Gene3D: Multi-domain annotations for protein sequence and comparative genome analysis

Bioinformatics analysis of the Microsporidia sp. MB genome: a malaria transmission-blocking symbiont of the Anopheles arabiensis mosquito

BACKGROUND

The use of microsporidia as a disease-transmission-blocking tool has garnered significant attention. Microsporidia sp. MB, known for its ability to block malaria development in mosquitoes, is an optimal candidate for supplementing malaria vector control methods. This symbiont, found in Anopheles mosquitoes, can be transmitted both vertically and horizontally with minimal effects on its mosquito host. Its genome, recently sequenced from An. arabiensis, comprises a compact 5.9 Mbp.

RESULTS

Here, we analyze the Microsporidia sp. MB genome, highlighting its major genomic features, gene content, and protein function. The genome contains 2247 genes, predominantly encoding enzymes. Unlike other members of the Enterocytozoonida group, Microsporidia sp. MB has retained most of the genes in the glycolytic pathway. Genes involved in RNA interference (RNAi) were also identified, suggesting a mechanism for host immune suppression. Importantly, meiosis-related genes (MRG) were detected, indicating potential for sexual reproduction in this organism. Comparative analyses revealed similarities with its closest relative, Vittaforma corneae, despite key differences in host interactions.

CONCLUSION

This study provides an in-depth analysis of the newly sequenced Microsporidia sp. MB genome, uncovering its unique adaptations for intracellular parasitism, including retention of essential metabolic pathways and RNAi machinery. The identification of MRGs suggests the possibility of sexual reproduction, offering insights into the symbiont's evolutionary strategies. Establishing a reference genome for Microsporidia sp. MB sets the foundation for future studies on its role in malaria transmission dynamics and host-parasite interactions.

Clustering protein functional families at large scale with hierarchical approaches

Proteins, fundamental to cellular activities, reveal their function and evolution through their structure and sequence. CATH functional families (FunFams) are coherent clusters of protein domain sequences in which the function is conserved across their members. The increasing volume and complexity of protein data enabled by large-scale repositories like MGnify or AlphaFold Database requires more powerful approaches that can scale to the size of these new resources. In this work, we introduce MARC and FRAN, two algorithms developed to build upon and address limitations of GeMMA/FunFHMMER, our original methods developed to classify proteins with related functions using a hierarchical approach. We also present CATH-eMMA, which uses embeddings or Foldseek distances to form relationship trees from distance matrices, reducing computational demands and handling various data types effectively. CATH-eMMA offers a highly robust and much faster tool for clustering protein functions on a large scale, providing a new tool for future studies in protein function and evolution.

Deciphering the Genomic Landscape and Virulence Mechanisms of the Wheat Powdery Mildew Pathogen Blumeria graminis f. sp. tritici Wtn1: Insights from Integrated Genome Assembly and Conidial Transcriptomics

A high-quality genome sequence from an Indian isolate of Blumeria graminis f. sp. tritici Wtn1, a persistent threat in wheat farming, was obtained using a hybrid method. The assembly of over 9.24 million DNA-sequence reads resulted in 93 contigs, totaling a 140.61 Mb genome size, potentially encoding 8480 genes. Notably, more than 73.80% of the genome, spanning approximately 102.14 Mb, comprises retro-elements, LTR elements, and P elements, influencing evolution and adaptation significantly. The phylogenomic analysis placed B. graminis f. sp. tritici Wtn1 in a distinct monocot-infecting clade. A total of 583 tRNA anticodon sequences were identified from the whole genome of the native virulent strain B. graminis f. sp. tritici, which comprises distinct genome features with high counts of tRNA anticodons for leucine (70), cysteine (61), alanine (58), and arginine (45), with only two stop codons (Opal and Ochre) present and the absence of the Amber stop codon. Comparative InterProScan analysis unveiled "shared and unique" proteins in B. graminis f. sp. tritici Wtn1. Identified were 7707 protein-encoding genes, annotated to different categories such as 805 effectors, 156 CAZymes, 6102 orthologous proteins, and 3180 distinct protein families (PFAMs). Among the effectors, genes like Avra10, Avrk1, Bcg-7, BEC1005, CSEP0105, CSEP0162, BEC1016, BEC1040, and HopI1 closely linked to pathogenesis and virulence were recognized. Transcriptome analysis highlighted abundant proteins associated with RNA processing and modification, post-translational modification, protein turnover, chaperones, and signal transduction. Examining the Environmental Information Processing Pathways in B. graminis f. sp. tritici Wtn1 revealed 393 genes across 33 signal transduction pathways. The key pathways included yeast MAPK signaling (53 genes), mTOR signaling (38 genes), PI3K-Akt signaling (23 genes), and AMPK signaling (21 genes). Additionally, pathways like FoxO, Phosphatidylinositol, the two-component system, and Ras signaling showed significant gene representation, each with 15-16 genes, key SNPs, and Indels in specific chromosomes highlighting their relevance to environmental responses and pathotype evolution. The SNP and InDel analysis resulted in about 3.56 million variants, including 3.45 million SNPs, 5050 insertions, and 5651 deletions within the whole genome of B. graminis f. sp. tritici Wtn1. These comprehensive genome and transcriptome datasets serve as crucial resources for understanding the pathogenicity, virulence effectors, retro-elements, and evolutionary origins of B. graminis f. sp. tritici Wtn1, aiding in developing robust strategies for the effective management of wheat powdery mildew.

