From protein sequences to 3D-structures and beyond: the example of the UniProt knowledgebase

Naringenin inhibits ferroptosis to reduce radiation-induced lung injury: insights from network Pharmacology and molecular docking

CONTEXT

Naringenin is a natural flavanone with potent pharmacological properties. It has demonstrated therapeutic potential in treating various diseases and organ injuries, including radiation-induced lung injury (RILI). Ferroptosis is a newly type of cell death, and naringenin has been shown to attenuates ferroptosis.

OBJECTIVE

To evaluate the inhibitory effect and molecular mechanism of naringenin on ferroptosis during RILI process.

MATERIALS & METHODS

Firstly, BEAS-2B and HUVECs cells were pre-incubated with naringenin for 1 h prior to 8 Gy of X-ray irradiation to evaluate oxidative stress, inflammation, and the mRNA levels of ferroptosis-related genes. Next, target genes of naringenin, RILI, and ferroptosis were identified using the TCMSP, SwissTargetPrediction, and GeneCards databases. The target network was constructed with Cytoscape and STRING. Finally, the core target genes were identified through in vitro experiments by qRT-PCR, western blot and immunofluorescence staining.

RESULTS

Naringenin effectively reduced radiation-induced increasement of oxidative stress, inflammation, and ferroptosis markers in both cell lines. Network pharmacology identified 14 target genes, with prostaglandin endoperoxide synthase (PTGS2) and Valosin-containing protein (VCP) mRNA levels being prominent, which were crucial for ferroptosis regulation. Molecular docking revealed strong binding interactions between naringenin and the two target proteins. Subsequently, experimental validation confirmed that naringenin reduced the elevated levels of PTGS2 and VCP induced by radiation.

DISCUSSION & CONCLUSION

Naringenin alleviates radiation-induced lung damage by inhibiting ferroptosis, with PTGS2 and VCP emerging as potential therapeutic targets.

In-silico structural and functional analysis of nonsynonymous single nucleotide polymorphisms in human FOLH1 gene

Non-synonymous single nucleotide polymorphisms (nsSNPs), also known as missense SNPs, can seriously affect an individual's vulnerability to numerous diseases, including cancer. In this study, we conducted a comprehensive in-silico analysis to examine the structural and functional implications of nsSNPs within the Folate Hydrolase 1(FOLH1) gene, which encodes the Prostate-Specific Membrane Antigen (PSMA). A total of 504 SNPs were retrieved, and after filtering, 15 pathogenic nsSNPs were identified using five different in-silico tools. Three of these SNPs-R255H (rs375565491), R255C (rs201789325), and G168E (rs267602926)-were consistently predicted to be pathogenic across all in-silico tools. MutPred2 was used to predict the structural and functional consequences of the identified mutations. The analysis revealed multiple alterations in the PSMA protein, including changes in helical conformations, glycosylation patterns, transmembrane properties, and solvent accessibility. Furthermore, I-Mutant 2.0 analysis demonstrated a decrease in protein stability for most nsSNPs, except for rs267602926 (G168E), which was predicted to increase stability. Conservation analysis using ConSurf revealed varying degrees of amino acid conservation, with R255H and R255C identified as highly conserved residues, indicating their potential functional and structural significance. Additionally, post-translational modification (PTM) analysis indicated that while phosphorylation and methylation sites remained unchanged, specific glycosylation sites were lost in two pathogenic mutant variants (R255H and R255C), potentially affecting PSMA function and adversely impacting prostate cancer. Our findings highlight the importance of in silico studies to investigate the structural and functional impacts of FOLH1 nsSNPs on the PSMA protein. Such in silico studies can deepen our understanding of the roles of nsSNPs in prostate cancer onset, progression, and drug resistance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00319-3.

Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins

Proteins serve as the building blocks of life and play essential roles in almost every cellular process [...].

