Archive for the 'Releases' Category

Dfam 3.7 : ~3.4 million TE models across 2346 taxa

January 12, 2023

We at Dfam are pleased to announce the latest data release! The Dfam 3.7 release includes additional raw and curated datasets, resulting in a ~4.5x increase in the number of families compared to the previous Dfam 3.6 data release over a wide range of taxa. Please note the large size of the newest release and plan accordingly. It may be beneficial to filter and download the relevant data to your project by utilizing the API. 

EBI dataset contributes to the quadrupling of the Dfam database 

Our continued collaboration with Fergal Martin and Denye Ogeh from the European Bioinformatics Institute (EBI) has provided an additional 771 assemblies and their associated TE models that are now a part of the DR records in Dfam. This brings the total contribution of genomic data from EBI to 1551 species. The new data expands taxa such as Viridiplantae (green plants) and Actinopterygii (bony fishes), and broadens Dfam coverage with the addition of Echinodermata (starfishes, sea urchins/cucumbers) and Petromyzontiformes (lampreys). 

Community submissions – adding diversity to Dfam

Taro (Colocasia esculenta) – a threatened food staple

One of the most ancient cultivated crops, taro is a food staple in the Pacific Islands and the Caribbean, which is currently threatened by taro leaf blight (TLB). Some populations of taro are resistant to TLB, but the genetic basis for this resistance is unknown. As part of an effort to understand the genetic basis of TLB resistance, a taro de novo assembly was generated and the repetitive content was analyzed [1]. The high repetitive content (~82%) of this genome was positively correlated with genome size, with the potential to be linked to TLB resistance. Contributed by M. Renee Bellinger.

Gesneriaceae – understanding angiosperm morphological variation

A member of the plant family Gesneriaceae, the Cape Primrose Streptocarpus rexii has long been studied by evolutionary biologists due to its unique morphological aspects. Genetic resources are critical in order to study the unique meristem evolution of this plant family. As such, a genome annotation pipeline was generated in order to handle the shortcomings of current technical challenges of genome annotation. Part of this effort included generating repeat libraries for not only the Cape Primrose, but also for Dorcoceras hygrometricum and Primulina huaijiensis [2]. Providing these libraries to Dfam will enhance the resources available for future genomic characterization of this plant family.  Contributed by Kanae Nishii.

Mosquito (Anopheles coluzzii) – a human malaria vector

The adaptive flexibility of Anopheles coluzzii, a primary vector of human malaria, allows it escape efforts to control the mosquito population with insecticides. As TEs are integral to adaptive processes in other species, it was hypothesized that TEs could be what is allowing the rapid resistance of A. coluzzii to classic methods of intervention. Analyzing six individuals from two African localities allowed the authors to provide a comprehensive TE library [3]. This effort enhances the resources available to study the genomic architecture and gene regulation underpinning the success of this malaria vector. Contributed by Carlos Vargas and Josefa Gonzalez.

Water flea (Daphnia pulicaria) – a model organism to study climate change

Due to their short lifespans and reproductive capabilities, water fleas are used as a bioindicator to study the effects of toxins on an ecosystem, and are thus useful in studying climate change. A study of two ecological sister taxa – Daphnia pulicaria and Daphnia pulex – analyzed the evolutionary forces of recombination and gene density in driving the differentiation and divergence of the two aforementioned species [4]. TE content was analyzed as part of generating the new Daphnia pulicaria genome assembly.  Contributed by Mathew Wersebe.

601 insects – transposable element influence on species diversity 

TEs are drivers of evolution eukaryotes. However, in some underrepresented taxa, TE dynamics are less well understood. To this end, 601 insect genomes over 20 Orders were analyzed for TE content to analyze the variation between and among insect Orders. This work highlights the need for community-submitted high-quality libraries.  Contributed by John Sproul and Jacqueline Heckenhauer.

Analysis of six bat genomes – evolution of bat adaptations

Bats are an excellent example of complex adaptations, such as flight, echolocation, longevity and immunity. In order to enhance the genomic resources to study the development of complex traits, six high-quality genomes assemblies using long- and short-read technologies were generated (Rhinolophus ferrumequinumRousettus aegyptiacusPhyllostomus discolorMyotis myotisPipistrellus kuhlii and Molossus molossus) [6]. As part of the effort to annotate these new genome assemblies, the TE content was analyzed. These six genomes displayed a wide range of diversity in TE content, perhaps contributing to their complex traits.  Contributed by Kevin Sullivan and David Ray.