Improved global protein homolog detection with major gains in function identification

There are several hundred million protein sequences, but the relationships among them are not fully available from existing homolog detection methods. There is an essential need for an improved method to push homolog detection to lower levels of sequence identity. The method used here relies on a language model to represent proteins numerically in a matrix (an embedding) and uses discrete cosine transforms to compress the data to extract the most essential part, significantly reducing the data size. This PRotein Ortholog Search Tool (PROST) is significantly faster with linear runtimes, and most importantly, computes the distances between pairs of protein sequences to yield homologs at significantly lower levels of sequence identity than previously. The extent of allosteric effects in proteins points out the importance of global aspects of structure and sequence. PROST excels at global homology detection but not at detecting local homologs. Results are validated by strong similarities between the corresponding pairs of structures. The number of remote homologs detected increased significantly and pushes the effective sequence matches more deeply into the twilight zone. Human protein sequences presently having no assigned function now find significant numbers of putative homologs for 93% of cases and structurally verified assigned functions for 76.4% of these cases. The data compression enables massive searches for homologs with short search times while yielding significant gains in the numbers of remote homologs detected. The method is sufficiently efficient to permit whole-genome/proteome comparisons. The PROST web server is accessible at https://mesihk.github.io/prost.

Structural and Functional Annotation of Hypothetical Proteins from the Microsporidia Species Vittaforma corneae ATCC 50505 Using in silico Approaches

Microsporidia are spore-forming eukaryotes that are related to fungi but have unique traits that set them apart. They have compact genomes as a result of evolutionary gene loss associated with their complete dependency on hosts for survival. Despite having a relatively small number of genes, a disproportionately high percentage of the genes in microsporidia genomes code for proteins whose functions remain unknown (hypothetical proteins-HPs). Computational annotation of HPs has become a more efficient and cost-effective alternative to experimental investigation. This research developed a robust bioinformatics annotation pipeline of HPs from Vittaforma corneae, a clinically important microsporidian that causes ocular infections in immunocompromised individuals. Here, we describe various steps to retrieve sequences and homologs and to carry out physicochemical characterization, protein family classification, identification of motifs and domains, protein-protein interaction network analysis, and homology modelling using a variety of online resources. Classification of protein families produced consistent findings across platforms, demonstrating the accuracy of annotation utilizing in silico methods. A total of 162 out of 2034 HPs were fully annotated, with the bulk of them categorized as binding proteins, enzymes, or regulatory proteins. The protein functions of several HPs from Vittaforma corneae were accurately inferred. This improved our understanding of microsporidian HPs despite challenges related to the obligate nature of microsporidia, the absence of fully characterized genes, and the lack of homologous genes in other systems.

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

Deep-learning (DL) methods like DeepMind's AlphaFold2 (AF2) have led to substantial improvements in protein structure prediction. We analyse confident AF2 models from 21 model organisms using a new classification protocol (CATH-Assign) which exploits novel DL methods for structural comparison and classification. Of ~370,000 confident models, 92% can be assigned to 3253 superfamilies in our CATH domain superfamily classification. The remaining cluster into 2367 putative novel superfamilies. Detailed manual analysis on 618 of these, having at least one human relative, reveal extremely remote homologies and further unusual features. Only 25 novel superfamilies could be confirmed. Although most models map to existing superfamilies, AF2 domains expand CATH by 67% and increases the number of unique 'global' folds by 36% and will provide valuable insights on structure function relationships. CATH-Assign will harness the huge expansion in structural data provided by DeepMind to rationalise evolutionary changes driving functional divergence.

Children's Neurological Status Epilepticus and Poor Prognostic Factors through Electroencephalogram Image under Composite Domain Analysis Algorithm

This study aimed to analyze the application of composite domain analysis algorithm for electroencephalogram (EEG) images of children with epilepsy and to investigate the risk factors related to poor prognosis. 70 children with neurological epilepsy admitted to the hospital were selected as the research objects. Besides, the EEG of the children during the intermittent and seizure phases of epilepsy were collected, so as to establish a composite domain analysis algorithm model. Then, the model was applied in EEG analysis. The clinical disease type and prognosis of children were statistically analyzed, and the risk factors that affected the prognosis of children were investigated. The results showed that the EEG signal values of the detail coefficients (d51 and d52) and the approximate coefficient (c5) during the epileptic seizure period were higher markedly than the signal values of the epileptic intermittent period; the EEG signal of the epileptic intermittent period was a transient waveform, which appeared as sharp waves or spikes. The EEG signal of epileptic seizures was continuous, with a composite waveform of sharp waves and spikes, and the change amplitude of the wavelet envelope spectrum during epileptic seizures was also higher hugely than that of intermittent epilepsy. The accurate identification rate, specificity, and sensitivity of EEG analysis with the composite domain algorithm were higher than those without the algorithm. Among the five types of epileptic seizures in children, the proportion of systemic tonic-clonic status was the largest, and the proportion of myoclonic status was equal to that of complex partial epileptic status, both of which were relatively small. The proportion of children with a better prognosis was 75.71% (53/70), which was higher than those with a poor prognosis 24.29% (17/70). Abnormal imaging examination (odds ratio (OR) = 3.823 and 95% confidence interval (CI) = 1.643–8.897); seizure duration greater than 1 hour (OR = 1.855 and 95% CI = 1.076–3.199); C-reactive protein (CRP) (OR = 5.089 and 95% CI = 1.507–17.187); and abnormal blood glucose (OR = 3.077, 95%CI = 1.640–5.773) were all independent risk factors for poor prognosis (all P < 0.05). The composite domain analysis algorithm was helpful for clinicians to find the difference in the EEG signals between the epileptic seizure period and the epileptic intermittent period in a short time, thereby improving the doctor's analysis of the results, which could reflect its marked superiority. In addition, abnormal imaging examinations, convulsion duration greater than 1 hour, CRP, and abnormal blood glucose were independent risk factors for poor prognosis in children. Therefore, the invasion of related risk factors could be reduced clinically by prognostic review with medical advice, attention to food safety and hygiene, and improvement of children's immunity.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Genomic Insights Into the Archaea Inhabiting an Australian Radioactive Legacy Site