Bioprospecting Microbial Diversity for Lignin Valorization: Dry and Wet Screening Methods

Lignin is an abundant cell wall component, and it has been used mainly for generating steam and electricity. Nevertheless, lignin valorization, i.e. the conversion of lignin into high value-added fuels, chemicals, or materials, is crucial for the full implementation of cost-effective lignocellulosic biorefineries. From this perspective, rapid screening methods are crucial for time- and resource-efficient development of novel microbial strains and enzymes with applications in the lignin biorefinery. The present review gives an overview of recent developments and applications of a vast arsenal of activity and sequence-based methodologies for uncovering novel microbial strains with ligninolytic potential, novel enzymes for lignin depolymerization and for unraveling the main metabolic routes during growth on lignin. Finally, perspectives on the use of each of the presented methods and their respective advantages and disadvantages are discussed.

S-layer protein-AuNP systems for the colorimetric detection of metal and metalloid ions in water

Bacterial surface layer proteins (S-layer) possess unique binding properties for metal ions. By combining the binding capability of S-layer proteins with the optical properties of gold nanoparticles (AuNP), namely plasmonic resonance, a colorimetric detection system for metal and metalloid ions in water was developed. Eight S-layer proteins from different bacteria species were used for the functionalization of AuNP. The thus developed biohybrid systems, AuNP functionalized with S-layer proteins, were tested with different metal salt solutions, e.g. Indium(III)-chloride, Yttrium(III)-chloride or Nickel(II)-chloride, to determine their selective and sensitive binding to ionic analytes. All tested S-layer proteins displayed unique binding affinities for the different metal ions. For each S-layer and metal ion combination markedly different reaction patterns and differences in concentration range and absorption spectra were detected by UV/vis spectroscopy. In this way, the selective detection of tested metal ions was achieved by differentiated analysis of a colorimetric screening assay of these biohybrid systems. A highly selective and sensitive detection of yttrium ions down to a concentration of 1.67 × 10^-5 mol/l was achieved with S-layer protein SslA functionalized AuNP. The presented biohybrid systems can thus be used as a sensitive and fast sensor system for metal and metalloid ions in aqueous systems.

Transcriptomic and Proteomic Analysis of the Tentacles and Mucus of Anthopleura dowii Verrill, 1869

Sea anemone venom contains a complex and diverse arsenal of peptides and proteins of pharmacological and biotechnological interest, however, only venom from a few species has been explored from a global perspective to date. In the present study, we identified the polypeptides present in the venom of the sea anemone Anthopleura dowii Verrill, 1869 through a transcriptomic and proteomic analysis of the tentacles and the proteomic profile of the secreted mucus. In our transcriptomic results, we identified 261 polypeptides related to or predicted to be secreted in the venom, including proteases, neurotoxins that could act as either potassium (K⁺) or sodium (Na⁺) channels inhibitors, protease inhibitors, phospholipases A2, and other polypeptides. Our proteomic data allowed the identification of 156 polypeptides—48 exclusively identified in the mucus, 20 in the tentacles, and 88 in both protein samples. Only 23 polypeptides identified by tandem mass spectrometry (MS/MS) were related to the venom and 21 exclusively identified in the mucus, most corresponding to neurotoxins and hydrolases. Our data contribute to the knowledge of evolutionary and venomic analyses of cnidarians, particularly of sea anemones.

NIPS, a 3D network-integrated predictor of deleterious protein SAPs, and its application in cancer prognosis

Identifying deleterious mutations remains a challenge in cancer genome sequencing projects, reflecting the vast number of candidate mutations per tumour and the existence of interpatient heterogeneity. Based on a 3D protein interaction network profiled via large-scale cross-linking mass spectrometry, we propose a weighted average formula involving the combination of three types of information into a 'meta-score'. We assume that a single amino acid polymorphism (SAP) may have a deleterious effect if the mutation rarely occurs naturally during evolution, if it inhibits binding between a pair of interacting proteins when located at their interface, or if it plays an important role in a protein interaction (PPI) network. Cross-validation indicated that this new method presents an AUC value of 0.93 and outperforms other widely used tools. The application of this method to the CPTAC colorectal cancer dataset enabled the accurate identification of validated deleterious mutations and yielded insights into their potential pathogenesis. Survival analysis showed that the accumulation of deleterious SAPs is significantly associated with a poor prognosis. The new method provides an alternative method to identifying and ranking deleterious cancer SAPs based on a 3D PPI network and will contribute to the understanding of pathogenesis and the discovery of prognostic biomarkers.