LTR7/ERVH – transcriptional regulation in the human embryo

The mechanism by which human endogenous retrovirus type-H (HERVH) exerts regulatory activities fostering self-renewal and pluripotency in the pre-implantation embryo is unknown. In order to elucidate the aforementioned mechanism, the transcription dynamics and sequence signature evolution of HERVH were analyzed [7]. This study not only revealed previously undefined LTR7 subfamilies, but also provided a comprehensive phytoregulatory analysis of all the identified subfamilies against locus-specific regulatory data available in genome-wide assays of embryonic stem cells (ESCs), providing evidence for subfamily-specific promoter activity. The complex evolutionary history of LTR7 is mirrored in the transcriptional partitioning that takes place during early embryonic development.  Contributed by Thomas Carter, Cédric Feschotte, and Arian Smit.

References

1. Bellinger, M. R., Paudel, R., Starnes, S., Kambic, L., Kantar, M. B., Wolfgruber, T., Lamour, K., Geib, S., Sim, S., Miyasaka, S. C., Helmkampf, M., & Shintaku, M. (2020). Taro Genome Assembly and Linkage Map Reveal QTLs for Resistance to Taro Leaf Blight. G3 (Bethesda, Md.)10(8), 2763–2775. https://doi.org/10.1534/g3.120.401367

    2. Nishii, K., Hart, M., Kelso, N., Barber, S., Chen, Y. Y., Thomson, M., Trivedi, U., Twyford, A. D., & Möller, M. (2022). The first genome for the Cape Primrose Streptocarpus rexii (Gesneriaceae), a model plant for studying meristem-driven shoot diversity. Plant direct6(4), e388. https://doi.org/10.1002/pld3.388

    3. Vargas-Chavez, C., Longo Pendy, N. M., Nsango, S. E., Aguilera, L., Ayala, D., & González, J. (2022). Transposable element variants and their potential adaptive impact in urban populations of the malaria vector Anopheles coluzziiGenome research32(1), 189–202. https://doi.org/10.1101/gr.275761.121

    4. Wersebe, M. J., Sherman, R. E., Jeyasingh, P. D., & Weider, L. J. (2022). The roles of recombination and selection in shaping genomic divergence in an incipient ecological species complex. Molecular ecology, 10.1111/mec.16383. Advance online publication. https://doi.org/10.1111/mec.16383

    5. Sproul, J.S., Hotaling, S., Heckenhauer, J., Powell, A., Larracuente, A.M., Kelley, J.L., Pauls, S.U., Frandsen, P.B. (2022). Repetitive elements in the era of biodiversity genomics: insights from 600+ insect genomes. bioRxiv 2022.06.02.494618; doi: https://doi.org/10.1101/2022.06.02.494618

    6. Jebb, D., Huang, Z., Pippel, M., Hughes, G. M., Lavrichenko, K., Devanna, P., Winkler, S., Jermiin, L. S., Skirmuntt, E. C., Katzourakis, A., Burkitt-Gray, L., Ray, D. A., Sullivan, K. A. M., Roscito, J. G., Kirilenko, B. M., Dávalos, L. M., Corthals, A. P., Power, M. L., Jones, G., Ransome, R. D., … Teeling, E. C. (2020). Six reference-quality genomes reveal evolution of bat adaptations. Nature583(7817), 578–584. https://doi.org/10.1038/s41586-020-2486-3

    7. Carter, T. A., Singh, M., Dumbović, G., Chobirko, J. D., Rinn, J. L., & Feschotte, C. (2022). Mosaic cis-regulatory evolution drives transcriptional partitioning of HERVH endogenous retrovirus in the human embryo. eLife11, e76257. https://doi.org/10.7554/eLife.76257

    Dfam 3.6 release

    April 21, 2022

    We are pleased to announce the latest data release of the Dfam database! This latest release approximately doubles the number of species from the Dfam 3.5 release (595 to 1,109), and increases the number of transposable element (TE) families by ~2.5x (285,542 to 732,993. A more detailed summary of the species included can be seen in Table 1, and in the Dfam 3.6 release notes.