During the 1960s, small quantities of radioactive materials were co-disposed with chemical waste at the Little Forest Legacy Site (LFLS, Sydney, Australia). The microbial function and population dynamics in a waste trench during a rainfall event have been previously investigated revealing a broad abundance of candidate and potentially undescribed taxa in this iron-rich, radionuclide-contaminated environment. Applying genome-based metagenomic methods, we recovered 37 refined archaeal MAGs, mainly from undescribed DPANN Archaea lineages without standing in nomenclature and 'Candidatus Methanoperedenaceae' (ANME-2D). Within the undescribed DPANN, the newly proposed orders 'Ca. Gugararchaeales', 'Ca. Burarchaeales' and 'Ca. Anstonellales', constitute distinct lineages with a more comprehensive central metabolism and anabolic capabilities within the 'Ca. Micrarchaeota' phylum compared to most other DPANN. The analysis of new and extant 'Ca. Methanoperedens spp.' MAGs suggests metal ions as the ancestral electron acceptors during the anaerobic oxidation of methane while the respiration of nitrate/nitrite via molybdopterin oxidoreductases would have been a secondary acquisition. The presence of genes for the biosynthesis of polyhydroxyalkanoates in most 'Ca. Methanoperedens' also appears to be a widespread characteristic of the genus for carbon accumulation. This work expands our knowledge about the roles of the Archaea at the LFLS, especially, DPANN Archaea and 'Ca. Methanoperedens', while exploring their diversity, uniqueness, potential role in elemental cycling, and evolutionary history.

TIAMMAt: Leveraging biodiversity to revise protein domain models, evidence from innate immunity

Sequence annotation is fundamental for studying the evolution of protein families, particularly when working with nonmodel species. Given the rapid, ever-increasing number of species receiving high-quality genome sequencing, accurate domain modeling that is representative of species diversity is crucial for understanding protein family sequence evolution and their inferred function(s). Here, we describe a bioinformatic tool called Taxon-Informed Adjustment of Markov Model Attributes (TIAMMAt) which revises domain profile hidden Markov models (HMMs) by incorporating homologous domain sequences from underrepresented and nonmodel species. Using innate immunity pathways as a case study, we show that revising profile HMM parameters to directly account for variation in homologs among underrepresented species provides valuable insight into the evolution of protein families. Following adjustment by TIAMMAt, domain profile HMMs exhibit changes in their per-site amino acid state emission probabilities and insertion/deletion probabilities while maintaining the overall structure of the consensus sequence. Our results show that domain revision can heavily impact evolutionary interpretations for some families (i.e., NLR’s NACHT domain), whereas impact on other domains (e.g., rel homology domain and interferon regulatory factor domains) is minimal due to high levels of sequence conservation across the sampled phylogenetic depth (i.e., Metazoa). Importantly, TIAMMAt revises target domain models to reflect homologous sequence variation using the taxonomic distribution under consideration by the user. TIAMMAt’s flexibility to revise any subset of the Pfam database using a user-defined taxonomic pool will make it a valuable tool for future protein evolution studies, particularly when incorporating (or focusing) on nonmodel species.

Mantis: flexible and consensus-driven genome annotation

BACKGROUND

The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources.

RESULTS

We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations.

CONCLUSIONS

Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis.

An improved high-quality genome assembly and annotation of Tibetan hulless barley

Hulless barley (Hordeum vulgare L. var. nudum) is a barley variety that has loose husk cover of the caryopses. Because of the ease in processing and edibility, hulless barley has been locally cultivated and used as human food. For example, in Tibetan Plateau, hulless barley is the staple food for human and essential livestock feed. Although the draft genome of hulless barley has been sequenced, the assembly remains fragmented. Here, we reported an improved high-quality assembly and annotation of the Tibetan hulless barley genome using more than 67X PacBio long-reads. The N50 contig length of the new assembly is at least more than 19 times larger than other available barley assemblies. The new genome assembly also showed high gene completeness and high collinearity of genome synteny with the previously reported barley genome. The new genome assembly and annotation will not only remove major hurdles in genetic analysis and breeding of hulless barley, but will also serve as a key resource for studying barley genomics and genetics.

A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling

BACKGROUND

Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism and one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires the reconstruction of biochemical and regulatory pathways at the community level in a given environment. In order to understand how environmental factors affect the genetic material and the dynamics of the expression from one environment to another, we want to evaluate the quantity of gene protein sequences or transcripts associated to a given pathway by precisely estimating the abundance of protein domains, their weak presence or absence in environmental samples.

RESULTS

MetaCLADE is a novel profile-based domain annotation pipeline based on a multi-source domain annotation strategy. It applies directly to reads and improves identification of the catalog of functions in microbiomes. MetaCLADE is applied to simulated data and to more than ten metagenomic and metatranscriptomic datasets from different environments where it outperforms InterProScan in the number of annotated domains. It is compared to the state-of-the-art non-profile-based and profile-based methods, UProC and HMM-GRASPx, showing complementary predictions to UProC. A combination of MetaCLADE and UProC improves even further the functional annotation of environmental samples.

CONCLUSIONS

Learning about the functional activity of environmental microbial communities is a crucial step to understand microbial interactions and large-scale environmental impact. MetaCLADE has been explicitly designed for metagenomic and metatranscriptomic data and allows for the discovery of patterns in divergent sequences, thanks to its multi-source strategy. MetaCLADE highly improves current domain annotation methods and reaches a fine degree of accuracy in annotation of very different environments such as soil and marine ecosystems, ancient metagenomes and human tissues.

