@article {69825, title = {HortaCloud: An Open and Collaborative Platform for Whole Brain Neuronal Reconstructions}, journal = {bioRxiv}, year = {2025}, month = {2025 Mar 13}, abstract = {

HortaCloud is a cloud-based, open-source platform designed to facilitate the collaborative reconstruction of long-range projection neurons from whole-brain light microscopy data. By providing virtual environments directly within the cloud, it eliminates the need for costly and time-consuming data downloads, allowing researchers to work efficiently with terabyte- scale volumetric datasets. Standardization of computational resources in the cloud make deployment easier and more predictable. The pay-as-you-go cloud model reduces adoption barriers by eliminating upfront investments in expensive hardware. Finally, HortaCloud{\textquoteright}s decentralized architecture enables global collaboration between researchers and between institutions.

}, doi = {10.1101/2025.03.13.642887}, url = {https://www.biorxiv.org/content/early/2025/03/14/2025.03.13.642887}, author = {Rokicki, Konrad and Schauder, David and Olbris, Donald J. and Goina, Cristian and Clements, Jody and Edson, Patrick and Kawase, Takashi and Svirskas, Robert and Arshadi, Cameron and Feng, David and Chandrashekar, Jayaram and Ferreira, Tiago A. and MouseLight Project Team} } @article {69251, title = {The cell-type-specific spatial organization of the anterior thalamic nuclei of the mouse brain.}, journal = {Cell Reports}, volume = {43}, year = {2024}, month = {2024 Feb 29}, pages = {113842}, abstract = {

Understanding the cell-type composition and spatial organization of brain regions is crucial for interpreting brain computation and function. In the thalamus, the anterior thalamic nuclei (ATN) are involved in a wide variety of functions, yet the cell-type composition of the ATN remains unmapped at a single-cell and spatial resolution. Combining single-cell RNA sequencing, spatial transcriptomics, and multiplexed fluorescent in situ hybridization, we identify three discrete excitatory cell-type clusters that correspond to the known nuclei of the ATN and uncover marker genes, molecular pathways, and putative functions of these cell types. We further illustrate graded spatial variation along the dorsomedial-ventrolateral axis for all individual nuclei of the ATN and additionally demonstrate that the anteroventral nucleus exhibits spatially covarying protein products and long-range inputs. Collectively, our study reveals discrete and continuous cell-type organizational principles of the ATN, which will help to guide and interpret experiments on ATN computation and function.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2024.113842}, author = {Kapustina, Margarita and Zhang, Angela A and Tsai, Jennifer Y J and Bristow, Brianna N and Kraus, Larissa and Sullivan, Kaitlin E and Erwin, Sarah R and Wang, Lihua and Stach, Tara R and Clements, Jody and Lemire, Andrew L and Cembrowski, Mark S} } @article {68629, title = {A searchable image resource of Drosophila GAL4-driver expression patterns with single neuron resolution.}, journal = {eLife}, volume = {12}, year = {2023}, month = {2023 Feb 23}, abstract = {

Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

}, issn = {2050-084X}, doi = {10.7554/eLife.80660}, author = {Meissner, Geoffrey Wilson and Nern, Aljoscha and Dorman, Zachary and Depasquale, Gina M and Forster, Kaitlyn and Gibney, Theresa and Hausenfluck, Joanna H and He, Yisheng and Iyer, Nirmala A and Jeter, Jennifer and Johnson, Lauren and Johnston, Rebecca M and Lee, Kelley and Melton, Brian and Yarbrough, Brianna and Zugates, Christopher T and Clements, Jody and Goina, Cristian and Otsuna, Hideo and Rokicki, Konrad and Svirskas, Robert R and Aso, Yoshinori and Card, Gwyneth M and Dickson, Barry J and Ehrhardt, Erica and Goldammer, Jens and Ito, Masayoshi and Kainmueller, Dagmar and Korff, Wyatt and Mais, Lisa and minegishi, ryo and Namiki, Shigehiro and Rubin, Gerald M and Sterne, Gabriella R and Wolff, Tanya and Malkesman, Oz} } @article {68958, title = {Towards Generalizable Organelle Segmentation in Volume Electron Microscopy.}, journal = {Microscopy and Microanalysis}, volume = {29}, year = {2023}, month = {2023 Jul 22}, pages = {975}, keywords = {Organelles, Volume Electron Microscopy}, issn = {1435-8115}, doi = {10.1093/micmic/ozad067.487}, author = {Heinrich, Larissa and Patton, Will and Bennett, Davis and Ackerman, David and Park, Grace and Bogovic, John A and Eckstein, Nils and Petruncio, Alyson and Clements, Jody and Pang, Song and Shan Xu, C and Funke, Jan and Korff, Wyatt and Hess, Harald and Lippincott-Schwartz, Jennifer and Saalfeld, Stephan and Weigel, Aubrey} } @article {68272, title = {neuPrint: An open access tool for EM connectomics.}, journal = {Frontiers in Neuroinformatics}, volume = {16}, year = {2022}, month = {2022 Jul 20}, pages = {896292}, abstract = {

Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication-it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components-a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research.

}, issn = {1662-5196}, doi = {10.3389/fninf.2022.896292}, author = {Plaza, Stephen M and Clements, Jody and Dolafi, Tom and Umayam, Lowell and Neubarth, Nicole N and Scheffer, Louis K and Berg, Stuart} } @article {68501, title = {Neuronal cell types, projections, and spatial organization of the central amygdala}, journal = {iScience}, volume = {25}, year = {2022}, month = {2022 Dec 22}, pages = {105497}, abstract = {

The central amygdala (CEA) has been richly studied for interpreting function and behavior according to specific cell types and circuits. Such work has typically defined molecular cell types by classical inhibitory marker genes; consequently, whether marker-gene-defined cell types exhaustively cover the CEA and co-vary with connectivity remains unresolved. Here, we combined single-cell RNA sequencing, multiplexed fluorescent\ in situ\ hybridization, immunohistochemistry, and long-range projection mapping to derive a {\textquotedblleft}bottom-up{\textquotedblright} understanding of CEA cell types. In doing so, we identify two major cell types, encompassing one-third of all CEA neurons, that have gone unresolved in previous studies. In spatially mapping these novel types, we identify a non-canonical CEA subdomain associated with\ Nr2f2\ expression and uncover an\ Isl1-expressing medial cell type that accounts for many long-range CEA projections. Our results reveal new CEA organizational principles across cell types and spatial scales and provide a framework for future work examining cell-type-specific behavior and function.

}, issn = {25890042}, doi = {10.1016/j.isci.2022.105497}, url = {https://linkinghub.elsevier.com/retrieve/pii/S2589004222017692}, author = {O{\textquoteright}Leary, Timothy P. and Kendrick, Rennie M. and Bristow, Brianna N. and Sullivan, Kaitlin E. and Wang, Lihua and Clements, Jody and Lemire, Andrew L. and Cembrowski, Mark S.} } @article {67673, title = {Spatially patterned excitatory neuron subtypes and projections of the claustrum.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Aug 16}, abstract = {

The claustrum is a functionally and structurally complex brain region, whose very spatial extent remains debated. Histochemical-based approaches typically treat the claustrum as a relatively narrow anatomical region that primarily projects to the neocortex, whereas circuit-based approaches can suggest a broader claustrum region containing projections to the neocortex and other regions. Here, in the mouse, we took a bottom-up and cell-type-specific approach to complement and possibly unite these seemingly disparate conclusions. Using single-cell RNA-sequencing, we found that the claustrum comprises two excitatory neuron subtypes that are differentiable from the surrounding cortex. Multicolor retrograde tracing in conjunction with 12-channel multiplexed in situ hybridization revealed a core-shell spatial arrangement of these subtypes, as well as differential downstream targets. Thus, the claustrum comprises excitatory neuron subtypes with distinct molecular and projection properties, whose spatial patterns reflect the narrower and broader claustral extents debated in previous research. This subtype-specific heterogeneity likely shapes the functional complexity of the claustrum.