The Resistome of Low-Impacted Marine Environments Is Composed by Distant Metallo-β-Lactamases Homologs

The worldwide dispersion and sudden emergence of new antibiotic resistance genes (ARGs) determined the need in uncovering which environment participate most as their source and reservoir. ARGs closely related to those currently found in human pathogens occur in the resistome of anthropogenic impacted environments. However, the role of pristine environment as the origin and source of ARGs remains underexplored and controversy, particularly, the marine environments represented by the oceans. Here, due to the ocean nature, we hypothesized that the resistome of this pristine/low-impacted marine environment is represented by distant ARG homologs. To test this hypothesis we performed an in silico analysis on the Global Ocean Sampling (GOS) metagenomic project dataset focusing on the metallo-β-lactamases (MβLs) as the ARG model. MβLs have been a challenge to public health, since they hydrolyze the carbapenems, one of the last therapeutic choice in clinics. Using Hidden Markov Model (HMM) profiles, we were successful in identifying a high diversity of distant MβL homologs, related to the B1, B2, and B3 subclasses. The majority of them were distributed across the Atlantic, Indian, and Pacific Oceans being related to the chromosomally encoded MβL GOB present in Elizabethkingia genus. It was observed only a reduced number of metagenomic sequence homologs related to the acquired MβL enzymes (VIM, SPM-1, and AIM-1) that currently have impact in clinics. Therefore, low antibiotic impacted marine environment, as the ocean, are unlikely the source of ARGs that have been causing enormous threat to the public health.

Convergent Balancing Selection on the Mu-Opioid Receptor in Primates

The mu opioid receptor is involved in many natural processes including stress response, pleasure, and pain. Mutations in the gene also have been associated with opiate and alcohol addictions as well as with responsivity to medication targeting these disorders. Two common and mutually exclusive polymorphisms have been identified in humans, A118G (N40D), found commonly in non-African populations, and C17T (V6A), found almost exclusively in African populations. Although A118G has been studied extensively for associations and in functional assays, C17T is much less well understood. In addition to a parallel polymorphism previously identified in rhesus macaques (Macaca mulatta), C77G (P26R), resequencing in additional non-human primate species identifies further common variation: C140T (P47L) in cynomolgus macaques (Macaca fascicularis), G55C (D19H) in vervet monkeys (Chlorocebus aethiops sabeus), A111T (L37F) in marmosets (Callithrix jacchus), and C55T (P19S) in squirrel monkeys (Saimiri boliviensis peruviensis). Functional effects on downstream signaling are observed for each of these variants following treatment with the endogenous agonist β-endorphin and the exogenous agonists morphine, DAMGO ([d-Ala2, N-Me-Phe4, Gly5-ol]-enkephalin), and fentanyl. In addition to demonstrating the importance of functional equivalency in reference to population variation for minority health, this also shows how common evolutionary pressures have produced similar phenotypes across species, suggesting a shared response to environmental needs and perhaps elucidating the mechanism by which these organism-environment interactions are mediated physiologically and molecularly. These studies set the stage for future investigations of shared functional polymorphisms across species as a new genetic tool for translational research.

GlycoMinestruct: a new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features

Glycosylation plays an important role in cell-cell adhesion, ligand-binding and subcellular recognition. Current approaches for predicting protein glycosylation are primarily based on sequence-derived features, while little work has been done to systematically assess the importance of structural features to glycosylation prediction. Here, we propose a novel bioinformatics method called GlycoMine^struct(http://glycomine.erc.monash.edu/Lab/GlycoMine_Struct/) for improved prediction of human N- and O-linked glycosylation sites by combining sequence and structural features in an integrated computational framework with a two-step feature-selection strategy. Experiments indicated that GlycoMine^struct outperformed NGlycPred, the only predictor that incorporated both sequence and structure features, achieving AUC values of 0.941 and 0.922 for N- and O-linked glycosylation, respectively, on an independent test dataset. We applied GlycoMine^struct to screen the human structural proteome and obtained high-confidence predictions for N- and O-linked glycosylation sites. GlycoMine^struct can be used as a powerful tool to expedite the discovery of glycosylation events and substrates to facilitate hypothesis-driven experimental studies.

