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 our previous report in Rfam 14.5 release 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.


AlphaFolding the Protein Universe

July 22, 2021

Hot on the tail of our inclusion of the Baker group’s trRosetta structural models we are excited to announce the inclusion of models from AlphaFold 2.0 generated by DeepMind and stored in the AlphaFold Database (AlphaFold DB). AlphaFold 2.0’s performance in the CASP14 competition was spectacular, producing near experimental quality structure models.

The new AlphaFold models have been constructed for over 375,000 proteins from 22 model organisms and the very large majority of the models are full length proteins. This is in contrast to the trRosetta models, which were built from the domain region predicted by Pfam. Having full length protein models is very exciting for us because it will allow us to more easily check whether we need to extend or change the Pfam domain boundaries.  We will also be able to look for missing domains in the protein structures. AlphaFold models also help to fill in gaps when only a part of a longer family has been structurally characterised.

When looking at the AlphaFold models it is important to look at the quality scores of the model overall. Sometimes a good quality structural model cannot be created, but in these cases it is usually obvious from the quality scores shown as orange regions of the model.  Disordered regions of proteins are usually of low confidence.

We think that there are many thousands of Pfam families that could be improved using the AlphaFold and trRosetta models. Feel free to tell us where we could improve them. We are really enjoying mining this treasure trove of data and we hope you find some (not so) hidden gems. 

The Pfam team


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


Pfam 34.0 is released

March 24, 2021

Pfam 34.0 contains a total of 19,179 families and 645 clans. Since the last release, we have built 935 new families, killed 15 families and created 11 new clans. UniProt Reference Proteomes has increased by 21% since Pfam 33.1, and now contains 47 million sequences. Of the sequences that are in reference proteomes, 74.5% have at least one Pfam match, and 48.8% of all residues fall within a Pfam family.

Structural models

In our previous blog post, we announced the release of ~6,000 structural models in Pfam and InterPro. Many of the new families that we have created since the last release are large enough to be suitable for structure prediction. We have sent the alignments for new and modified Pfam families to the Baker group, who are currently generating structural models for them using their pipeline. We will release the next set of structural models when Pfam 34.0 is integrated into InterPro.

Collaboration with Google Research

We have been working with Dr Lucy Colwell’s research team at Google Research to expand Pfam coverage using deep learning methods. The deep learning approach, trained on Pfam HMMER matches, has found many additional matches which can be found in a new file called Pfam-N. There is another Pfam blog post which describes the work in more detail here.


Join Rfam team!

March 19, 2021

We are looking for a Software Developer to join the Rfam team and contribute to the world’s largest database of RNA families. The post holder will be responsible for keeping Rfam up-to-date, developing Rfam Cloud, and improving the website. More information about the position can be found at https://bit.ly/rfam-software-developer

Apply now or help spread the word. Closing date: April 20th, 2021.


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


Folding the Protein Universe

March 3, 2021

Today signifies the realization of a long-held dream to have the structure of every (well nearly every) family in Pfam. The Pfam and InterPro databases have made available structural models of 6,370 protein families created by Ivan Anishchanka from David Baker’s group at the University of Washington in Seattle. The models are made using their latest prediction method called trRosetta which can predict protein structures, based on large multiple sequence alignments, with incredible accuracy.

The Baker group have had remarkable success over the years in the field of structure prediction, and in the recent CASP14 event the group’s predictions were the most accurate from an academic group. Although not quite as accurate as Deep Mind’s AlphaFold 2.0 predictions, they are certainly of a high enough quality for many applications. For example, I am interested to understand when a Pfam family is part of a larger superfamily, or clan as we call them in Pfam. I have been able to take the structural models and identify distant homologues in the PDB using tools such as DALI and PDBeFold that compare protein structures. For longer Pfam families we can look at the structure model and identify likely domain boundaries to split up the existing Pfam family into the domain sized chunks (for example Calmodulin_bind could be split into 3 domains).

Within the InterPro website we have developed a completely novel view that allows you to see which residues in the Pfam seed alignment are predicted to be close in space. By clicking on columns in the alignment, one can see where they are in the structural model and which residues are predicted to be nearby (see the documentation for further details). We would be very interested in getting your feedback on this feature. We could provide a similar view based on contacts found in known structures. The PDB file for individual models can be downloaded from the structural model tab on the family pages within the InterPro and Pfam websites. You can also download all of the structural model and contact map data from the Pfam ftp site and InterPro ftp site.

The figure above shows the contact map and structural model as seen through the InterPro website. Links to some example Pfam families that have a structure model are shown below (for the Pfam links, click on the Structural Model tab):

This is not the first time we have made a large set of structure predictions available.  Back in 2002, again in collaboration with David Baker and Rich Bonneau we made many models available.  The overall accuracy of these models was much lower and we did not know which models were good since we lacked an accurate quality metric. The new models come with a quality score called lDDT and broadly, we can consider a model with lDDT > 0.6 to be good, and one with lDDT > 0.8 to be excellent.

Today marks an amazing milestone, with 88% of Pfam families now having a PDB structure or a structural model. The story is not quite finished though, as there remain 2,202 families that do not have structural data. We plan to investigate different sequence sets to make an even larger set of models available in the coming months. We felt however that it was useful to release this data set to the community as fast as we could. The work described here has been possible only due to funding from the BBSRC BBR, which has been a critical part of the funding landscape for UK data resources for many years.

There will be many exciting stories to be told using this structural treasure trove, and we hope it is a beneficial resource to the community. Please let us know what you think of the data, and whether you find the contact maps and models useful.

Posted by Alex Bateman


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.