}, issn = {2050-084X}, doi = {10.7554/eLife.68967}, author = {Erwin, Sarah R and Bristow, Brianna N and Sullivan, Kaitlin E and Kendrick, Rennie M and Marriott, Brian and Wang, Lihua and Clements, Jody and Lemire, Andrew L and Jackson, Jesse and Cembrowski, Mark S} } @article {67750, title = {Whole-cell organelle segmentation in volume electron microscopy.}, journal = {Nature}, volume = {599}, year = {2021}, month = {2021 Nov 01}, pages = {141-46}, abstract = {

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes-ranging from endoplasmic reticulum to microtubules to ribosomes-in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM). We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, {\textquoteright}OpenOrganelle{\textquoteright}, to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.

}, issn = {1476-4687}, doi = {10.1038/s41586-021-03977-3}, author = {Heinrich, Larissa and Bennett, Davis and Ackerman, David and Park, Woohyun and Bogovic, John and Eckstein, Nils and Petruncio, Alyson and Clements, Jody and Pang, Song and Xu, C Shan and Funke, Jan and Korff, Wyatt and Hess, Harald F and Lippincott-Schwartz, Jennifer and Saalfeld, Stephan and Weigel, Aubrey V} } @article {68507, title = {A connectome and analysis of the adult Drosophila central brain.}, journal = {Elife}, volume = {9}, year = {2020}, month = {2020 Sep 07}, abstract = {

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly . Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly{\textquoteright}s brain.

}, keywords = {Animals, Brain, Connectome, Drosophila melanogaster, Female, Male, Neurons, Synapses}, issn = {2050-084X}, doi = {10.7554/eLife.57443}, author = {Scheffer, Louis K and Xu, C Shan and Januszewski, Michal and Lu, Zhiyuan and Takemura, Shin-ya and Hayworth, Kenneth J and Huang, Gary B and Shinomiya, Kazunori and Maitlin-Shepard, Jeremy and Berg, Stuart and Clements, Jody and Hubbard, Philip M and Katz, William T and Umayam, Lowell and Zhao, Ting and Ackerman, David and Blakely, Tim and Bogovic, John and Dolafi, Tom and Kainmueller, Dagmar and Kawase, Takashi and Khairy, Khaled A and Leavitt, Laramie and Li, Peter H and Lindsey, Larry and Neubarth, Nicole and Olbris, Donald J and Otsuna, Hideo and Trautman, Eric T and Ito, Masayoshi and Bates, Alexander S and Goldammer, Jens and Wolff, Tanya and Svirskas, Robert and Schlegel, Philipp and Neace, Erika and Knecht, Christopher J and Alvarado, Chelsea X and Bailey, Dennis A and Ballinger, Samantha and Borycz, Jolanta A and Canino, Brandon S and Cheatham, Natasha and Cook, Michael and Dreher, Marisa and Duclos, Octave and Eubanks, Bryon and Fairbanks, Kelli and Finley, Samantha and Forknall, Nora and Francis, Audrey and Hopkins, Gary Patrick and Joyce, Emily M and Kim, SungJin and Kirk, Nicole A and Kovalyak, Julie and Lauchie, Shirley A and Lohff, Alanna and Maldonado, Charli and Manley, Emily A and McLin, Sari and Mooney, Caroline and Ndama, Miatta and Ogundeyi, Omotara and Okeoma, Nneoma and Ordish, Christopher and Padilla, Nicholas and Patrick, Christopher M and Paterson, Tyler and Phillips, Elliott E and Phillips, Emily M and Rampally, Neha and Ribeiro, Caitlin and Robertson, Madelaine K and Rymer, Jon Thomson and Ryan, Sean M and Sammons, Megan and Scott, Anne K and Scott, Ashley L and Shinomiya, Aya and Smith, Claire and Smith, Kelsey and Smith, Natalie L and Sobeski, Margaret A and Suleiman, Alia and Swift, Jackie and Takemura, Satoko and Talebi, Iris and Tarnogorska, Dorota and Tenshaw, Emily and Tokhi, Temour and Walsh, John J and Yang, Tansy and Horne, Jane Anne and Li, Feng and Parekh, Ruchi and Rivlin, Patricia K and Jayaraman, Vivek and Costa, Marta and Jefferis, Gregory Sxe and Ito, Kei and Saalfeld, Stephan and George, Reed and Meinertzhagen, Ian A and Rubin, Gerald M and Hess, Harald F and Jain, Viren and Plaza, Stephen M} } @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 {65720, title = {Extensive and spatially variable within-cell-type heterogeneity across the basolateral amygdala.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Sep 01}, abstract = {