Evolutionary Conserved Positions Define Protein Conformational Diversity

Conformational diversity of the native state plays a central role in modulating protein function. The selection paradigm sustains that different ligands shift the conformational equilibrium through their binding to highest-affinity conformers. Intramolecular vibrational dynamics associated to each conformation should guarantee conformational transitions, which due to its importance, could possibly be associated with evolutionary conserved traits. Normal mode analysis, based on a coarse-grained model of the protein, can provide the required information to explore these features. Herein, we present a novel procedure to identify key positions sustaining the conformational diversity associated to ligand binding. The method is applied to an adequate refined dataset of 188 paired protein structures in their bound and unbound forms. Firstly, normal modes most involved in the conformational change are selected according to their corresponding overlap with structural distortions introduced by ligand binding. The subspace defined by these modes is used to analyze the effect of simulated point mutations on preserving the conformational diversity of the protein. We find a negative correlation between the effects of mutations on these normal mode subspaces associated to ligand-binding and position-specific evolutionary conservations obtained from multiple sequence-structure alignments. Positions whose mutations are found to alter the most these subspaces are defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. These positions are shown to be evolutionary conserved, mostly buried aliphatic residues localized in regular structural regions of the protein like β-sheets and α-helix.

Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer

Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals.

Human germline and pan-cancer variomes and their distinct functional profiles

Identification of non-synonymous single nucleotide variations (nsSNVs) has exponentially increased due to advances in Next-Generation Sequencing technologies. The functional impacts of these variations have been difficult to ascertain because the corresponding knowledge about sequence functional sites is quite fragmented. It is clear that mapping of variations to sequence functional features can help us better understand the pathophysiological role of variations. In this study, we investigated the effect of nsSNVs on more than 17 common types of post-translational modification (PTM) sites, active sites and binding sites. Out of 1 705 285 distinct nsSNVs on 259 216 functional sites we identified 38 549 variations that significantly affect 10 major functional sites. Furthermore, we found distinct patterns of site disruptions due to germline and somatic nsSNVs. Pan-cancer analysis across 12 different cancer types led to the identification of 51 genes with 106 nsSNV affected functional sites found in 3 or more cancer types. 13 of the 51 genes overlap with previously identified Significantly Mutated Genes (Nature. 2013 Oct 17;502(7471)). 62 mutations in these 13 genes affecting functional sites such as DNA, ATP binding and various PTM sites occur across several cancers and can be prioritized for additional validation and investigations.

The protein-protein interaction network of eyestalk, Y-organ and hepatopancreas in Chinese mitten crab Eriocheir sinensis

Background

The protein-protein interaction network (PIN) is an effective information tool for understanding the complex biological processes inside the cell and solving many biological problems such as signaling pathway identification and prediction of protein functions. Eriocheir sinensis is a highly-commercial aquaculture species with an unclear proteome background which hinders the construction and development of PIN for E. sinensis. However, in recent years, the development of next-generation deep-sequencing techniques makes it possible to get high throughput data of E. sinensis tanscriptome and subsequently obtain a systematic overview of the protein-protein interaction system.

Results

In this work we sequenced the transcriptional RNA of eyestalk, Y-organ and hepatopancreas in E. sinensis and generated a PIN of E. sinensis which included 3,223 proteins and 35,787 interactions. Each protein-protein interaction in the network was scored according to the homology and genetic relationship. The signaling sub-network, representing the signal transduction pathways in E. sinensis, was extracted from the global network, which depicted a global view of the signaling systems in E. sinensis. Seven basic signal transduction pathways were identified in E. sinensis. By investigating the evolution paths of the seven pathways, we found that these pathways got mature in different evolutionary stages. Moreover, the functions of unclassified proteins and unigenes in the PIN of E. sinensis were predicted. Specifically, the functions of 549 unclassified proteins related to 864 unclassified unigenes were assigned, which respectively covered 76% and 73% of all the unclassified proteins and unigenes in the network.

Conclusions

The PIN generated in this work is the first large-scale PIN of aquatic crustacean, thereby providing a paradigmatic blueprint of the aquatic crustacean interactome. Signaling sub-network extracted from the global PIN depicts the interaction of different signaling proteins and the evolutionary paths of the identified signal transduction pathways. Furthermore, the function assignment of unclassified proteins based on the PIN offers a new reference in protein function exploration. More importantly, the construction of the E. sinensis PIN provides necessary experience for the exploration of PINs in other aquatic crustacean species.