Biocuration in the structure-function linkage database: the anatomy of a superfamily

With ever-increasing amounts of sequence data available in both the primary literature and sequence repositories, there is a bottleneck in annotating molecular function to a sequence. This article describes the biocuration process and methods used in the structure-function linkage database (SFLD) to help address some of the challenges. We discuss how the hierarchy within the SFLD allows us to infer detailed functional properties for functionally diverse enzyme superfamilies in which all members are homologous, conserve an aspect of their chemical function and have associated conserved structural features that enable the chemistry. Also presented is the Enzyme Structure-Function Ontology (ESFO), which has been designed to capture the relationships between enzyme sequence, structure and function that underlie the SFLD and is used to guide the biocuration processes within the SFLD.

Database URL:http://sfld.rbvi.ucsf.edu/

Drug repurposing for aging research using model organisms

Many increasingly prevalent diseases share a common risk factor: age. However, little is known about pharmaceutical interventions against aging, despite many genes and pathways shown to be important in the aging process and numerous studies demonstrating that genetic interventions can lead to a healthier aging phenotype. An important challenge is to assess the potential to repurpose existing drugs for initial testing on model organisms, where such experiments are possible. To this end, we present a new approach to rank drug-like compounds with known mammalian targets according to their likelihood to modulate aging in the invertebrates Caenorhabditis elegans and Drosophila. Our approach combines information on genetic effects on aging, orthology relationships and sequence conservation, 3D protein structures, drug binding and bioavailability. Overall, we rank 743 different drug-like compounds for their likelihood to modulate aging. We provide various lines of evidence for the successful enrichment of our ranking for compounds modulating aging, despite sparse public data suitable for validation. The top ranked compounds are thus prime candidates for in vivo testing of their effects on lifespan in C. elegans or Drosophila. As such, these compounds are promising as research tools and ultimately a step towards identifying drugs for a healthier human aging.

CATH-Gene3D: Generation of the Resource and Its Use in Obtaining Structural and Functional Annotations for Protein Sequences

This chapter describes the generation of the data in the CATH-Gene3D online resource and how it can be used to study protein domains and their evolutionary relationships. Methods will be presented for: comparing protein structures, recognizing homologs, predicting domain structures within protein sequences, and subclassifying superfamilies into functionally pure families, together with a guide on using the webpages.

Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence

Traditional protein annotation methods describe known domains with probabilistic models representing consensus among homologous domain sequences. However, when relevant signals become too weak to be identified by a global consensus, attempts for annotation fail. Here we address the fundamental question of domain identification for highly divergent proteins. By using high performance computing, we demonstrate that the limits of state-of-the-art annotation methods can be bypassed. We design a new strategy based on the observation that many structural and functional protein constraints are not globally conserved through all species but might be locally conserved in separate clades. We propose a novel exploitation of the large amount of data available: 1. for each known protein domain, several probabilistic clade-centered models are constructed from a large and differentiated panel of homologous sequences, 2. a decision-making protocol combines outcomes obtained from multiple models, 3. a multi-criteria optimization algorithm finds the most likely protein architecture. The method is evaluated for domain and architecture prediction over several datasets and statistical testing hypotheses. Its performance is compared against HMMScan and HHblits, two widely used search methods based on sequence-profile and profile-profile comparison. Due to their closeness to actual protein sequences, clade-centered models are shown to be more specific and functionally predictive than the broadly used consensus models. Based on them, we improved annotation of Plasmodium falciparum protein sequences on a scale not previously possible. We successfully predict at least one domain for 72% of P. falciparum proteins against 63% achieved previously, corresponding to 30% of improvement over the total number of Pfam domain predictions on the whole genome. The method is applicable to any genome and opens new avenues to tackle evolutionary questions such as the reconstruction of ancient domain duplications, the reconstruction of the history of protein architectures, and the estimation of protein domain age. Website and software: http://www.lcqb.upmc.fr/CLADE.

Current sequence databases contain hundreds of billions of nucleotides coding for genes and a classification of these sequences is a primary problem in genomics. A reasonable way to organize these sequences is through their predicted domains, but the identification of domains in very divergent sequences, spanning the entire phylogenetic tree of species, is a difficult problem. By generating multiple probabilistic models for a domain, describing the spread of evolutionary patterns in different phylogenetic clades, we can effectively explore domains that are likely to be coded in gene sequences. Through a machine learning approach and optimization techniques, coding for expected evolutionary constraints, we filter the many possibilities of domain identification found for a gene and propose the most likely domain architecture associated to it. The application of this novel approach to the full genome of Plasmodium falciparum, to a dataset of sequences from three SCOP datasets highlights the interest of exploring multiple pathways of domain evolution in the aim of extracting biological information from genomic sequences. Our new computational approach was developed with the hope of providing a novel tier of accurate and precise tools that complement existing tools such as HMMer, HHblits and PSI-BLAST, by exploring in a novel way the large amount of sequence data available. The existence of powerful databases for sequences, domains and architectures help make this hope a reality.

Reannotation of Yersinia pestis Strain 91001 Based on Omics Data

Yersinia pestis is among the most dangerous human pathogens, and systematic research of this pathogen is important in bacterial pathogenomics research. To fully interpret the biological functions, physiological characteristics, and pathogenesis of Y. pestis, a comprehensive annotation of its entire genome is necessary. The emergence of omics-based research has brought new opportunities to better annotate the genome of this pathogen. Here, the complete genome of Y. pestis strain 91001 was reannotated using genomics and proteogenomics data. One hundred and thirty-seven unreliable coding sequences were removed, and 41 homologous genes were relocated with their translational initiation sites, while the functions of seven pseudogenes and 392 hypothetical genes were revised. Moreover, annotations of noncoding RNAs, repeat sequences, and transposable elements have also been incorporated. The reannotated results are freely available at http://tody.bmi.ac.cn.

