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30 Publications

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    01/01/17 | A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs.
    Rivas E, Clements J, Eddy SR
    Nature Methods. 2017 Jan 31;14(1):45-8

    Many functional RNAs have an evolutionarily conserved secondary structure. Conservation of RNA base pairing induces pairwise covariations in sequence alignments. We developed a computational method, R-scape (RNA Structural Covariation Above Phylogenetic Expectation), that quantitatively tests whether covariation analysis supports the presence of a conserved RNA secondary structure. R-scape analysis finds no statistically significant support for proposed secondary structures of the long noncoding RNAs HOTAIR, SRA, and Xist.

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    Eddy/Rivas Lab
    03/07/16 | Epigenomic landscapes of retinal rods and cones.
    Mo A, Luo C, Davis FP, Mukamel EA, Henry GL, Nery JR, Urich MA, Picard S, Lister R, Eddy SR, Beer MA, Ecker JR, Nathans J
    eLife. 2016 Mar 07;5:. doi: 10.7554/eLife.11613

    Rod and cone photoreceptors are highly similar in many respects but they have important functional and molecular differences. Here, we investigate genome-wide patterns of DNA methylation and chromatin accessibility in mouse rods and cones and correlate differences in these features with gene expression, histone marks, transcription factor binding, and DNA sequence motifs. Loss of NR2E3 in rods shifts their epigenomes to a more cone-like state. The data further reveal wide differences in DNA methylation between retinal photoreceptors and brain neurons. Surprisingly, we also find a substantial fraction of DNA hypo-methylated regions in adult rods that are not in active chromatin. Many of these regions exhibit hallmarks of regulatory regions that were active earlier in neuronal development, suggesting that these regions could remain undermethylated due to the highly compact chromatin in mature rods. This work defines the epigenomic landscapes of rods and cones, revealing features relevant to photoreceptor development and function.

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    Eddy/Rivas Lab
    09/02/15 | Combinatorial DNA rearrangement facilitates the origin of new genes in ciliates.
    Chen X, Jung S, Beh LY, Eddy SR, Landweber LF
    Genome Biology and Evolution. 2015 Sep 2;7(10):2859-70. doi: 10.1093/gbe/evv172

    Programmed genome rearrangements in the unicellular eukaryote Oxytricha trifallax produce a transcriptionally active somatic nucleus from a copy of its germline nucleus during development. This process eliminates noncoding sequences that interrupt coding regions in the germline genome, and joins over 225,000 remaining DNA segments, some of which require inversion or complex permutation to build functional genes. This dynamic genomic organization permits some single DNA segments in the germline to contribute to multiple, distinct somatic genes via alternative processing. Like alternative mRNA splicing, the combinatorial assembly of DNA segments contributes to genetic variation and facilitates the evolution of new genes. In this study, we use comparative genomic analysis to demonstrate that the emergence of alternative DNA splicing is associated with the origin of new genes. Short duplications give rise to alternative gene segments that are spliced to the shared gene segments. Alternative gene segments evolve faster than shared, constitutive segments. Genes with shared segments frequently have different expression profiles, permitting functional divergence. This study reports alternative DNA splicing as a mechanism of new gene origination, illustrating how the process of programmed genome rearrangement gives rise to evolutionary innovation.

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    06/17/15 | Epigenomic signatures of neuronal diversity in the mammalian brain.
    Mo A, Mukamel EA, Davis FP, Luo C, Henry GL, Picard S, Urich MA, Nery JR, Sejnowski TJ, Lister R, Eddy SR, Ecker JR, Nathans J
    Neuron. 2015 Jun 17;86(6):1369-1384. doi: 10.1016/j.neuron.2015.05.018

    Neuronal diversity is essential for mammalian brain function but poses a challenge to molecular profiling. To address the need for tools that facilitate cell-type-specific epigenomic studies, we developed the first affinity purification approach to isolate nuclei from genetically defined cell types in a mammal. We combine this technique with next-generation sequencing to show that three subtypes of neocortical neurons have highly distinctive epigenomic landscapes. Over 200,000 regions differ in chromatin accessibility and DNA methylation signatures characteristic of gene regulatory regions. By footprinting and motif analyses, these regions are predicted to bind distinct cohorts of neuron subtype-specific transcription factors. Neuronal epigenomes reflect both past and present gene expression, with DNA hyper-methylation at developmentally critical genes appearing as a novel epigenomic signature in mature neurons. Taken together, our findings link the functional and transcriptional complexity of neurons to their underlying epigenomic diversity.

