KnockTF: a comprehensive human gene expression profile database with knockdown/knockout of transcription factors

Identification and Validation of Autophagy-Related Genes in Primary Ovarian Insufficiency by Gene Expression Profile and Bioinformatic Analysis

Background

To investigate the relationship between primary ovarian insufficiency and autophagy, we detected and got the expression profile of human granulosa cell line SVOG, which was with or without LPS induced. The expression profile was analyzed with the focus on the autophagy genes, among which hub genes were identified.

Results

Totally, 6 genes were selected as candidate hub genes which might correlate with the process of primary ovarian insufficiency. The expression of hub genes was then validated by quantitative real-time PCR and two of them had significant expression change. Bioinformatics analysis was performed to observe the features of hub genes, including hub gene-RBP/TF/miRNA/drug network construction, functional analysis, and protein-protein interaction network. Pearson's correlation analysis was also performed to identify the correlation between hub genes and autophagy genes, among which there were four autophagy genes significantly correlated with hub genes, including ATG4B, ATG3, ATG13, and ULK1.

Conclusion

The results indicated that autophagy might play an essential role in the process and underlying molecular mechanism of primary ovarian insufficiency, which was revealed for the first time and may help to provide a molecular foundation for the development of diagnostic and therapeutic approaches for primary ovarian insufficiency.

Identification of Transcription Factors Regulating SARS-CoV-2 Tropism Factor Expression by Inferring Cell-Type-Specific Transcriptional Regulatory Networks in Human Lungs

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that caused the coronavirus disease 2019 (COVID-19) pandemic. Though previous studies have suggested that SARS-CoV-2 cellular tropism depends on the host-cell-expressed proteins, whether transcriptional regulation controls SARS-CoV-2 tropism factors in human lung cells remains unclear. In this study, we used computational approaches to identify transcription factors (TFs) regulating SARS-CoV-2 tropism for different types of lung cells. We constructed transcriptional regulatory networks (TRNs) controlling SARS-CoV-2 tropism factors for healthy donors and COVID-19 patients using lung single-cell RNA-sequencing (scRNA-seq) data. Through differential network analysis, we found that the altered regulatory role of TFs in the same cell types of healthy and SARS-CoV-2-infected networks may be partially responsible for differential tropism factor expression. In addition, we identified the TFs with high centralities from each cell type and proposed currently available drugs that target these TFs as potential candidates for the treatment of SARS-CoV-2 infection. Altogether, our work provides valuable cell-type-specific TRN models for understanding the transcriptional regulation and gene expression of SARS-CoV-2 tropism factors.

Comprehensive Analyses of the Expression, Genetic Alteration, Prognosis Significance, and Interaction Networks of m6A Regulators Across Human Cancers

Accumulating lines of evidence indicate that the deregulation of m⁶A is involved in various cancer types. The m⁶A RNA methylation is modulated by m⁶A methyltransferases, demethylases, and reader proteins. Although the aberrant expression of m⁶A RNA methylation contributes to the development and progression of multiple cancer types, the roles of m⁶A regulators across numerous types of cancers remain largely unknown. Here, we comprehensively investigated the expression, genetic alteration, and prognosis significance of 20 commonly studied m⁶A regulators across diverse cancer types using TCGA datasets via bioinformatic analyses. The results revealed that the m⁶A regulators exhibited widespread dysregulation, genetic alteration, and the modulation of oncogenic pathways across TCGA cancer types. In addition, most of the m⁶A regulators were closely relevant with significant prognosis in many cancer types. Furthermore, we also constructed the protein–protein interacting network of the 20 m⁶A regulators, and a more complex interacting regulatory network including m⁶A regulators and their corresponding interacting factors. Besides, the networks between m⁶A regulators and their upstream regulators such as miRNAs or transcriptional factors were further constructed in this study. Finally, the possible chemicals targeting each m⁶A regulator were obtained by bioinformatics analysis and the m⁶A regulators–potential drugs network was further constructed. Taken together, the comprehensive analyses of m⁶A regulators might provide novel insights into the m⁶A regulators’ roles across cancer types and shed light on their potential molecular mechanisms as well as help develop new therapy approaches for cancers.

TF-Marker: a comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human

Transcription factors (TFs) play key roles in biological processes and are usually used as cell markers. The emerging importance of TFs and related markers in identifying specific cell types in human diseases increases the need for a comprehensive collection of human TFs and related markers sets. Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/), aiming to provide cell/tissue-specific TFs and related markers for human. By manually curating thousands of published literature, 5905 entries including information about TFs and related markers were classified into five types according to their functions: (i) TF: TFs which regulate expression of the markers; (ii) T Marker: markers which are regulated by the TF; (iii) I Marker: markers which influence the activity of TFs; (iv) TFMarker: TFs which play roles as markers and (v) TF Pmarker: TFs which play roles as potential markers. The 5905 entries of TF-Marker include 1316 TFs, 1092 T Markers, 473 I Markers, 1600 TFMarkers and 1424 TF Pmarkers, involving 383 cell types and 95 tissue types in human. TF-Marker further provides a user-friendly interface to browse, query and visualize the detailed information about TFs and related markers. We believe TF-Marker will become a valuable resource to understand the regulation patterns of different tissues and cells.

