m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq.

We present m6ACali, a novel machine-learning framework aimed at enhancing the accuracy of N6-methyladenosine (m6A) epitranscriptome profiling by reducing the impact of non-specific antibody enrichment in MeRIP-Seq. The calibration model serves as a genomic feature-based classifier that refines the identification of m6A sites, distinguishing those genuinely present from those that can be detected in in-vitro transcribed (IVT) control experiments. We find that m6ACali effectively identifies non-specific binding peaks reported by exomePeak2 and MACS2 in novel MeRIP-Seq datasets without the need for paired IVT controls. The model interpretation revealed that off-target antibody binding sites commonly occur at short exons and short mRNAs, originating from high read coverage regions that share the motif sequence with true m6A sites. We also reveal that the ML strategy can efficiently adjust differentially methylated peaks and other antibody-dependent, base-resolution m6A detection techniques. As a result, m6ACali offers a promising method for the universal enhancement of m6A profiles generated by MeRIP-Seq experiments, elevating the benchmark for omics-level m6A data integration.

KH-like Domains in PARP9/DTX3L and PARP14 Coordinate Protein-Protein Interactions to Promote Cancer Cell Survival.

Certain members of the ADP-ribosyltransferase superfamily (ARTD or PARP enzymes) catalyse ADP-ribosylation in response to cellular stress, DNA damage and viral infection and are upregulated in various tumours. PARP9, its binding partner DTX3L and PARP14 protein levels are significantly correlated in head and neck squamous cell carcinoma (HNSCC) and other tumour types though a mechanism where PARP9/DTX3L regulates PARP14 post-transcriptionally. Depleting PARP9, DTX3L or PARP14 expression in HNSCC or HeLa cell lines decreases cell survival through a reduction of proliferation and an increase in apoptosis. A partial rescue of survival was achieved by expressing a PARP14 truncation containing a predicted eukaryotic type I KH domain. KH-like domains were also found in PARP9 and in DTX3L and contributed to protein-protein interactions between PARP9-DTX3L and PARP14-DTX3L. Homodimerization of DTX3L was also coordinated by a KH-like domain and was disrupted by site-specific mutation. Although, cell survival promoted by PARP14 did not require ADP-ribosyltransferase activity, interaction of DTX3L in vitro suppressed PARP14 auto-ADP-ribosylation and promoted trans-ADP-ribosylation of PARP9 and DTX3L. In summary, we characterised PARP9-DTX3L-PARP14 interactions important to pro-survival signalling in HNSCC cells, albeit in PARP14 catalytically independent fashion.

The 2024 Nucleic Acids Research database issue and the online molecular biology database collection.

The 2024 Nucleic Acids Research database issue contains 180 papers from across biology and neighbouring disciplines. There are 90 papers reporting on new databases and 83 updates from resources previously published in the Issue. Updates from databases most recently published elsewhere account for a further seven. Nucleic acid databases include the new NAKB for structural information and updates from Genbank, ENA, GEO, Tarbase and JASPAR. The Issue's Breakthrough Article concerns NMPFamsDB for novel prokaryotic protein families and the AlphaFold Protein Structure Database has an important update. Metabolism is covered by updates from Reactome, Wikipathways and Metabolights. Microbes are covered by RefSeq, UNITE, SPIRE and P10K; viruses by ViralZone and PhageScope. Medically-oriented databases include the familiar COSMIC, Drugbank and TTD. Genomics-related resources include Ensembl, UCSC Genome Browser and Monarch. New arrivals cover plant imaging (OPIA and PlantPAD) and crop plants (SoyMD, TCOD and CropGS-Hub). The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Over the last year the NAR online Molecular Biology Database Collection has been updated, reviewing 1060 entries, adding 97 new resources and eliminating 388 discontinued URLs bringing the current total to 1959 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

m6A-Atlas v2.0: updated resources for unraveling the N6-methyladenosine (m6A) epitranscriptome among multiple species.

