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Manolis Kellis

Other affiliations: Broad Institute, Epigenomics AG, Harvard University  ...read more
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.


Papers
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Journal ArticleDOI
TL;DR: The authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling that leverages the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions.
Abstract: Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics.

39 citations

Journal ArticleDOI
14 Feb 2011-PLOS ONE
TL;DR: The extent of sequencing error in these 2× assemblies, and its potential impact in downstream analyses, is examined, finding that most errors are contributed by a small fraction of bases with low quality scores, in particular, by the ends of reads in regions of single-read coverage in the assembly.
Abstract: The recent release of twenty-two new genome sequences has dramatically increased the data available for mammalian comparative genomics, but twenty of these new sequences are currently limited to ∼2× coverage. Here we examine the extent of sequencing error in these 2× assemblies, and its potential impact in downstream analyses. By comparing 2× assemblies with high-quality sequences from the ENCODE regions, we estimate the rate of sequencing error to be 1–4 errors per kilobase. While this error rate is fairly modest, sequencing error can still have surprising effects. For example, an apparent lineage-specific insertion in a coding region is more likely to reflect sequencing error than a true biological event, and the length distribution of coding indels is strongly distorted by error. We find that most errors are contributed by a small fraction of bases with low quality scores, in particular, by the ends of reads in regions of single-read coverage in the assembly. We explore several approaches for automatic sequencing error mitigation (SEM), making use of the localized nature of sequencing error, the fact that it is well predicted by quality scores, and information about errors that comes from comparisons across species. Our automatic methods for error mitigation cannot replace the need for additional sequencing, but they do allow substantial fractions of errors to be masked or eliminated at the cost of modest amounts of over-correction, and they can reduce the impact of error in downstream phylogenomic analyses. Our error-mitigated alignments are available for download.

38 citations

Journal ArticleDOI
TL;DR: In this paper, a prospective case-control study was performed on human subjects to characterize the differential expression of mRNA and miRNA in unruptured cerebral aneurysms in comparison with healthy superficial temporal arteries (STA).
Abstract: OBJECTIVE The molecular mechanisms behind cerebral aneurysm formation and rupture remain poorly understood. In the past decade, microRNAs (miRNAs) have been shown to be key regulators in a host of biological processes. They are noncoding RNA molecules, approximately 21 nucleotides long, that posttranscriptionally inhibit mRNAs by attenuating protein translation and promoting mRNA degradation. The miRNA and mRNA interactions and expression levels in cerebral aneurysm tissue from human subjects were profiled. METHODS A prospective case-control study was performed on human subjects to characterize the differential expression of mRNA and miRNA in unruptured cerebral aneurysms in comparison with control tissue (healthy superficial temporal arteries [STA]). Ion Torrent was used for deep RNA sequencing. Affymetrix miRNA microarrays were used to analyze miRNA expression, whereas NanoString nCounter technology was used for validation of the identified targets. RESULTS Overall, 7 unruptured cerebral aneurysm and 10 STA specimens were collected. Several differentially expressed genes were identified in aneurysm tissue, with MMP-13 (fold change 7.21) and various collagen genes (COL1A1, COL5A1, COL5A2) being among the most upregulated. In addition, multiple miRNAs were significantly differentially expressed, with miR-21 (fold change 16.97) being the most upregulated, and miR-143-5p (fold change -11.14) being the most downregulated. From these, miR-21, miR-143, and miR-145 had several significantly anticorrelated target genes in the cohort that are associated with smooth muscle cell function, extracellular matrix remodeling, inflammation signaling, and lipid accumulation. All these processes are crucial to the pathophysiology of cerebral aneurysms. CONCLUSIONS This analysis identified differentially expressed genes and miRNAs in unruptured human cerebral aneurysms, suggesting the possibility of a role for miRNAs in aneurysm formation. Further investigation for their importance as therapeutic targets is needed.

38 citations

Journal ArticleDOI
TL;DR: A phylogenetic codon model-based framework, FRESCo, designed to find regions of excess synonymous constraint in short, deep alignments, such as individual viral genes across many sequenced isolates, and predicts many novel functional elements overlapping viral genes, including in Lassa and Ebola viruses.
Abstract: Background: The increasing availability of sequence data for many viruses provides power to detect regions under unusual evolutionary constraint at a high resolution. One approach leverages the synonymous substitution rate as a signature to pinpoint genic regions encoding overlapping or embedded functional elements. Protein-coding regions in viral genomes often contain overlapping RNA structural elements, reading frames, regulatory elements, microRNAs, and packaging signals. Synonymous substitutions in these regions would be selectively disfavored and thus these regions are characterized by excess synonymous constraint. Codon choice can also modulate transcriptional efficiency, translational accuracy, and protein folding. Results: We developed a phylogenetic codon model-based framework, FRESCo, designed to find regions of excess synonymous constraint in short, deep alignments, such as individual viral genes across many sequenced isolates. We demonstrated the high specificity of our approach on simulated data and applied our framework to the protein-coding regions of approximately 30 distinct species of viruses with diverse genome architectures. Conclusions: FRESCo recovers known multifunctional regions in well-characterized viruses such as hepatitis B virus, poliovirus, and West Nile virus, often at a single-codon resolution, and predicts many novel functional elements overlapping viral genes, including in Lassa and Ebola viruses. In a number of viruses, the synonymously constrained regions that we identified also display conserved, stable predicted RNA structures, including putative novel elements in multiple viral species.

38 citations

Journal ArticleDOI
TL;DR: Predictive properties in isolation are inspected, and meta‐analysis over the competing methods find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results.
Abstract: In many human diseases, associated genetic changes tend to occur within noncoding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such noncoding regions: given a region that is statistically associated with changes in gene expression (expression quantitative trait locus [eQTL]), does it in fact play a regulatory role? And if so, how is this role "coded" in its sequence? These questions were the subject of the Critical Assessment of Genome Interpretation eQTL challenge. Participants were given a set of sequences that flank eQTLs in humans and were asked to predict whether these are capable of regulating transcription (as evaluated by massively parallel reporter assays), and whether this capability changes between alternative alleles. Here, we report lessons learned from this community effort. By inspecting predictive properties in isolation, and conducting meta-analysis over the competing methods, we find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results. We then characterize the loci that are harder to predict, putting the spotlight on areas of weakness, which we expect to be the subject of future studies.

37 citations


Cited by
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Journal ArticleDOI
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
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.

34,830 citations

Journal ArticleDOI
TL;DR: 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 outperforms other aligners by a factor of >50 in mapping speed.
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/.

30,684 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组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�ly.
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。

18,940 citations

Journal ArticleDOI
23 Jan 2009-Cell
TL;DR: The current understanding of miRNA target recognition in animals is outlined and the widespread impact of miRNAs on both the expression and evolution of protein-coding genes is discussed.

18,036 citations

Journal ArticleDOI
TL;DR: 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, providing a unified solution for transcriptome reconstruction in any sample.
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.

15,665 citations