The basolateral amygdala complex (BLA), extensively connected with both local amygdalar nuclei as well as long-range circuits, is involved in a diverse array of functional roles. Understanding the mechanisms of such functional diversity will be greatly informed by understanding the cell-type-specific landscape of the BLA. Here, beginning with single-cell RNA sequencing, we identified both discrete and graded continuous gene-expression differences within the mouse BLA. Via in situ hybridization, we next mapped this discrete transcriptomic heterogeneity onto a sharp spatial border between the basal and lateral amygdala nuclei, and identified continuous spatial gene-expression gradients within each of these regions. These discrete and continuous spatial transformations of transcriptomic cell-type identity were recapitulated by local morphology as well as long-range connectivity. Thus, BLA excitatory neurons are a highly heterogenous collection of neurons that spatially covary in molecular, cellular, and circuit properties. This heterogeneity likely drives pronounced spatial variation in BLA computation and function.

}, issn = {2050-084X}, doi = {10.7554/eLife.59003}, author = {O{\textquoteright}Leary, Timothy P and Sullivan, Kaitlin E and Wang, Lihua and Clements, Jody and Lemire, Andrew L and Cembrowski, Mark S} } @article {49492, title = {The subiculum is a patchwork of discrete subregions.}, journal = {eLife}, volume = {7}, year = {2018}, month = {2018 Oct 30}, abstract = {

In the hippocampus, the classical pyramidal cell type of the subiculum acts as a primary output, conveying hippocampal signals to a diverse suite of downstream regions. Accumulating evidence suggests that the subiculum pyramidal cell population may actually be comprised of discrete subclasses. Here, we investigated the extent and organizational principles governing pyramidal cell heterogeneity throughout the mouse subiculum. Using single-cell RNA-seq, we find that the subiculum pyramidal cell population can be deconstructed into eight separable subclasses. These subclasses were mapped onto abutting spatial domains, ultimately producing a complex laminar and columnar organization with heterogeneity across classical dorsal-ventral, proximal-distal, and superficial-deep axes. We further show that these transcriptomically defined subclasses correspond to differential protein products and can be associated with specific projection targets. This work deconstructs the complex landscape of subiculum pyramidal cells into spatially segregated subclasses that may be observed, controlled, and interpreted in future experiments.

}, issn = {2050-084X}, doi = {10.7554/eLife.37701}, author = {Cembrowski, Mark S and Wang, Lihua and Lemire, Andrew L and Copeland, Monique and DiLisio, Salvatore F and Clements, Jody and Spruston, Nelson} } @article {48009, title = {A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs.}, journal = {Nature Methods}, volume = {14}, year = {2017}, month = {2017 Jan 31}, pages = {45-8}, abstract = {

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.