N 6-Methyladenosine (m6A) is one of the most abundant internal chemical modifications on eukaryote mRNA and is involved in numerous essential molecular functions and biological processes. To facilitate the study of this important post-transcriptional modification, we present here m6A-Atlas v2.0, an updated version of m6A-Atlas. It was expanded to include a total of 797 091 reliable m6A sites from 13 high-resolution technologies and two single-cell m6A profiles. Additionally, three methods (exomePeaks2, MACS2 and TRESS) were used to identify >16 million m6A enrichment peaks from 2712 MeRIP-seq experiments covering 651 conditions in 42 species. Quality control results of MeRIP-seq samples were also provided to help users to select reliable peaks. We also estimated the condition-specific quantitative m6A profiles (i.e. differential methylation) under 172 experimental conditions for 19 species. Further, to provide insights into potential functional circuitry, the m6A epitranscriptomics were annotated with various genomic features, interactions with RNA-binding proteins and microRNA, potentially linked splicing events and single nucleotide polymorphisms. The collected m6A sites and their functional annotations can be freely queried and downloaded via a user-friendly graphical interface at: http://rnamd.org/m6a.

m7GHub V2.0: an updated database for decoding the N7-methylguanosine (m7G) epitranscriptome.

With recent progress in mapping N7-methylguanosine (m7G) RNA methylation sites, tens of thousands of experimentally validated m7G sites have been discovered in various species, shedding light on the significant role of m7G modification in regulating numerous biological processes including disease pathogenesis. An integrated resource that enables the sharing, annotation and customized analysis of m7G data will greatly facilitate m7G studies under various physiological contexts. We previously developed the m7GHub database to host mRNA m7G sites identified in the human transcriptome. Here, we present m7GHub v.2.0, an updated resource for a comprehensive collection of m7G modifications in various types of RNA across multiple species: an m7GDB database containing 430 898 putative m7G sites identified in 23 species, collected from both widely applied next-generation sequencing (NGS) and the emerging Oxford Nanopore direct RNA sequencing (ONT) techniques; an m7GDiseaseDB hosting 156 206 m7G-associated variants (involving addition or removal of an m7G site), including 3238 disease-relevant m7G-SNPs that may function through epitranscriptome disturbance; and two enhanced analysis modules to perform interactive analyses on the collections of m7G sites (m7GFinder) and functional variants (m7GSNPer). We expect that m7Ghub v.2.0 should serve as a valuable centralized resource for studying m7G modification. It is freely accessible at: www.rnamd.org/m7GHub2.

Microtubule association of TRIM3 revealed by differential extraction proteomics.

The microtubule network is formed from polymerised tubulin subunits and associating proteins, which govern microtubule dynamics and a diverse array of functions. To identify novel microtubule-binding proteins, we have developed an unbiased biochemical assay, which relies on the selective extraction of cytosolic proteins from U2OS cells, while leaving behind the microtubule network. Candidate proteins are linked to microtubules by their sensitivities to the depolymerising drug nocodazole or the microtubule-stabilising drug taxol, which is quantitated by mass spectrometry. Our approach is benchmarked by co-segregation of tubulin and previously established microtubule-binding proteins. We then identify several novel candidate microtubule-binding proteins, from which we have selected the ubiquitin E3 ligase tripartite motif-containing protein 3 (TRIM3) for further characterisation. We map TRIM3 microtubule binding to its C-terminal NHL-repeat region. We show that TRIM3 is required for the accumulation of acetylated tubulin, following treatment with taxol. Furthermore, loss of TRIM3 partially recapitulates the reduction in nocodazole-resistant microtubules characteristic of α-tubulin acetyltransferase 1 (ATAT1) depletion. These results can be explained by a decrease in ATAT1 following depletion of TRIM3 that is independent of transcription.

CASP15 cryo-EM protein and RNA targets: Refinement and analysis using experimental maps.

CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, experimental structures by their nature are only models themselves-their construction involves a certain degree of subjectivity in interpreting density maps and translating them to atomic coordinates. Here, we directly utilized density maps to evaluate the predictions by employing a method for ranking the quality of protein chain predictions based on their fit into the experimental density. The fit-based ranking was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy, and occasionally even better than the reference structure in some regions of the model. Local assessment of predicted side chains in a 1.52 Å resolution map showed that side-chains are sometimes poorly positioned. Additionally, the top 118 predictions associated with 9 protein target reference structures were selected for automated refinement, in addition to the top 40 predictions for 11 RNA targets. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure. This refinement was successful despite large conformational changes often being required, showing that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryo-EM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors, and together with the lack of consensus amongst models in these regions suggests that modeling, in combination with model-fit to the density, holds the potential for identifying more flexible regions within the structure.

Assessment of three-dimensional RNA structure prediction in CASP15.

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15.

For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.

Tertiary structure assessment at CASP15.

The results of tertiary structure assessment at CASP15 are reported. For the first time, recognizing the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single-chain predictions were assessed together, irrespective of whether a template was available. At CASP15, there was no single stand-out group, with most of the best-scoring groups-led by PEZYFoldings, UM-TBM, and Yang Server-employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues. Local divergence between prediction and target correlated with localization at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, and should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups produced high-quality predictions for most targets, which are valuable for experimental structure determination, functional analysis, and many other tasks across biology. These include those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas: the confidence estimates of the former were also notably accurate.

CASP15 cryoEM protein and RNA targets: refinement and analysis using experimental maps.

CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, errors in the reference structures can potentially reduce the accuracy of the assessment. This issue is particularly prominent in cryoEM-determined structures, and therefore, in the assessment of CASP15 cryoEM targets, we directly utilized density maps to evaluate the predictions. A method for ranking the quality of protein chain predictions based on rigid fitting to experimental density was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy although local assessment of predicted side chains in a 1.52 Å resolution map showed that side-chains are sometimes poorly positioned. The top 136 predictions associated with 9 protein target reference structures were selected for refinement, in addition to the top 40 predictions for 11 RNA targets. To this end, we have developed an automated hierarchical refinement pipeline in cryoEM maps. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure, including some regions with better fit to the density. This refinement was successful despite large conformational changes and secondary structure element movements often being required, suggesting that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryoEM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors with even short loops failing to be accurately modeled or refined at times. The lack of consensus amongst models suggests that modeling holds the potential for identifying more flexible regions within the structure.

Predicted models and CCP4.

In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement.

Deep Learning-based structure modelling illuminates structure and function in uncharted regions of ß-solenoid fold space.

Repeat proteins are common in all domains of life and exhibit a wide range of functions. One class of repeat protein contains solenoid folds where the repeating unit consists of ß-strands separated by tight turns. ß-solenoids have distinguishing structural features such as handedness, twist, oligomerisation state, coil shape and size which give rise to their diversity. Characterised ß-solenoid repeat proteins are known to form regions in bacterial and viral virulence factors, antifreeze proteins and functional amyloids. For many of these proteins, the experimental structure has not been solved, as they are difficult to crystallise or model. Here we use various deep learning-based structure-modelling methods to discover novel predicted ß-solenoids, perform structural database searches to mine further structural neighbours and relate their predicted structure to possible functions. We find both eukaryotic and prokaryotic adhesins, confirming a known functional linkage between adhesin function and the ß-solenoid fold. We further identify exceptionally long, flat ß-solenoid folds as possible structures of mucin tandem repeat regions and unprecedentedly small ß-solenoid structures. Additionally, we characterise a novel ß-solenoid coil shape, the FapC Greek key ß-solenoid as well as plausible complexes between it and other proteins involved in Pseudomonas functional amyloid fibres.