    Community-submitted libraries

    A huge thank you to the TE community for submitting your data to us! In this release, we have: 1) 3,360 curated rice weevil TE models, submitted by Clément Goubert and Rita Rebollo1; 2) 22 SINE families obtained from 15 moth species (Lepidoptera insects) submitted by Guangjie Han et al.2; 3) 120 Penelope-classified families – something about how they span several kingdoms/orders? submitted by Rory Craig et al.3; and 4) 41 repeat families generated as part of the T2T human assembly project4 – not including the 22 “composite” repetitive families, which will be available as part of a later Dfam release. To read more about the studies associated with these submissions, please see the references below.

    Rice weevil: an agricultural pest

    (Background copied from paper): The rice weevil Sitophilus oryzae is one of the most important agricultural pests, causing extensive damage to cereal in fields and to stored grains. S. oryzae has an intracellular symbiotic relationship (endosymbiosis) with the Gram-negative bacterium Sodalis pierantonius and is a valuable model to decipher host-symbiont molecular interactions. In the paper (see below), the authors show that many TE families are transcriptionally active, and changes in their expression are associated with insect endosymbiotic state.

    Moth SINEs: high diversity

    (Conclusions copied from paper): Lepidopteran insect genomes harbor a diversity of SINEs. The retrotransposition activity and copy number of these SINEs varies considerably between host lineages and SINE lineages. Host-parasite interactions facilitate the horizontal transfer of SINE between baculovirus and its lepidopteran hosts.

    Penelope elements: far-reaching impacts

    The authors investigate the Penelope (PLE) content of a wide variety of eukaryotes. (copied from paper): This paper uncovers the hitherto unknown PLE diversity, which spans all eukaryotic kingdoms, testifying to their ancient origins. 

    T2T entries: previously hidden genomic content

    A new human genome assembly has been released! The new assembly (T2T or chm13) has sequenced and assembled the remaining 10% of the human genome that was previously unattainable. The entries described in the manuscript are part of this newly-analyzed sequence.

    EBI libraries

    In collaboration the European Bioinformatic Institute (EBI), we processed and imported RepeatModeler runs on 444 additional species, resulting in the addition of 440,543 families. Additional extension and re-classification sites were run on each models and fate final consensus and HMMs were produced. Please note that the relationship data is not available on these uncreated imports at this time.

    References associated with community submissions

    1 Parisot, N., et al (2021). The transposable element-rich genome of the cereal pest Sitophilus oryzae. BMC biology19(1), 241. https://doi.org/10.1186/s12915-021-01158-2
    2 Han, G., et al (2021). Diversity of short interspersed nuclear elements (SINEs) in lepidopteran insects and evidence of horizontal SINE transfer between baculovirus and lepidopteran hosts. BMC genomics22(1), 226. https://doi.org/10.1186/s12864-021-07543-z
    3 Craig, R. J., et al (2021). An Ancient Clade of Penelope-Like Retroelements with Permuted Domains Is Present in the Green Lineage and Protists, and Dominates Many Invertebrate Genomes. Molecular biology and evolution38(11), 5005–5020. https://doi.org/10.1093/molbev/msab225
    4 Hoyt, S. J., et al (2022). From telomere to telomere: The transcriptional and epigenetic state of human repeat elements. Science (New York, N.Y.)376(6588), eabk3112. https://doi.org/10.1126/science.abk3112

    Pfam 35.0 is released

    November 19, 2021

    Pfam 35.0 contains a total of 19,632 families and clans. Since the last release, we have built 460 new families, killed 7 families and created 12 new clans. UniProt Reference Proteomes has increased by 7% since Pfam 34.0, and now contains 61 million sequences. Of the sequences that are in UniProt Reference Proteomes, 75.2% have at least one Pfam match, and 48.7% of all residues fall within a Pfam family.

    Sources of new families

    In an effort to increase the Pfam coverage of metagenomic sequence space, we have created 250 metagenomic protein families. These families were built by clustering protein sequences from the MGnify and UniProt databases, aligning the sequences in each cluster, and using the resulting alignments to create new SEED alignments. We then used our usual building process to create new families from the SEED alignments.