Reconstruction of tissue-specific genome-scale metabolic models for human cancer stem cells

Cancer Stem Cells (CSCs) contribute to cancer aggressiveness, metastasis, chemo/radio-therapy resistance, and tumor recurrence. Recent studies emphasized the importance of metabolic reprogramming of CSCs for the maintenance and progression of the cancer phenotype through both the fulfillment of the energetic requirements and the supply of substrates fundamental for fast-cell growth, as well as through metabolite-induced epigenetic regulation. Therefore, it is of paramount importance to develop therapeutic strategies tailored to target the metabolism of CSCs. In this work, we built computational Genome-Scale Metabolic Models (GSMMs) for CSCs of different tissues. Flux simulations were then used to predict metabolic phenotypes, identify potential therapeutic targets, and spot already-known Transcription Factors (TFs), miRNAs and antimetabolites that could be used as part of drug repurposing strategies against cancer. Results were in accordance with experimental evidence, provided insights of new metabolic mechanisms for already known agents, and allowed for the identification of potential new targets and compounds that could be interesting for further in vitro and in vivo validation.

A Functional Network Model of the Metastasis Suppressor PEBP1/RKIP and Its Regulators in Breast Cancer Cells

Drug screening strategies focus on quantifying the phenotypic effects of different compounds on biological systems. High-throughput technologies have the potential to understand further the mechanisms by which these drugs produce the desired outcome. Reverse causal reasoning integrates existing biological knowledge and measurements of gene and protein abundances to infer their function. This approach can be employed to appraise the existing biological knowledge and data to prioritize targets for cancer therapies. We applied text mining and a manual literature search to extract known interactions between several metastasis suppressors and their regulators. We then identified the relevant interactions in the breast cancer cell line MCF7 using a knockdown dataset. We finally adopted a reverse causal reasoning approach to evaluate and prioritize pathways that are most consistent and responsive to drugs that inhibit cell growth. We evaluated this model in terms of agreement with the observations under treatment of several drugs that produced growth inhibition of cancer cell lines. In particular, we suggested that the metastasis suppressor PEBP1/RKIP is on the receiving end of two significant regulatory mechanisms. One involves RELA (transcription factor p65) and SNAI1, which were previously reported to inhibit PEBP1. The other involves the estrogen receptor (ESR1), which induces PEBP1 through the kinase NME1. Our model was derived in the specific context of breast cancer, but the observed responses to drug treatments were consistent in other cell lines. We further validated some of the predicted regulatory links in the breast cancer cell line MCF7 experimentally and highlighted the points of uncertainty in our model. To summarize, our model was consistent with the observed changes in activity with drug perturbations. In particular, two pathways, including PEBP1, were highly responsive and would be likely targets for intervention.

Experiment level curation of transcriptional regulatory interactions in neurodevelopment

To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we reason that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the theoretical importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965; 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high resolution DTRI resource to support the development of large-scale TRNs.

The capacity to computationally reconstruct gene regulatory networks using large-scale biological data is currently limited by the absence of a high confidence set of one-to-one regulatory interactions. Given the lengthy history of using small scale experimental assays to investigate individual interactions, we reason that a reliable collection of gene regulatory interactions could be compiled by systematically inspecting the published literature. To this end, we developed a curation protocol to examine and record evidence of regulatory interactions at the individual experiment level. Focusing on the area of brain development, we applied our pipeline to 1,310 publications. We identified 3,601 individual experiments, providing detailed information about 1,499 regulatory interactions. Many of these interactions have verified activity specifically in the embryonic brain. By capturing reports of regulatory interactions at this level of detail, we equip the users with more granular information than other similar resources, enabling more informed assessments of reliability.

Pan-Cancer Analysis of Prognostic and Immune Infiltrates for CXCs

Simple Summary

CXCs are important genes that regulate inflammation and tumor metastasis. While there are many studies with a focus on individual CXCs, few present a pan-cancer analysis of the whole CXC family. Our results indicate that CXCs are a potential therapeutic target in a variety of tumors and a potential prognostic marker that could improve the survival of cancer patients and the accuracy of prognosis. Meanwhile, we found that CXCs may be involved in diseases caused by intestinal flora.