Selecting targets from eukaryotic parasites for structural genomics and drug discovery

The selection of targets is the first step for any structural genomics project. The application of structural genomics approaches to drug discovery also starts with the selection of targets. Here, three protocols are described that were developed to select targets from eukaryotic pathogens. These protocols could also be applied to other drug discovery projects.

Online tools for bioinformatics analyses in nutrition sciences

Recent advances in "omics" research have resulted in the creation of large datasets that were generated by consortiums and centers, small datasets that were generated by individual investigators, and bioinformatics tools for mining these datasets. It is important for nutrition laboratories to take full advantage of the analysis tools to interrogate datasets for information relevant to genomics, epigenomics, transcriptomics, proteomics, and metabolomics. This review provides guidance regarding bioinformatics resources that are currently available in the public domain, with the intent to provide a starting point for investigators who want to take advantage of the opportunities provided by the bioinformatics field.

Protein profiling of epidermal bladder cells from the halophyte Mesembryanthemum crystallinum

Plant epidermal trichomes are as varied in morphology as they are in function. In the halophyte Mesembryanthemum crystallinum, specialized trichomes called epidermal bladder cells (EBC) line the surface of leaves and stems, and increase dramatically in size and volume upon plant salt-treatment. These cells have been proposed to have roles in plant defense and UV protection, but primarily in sodium sequestration and as water reservoirs. To gain further understanding into the roles of EBC, a cell-type-specific proteomics approach was taken in which precision single-cell sampling of cell sap from individual EBC was combined with shotgun peptide sequencing (LC-MS/MS). Identified proteins showed diverse biological functions and cellular locations, with a high representation of proteins involved in H(+)-transport, carbohydrate metabolism, and photosynthesis. The proteome of EBC provides insight into the roles of these cells in ion and water homeostasis and raises the possibility that they are photosynthetically active and functioning in Crassulacean acid metabolism.

Mutations of the serine protease CAP1/Prss8 lead to reduced embryonic viability, skin defects, and decreased ENaC activity

CAP1/Prss8 is a membrane-bound serine protease involved in the regulation of several different effectors, such as the epithelial sodium channel ENaC, the protease-activated receptor PAR2, the tight junction proteins, and the profilaggrin polypeptide. Recently, the V170D and the G54-P57 deletion mutations within the CAP1/Prss8 gene, identified in mouse frizzy (fr) and rat hairless (fr(CR)) animals, respectively, have been proposed to be responsible for their skin phenotypes. In the present study, we analyzed those mutations, revealing a change in the protein structure, a modification of the glycosylation state, and an overall reduction in the activation of ENaC of the two mutant proteins. In vivo analyses demonstrated that both fr and fr(CR) mutant animals present analogous reduction of embryonic viability, similar histologic aberrations at the level of the skin, and a significant decrease in the activity of ENaC in the distal colon compared with their control littermates. Hairless rats additionally had dehydration defects in skin and intestine and significant reduction in the body weight. In conclusion, we provided molecular and functional evidence that CAP1/Prss8 mutations are accountable for the defects in fr and fr(CR) animals, and we furthermore demonstrate a decreased function of the CAP1/Prss8 mutant proteins. Therefore, fr and fr(CR) animals are suitable models to investigate the consequences of CAP1/Prss8 action on its target proteins in the whole organism.

Identification of new hematopoietic cell subsets with a polyclonal antibody library specific for neglected proteins