    We have also created 52 new families based on clusters from a new resource called DPCfam based on Density Peak Clustering, created by Allesandro Laio, Marco Punta and Elena Tea Russo. An interesting example of these families is the N-terminal domain of the Crinkler effector protein (PF20147). Crinkling- and necrosis-inducing proteins (CRNs) or Crinkler, are ubiquitously present and first described in plant pathogenic oomycetes, and have been shown to participate in processes controlling plant cell death and immunity. However, Crinkler is also found outside oomycetes, such as in the Rhizophagus irregularis crinkler effector protein 1 (RiCRN1) which, like other CRNs, functions in the plant nucleus, but plays an essential role in symbiosis progression and the proper initiation of arbuscule development. This suggests that Crinkler proteins are more ubiquitously distributed than first predicted, and that their function is not limited to plant death (PMID:30233541). The Pfam domain contains the conserved motif FLAK, and, from structure predictions, adopts the ubiquitin-like fold, as seen in the image below. 

    Figure 1: N-terminal domain of RiCRN. The image was generated using an AlphaFold colab notebook and is displayed using Molstar.

    We continue to be provided with new families from the group of L. Aravind from NCBI, and have added 42 of them to this release of Pfam. Many of these families represent novel domains and proteins found in phage defence systems of bacteria.

    Pfam-N

    We are really excited about the Pfam-N matches for Pfam 35.0, but there is still a bit of work to do before we can release them. In particular, we’re working on neural networks that can predict the location of the domains themselves, instead of relying on HMMER to do so, as with the previous Pfam-N release. It will take a little more time to ensure the quality of these annotations, and we will make another announcement when they are ready.

    Enjoy Pfam 35.0!

    Posted by the Pfam team

    Dfam 3.5 release

    October 11, 2021

    We are pleased to announce the the Dfam 3.5 release, which includes both new annotation data (available for download) and additional TE (transposable element) models and species.

    TE annotation data

    As part of this release, we have added annotation data for the curated TE entries for all of the extant species as part of the Zoonomia project (Figure 1). These data were curated by the combined work of David Ray’s lab at Texas Tech University (TTU) as well as the Smit group at the Institute for Systems Biology (ISB), and include young, lineage-specific TE models.

    Figure 1: Example of the genomic annotation for a Dfam TE model.

    TE models

    271 Lineage-specific, curated LTR TE models for the reconstructed ancestor of New World monkeys as part of the Zoonomia project have also been added.Additional uncurated entries (DR records) have also been added for the duckweed (607 models) and Atlantic cod (2751 models) as part of TE families submitted to Dfam via the website interface. The next Dfam release will include additional submitted datasets. With the addition of these new families, Dfam now houses 285,542 TE models across 595 species (Figure 2; Figure 3). We look forward to the continued growth of Dfam!

    Figure 2: Dfam model growth. Numbers above each bar indicate the number of total models in Dfam at the time of the indicated release.
    Figure 3: Dfam species growth. Numbers above each bar indicates the number of total species in Dfam at the time of the indicated release.

    Rfam 14.6 is out

    July 27, 2021

    We are happy to announce a new release of Rfam (14.6) that includes 121 new microRNA families, a new ribozyme family, 8 new small RNA families found in Bacteroides, as well as 10 additional families with updated secondary structures using 3D structural information. Read on for more information or explore the data in Rfam.

    New microRNA families

    The new release includes 121 new microRNA families bringing the total number of microRNA families in Rfam to 1,506. This work is part of the ongoing collaboration with miRBase that aims to synchronise microRNAs across miRBase, Rfam, and RNAcentral. Browse Rfam microRNAs or find out more about the microRNA project.

    We also resolved an issue with 6 microRNA families that were missing a covariance model on the website and in the FTP archive. Many thanks to Dr Christian Anthon (University of Copenhagen) for pointing out this problem!

    Updating families using information from 3D structures

    Following on from Rfam 14.5, we updated the secondary structure of 10 additional families with 3D information, including 6 riboswitches, 1 ribozyme, 1 telomerase, 1 localization element and 1 microRNA precursor.

    In some families, the updated structure is substantially changed. For example, the central part of the flavin mononucleotide (FMN) riboswitch is now organised by several additional base pairs and two pseudoknots (pK). As a result, the updated structure is more compact and more accurately reflects the experimentally determined 3D structures.

