@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 {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 {4252, title = {A probabilistic model of local sequence alignment that simplifies statistical significance estimation.}, journal = {PLoS Computational Biology}, volume = {4}, year = {2008}, month = {2008 May 30}, pages = {e1000069}, abstract = {

Sequence database searches require accurate estimation of the statistical significance of scores. Optimal local sequence alignment scores follow Gumbel distributions, but determining an important parameter of the distribution (lambda) requires time-consuming computational simulation. Moreover, optimal alignment scores are less powerful than probabilistic scores that integrate over alignment uncertainty ("Forward" scores), but the expected distribution of Forward scores remains unknown. Here, I conjecture that both expected score distributions have simple, predictable forms when full probabilistic modeling methods are used. For a probabilistic model of local sequence alignment, optimal alignment bit scores ("Viterbi" scores) are Gumbel-distributed with constant lambda = log 2, and the high scoring tail of Forward scores is exponential with the same constant lambda. Simulation studies support these conjectures over a wide range of profile/sequence comparisons, using 9,318 profile-hidden Markov models from the Pfam database. This enables efficient and accurate determination of expectation values (E-values) for both Viterbi and Forward scores for probabilistic local alignments.

}, keywords = {Algorithms, Base Sequence, Chromosome Mapping, Computer Simulation, Data Interpretation, DNA, Genetic, Models, Molecular Sequence Data, Sequence Alignment, Sequence Analysis, Statistical}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000069}, author = {Sean R. Eddy} } @article {4259, title = {Probabilistic phylogenetic inference with insertions and deletions.}, journal = {PLoS Computational Biology}, volume = {4}, year = {2008}, month = {2008 Sep 19}, pages = {e1000172}, abstract = {

A fundamental task in sequence analysis is to calculate the probability of a multiple alignment given a phylogenetic tree relating the sequences and an evolutionary model describing how sequences change over time. However, the most widely used phylogenetic models only account for residue substitution events. We describe a probabilistic model of a multiple sequence alignment that accounts for insertion and deletion events in addition to substitutions, given a phylogenetic tree, using a rate matrix augmented by the gap character. Starting from a continuous Markov process, we construct a non-reversible generative (birth-death) evolutionary model for insertions and deletions. The model assumes that insertion and deletion events occur one residue at a time. We apply this model to phylogenetic tree inference by extending the program dnaml in phylip. Using standard benchmarking methods on simulated data and a new "concordance test" benchmark on real ribosomal RNA alignments, we show that the extended program dnamlepsilon improves accuracy relative to the usual approach of ignoring gaps, while retaining the computational efficiency of the Felsenstein peeling algorithm.

}, keywords = {Algorithms, Computational Biology, Computer Simulation, DNA, Evolution, Genetic, Insertional, Markov Chains, Models, Molecular, Mutagenesis, Phylogeny, Poisson Distribution, Ribosomal, RNA, Sequence Alignment, Sequence Deletion, Software, Statistical}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000172}, author = {Rivas, Elena and Sean R. Eddy} }