}, author = {Rivas, Elena and Clements, Jody and Eddy, Sean R.} } @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 {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 {6887, title = {iPfam: a database of protein family and domain interactions found in the Protein Data Bank.}, journal = {Nucleic acids research}, volume = {42}, year = {2014}, month = {2014 Jan}, pages = {D364-73}, abstract = {The database iPfam, available at http://ipfam.org, catalogues Pfam domain interactions based on known 3D structures that are found in the Protein Data Bank, providing interaction data at the molecular level. Previously, the iPfam domain-domain interaction data was integrated within the Pfam database and website, but it has now been migrated to a separate database. This allows for independent development, improving data access and giving clearer separation between the protein family and interactions datasets. In addition to domain-domain interactions, iPfam has been expanded to include interaction data for domain bound small molecule ligands. Functional annotations are provided from source databases, supplemented by the incorporation of Wikipedia articles where available. iPfam (version 1.0) contains >9500 domain-domain and 15 500 domain-ligand interactions. The new website provides access to this data in a variety of ways, including interactive visualizations of the interaction data.}, keywords = {Databases, Internet, Ligands, Protein, Protein Interaction Domains and Motifs, Protein Interaction Mapping, Protein Interaction Maps, Proteins, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkt1210}, author = {Finn, Robert D and Miller, Benjamin L and Clements, Jody and Bateman, Alex} } @article {6886, title = {Pfam: the protein families database.}, journal = {Nucleic acids research}, volume = {42}, year = {2014}, month = {2014 Jan}, pages = {D222-30}, abstract = {Pfam, available via servers in the UK (http://pfam.sanger.ac.uk/) and the USA (http://pfam.janelia.org/), is a widely used database of protein families, containing 14 831 manually curated entries in the current release, version 27.0. Since the last update article 2 years ago, we have generated 1182 new families and maintained sequence coverage of the UniProt Knowledgebase (UniProtKB) at nearly 80\%, despite a 50\% increase in the size of the underlying sequence database. Since our 2012 article describing Pfam, we have also undertaken a comprehensive review of the features that are provided by Pfam over and above the basic family data. For each feature, we determined the relevance, computational burden, usage statistics and the functionality of the feature in a website context. As a consequence of this review, we have removed some features, enhanced others and developed new ones to meet the changing demands of computational biology. Here, we describe the changes to Pfam content. Notably, we now provide family alignments based on four different representative proteome sequence data sets and a new interactive DNA search interface. We also discuss the mapping between Pfam and known 3D structures.}, keywords = {Databases, DNA, Internet, Intrinsically Disordered Proteins, Protein, Protein Conformation, Proteins, Proteome, Sequence Alignment, Sequence Analysis}, issn = {1362-4962}, doi = {10.1093/nar/gkt1223}, author = {Finn, Robert D and Bateman, Alex and Clements, Jody and Coggill, Penelope and Eberhardt, Ruth Y and Sean R. Eddy and Heger, Andreas and Hetherington, Kirstie and Holm, Liisa and Mistry, Jaina and Sonnhammer, Erik L L and Tate, John and Punta, Marco} } @article {6878, title = {Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models.}, journal = {BMC Bioinformatics}, volume = {15}, year = {2014}, month = {2014 Jan 13}, pages = {7}, abstract = {

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{\textquoteright}s interactive logos are easily incorporated into a web page with just a few lines of HTML markup. Skylign may be found at http://skylign.org.

}, issn = {1471-2105}, doi = {10.1186/1471-2105-15-7}, author = {Wheeler, Travis J and Clements, Jody and Finn, Robert D} } @article {6285, title = {Dfam: a database of repetitive DNA based on profile hidden Markov models.}, journal = {Nucleic Acids Research}, volume = {41}, year = {2013}, month = {2013 Jan}, pages = {D70-82}, abstract = {

We present a database of repetitive DNA elements, called Dfam (http://dfam.janelia.org). Many genomes contain a large fraction of repetitive DNA, much of which is made up of remnants of transposable elements (TEs). Accurate annotation of TEs enables research into their biology and can shed light on the evolutionary processes that shape genomes. Identification and masking of TEs can also greatly simplify many downstream genome annotation and sequence analysis tasks. The commonly used TE annotation tools RepeatMasker and Censor depend on sequence homology search tools such as cross_match and BLAST variants, as well as Repbase, a collection of known TE families each represented by a single consensus sequence. Dfam contains entries corresponding to all Repbase TE entries for which instances have been found in the human genome. Each Dfam entry is represented by a profile hidden Markov model, built from alignments generated using RepeatMasker and Repbase. When used in conjunction with the hidden Markov model search tool nhmmer, Dfam produces a 2.9\% increase in coverage over consensus sequence search methods on a large human benchmark, while maintaining low false discovery rates, and coverage of the full human genome is 54.5\%. The website provides a collection of tools and data views to support improved TE curation and annotation efforts. Dfam is also available for download in flat file format or in the form of MySQL table dumps.