    Seven of the updated families include newly annotated pseudoknots, which is an important improvement that helps better model long-distance non-nested interactions. We will continue reviewing and updating the families with 3D structure in future releases. The full list of the updated families can be found in the table below.

    FamilyPDB structuresNew  pK
    RF00008 – Hammerhead ribozyme (type III)2QUS_A, 2QUS_B, 2QUW_A, 2QUW_B, 2QUW_C, 2QUW_D, 5DI2_A, 5DI4_A, 5DQK_A, 5EAO_A, 5EAQ_A1
    RF00025 – Ciliate telomerase RNA6D6V2
    RF00050 – FMN riboswitch (RFN element)3F2Q, 3F2T, 3F2W, 3F2X, 3F2Y, 3F3O 2
    RF00059 – TPP riboswitch (THI element)2CKY_A, 2CKY_B, 2GDI_X, 2GDI_Y, 2HOJ_A, 2HOK_A, 2HOL_A, 2HOM_A, 2HOO_A, 2HOP_A, 3D2G_A, 3D2G_B, 3D2V_A, 3D2V_B, 3D2X_A, 3D2X_B, 3K0J_E, 3K0J_F, 4NYA_A, 4NYA_B, 4NYB_A, 4NYC_A, 4NYD_A, 4NYG_A
    RF00207 – K10 transport/localisation element (TLS)2KE6, 2KUR, 2KUU, 2KU, 2KUW
    RF00174 – Cobalamin riboswitch4GMA, 4GXY1
    RF00380 – ykoK leader / M-box riboswitch2QBZ_X, 3PDR_X, 3PDR_A1
    RF01689 – AdoCbl variant RNA4FRN_A, 4FRN_B, 4FRG_X, 4FRG_B1
    RF01831 – THF riboswitch3SD3, 3SUH, 3SUX, 3SUY, 4LVV, 4LVW, 4LVX, 4LVY, 4LVZ, 4LW01
    RF02095 – mir-2985-2 microRNA precursor2L3J

    Hovlinc ribozyme

    A recent paper by Chen Y et al. 2021 describes Hovlinc, a new type of self-cleaving ribozymes found in human and other hominids. Hovlinc was detected in a very long intergenic noncoding RNA in hominids (hominin vlincRNA-located) using a genome-wide approach designed to discover self-cleaving ribozymes. The functions of vlincRNA and the hovlinc ribozyme remain unclear. Hovlinc joins 3 known classes of small, self cleaving ribozymes found in human: (1) Mammalian CPEB3 ribozyme, (2) Hammerhead ribozyme and (3) B2 and ALU retrotransposons. We would like to thank Dr Fei Qi (Huaqiao University) for providing the Hovlinc alignment. View hovlinc family in Rfam.

    New Bacteroides families

    In a recent article by Ryan et al. 2020, the authors report a high-resolution transcriptome map of the model organism Bacteroides thetaiotaomicron, a common bacteria of the human gut. They recognize 269 non-coding RNAs (ncRNAs) candidates from which nine were validated. Eight of these ncRNAs were integrated as new families:

    1. RF04177 – Bacteroides sRNA BTnc201
    2. RF04178 – Bacteroides sRNA BTnc005
    3. RF04179 – Bacteroides sRNA BTnc049
    4. RF04180 – Bacteroides sRNA BTnc231
    5. RF04181 – rteR sRNA
    6. RF04182 – GibS sRNA
    7. RF04183 – Bacteroidales small SRP
    8. RF04184 – Bacteroides sRNA BTnc060

    In addition, the RF01693 – Bacteroidales-1 family was renamed to 6S-Bacteroidales RNA. Bacteroidales-1 was first reported in a comparative genomics-based approach of genome and metagenome sequences from Weinberg et al. 2010. It was identified downstream of L20 ribosomal subunit genes in the order Bacteroidales. Ryan et al. 2020 report that this sRNA is a 6S RNA homolog in Bacteroides thetaiotaomicron. Rfam also has other two families of 6S RNA RF00013-6S/SsrS RNA and RF01685-6S-Flavo. We would like to thank Dr Lars Barquist (University of Würzburg) for providing the data.