Structural insights into pink-eyed dilution protein (Oca2).

Recent innovations in computational structural biology have opened an opportunity to revise our current understanding of the structure and function of clinically important proteins. This study centres on human Oca2 which is located on mature melanosomal membranes. Mutations of Oca2 can result in a form of oculocutanous albinism, which is the most prevalent and visually identifiable form of albinism. Sequence analysis predicts Oca2 to be a member of the SLC13 transporter family, but it has not been classified into any existing SLC families. The modelling of Oca2 with AlphaFold2 and other advanced methods show that, like SLC13 members, it consists of a scaffold and transport domain and displays a pseudo inverted repeat topology that includes re-entrant loops. This finding contradicts the prevailing consensus view of its topology. In addition to the scaffold and transport domains, the presence of a cryptic GOLD domain is revealed that is likely responsible for its trafficking from the endoplasmic reticulum to the Golgi prior to localisation at the melanosomes. The GOLD domain harbours some known glycosylation sites. Analysis of the putative ligand binding site of the model shows the presence of highly conserved key asparagine residues that suggest Oca2 may be a Na+/dicarboxylate symporter. Known critical pathogenic mutations map to structural features present in the repeat regions that form the transport domain. Exploiting the AlphaFold2 multimeric modelling protocol in combination with conventional homology modelling allowed the building of plausible homodimers in both inward- and outward-facing conformations, supporting an elevator-type transport mechanism.

Assessment of three-dimensional RNA structure prediction in CASP15.

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

To split or not to split: CASP15 targets and their processing into tertiary structure evaluation units.

Processing of CASP15 targets into evaluation units (EUs) and assigning them to evolutionary-based prediction classes is presented in this study. The targets were first split into structural domains based on compactness and similarity to other proteins. Models were then evaluated against these domains and their combinations. The domains were joined into larger EUs if predictors' performance on the combined units was similar to that on individual domains. Alternatively, if most predictors performed better on the individual domains, then they were retained as EUs. As a result, 112 evaluation units were created from 77 tertiary structure prediction targets. The EUs were assigned to four prediction classes roughly corresponding to target difficulty categories in previous CASPs: TBM (template-based modeling, easy or hard), FM (free modeling), and the TBM/FM overlap category. More than a third of CASP15 EUs were attributed to the historically most challenging FM class, where homology or structural analogy to proteins of known fold cannot be detected.

An exonuclease V homologue is expressed predominantly during early megasporogenesis in apomictic Brachiaria brizantha.

MAIN CONCLUSION: An exonuclease V homologue from apomictic Brachiaria brizantha is expressed and localized in nucellar cells at key moments when these cells differentiate to give rise to unreduced gametophytes. Brachiaria is a genus of forage grasses with economical and agricultural importance to Brazil. Brachiaria reproduces by aposporic apomixis, in which unreduced embryo sacs, derived from nucellar cells, other than the megaspore mother cell (MMC), are formed. The unreduced embryo sacs produce an embryo without fertilization resulting in clones of the mother plant. Comparative gene expression analysis in ovaries of sexual and apomictic Brachiaria spp. revealed a sequence from B. brizantha that showed a distinct pattern of expression in ovaries of sexual and apomictic plants. In this work, we describe a gene named BbrizExoV with strong identity to exonuclease V (Exo V) genes from other grasses. Sequence analysis in signal prediction tools showed that BbrizExoV might have dual localization, depending on the translation point. A longer form to the nucleus and a shorter form which would be directed to the chloroplast. This is also the case for monocot sequences analyzed from other species. The long form of BbrizExoV protein localizes to the nucleus of onion epidermal cells. Analysis of ExoV proteins from dicot species, with exception of Arabidopsis thaliana ExoVL protein, showed only one localization. Using a template-based AlphaFold 2 modelling approach the structure of BbrizExoV in complex with metal and ssDNA was predicted based on the holo structure of the human counterpart. Features predicted to define ssDNA binding but a lack of sequence specificity are shared between the human enzyme and BbrizExoV. Expression analyses indicated the precise site and timing of transcript accumulation during ovule development, which coincides with the differentiation of nucelar cells to form the typical aposporic four-celled unreduced gametophyte. A putative function for this protein is proposed based on its homology and expression pattern.