}, keywords = {Databases, DNA Transposable Elements, Genome, Human, Humans, Internet, Markov Chains, Models, Molecular Sequence Annotation, Nucleic Acid, Statistical}, issn = {1362-4962}, doi = {10.1093/nar/gks1265}, author = {Wheeler, Travis J and Clements, Jody and Eddy, Sean R. and Hubley, Robert and Jones, Thomas A and Jurka, Jerzy and Smit, Arian F A and Finn, Robert D} } @article {6296, title = {The Pfam protein families database.}, journal = {Nucleic acids research}, volume = {40}, year = {2012}, month = {2012 Jan}, pages = {D290-301}, abstract = {

Pfam is a widely used database of protein families, currently containing more than 13,000 manually curated protein families as of release 26.0. Pfam is available via servers in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/). Here, we report on changes that have occurred since our 2010 NAR paper (release 24.0). Over the last 2 years, we have generated 1840 new families and increased coverage of the UniProt Knowledgebase (UniProtKB) to nearly 80\%. Notably, we have taken the step of opening up the annotation of our families to the Wikipedia community, by linking Pfam families to relevant Wikipedia pages and encouraging the Pfam and Wikipedia communities to improve and expand those pages. We continue to improve the Pfam website and add new visualizations, such as the {\textquoteright}sunburst{\textquoteright} representation of taxonomic distribution of families. In this work we additionally address two topics that will be of particular interest to the Pfam community. First, we explain the definition and use of family-specific, manually curated gathering thresholds. Second, we discuss some of the features of domains of unknown function (also known as DUFs), which constitute a rapidly growing class of families within Pfam.

}, keywords = {Amino Acid, Databases, Encyclopedias as Topic, Internet, Protein, Protein Structure, Proteins, Sequence Homology, Tertiary}, issn = {1362-4962}, doi = {10.1093/nar/gkr1065}, author = {Punta, Marco and Coggill, Penny C and Eberhardt, Ruth Y and Mistry, Jaina and Tate, John and Boursnell, Chris and Pang, Ningze and Forslund, Kristoffer and Ceric, Goran and Clements, Jody and Heger, Andreas and Holm, Liisa and Sonnhammer, Erik L L and Sean R. Eddy and Bateman, Alex and Finn, Robert D} } @article {4302, title = {HMMER web server: interactive sequence similarity searching.}, journal = {Nucleic Acids Research}, volume = {39}, year = {2011}, month = {2011 Jul}, pages = {W29-37}, abstract = {

HMMER is a software suite for protein sequence similarity searches using probabilistic methods. Previously, HMMER has mainly been available only as a computationally intensive UNIX command-line tool, restricting its use. Recent advances in the software, HMMER3, have resulted in a 100-fold speed gain relative to previous versions. It is now feasible to make efficient profile hidden Markov model (profile HMM) searches via the web. A HMMER web server (http://hmmer.janelia.org) has been designed and implemented such that most protein database searches return within a few seconds. Methods are available for searching either a single protein sequence, multiple protein sequence alignment or profile HMM against a target sequence database, and for searching a protein sequence against Pfam. The web server is designed to cater to a range of different user expertise and accepts batch uploading of multiple queries at once. All search methods are also available as RESTful web services, thereby allowing them to be readily integrated as remotely executed tasks in locally scripted workflows. We have focused on minimizing search times and the ability to rapidly display tabular results, regardless of the number of matches found, developing graphical summaries of the search results to provide quick, intuitive appraisement of them.

}, keywords = {Internet, Protein, Sequence Alignment, Sequence Analysis, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkr367}, author = {Finn, Robert D and Clements, Jody and Eddy, Sean R.} }