@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 {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 {6435, title = {nhmmer: DNA homology search with profile HMMs.}, journal = {Bioinformatics}, volume = {29}, year = {2013}, month = {2013 Oct 1}, pages = {2487-9}, abstract = {

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 http://hmmer.org. 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: wheelert@janelia.hhmi.org.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btt403}, author = {Wheeler, Travis J and Eddy, Sean R.} }