A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies

Background

Comparative analysis of whole genome sequence data from closely related prokaryotic species or strains is becoming an increasingly important and accessible approach for addressing both fundamental and applied biological questions. While there are number of excellent tools developed for performing this task, most scale poorly when faced with hundreds of genome sequences, and many require extensive manual curation.

Results

We have developed a de-novo genome analysis pipeline (DeNoGAP) for the automated, iterative and high-throughput analysis of data from comparative genomics projects involving hundreds of whole genome sequences. The pipeline is designed to perform reference-assisted and de novo gene prediction, homolog protein family assignment, ortholog prediction, functional annotation, and pan-genome analysis using a range of proven tools and databases. While most existing methods scale quadratically with the number of genomes since they rely on pairwise comparisons among predicted protein sequences, DeNoGAP scales linearly since the homology assignment is based on iteratively refined hidden Markov models. This iterative clustering strategy enables DeNoGAP to handle a very large number of genomes using minimal computational resources. Moreover, the modular structure of the pipeline permits easy updates as new analysis programs become available.

Conclusion

DeNoGAP integrates bioinformatics tools and databases for comparative analysis of a large number of genomes. The pipeline offers tools and algorithms for annotation and analysis of completed and draft genome sequences. The pipeline is developed using Perl, BioPerl and SQLite on Ubuntu Linux version 12.04 LTS. Currently, the software package accompanies script for automated installation of necessary external programs on Ubuntu Linux; however, the pipeline should be also compatible with other Linux and Unix systems after necessary external programs are installed. DeNoGAP is freely available at https://sourceforge.net/projects/denogap/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1142-2) contains supplementary material, which is available to authorized users.

Benchmarking the next generation of homology inference tools

Motivation: Over the last decades, vast numbers of sequences were deposited in public databases. Bioinformatics tools allow homology and consequently functional inference for these sequences. New profile-based homology search tools have been introduced, allowing reliable detection of remote homologs, but have not been systematically benchmarked. To provide such a comparison, which can guide bioinformatics workflows, we extend and apply our previously developed benchmark approach to evaluate the ‘next generation’ of profile-based approaches, including CS-BLAST, HHSEARCH and PHMMER, in comparison with the non-profile based search tools NCBI-BLAST, USEARCH, UBLAST and FASTA.

Method: We generated challenging benchmark datasets based on protein domain architectures within either the PFAM + Clan, SCOP/Superfamily or CATH/Gene3D domain definition schemes. From each dataset, homologous and non-homologous protein pairs were aligned using each tool, and standard performance metrics calculated. We further measured congruence of domain architecture assignments in the three domain databases.

Results: CSBLAST and PHMMER had overall highest accuracy. FASTA, UBLAST and USEARCH showed large trade-offs of accuracy for speed optimization.

Conclusion: Profile methods are superior at inferring remote homologs but the difference in accuracy between methods is relatively small. PHMMER and CSBLAST stand out with the highest accuracy, yet still at a reasonable computational cost. Additionally, we show that less than 0.1% of Swiss-Prot protein pairs considered homologous by one database are considered non-homologous by another, implying that these classifications represent equivalent underlying biological phenomena, differing mostly in coverage and granularity.

Availability and Implementation: Benchmark datasets and all scripts are placed at (http://sonnhammer.org/download/Homology_benchmark).

Contact:forslund@embl.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Novel Computational Protocols for Functionally Classifying and Characterising Serine Beta-Lactamases

Beta-lactamases represent the main bacterial mechanism of resistance to beta-lactam antibiotics and are a significant challenge to modern medicine. We have developed an automated classification and analysis protocol that exploits structure- and sequence-based approaches and which allows us to propose a grouping of serine beta-lactamases that more consistently captures and rationalizes the existing three classification schemes: Classes, (A, C and D, which vary in their implementation of the mechanism of action); Types (that largely reflect evolutionary distance measured by sequence similarity); and Variant groups (which largely correspond with the Bush-Jacoby clinical groups). Our analysis platform exploits a suite of in-house and public tools to identify Functional Determinants (FDs), i.e. residue sites, responsible for conferring different phenotypes between different classes, different types and different variants. We focused on Class A beta-lactamases, the most highly populated and clinically relevant class, to identify FDs implicated in the distinct phenotypes associated with different Class A Types and Variants. We show that our FunFHMMer method can separate the known beta-lactamase classes and identify those positions likely to be responsible for the different implementations of the mechanism of action in these enzymes. Two novel algorithms, ASSP and SSPA, allow detection of FD sites likely to contribute to the broadening of the substrate profiles. Using our approaches, we recognise 151 Class A types in UniProt. Finally, we used our beta-lactamase FunFams and ASSP profiles to detect 4 novel Class A types in microbiome samples. Our platforms have been validated by literature studies, in silico analysis and some targeted experimental verification. Although developed for the serine beta-lactamases they could be used to classify and analyse any diverse protein superfamily where sub-families have diverged over both long and short evolutionary timescales.

Comprehensive Annotation of the Parastagonospora nodorum Reference Genome Using Next-Generation Genomics, Transcriptomics and Proteogenomics

Parastagonospora nodorum, the causal agent of Septoria nodorum blotch (SNB), is an economically important pathogen of wheat (Triticum spp.), and a model for the study of necrotrophic pathology and genome evolution. The reference P. nodorum strain SN15 was the first Dothideomycete with a published genome sequence, and has been used as the basis for comparison within and between species. Here we present an updated reference genome assembly with corrections of SNP and indel errors in the underlying genome assembly from deep resequencing data as well as extensive manual annotation of gene models using transcriptomic and proteomic sources of evidence (https://github.com/robsyme/Parastagonospora_nodorum_SN15). The updated assembly and annotation includes 8,366 genes with modified protein sequence and 866 new genes. This study shows the benefits of using a wide variety of experimental methods allied to expert curation to generate a reliable set of gene models.