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    Eddy/Rivas Lab
    04/01/15 | Homology searches for structural RNAs: from proof of principle to practical use.
    Eddy SR
    RNA. 2015 Apr;21(4):605-7. doi: 10.1261/rna.050484.115
    Eddy/Rivas Lab
    01/28/15 | Rfam 12.0: updates to the RNA families database.
    Nawrocki EP, Burge SW, Bateman A, Daub J, Eberhardt RY, Eddy SR, Floden EW, Gardner PP, Jones TA, Tate J, Finn RD
    Nucleic Acids Research. 2015 Jan 28;43(Database issue):D130-7. doi: 10.1093/nar/gku1063

    The Rfam database (available at is a collection of non-coding RNA families represented by manually curated sequence alignments, consensus secondary structures and annotation gathered from corresponding Wikipedia, taxonomy and ontology resources. In this article, we detail updates and improvements to the Rfam data and website for the Rfam 12.0 release. We describe the upgrade of our search pipeline to use Infernal 1.1 and demonstrate its improved homology detection ability by comparison with the previous version. The new pipeline is easier for users to apply to their own data sets, and we illustrate its ability to annotate RNAs in genomic and metagenomic data sets of various sizes. Rfam has been expanded to include 260 new families, including the well-studied large subunit ribosomal RNA family, and for the first time includes information on short sequence- and structure-based RNA motifs present within families.

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    01/13/14 | Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models.
    Wheeler TJ, Clements J, Finn RD
    BMC Bioinformatics. 2014 Jan 13;15:7. doi: 10.1186/1471-2105-15-7

    BACKGROUND: Logos are commonly used in molecular biology to provide a compact graphical representation of the conservation pattern of a set of sequences. They render the information contained in sequence alignments or profile hidden Markov models by drawing a stack of letters for each position, where the height of the stack corresponds to the conservation at that position, and the height of each letter within a stack depends on the frequency of that letter at that position. RESULTS: We present a new tool and web server, called Skylign, which provides a unified framework for creating logos for both sequence alignments and profile hidden Markov models. In addition to static image files, Skylign creates a novel interactive logo plot for inclusion in web pages. These interactive logos enable scrolling, zooming, and inspection of underlying values. Skylign can avoid sampling bias in sequence alignments by down-weighting redundant sequences and by combining observed counts with informed priors. It also simplifies the representation of gap parameters, and can optionally scale letter heights based on alternate calculations of the conservation of a position. CONCLUSION: Skylign is available as a website, a scriptable web service with a RESTful interface, and as a software package for download. Skylign’s interactive logos are easily incorporated into a web page with just a few lines of HTML markup. Skylign may be found at

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    Eddy/Rivas Lab
    01/01/14 | Annotating functional RNAs in genomes using infernal.
    Nawrocki EP
    Methods in Molecular Biology. 2014;1097:163-97. doi: 10.1007/978-1-62703-709-9_9

    Many different types of functional non-coding RNAs participate in a wide range of important cellular functions but the large majority of these RNAs are not routinely annotated in published genomes. Several programs have been developed for identifying RNAs, including specific tools tailored to a particular RNA family as well as more general ones designed to work for any family. Many of these tools utilize covariance models (CMs), statistical models of the conserved sequence, and structure of an RNA family. In this chapter, as an illustrative example, the Infernal software package and CMs from the Rfam database are used to identify RNAs in the genome of the archaeon Methanobrevibacter ruminantium, uncovering some additional RNAs not present in the genome’s initial annotation. Analysis of the results and comparison with family-specific methods demonstrate some important strengths and weaknesses of this general approach.

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    Eddy/Rivas Lab
    01/01/14 | Computational analysis of conserved RNA secondary structure in transcriptomes and genomes.
    Eddy SR
    Annual Review of Biophysics and Biomolecular Structure. 2014;43:433-56. doi: 10.1146/annurev-biophys-051013-022950

    Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.

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    Eddy/Rivas Lab
    10/01/13 | nhmmer: DNA homology search with profile HMMs.
    Wheeler TJ, Eddy SR
    Bioinformatics. 2013 Oct 1;29:2487-9. doi: 10.1093/bioinformatics/btt403

    SUMMARY: Sequence database searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. We present a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome. AVAILABILITY: nhmmer is a part of the new HMMER3.1 release. Source code and documentation can be downloaded from HMMER3.1 is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. CONTACT:

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