Engineering a Carboxyl Methyltransferase for the Formation of a Furan-Based Bioplastic Precursor.

FtpM from Aspergillus fumigatus was the first carboxyl methyltransferase reported to catalyse the dimethylation of dicarboxylic acids. Here the creation of mutant R166M that can catalyse the quantitative conversion of bio-derived 2,5-furandicarboxylic acid (FDCA) to its dimethyl ester (FDME), a bioplastics precursor, was reported. Wild type FtpM gave low conversion due to its reduced catalytic efficiency for the second methylation step. An AlphaFold 2 model revealed a highly electropositive active site, due to the presence of 4 arginine residues, postulated to favour the binding of the dicarboxylic acid over the intermediate monoester. The R166M mutation improved both binding and turnover of the monoester to permit near quantitative conversion to the target dimethyl ester product. The mutant also had improved activity for other diacids and a range of monoacids. R166M was incorporated into 2 multienzyme cascades for the synthesis of the bioplastics precursor FDME from bioderived 5-hydroxymethylfurfural (HMF) as well as from poly(ethylene furanoate) (PEF) plastic, demonstrating the potential to recycle waste plastic.

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection.

The 2023 Nucleic Acids Research Database Issue contains 178 papers ranging across biology and related fields. There are 90 papers reporting on new databases and 82 updates from resources previously published in the Issue. Six more papers are updates from databases most recently published elsewhere. Major nucleic acid databases reporting updates include Genbank, ENA, ChIPBase, JASPAR, mirDIP and the Issue's first Breakthrough Article, NACDDB for Circular Dichroism data. Updates from BMRB and RCSB cover experimental protein structural data while AlphaFold 2 computational structure predictions feature widely. STRING and REBASE are stand-out updates in the signalling and enzymes section. Immunology-related databases include CEDAR, the second Breakthrough Article, for cancer epitopes and receptors alongside returning IPD-IMGT/HLA and the new PGG.MHC. Genomics-related resources include Ensembl, GWAS Central and UCSC Genome Browser. Major returning databases for drugs and their targets include Open Targets, DrugCentral, CTD and Pubchem. The EMPIAR image archive appears in the Issue for the first time. The entire database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, revisiting 463 entries, adding 92 new resources and eliminating 96 discontinued URLs so bringing the current total to 1764 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication.

Recent advances in epitranscriptomics have unveiled functional associations between RNA modifications (RMs) and multiple human diseases, but distinguishing the functional or disease-related single nucleotide variants (SNVs) from the majority of 'silent' variants remains a major challenge. We previously developed the RMDisease database for unveiling the association between genetic variants and RMs concerning human disease pathogenesis. In this work, we present RMDisease v2.0, an updated database with expanded coverage. Using deep learning models and from 873 819 experimentally validated RM sites, we identified a total of 1 366 252 RM-associated variants that may affect (add or remove an RM site) 16 different types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G, A-to-I, ac4C, Am, Cm, Um, Gm, hm5C, D and f5C) in 20 organisms (human, mouse, rat, zebrafish, maize, fruit fly, yeast, fission yeast, Arabidopsis, rice, chicken, goat, sheep, pig, cow, rhesus monkey, tomato, chimpanzee, green monkey and SARS-CoV-2). Among them, 14 749 disease- and 2441 trait-associated genetic variants may function via the perturbation of epitranscriptomic markers. RMDisease v2.0 should serve as a useful resource for studying the genetic drivers of phenotypes that lie within the epitranscriptome layer circuitry, and is freely accessible at: www.rnamd.org/rmdisease2.