Gene3D: expanding the utility of domain assignments

Gene3D http://gene3d.biochem.ucl.ac.uk is a database of domain annotations of Ensembl and UniProtKB protein sequences. Domains are predicted using a library of profile HMMs representing 2737 CATH superfamilies. Gene3D has previously featured in the Database issue of NAR and here we report updates to the website and database. The current Gene3D (v14) release has expanded its domain assignments to ∼ 20,000 cellular genomes and over 43 million unique protein sequences, more than doubling the number of protein sequences since our last publication. Amongst other updates, we have improved our Functional Family annotation method. We have also improved the quality and coverage of our 3D homology modelling pipeline of predicted CATH domains. Additionally, the structural models have been expanded to include an extra model organism (Drosophila melanogaster). We also document a number of additional visualization tools in the Gene3D website.

Why do Sequence Signatures Predict Enzyme Mechanism? Homology versus Chemistry

First, we identify InterPro sequence signatures representing evolutionary relatedness and, second, signatures identifying specific chemical machinery. Thus, we predict the chemical mechanisms of enzyme-catalyzed reactions from catalytic and non-catalytic subsets of InterPro signatures. We first scanned our 249 sequences using InterProScan and then used the MACiE database to identify those amino acid residues that are important for catalysis. The sequences were mutated in silico to replace these catalytic residues with glycine and then again scanned using InterProScan. Those signature matches from the original scan that disappeared on mutation were called catalytic. Mechanism was predicted using all signatures, only the 78 “catalytic” signatures, or only the 519 “non-catalytic” signatures. The non-catalytic signatures gave indistinguishable results from those for the whole feature set, with precision of 0.991 and sensitivity of 0.970. The catalytic signatures alone gave less impressive predictivity, with precision and sensitivity of 0.791 and 0.735, respectively. These results show that our successful prediction of enzyme mechanism is mostly by homology rather than by identifying catalytic machinery.

Kiwifruit Information Resource (KIR): a comparative platform for kiwifruit genomics

The Kiwifruit Information Resource (KIR) is dedicated to maintain and integrate comprehensive datasets on genomics, functional genomics and transcriptomics of kiwifruit (Actinidiaceae). KIR serves as a central access point for existing/new genomic and genetic data. KIR also provides researchers with a variety of visualization and analysis tools. Current developments include the updated genome structure of Actinidia chinensis cv. Hongyang and its newest genome annotation, putative transcripts, gene expression, physical markers of genetic traits as well as relevant publications based on the latest genome assembly. Nine thousand five hundred and forty-seven new transcripts are detected and 21 132 old transcripts are changed. At the present release, the next-generation transcriptome sequencing data has been incorporated into gene models and splice variants. Protein–protein interactions are also identified based on experimentally determined orthologous interactions. Furthermore, the experimental results reported in peer-reviewed literature are manually extracted and integrated within a well-developed query page. In total, 122 identifications are currently associated, including commonly used gene names and symbols. All KIR datasets are helpful to facilitate a broad range of kiwifruit research topics and freely available to the research community.

Database URL: http://bdg.hfut.edu.cn/kir/index.html.

Diversity in protein domain superfamilies

Whilst ∼93% of domain superfamilies appear to be relatively structurally and functionally conserved based on the available data from the CATH-Gene3D domain classification resource, the remainder are much more diverse. In this review, we consider how domains in some of the most ubiquitous and promiscuous superfamilies have evolved, in particular the plasticity in their functional sites and surfaces which expands the repertoire of molecules they interact with and actions performed on them. To what extent can we identify a core function for these superfamilies which would allow us to develop a ‘domain grammar of function’ whereby a protein's biological role can be proposed from its constituent domains? Clearly the first step is to understand the extent to which these components vary and how changes in their molecular make-up modifies function.

The language of the protein universe

Proteins, the main cell machinery which play a major role in nearly every cellular process, have always been a central focus in biology. We live in the post-genomic era, and inferring information from massive data sets is a steadily growing universal challenge. The increasing availability of fully sequenced genomes can be regarded as the 'Rosetta Stone' of the protein universe, allowing the understanding of genomes and their evolution, just as the original Rosetta Stone allowed Champollion to decipher the ancient Egyptian hieroglyphics. In this review, we consider aspects of the protein domain architectures repertoire that are closely related to those of human languages and aim to provide some insights about the language of proteins.

Identifying and characterising key alternative splicing events in Drosophila development

BACKGROUND

In complex Metazoans a given gene frequently codes for multiple protein isoforms, through processes such as alternative splicing. Large scale functional annotation of these isoforms is a key challenge for functional genomics. This annotation gap is increasing with the large numbers of multi transcript genes being identified by technologies such as RNASeq. Furthermore attempts to characterise the functions of splicing in an organism are complicated by the difficulty in distinguishing functional isoforms from those produced by splicing errors or transcription noise. Tools to help prioritise candidate isoforms for testing are largely absent.

RESULTS

In this study we implement a Time-course Switch (TS) score for ranking isoforms by their likelihood of producing additional functions based on their developmental expression profiles, as reported by modENCODE. The TS score allows us to better investigate functional roles of different isoforms expressed in multi transcript genes. From this analysis, we find that isoforms with high TS scores have sequence feature changes consistent with more deterministic splicing and functional changes and tend to gain domains or whole exons which could carry additional functions. Furthermore these functions appear to be particularly important for essential regulatory roles, establishing functional isoform switching as key for regulatory processes. Based on the TS score we develop a Transcript Annotations Pipeline for Alternative Splicing (TAPAS) that identifies functional neighbourhoods of potentially interesting isoforms.

