@article {65295, title = {Estimating the power of sequence covariation for detecting conserved RNA structure.}, journal = {Bioinformatics}, volume = {36}, year = {2020}, month = {2020 May 01}, pages = {3072-76}, abstract = {

Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long noncoding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures.

}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa080}, url = {https://doi.org/10.1093/bioinformatics/btaa080}, author = {Rivas, Elena and Clements, Jody and Eddy, Sean R}, editor = {Ponty, Yann} } @article {65282, title = {A genetic, genomic, and computational resource for exploring neural circuit function.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Jan 15}, abstract = {

The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize gene expression patterns in distinct neural cell types of the visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at http://www.opticlobe.com. Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. We describe examples that include identifying neurotransmitters, including cases of apparent co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types.

}, issn = {2050-084X}, doi = {10.7554/eLife.50901}, author = {Davis, Fred P and Nern, Aljoscha and Picard, Serge and Reiser, Michael B and Rubin, Gerald M and Eddy, Sean R and Henry, Gilbert L} } @article {47397, title = {The Dfam database of repetitive DNA families.}, journal = {Nucleic Acids Research}, volume = {44}, year = {2016}, month = {2016 Jan 4}, pages = {D81-9}, abstract = {

Repetitive DNA, especially that due to transposable elements (TEs), makes up a large fraction of many genomes. Dfam is an open access database of families of repetitive DNA elements, in which each family is represented by a multiple sequence alignment and a profile hidden Markov model (HMM). The initial release of Dfam, featured in the 2013 NAR Database Issue, contained 1143 families of repetitive elements found in humans, and was used to produce more than 100 Mb of additional annotation of TE-derived regions in the human genome, with improved speed. Here, we describe recent advances, most notably expansion to 4150 total families including a comprehensive set of known repeat families from four new organisms (mouse, zebrafish, fly and nematode). We describe improvements to coverage, and to our methods for identifying and reducing false annotation. We also describe updates to the website interface. The Dfam website has moved to http://dfam.org. Seed alignments, profile HMMs, hit lists and other underlying data are available for download.

}, issn = {1362-4962}, doi = {10.1093/nar/gkv1272}, author = {Hubley, Robert and Finn, Robert D and Clements, Jody and Eddy, Sean R and Jones, Thomas A and Bao, Weidong and Smit, Arian F A and Wheeler, Travis J} } @article {47412, title = {Epigenomic landscapes of retinal rods and cones.}, journal = {eLife}, volume = {5}, year = {2016}, month = {2016 Mar 07}, abstract = {

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.

}, issn = {2050-084X}, doi = {10.7554/eLife.11613}, author = {Mo, Alisa and Luo, Chongyuan and Davis, Fred P and Mukamel, Eran A and Henry, Gilbert L and Nery, Joseph R and Urich, Mark A and Picard, Serge and Lister, Ryan and Eddy, Sean R and Beer, Michael A and Ecker, Joseph R and Nathans, Jeremy} } @article {47035, title = {Combinatorial DNA rearrangement facilitates the origin of new genes in ciliates.}, journal = {Genome Biology and Evolution}, volume = {7}, year = {2015}, month = {2015 Sep 2}, pages = {2859-70}, abstract = {

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.

}, issn = {1759-6653}, doi = {10.1093/gbe/evv172}, author = {Chen, Xiao and Jung, Seolkyoung and Beh, Leslie Y and Eddy, Sean R and Landweber, Laura F} } @article {46852, title = {Epigenomic signatures of neuronal diversity in the mammalian brain.}, journal = {Neuron}, volume = {86}, year = {2015}, month = {2015 Jun 17}, pages = {1369-1384}, abstract = {

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.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2015.05.018}, author = {Mo, Alisa and Mukamel, Eran A and Davis, Fred P and Luo, Chongyuan and Henry, Gilbert L and Picard, Serge and Urich, Mark A and Nery, Joseph R and Sejnowski, Terrence J and Lister, Ryan and Eddy, Sean R and Ecker, Joseph R and Nathans, Jeremy} } @article {46742, title = {HMMER web server: 2015 update.}, journal = {Nucleic Acids Research}, year = {2015}, month = {2015 May 5}, abstract = {