    Welcome to Emma!

    A few weeks ago Emma Cooke joined the Rfam team as Software Developer and is already busy working on new features. Emma has studied Genetics and Software Engineering, and her previous roles have focused on release verification pipelines, software testing, and developing for cloud environments. Please join us in welcoming Emma to the Rfam community and stay tuned for new announcements based on her work.

    Get in touch

    As always, we would be very happy to hear from you if you have any feedback or suggestions for Rfam. Please feel free to email us or get in touch on Twitter.

    Dfam 3.4 Release

    July 24, 2021

    The Dfam Consortium is proud to announce the release of Dfam 3.4. This update includes over 8,200 curated transposable element (TE) families found in 240 mammalian genomes. The models therein have been carefully developed by David Ray’s lab at Texas Tech University (TTU) and further refined by Arian Smit. This is part of an ongoing effort to generate a comprehensive mammalian TE library using multi-species alignments and ancestral sequence reconstructions generated by the Zoonomia project (https://zoonomiaproject.org).

    In addition to releasing the curated TE families, full genome annotations are provided for 21 Old World monkeys (Figure 1; Figure 2).

    Figure 1: A portion of the available genomes aligned as part of the Zoonomia project, focused on the Primate Order.

    Discovery of young, species-specific TEs

    As a large portion of a mammalian genome, TEs serve as a source for genomic variation and innovation, including (but certainly not limited to) genomic rearrangement via movement and non-homologous recombination and providing novel transcription factor binding sites. David Ray’s lab has taken the first large-scale effort into examining the TE content of the extant genomes as part of the Zoonomia project in order to determine the TE type and location and subsequently the impact they might have on the evolution of each lineage of mammals. 

    Methods

    A total of 248 final genome assemblies of placental mammals were initially presented for analysis, most coming from the Zoonomia dataset. Low quality assemblies and previously analyzed genomes were excluded from analyses. To avoid wasted effort on re-curation of previously described TEs, manual curation efforts were focused towards identifying newer putative TEs that underwent relatively recent accumulation, with the main assumption being that many older TEs will be widely shared among large groups of placental mammals and that previous annotation efforts have thoroughly described these older elements in detail.

    To classify younger TEs, the filtered dataset was narrowed to elements that have undergone transposition in the recent past, i.e. TEs that have insertion sequences with Kimura 2 parameter (K2P) distances less than 4.4% (approximately ~20my or less since insertion, based on a general mammalian neutral mutation rate of 2.2×10-9). This approach yielded mostly lineage specific TEs, many of which were yet to be previously described.

    For each iteration of manual TE curation, new consensus sequences were generated from the 10-50 top BLAST hits, and aligning these sequences via MUSCLE and estimating a consensus sequence with EMBOSS.

    To reduce library redundancy, the potential TE consensus sequences were combined with those of known TEs from previous work as well as all known vertebrate TEs from Repbase. The program CD-HIT-EST was used to identify duplicate TEs among our combined TE library according to the 80-80-80 rule of Wicker et al.

    To confirm the TE type, each sequence in the library was subjected to a custom pipeline which used: blastx to confirm the presence of known ORFs in autonomous elements, RepBase to identify known elements, and TEclass to predict the TE type. In addition, structural criteria was also utilized for categorizing TEs: DNA transposons, elements with visible terminal inverted repeats; rolling circle transposons were required to have identifiable ACTAG at one end; putative SINEs were inspected for a repetitive tail as well as A and B boxes; LTR retrotransposons were required to have recognizable hallmarks, such as: TG, TGT, or TGTT at their 5’ and the inverse at the 3’ ends.   

    Zoonomia Project

    Figure 2: Summary of the Zoonomia project

    The Zoonomia project is an effort to understand the mammalian tree of life at a deeper level. This massive undertaking is the collaboration of 27 laboratories. Although far from a complete list, some current projects derived from the Zoonomia datasets include: studying mammalian speech development, regulatory element analyses, chromosome evolution and the evolution of microRNA genes.

    Future Work

    Future efforts will continue to analyze and catalog lineage-specific TEs in deeper branches of the 240-way genome alignment via the reconstructed genomes at each node of the phylogenetic tree as part of the alignment and expand the full genome annotations available on Dfam.