CONCLUSIONS

We have identified a subset of protein isoforms which appear to have high functional significance, particularly in regulation. This has been made possible through the development of novel methods that make use of transcript expression profiles. The methods and analyses we present here represent important first steps in the development of tools to address the near complete lack of isoform specific function annotation. In turn the tools allow us to better characterise the regulatory functions of alternative splicing in more detail.

The history of the CATH structural classification of protein domains

This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families.

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We present a historical review of the protein structure database CATH.

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We review the expansion of the CATH and SCOP resources with sequence data and functional annotations.

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How functional annotation resources allow insights into functional divergence and evolution within protein families.

Functional classification of CATH superfamilies: a domain-based approach for protein function annotation

Motivation: Computational approaches that can predict protein functions are essential to bridge the widening function annotation gap especially since <1.0% of all proteins in UniProtKB have been experimentally characterized. We present a domain-based method for protein function classification and prediction of functional sites that exploits functional sub-classification of CATH superfamilies. The superfamilies are sub-classified into functional families (FunFams) using a hierarchical clustering algorithm supervised by a new classification method, FunFHMMer.

Results: FunFHMMer generates more functionally coherent groupings of protein sequences than other domain-based protein classifications. This has been validated using known functional information. The conserved positions predicted by the FunFams are also found to be enriched in known functional residues. Moreover, the functional annotations provided by the FunFams are found to be more precise than other domain-based resources. FunFHMMer currently identifies 110 439 FunFams in 2735 superfamilies which can be used to functionally annotate > 16 million domain sequences.

Availability and implementation: All FunFam annotation data are made available through the CATH webpages (http://www.cathdb.info). The FunFHMMer webserver (http://www.cathdb.info/search/by_funfhmmer) allows users to submit query sequences for assignment to a CATH FunFam.

Contact:sayoni.das.12@ucl.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

CATH FunFHMMer web server: protein functional annotations using functional family assignments

The widening function annotation gap in protein databases and the increasing number and diversity of the proteins being sequenced presents new challenges to protein function prediction methods. Multidomain proteins complicate the protein sequence–structure–function relationship further as new combinations of domains can expand the functional repertoire, creating new proteins and functions. Here, we present the FunFHMMer web server, which provides Gene Ontology (GO) annotations for query protein sequences based on the functional classification of the domain-based CATH-Gene3D resource. Our server also provides valuable information for the prediction of functional sites. The predictive power of FunFHMMer has been validated on a set of 95 proteins where FunFHMMer performs better than BLAST, Pfam and CDD. Recent validation by an independent international competition ranks FunFHMMer as one of the top function prediction methods in predicting GO annotations for both the Biological Process and Molecular Function Ontology. The FunFHMMer web server is available at http://www.cathdb.info/search/by_funfhmmer.

Comparative transcriptome profiling approach to glean virulence and immunomodulation-related genes of Fasciola hepatica

BACKGROUND

Fasciola hepatica causes chronic liver disease, fasciolosis, leading to significant losses in the livestock economy and concerns for human health in many countries. The identification of F. hepatica genes involved in the parasite's virulence through modulation of host immune system is utmost important to comprehend evasion mechanisms of the parasite and develop more effective strategies against fasciolosis. In this study, to identify the parasite's putative virulence genes which are associated with host immunomodulation, we explored whole transcriptome of an adult F. hepatica using current transcriptome profiling approaches integrated with detailed in silico analyses. In brief, the comparison of the parasite transcripts with the specialised public databases containing sequence data of non-parasitic organisms (Dugesiidae species and Caenorhabditis elegans) or of numerous pathogens and investigation of the sequences in terms of nucleotide evolution (directional selection) and cytokine signaling relation were conducted.

RESULTS

NGS of the whole transcriptome resulted in 19,534,766 sequence reads, yielding a total of 40,260 transcripts (N₅₀ = 522 bp). A number of the parasite transcripts (n = 1,671) were predicted to be virulence-related on the basis of the exclusive homology with the pathogen-associated data, positive selection or relationship with cytokine signaling. Of these, a group of the virulence-related genes (n = 62), not previously described, were found likely to be associated with immunomodulation based on in silico functional categorisation, showing significant sequence similarities with various immune receptors (i.e. MHC I class, TGF-β receptor, toll/interleukin-1 receptor, T-cell receptor, TNF receptor, and IL-18 receptor accessory protein), cytokines (i.e. TGF-β, interleukin-4/interleukin-13 and TNF-α), cluster of differentiations (e.g. CD48 and CD147) or molecules associated with other immunomodulatory mechanisms (such as regulation of macrophage activation). Some of the genes (n = 5) appeared to be under positive selection (Ka/Ks > 1), imitating proteins associated with cytokine signaling (through sequence homologies with thrombospondin type 1, toll/interleukin-1 receptor, TGF-β receptor and CD147).

CONCLUSIONS

With a comparative transcriptome profiling approach, we have identified a number of potential immunomodulator genes of F. hepatica (n = 62), which are firstly described here, could be employed for the development of better strategies (including RNAi) in the battle against both zoonotically and economically important disease, fasciolosis.

Manual classification strategies in the ECOD database

ECOD (Evolutionary Classification Of protein Domains) is a comprehensive and up-to-date protein structure classification database. The majority of new structures released from the PDB (Protein Data Bank) each week already have close homologs in the ECOD hierarchy and thus can be reliably partitioned into domains and classified by software without manual intervention. However, those proteins that lack confidently detectable homologs require careful analysis by experts. Although many bioinformatics resources rely on expert curation to some degree, specific examples of how this curation occurs and in what cases it is necessary are not always described. Here, we illustrate the manual classification strategy in ECOD by example, focusing on two major issues in protein classification: domain partitioning and the relationship between homology and similarity scores. Most examples show recently released and manually classified PDB structures. We discuss multi-domain proteins, discordance between sequence and structural similarities, difficulties with assessing homology with scores, and integral membrane proteins homologous to soluble proteins. By timely assimilation of newly available structures into its hierarchy, ECOD strives to provide a most accurate and updated view of the protein structure world as a result of combined computational and expert-driven analysis.

