention will find applicability in the construction and analysis of large-scale protein–protein interaction networks (4).

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。