Manolis Kellis

Bio: Manolis Kellis is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 128, co-authored 405 publications receiving 112181 citations. Previous affiliations of Manolis Kellis include Broad Institute & Epigenomics AG.

Evidence of reduced recombination rate in human regulatory domains

Abstract: Recombination rate is non-uniformly distributed across the human genome. The variation of recombination rate at both fine and large scales cannot be fully explained by DNA sequences alone. Epigenetic factors, particularly DNA methylation, have recently been proposed to influence the variation in recombination rate. We study the relationship between recombination rate and gene regulatory domains, defined by a gene and its linked control elements. We define these links using expression quantitative trait loci (eQTLs), methylation quantitative trait loci (meQTLs), chromatin conformation from publicly available datasets (Hi-C and ChIA-PET), and correlated activity links that we infer across cell types. Each link type shows a “recombination rate valley” of significantly reduced recombination rate compared to matched control regions. This recombination rate valley is most pronounced for gene regulatory domains of early embryonic development genes, housekeeping genes, and constitutive regulatory elements, which are known to show increased evolutionary constraint across species. Recombination rate valleys show increased DNA methylation, reduced doublestranded break initiation, and increased repair efficiency, specifically in the lineage leading to the germ line. Moreover, by using only the overlap of functional links and DNA methylation in germ cells, we are able to predict the recombination rate with high accuracy. Our results suggest the existence of a recombination rate valley at regulatory domains and provide a potential molecular mechanism to interpret the interplay between genetic and epigenetic variations.

Genetic drivers of m6A methylation in human brain, lung, heart and muscle.

Abstract: The most prevalent post-transcriptional mRNA modification, N6-methyladenosine (m6A), plays diverse RNA-regulatory roles, but its genetic control in human tissues remains uncharted. Here we report 129 transcriptome-wide m6A profiles, covering 91 individuals and 4 tissues (brain, lung, muscle and heart) from GTEx/eGTEx. We integrate these with interindividual genetic and expression variation, revealing 8,843 tissue-specific and 469 tissue-shared m6A quantitative trait loci (QTLs), which are modestly enriched in, but mostly orthogonal to, expression QTLs. We integrate m6A QTLs with disease genetics, identifying 184 GWAS-colocalized m6A QTL, including brain m6A QTLs underlying neuroticism, depression, schizophrenia and anxiety; lung m6A QTLs underlying expiratory flow and asthma; and muscle/heart m6A QTLs underlying coronary artery disease. Last, we predict novel m6A regulators that show preferential binding in m6A QTLs, protein interactions with known m6A regulators and expression correlation with the m6A levels of their targets. Our results provide important insights and resources for understanding both cis and trans regulation of epitranscriptomic modifications, their interindividual variation and their roles in human disease. Analysis of 129 N6-methyladenosine (m6A) profiles across 4 tissues (brain, lung, muscle and heart) identifies 8,843 tissue-specific and 469 tissue-shared m6A quantitative trait loci (QTLs). Of these, 184 m6A QTLs colocalize with GWAS signals.

Distinct and Predictive Histone Lysine Acetylation Patterns at Promoters, Enhancers and Gene Bodies

Abstract: In eukaryotic cells, histone lysines are frequently acetylated. However, unlike modifications such as methylations, histone acetylation modifications are often considered redundant. As such, the functional roles of distinct histone acetylations are largely unexplored. We previously developed an algorithm RFECS to discover the most informative modifications associated with the classification or prediction of mammalian enhancers. Here, we used this tool to identify the modifications most predictive of promoters, enhancers, and gene bodies. Unexpectedly, we found that histone acetylation alone performs well in distinguishing these unique genomic regions. Further, we found the association of characteristic acetylation patterns with genic regions and association of chromatin state with splicing. Taken together, our work underscores the diverse functional roles of histone acetylation in gene regulation and provides several testable hypotheses to dissect these roles.

Motif discovery in physiological datasets: A methodology for inferring predictive elements

Abstract: In this article, we propose a methodology for identifying predictive physiological patterns in the absence of prior knowledge. We use the principle of conservation to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to efficiently search for such patterns in large datasets. This involves first transforming continuous physiological signals from patients into symbolic sequences, and then searching for patterns in these reduced representations that are strongly associated with an outcome.Our strategy of identifying conserved activity that is unlikely to have occurred purely by chance in symbolic data is analogous to the discovery of regulatory motifs in genomic datasets. We build upon existing work in this area, generalizing the notion of a regulatory motif and enhancing current techniques to operate robustly on non-genomic data. We also address two significant considerations associated with motif discovery in general: computational efficiency and robustness in the presence of degeneracy and noise. To deal with these issues, we introduce the concept of active regions and new subset-based techniques such as a two-layer Gibbs sampling algorithm. These extensions allow for a framework for information inference, where precursors are identified as approximately conserved activity of arbitrary complexity preceding multiple occurrences of an event.We evaluated our solution on a population of patients who experienced sudden cardiac death and attempted to discover electrocardiographic activity that may be associated with the endpoint of death. To assess the predictive patterns discovered, we compared likelihood scores for motifs in the sudden death population against control populations of normal individuals and those with non-fatal supraventricular arrhythmias. Our results suggest that predictive motif discovery may be able to identify clinically relevant information even in the absence of significant prior knowledge.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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/.

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。

Full-length transcriptome assembly from RNA-Seq data without a reference genome.

Abstract: Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here we present the Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. Our approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.