Genome-wide identification and evolution of HECT genes in soybean

Proteins containing domains homologous to the E6-associated protein (E6-AP) carboxyl terminus (HECT) are an important class of E3 ubiquitin ligases involved in the ubiquitin proteasome pathway. HECT-type E3s play crucial roles in plant growth and development. However, current understanding of plant HECT genes and their evolution is very limited. In this study, we performed a genome-wide analysis of the HECT domain-containing genes in soybean. Using high-quality genome sequences, we identified 19 soybean HECT genes. The predicted HECT genes were distributed unevenly across 15 of 20 chromosomes. Nineteen of these genes were inferred to be segmentally duplicated gene pairs, suggesting that in soybean, segmental duplications have made a significant contribution to the expansion of the HECT gene family. Phylogenetic analysis showed that these HECT genes can be divided into seven groups, among which gene structure and domain architecture was relatively well-conserved. The Ka/Ks ratios show that after the duplication events, duplicated HECT genes underwent purifying selection. Moreover, expression analysis reveals that 15 of the HECT genes in soybean are differentially expressed in 14 tissues, and are often highly expressed in the flowers and roots. In summary, this work provides useful information on which further functional studies of soybean HECT genes can be based.

What's that gene (or protein)? Online resources for exploring functions of genes, transcripts, and proteins

The genomic era has enabled research projects that use approaches including genome-scale screens, microarray analysis, next-generation sequencing, and mass spectrometry-based proteomics to discover genes and proteins involved in biological processes. Such methods generate data sets of gene, transcript, or protein hits that researchers wish to explore to understand their properties and functions and thus their possible roles in biological systems of interest. Recent years have seen a profusion of Internet-based resources to aid this process. This review takes the viewpoint of the curious biologist wishing to explore the properties of protein-coding genes and their products, identified using genome-based technologies. Ten key questions are asked about each hit, addressing functions, phenotypes, expression, evolutionary conservation, disease association, protein structure, interactors, posttranslational modifications, and inhibitors. Answers are provided by presenting the latest publicly available resources, together with methods for hit-specific and data set-wide information retrieval, suited to any genome-based analytical technique and experimental species. The utility of these resources is demonstrated for 20 factors regulating cell proliferation. Results obtained using some of these are discussed in more depth using the p53 tumor suppressor as an example. This flexible and universally applicable approach for characterizing experimental hits helps researchers to maximize the potential of their projects for biological discovery.

Protein structure annotation resources

A key reason three-dimensional (3-D) protein structures are annotated with supporting or derived information is to understand the molecular basis of protein function. To this end, protein structure annotation databases curate key facts and observations, based on community-accepted standards, about the ~100,000 3-D experimental protein structures to date. This review will introduce the primary structure repositories, databases, and value-added structural annotation databases, as well as the range of information they provide. The different levels of annotation data (primary vs. derived vs. inferred) and how they should all be considered accordingly will also be described.

MPIC: a mitochondrial protein import components database for plant and non-plant species

In the 2 billion years since the endosymbiotic event that gave rise to mitochondria, variations in mitochondrial protein import have evolved across different species. With the genomes of an increasing number of plant species sequenced, it is possible to gain novel insights into mitochondrial protein import pathways. We have generated the Mitochondrial Protein Import Components (MPIC) Database (DB; http://www.plantenergy.uwa.edu.au/applications/mpic) providing searchable information on the protein import apparatus of plant and non-plant mitochondria. An in silico analysis was carried out, comparing the mitochondrial protein import apparatus from 24 species representing various lineages from Saccharomyces cerevisiae (yeast) and algae to Homo sapiens (human) and higher plants, including Arabidopsis thaliana (Arabidopsis), Oryza sativa (rice) and other more recently sequenced plant species. Each of these species was extensively searched and manually assembled for analysis in the MPIC DB. The database presents an interactive diagram in a user-friendly manner, allowing users to select their import component of interest. The MPIC DB presents an extensive resource facilitating detailed investigation of the mitochondrial protein import machinery and allowing patterns of conservation and divergence to be recognized that would otherwise have been missed. To demonstrate the usefulness of the MPIC DB, we present a comparative analysis of the mitochondrial protein import machinery in plants and non-plant species, revealing plant-specific features that have evolved.

The InterPro protein families database: the classification resource after 15 years

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.

Genome3D: exploiting structure to help users understand their sequences

Genome3D (http://www.genome3d.eu) is a collaborative resource that provides predicted domain annotations and structural models for key sequences. Since introducing Genome3D in a previous NAR paper, we have substantially extended and improved the resource. We have annotated representatives from Pfam families to improve coverage of diverse sequences and added a fast sequence search to the website to allow users to find Genome3D-annotated sequences similar to their own. We have improved and extended the Genome3D data, enlarging the source data set from three model organisms to 10, and adding VIVACE, a resource new to Genome3D. We have analysed and updated Genome3D's SCOP/CATH mapping. Finally, we have improved the superposition tools, which now give users a more powerful interface for investigating similarities and differences between structural models.

From sequence to enzyme mechanism using multi-label machine learning

Background

In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling.

Results

In this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-the-shelf K-Nearest Neighbours multi-label algorithm.

Conclusion

We find that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also find that incorporating Catalytic Site Atlas attributes does not seem to provide additional accuracy. The software code (ml2db), data and results are available online at http://sourceforge.net/projects/ml2db/ and as supplementary files.