The HMMER website, available at http://www.ebi.ac.uk/Tools/hmmer/, provides access to the protein homology search algorithms found in the HMMER software suite. Since the first release of the website in 2011, the search repertoire has been expanded to include the iterative search algorithm, jackhmmer. The continued growth of the target sequence databases means that traditional tabular representations of significant sequence hits can be overwhelming to the user. Consequently, additional ways of presenting homology search results have been developed, allowing them to be summarised according to taxonomic distribution or domain architecture. The taxonomy and domain architecture representations can be used in combination to filter the results according to the needs of a user. Searches can also be restricted prior to submission using a new taxonomic filter, which not only ensures that the results are specific to the requested taxonomic group, but also improves search performance. The repertoire of profile hidden Markov model libraries, which are used for annotation of query sequences with protein families and domains, has been expanded to include the libraries from CATH-Gene3D, PIRSF, Superfamily and TIGRFAMs. Finally, we discuss the relocation of the HMMER webserver to the European Bioinformatics Institute and the potential impact that this will have.

}, issn = {1362-4962}, doi = {10.1093/nar/gkv397}, author = {Finn, Robert D and Clements, Jody and Arndt, William and Miller, Benjamin L and Wheeler, Travis J and Schreiber, Fabian and Bateman, Alex and Eddy, Sean R} } @article {46480, title = {Homology searches for structural RNAs: from proof of principle to practical use.}, journal = {RNA}, volume = {21}, year = {2015}, month = {2015 Apr}, pages = {605-7}, issn = {1469-9001}, doi = {10.1261/rna.050484.115}, author = {Eddy, Sean R} } @article {46185, title = {Rfam 12.0: updates to the RNA families database.}, journal = {Nucleic Acids Research}, volume = {43}, year = {2015}, month = {2015 Jan 28}, pages = {D130-7}, abstract = {

The Rfam database (available at http://rfam.xfam.org) 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.

}, issn = {1362-4962}, doi = {10.1093/nar/gku1063}, author = {Nawrocki, Eric P and Burge, Sarah W and Bateman, Alex and Daub, Jennifer and Eberhardt, Ruth Y and Eddy, Sean R and Floden, Evan W and Gardner, Paul P and Jones, Thomas A and Tate, John and Finn, Robert D} } @article {45856, title = {Computational analysis of conserved RNA secondary structure in transcriptomes and genomes.}, journal = {Annual Review of Biophysics and Biomolecular Structure}, volume = {43}, year = {2014}, month = {2014}, pages = {433-56}, abstract = {

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.

}, keywords = {Algorithms, Animals, Bacteria, Computational Biology, Humans, Nucleic Acid Conformation, RNA}, issn = {1936-1238}, doi = {10.1146/annurev-biophys-051013-022950}, author = {Eddy, Sean R} } @article {45849, title = {The Pfam protein families database.}, journal = {Nucleic Acids Research}, volume = {36}, year = {2008}, month = {2008 Jan}, pages = {D281-8}, abstract = {

Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI GenPept and on sequences from selected metagenomics projects. Pfam is available on the web from the consortium members using a new, consistent and improved website design in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/), as well as from mirror sites in France (http://pfam.jouy.inra.fr/) and South Korea (http://pfam.ccbb.re.kr/).

}, keywords = {Animals, Databases, Protein, Genomics, Internet, Protein Structure, Tertiary, Proteins, Sequence Alignment, Sequence Analysis, Protein, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkm960}, author = {Finn, Robert D and Tate, John and Mistry, Jaina and Coggill, Penny C and Sammut, Stephen John and Hotz, Hans-Rudolf and Ceric, Goran and Forslund, Kristoffer and Eddy, Sean R and Sonnhammer, Erik L L and Bateman, Alex} }