    Google Research Team bring Deep Learning to Pfam

    March 24, 2021

    We are delighted to announce the first fruits of a collaboration between the Pfam team and a Google Research team led by Dr Lucy Colwell, with Maxwell Bileschi and David Belanger. In 2019, Colwell’s team published a preprint describing a new deep learning method that was trained on Pfam data, and which improves upon the performance of the HMMER software (HMMER is the underlying software used by Pfam). Colwell’s team embraced our initial sceptical feedback and shared data that helped us to understand the new method’s performance. Over time our scepticism turned into interest as we explored novel findings from the method, and now we are very excited by the potential of these methods to improve our ability to classify sequences into domains and families.

    Introducing Pfam-N

    We are pleased to share a new file called Pfam-N (N for network), which provides additional Pfam 34.0 matches identified by the Google team. Pfam-N annotates 6.8 million protein regions into 11,438 Pfam families. These regions include nearly 1.8 million full-length protein sequences from UniProtKB Reference Proteomes that previously had no Pfam match, an improvement of 4.2% over the currently-annotated 42.5 million. We also note that among the sequences that get their first Pfam annotation, there are 360 human sequences.

    The figure above shows the number of matches to UniProtKB Reference Proteomes 2020_06 for each Pfam release over the last decade (orange). Pfam-N (blue) adds nearly 10% more regions to Pfam v34.0, which based on the current trend, would have taken several years for us to achieve.

    How was Pfam-N made?

    Deep learning approaches use training examples, much like HMMER, to learn the statistics of what it means for a protein to have a particular function. We use a subset of all the Pfam HMMER matches for training, and provide our deep learning model with both the sequence and Pfam family for each training example. 

    We trained a number of replicates (“ensemble elements”) of a convolutional neural network to predict the Pfam matches. We call this ensemble model ProtENN (ENN for Ensemble of Neural Networks). The method relies on HMMER to initially parse proteins into their constituent domains before giving these regions to ProtENN. 

    The Pfam-N file is in the standard Pfam Stockholm alignment format, and the ProtENN matches are aligned using the existing Pfam profile-HMM model. We only include a match in Pfam-N if it is not already included in Pfam.

    It should be noted that the deep learning model has access to the full set of matches for a Pfam family, whereas the Pfam profile-HMM models are trained on the much smaller Pfam seed alignments. Thus this is not a direct comparison between ProtENN and HMMER. 

    Improving Pfam using Pfam-N

    We plan to add Pfam-N matches to Pfam seed alignments to help improve the performance of the Pfam profile-HMMs in future releases. Some Pfam families gain huge numbers of additional matches in Pfam-N. For example, the TAT_signal family (PF10518) matches about 4,000 sequences in Pfam 34.0. Pfam-N identifies a further 37,000 protein sequences that were missed by the current Pfam model. The ACT domain (PF01842), which confers regulation to a variety of enzymes by binding to amino acids, is doubled in size by the 27,000 additional matches identified by the deep learning model. Overall, the deep learning models seem to perform particularly well for short families, where the profile-HMMs struggle to distinguish between signal and noise. Large gains are also made for short protein repeats such as TPRs, Leucine Rich Repeats and zinc fingers found in DNA-binding transcription factors.

    Funding

    The work to expand Pfam families with Pfam-N hits is funded by the Wellcome Trust as part of a Biomedical Resources grant awarded to the Pfam database.

    Future work

    Deep learning approaches have a number of potential upsides we’re excited to explore, including explicit modeling of interactions between amino acids that are quite far from each other in sequence, as well as the fact that these approaches build a shared model across all protein classes: they attempt to leverage shared information, about, say, a helix-turn-helix region for all of the large variety of biological processes that incorporate this motif. 

    If deep learning use in speech recognition and computer vision are any indications to go by, our current usage to functionally annotate proteins is in its infancy. We look forward to the development of these models to help us classify the protein universe. 

    Posted by Alex Bateman

    Rfam 14.5 is live

    March 18, 2021

    We are happy to announce a new Rfam release, version 14.5, featuring 112 updated microRNA families and 10 families improved using the 3D structure information. Read on for details or explore 3,940 RNA families at rfam.org.