The identification of new markers, the expression of which defines new phenotipically and functionally distinct cell subsets, is a main objective in cell biology. We have addressed the issue of identifying new cell specific markers with a reverse proteomic approach whereby approximately 1700 human open reading frames encoding proteins predicted to be transmembrane or secreted have been selected in silico for being poorly known, cloned and expressed in bacteria. These proteins have been purified and used to immunize mice with the aim of obtaining polyclonal antisera mostly specific for linear epitopes. Such a library, made of about 1600 different polyclonal antisera, has been obtained and screened by flow cytometry on cord blood derived CD34+CD45dim cells and on peripheral blood derived mature lymphocytes (PBLs). We identified three new proteins expressed by fractions of CD34+CD45dim cells and eight new proteins expressed by fractions of PBLs. Remarkably, we identified proteins the presence of which had not been demonstrated previously by transcriptomic analysis. From the functional point of view, looking at new proteins expressed on CD34+CD45dim cells, we identified one cell surface protein (MOSC-1) the expression of which on a minority of CD34+ progenitors marks those CD34+CD45dim cells that will go toward monocyte/granulocyte differentiation. In conclusion, we show a new way of looking at the membranome by assessing expression of generally neglected proteins with a library of polyclonal antisera, and in so doing we have identified new potential subsets of hematopoietic progenitors and of mature PBLs.

Intrinsically disordered N-terminus of calponin homology-associated smooth muscle protein (CHASM) interacts with the calponin homology domain to enable tropomyosin binding

The calponin homology-associated smooth muscle (CHASM) protein plays an important adaptive role in smooth and skeletal muscle contraction. CHASM is associated with increased muscle contractility and can be localized to the contractile thin filament via its binding interaction with tropomyosin. We sought to define the structural basis for the interaction of CHASM with smooth muscle tropomyosin as a first step to understanding the contribution of CHASM to the contractile capacity of smooth muscle. Herein, we provide a structure-based model for the tropomyosin-binding domain of CHASM using a combination of hydrogen/deuterium exchange mass spectrometry (HDX-MS) and NMR analyses. Our studies provide evidence that a portion of the N-terminal intrinsically disordered region forms intramolecular contacts with the globular C-terminal calponin homology (CH) domain. Ultimately, cooperativeness between these structurally dissimilar regions is required for CHASM binding to smooth muscle tropomyosin. Furthermore, it appears that the type-2 CH domain of CHASM is required for tropomyosin binding and presents a novel function for this protein domain.

PSORTdb--an expanded, auto-updated, user-friendly protein subcellular localization database for Bacteria and Archaea

The subcellular localization (SCL) of a microbial protein provides clues about its function, its suitability as a drug, vaccine or diagnostic target and aids experimental design. The first version of PSORTdb provided a valuable resource comprising a data set of proteins of known SCL (ePSORTdb) as well as pre-computed SCL predictions for proteomes derived from complete bacterial genomes (cPSORTdb). PSORTdb 2.0 (http://db.psort.org) extends user-friendly functionalities, significantly expands ePSORTdb and now contains pre-computed SCL predictions for all prokaryotes—including Archaea and Bacteria with atypical cell wall/membrane structures. cPSORTdb uses the latest version of the SCL predictor PSORTb (version 3.0), with higher genome prediction coverage and functional improvements over PSORTb 2.0, which has been the most precise bacterial SCL predictor available. PSORTdb 2.0 is the first microbial protein SCL database reported to have an automatic updating mechanism to regularly generate SCL predictions for deduced proteomes of newly sequenced prokaryotic organisms. This updating approach uses a novel sequence analysis we developed that detects whether the microbe being analyzed has an outer membrane. This identification of membrane structure permits appropriate SCL prediction in an auto-updated fashion and allows PSORTdb to serve as a practical resource for genome annotation and prokaryotic research.

The Sol Genomics Network (solgenomics.net): growing tomatoes using Perl

The Sol Genomics Network (SGN; http://solgenomics.net/) is a clade-oriented database (COD) containing biological data for species in the Solanaceae and their close relatives, with data types ranging from chromosomes and genes to phenotypes and accessions. SGN hosts several genome maps and sequences, including a pre-release of the tomato (Solanum lycopersicum cv Heinz 1706) reference genome. A new transcriptome component has been added to store RNA-seq and microarray data. SGN is also an open source software project, continuously developing and improving a complex system for storing, integrating and analyzing data. All code and development work is publicly visible on GitHub (http://github.com). The database architecture combines SGN-specific schemas and the community-developed Chado schema (http://gmod.org/wiki/Chado) for compatibility with other genome databases. The SGN curation model is community-driven, allowing researchers to add and edit information using simple web tools. Currently, over a hundred community annotators help curate the database. SGN can be accessed at http://solgenomics.net/.