    Updated microRNA families

    As described in our most recent paper, we are in the process of synchronising microRNA families between Rfam and miRBase. In this release 112 of the existing microRNA families have been updated with new manually curated seed alignments from miRBase, new gathering thresholds, and new family members found in the Rfamseq sequence database. 

    In total, 852 new microRNA families have been created (356 in release 14.3 and 496 in release 14.4) and 152 existing families have been updated (40 in release 14.3 and 112 in release 14.5). As the miRBase-Rfam synchronisation is about 50% complete, additional microRNA families will be made available in the upcoming releases. You can view a list of the 112 updated families or browse all 1,385 microRNA families on the Rfam website. 

    Updating families using information from 3D structures

    We are also in the process of reviewing the families with the experimentally determined 3D structures in order to compare the Rfam annotations with the 3D models. Our goal is to incorporate the 3D information into Rfam seed alignments as many families have been created before the corresponding 3D structures became available. We manually review each PDB structure, verify basepair annotations from matching PDBs, and obtain a more consistent consensus secondary structure model. 

    In multiple cases we were able to add missing base pairs and pseudoknots. For example, in the SAM riboswitch (RF00162), we added two base pairs in the base of helix P2, corrected a basepair in P3 and added four basepairs in P4 (one in the base of the helix and three near the terminal loop). The updated consensus secondary structure presents a more accurate central core annotation with more structure in the four-way junction.

    SAM riboswitch secondary structure before and after the updates

    In another example, one base pair was added in P1 and another one in P3 of the SAM-I/IV variant riboswitch (RF01725). We also corrected a base pair in P3 and included a P4 stem loop that was not integrated before.

    SAM-I/IV riboswitch secondary structure before and after the updates

    The SAM-I/IV riboswitch is characterised by a similar SAM binding core conformation to that of the SAM riboswitch but it differs in the k-turn motif in P2 which is found in SAM riboswitches but not in SAM-I/IV. These two families also have different pseudoknots interactions, where SAM riboswitch forms a pseudoknot between a P2 loop and the stem of P3, while the SAM-I/IV riboswitch contains a pseudoknot between a P3 loop and the 5′ region.

    The first 10 families updated with 3D information include:

    1. RF00162 – SAM riboswitch
    2. RF01725 – SAM-I/IV variant riboswitch
    3. RF00164 – Coronavirus 3’ stem-loop II-like motif (s2m)
    4. RF00013 – 6S / SsrS RNAP
    5. RF00003 – U1 spliceosomal RNA
    6. RF00015 – U4 spliceosomal RNA
    7. RF00442 – Guanidine-I riboswitch
    8. RF00027 – let-7 microRNA precursor
    9. RF01054 – preQ1-II (pre queuosine) riboswitch and
    10. RF02680 – preQ1-III riboswitch

    We will continue reviewing the families with known 3D structure in future releases.

    Other family updates

    Initially reported by Aspegren et al. 2004, Class I (RF01414) and Class II (RF01571) RNAs were found in social amoeba Dictyostelium discoideum and later on investigated in more detail by Avesson et al. 2011. Now a new report from Kjellin et al. 2021 presents a comprehensive analysis of the Class I RNA genes in dictyostelid social amoebas. Based on this study, we updated the Dicty Class I RNA family RF01414 with a new seed alignment and removed the family RF01571, thus merging both families into one. We thank Dr Jonas Kjellin (Uppsala University) for suggesting this update.

    Goodbye Ioanna!

    Rfam 14.5 is the last release prepared by Dr Ioanna Kalvari who will be leaving the team at the end of March 2021. We would like to take the opportunity to thank Ioanna for her contributions over the last 5.5 years and wish her best of luck in the future!

    Get in touch

    As always, we would be very happy to hear from you if you have any feedback or suggestions for Rfam. Please feel free to email us or get in touch on Twitter

    Rfam 14.4 is live

    December 18, 2020

    The last Rfam release of 2020 is now live! Rfam 14.4 contains 496 new microRNA families developed in collaboration with miRBase. Find out about the microRNA project in our new NAR paper and let us know if you have any feedback.

    Rfam 14.3

    September 15, 2020

    Rfam 14.3 includes 356 new and 40 updated microRNA families, as well as 12 new and 2 updated Flavivirus RNAs. Find out the details in our new NAR paper and get in touch if you have any feedback.