Abstract

Background: CXCs are important genes that regulate inflammation and tumor metastasis. However, the expression level, prognosis value, and immune infiltration of CXCs in cancers are not clear. Methods: Multiple online datasets were used to analyze the expression, prognosis, and immune regulation of CXCs in this study. Network analysis of the Amadis database and GEO dataset was used to analyze the regulation of intestinal flora on the expression of CXCs. A mouse model was used to verify the fact that intestinal bacterial dysregulation can affect the expression of CXCs. Results: In the three cancers, multiple datasets verified the fact that the mRNA expression of this family was significantly different; the mRNA levels of CXCL3, 8, 9, 10, 14, and 17 were significantly correlated with the prognosis of three cancers. CXCs were correlated with six types of immuno-infiltrating cells in three cancers. Immunohistochemistry of clinical samples confirmed that the expression of CXCL8 and 10 was higher in three cancer tissues. Animal experiments have shown that intestinal flora dysregulation can affect CXCL8 and 10 expressions. Conclusion: Our results further elucidate the function of CXCs in cancers and provide new insights into the prognosis and immune infiltration of breast, colon, and pancreatic cancers, and they suggest that intestinal flora may influence disease progression through CXCs.

Transcription factors: Bridge between cell signaling and gene regulation

Transcription factors (TFs) are key regulators of intrinsic cellular processes, such as differentiation and development, and of the cellular response to external perturbation through signaling pathways. In this review we focus on the role of TFs as a link between signaling pathways and gene regulation. Cell signaling tends to result in the modulation of a set of TFs that then lead to changes in the cell's transcriptional program. We highlight the molecular layers at which TF activity can be measured and the associated technical and conceptual challenges. These layers include post-translational modifications (PTMs) of the TF, regulation of TF binding to DNA through chromatin accessibility and epigenetics, and expression of target genes. We highlight that a large number of TFs are understudied in both signaling and gene regulation studies, and that our knowledge about known TF targets has a strong literature bias. We argue that TFs serve as a perfect bridge between the fields of gene regulation and signaling, and that separating these fields hinders our understanding of cell functions. Multi-omics approaches that measure multiple dimensions of TF activity are ideally suited to study the interplay of cell signaling and gene regulation using TFs as the anchor to link the two fields.

BARTweb: a web server for transcriptional regulator association analysis

Identifying active transcriptional regulators (TRs) associating with cis-regulatory elements in the genome to regulate gene expression is a key task in gene regulation research. TR binding profiles from numerous public ChIP-seq data can be utilized for association analysis with query data for TR identification, as an alternative to DNA sequence motif analysis. However, integration of the massive ChIP-seq datasets has been a major challenge in such approaches. Here we present BARTweb, an interactive web server for identifying TRs whose genomic binding patterns associate with input genomic features, by leveraging over 13 000 public ChIP-seq datasets for human and mouse. Using an updated binding analysis for regulation of transcription (BART) algorithm, BARTweb can identify functional TRs that regulate a gene set, have a binding profile correlated with a ChIP-seq profile or are enriched in a genomic region set, without a priori information of the cell type. BARTweb can be a useful web server for performing functional analysis of gene regulation. BARTweb is freely available at http://bartweb.org and the source code is available at https://github.com/zanglab/bart2.

Analysis in silico of the functional interaction between WNT5A and YAP/TEAD signaling in cancer

To date, most data regarding the crosstalk between the Wnt signaling pathway and the YAP/TAZ transcriptional coactivators focuses on the Wnt/β-catenin branch of the pathway. In contrast, the relationship between the non-canonical Wnt pathway and YAP/TAZ remains significantly less explored. Wnt5a is usually regarded as a prototypical non-canonical Wnt ligand, and its expression has been related to cancer progression. On the other hand, YAP/TAZ transcriptional coactivators act in concert with TEAD transcription factors to control gene expression. Although one article has shown previously that WNT5A is a YAP/TEAD target gene, there is a need for further evidence supporting this regulatory relationship, because a possible YAP/Wnt5a regulatory circuit might have profound implications for cancer biology. This article analyzes publicly available ChIP-Seq, gene expression, and protein expression data to explore this relationship, and shows that WNT5A might be a YAP/TEAD target gene in several contexts. Moreover, Wnt5a and YAP expression are significantly correlated in specific cancer types, suggesting that the crosstalk between YAP/TAZ and the Wnt pathway is more intricate than previously thought.

BART Cancer: a web resource for transcriptional regulators in cancer genomes

Dysregulation of gene expression plays an important role in cancer development. Identifying transcriptional regulators, including transcription factors and chromatin regulators, that drive the oncogenic gene expression program is a critical task in cancer research. Genomic profiles of active transcriptional regulators from primary cancer samples are limited in the public domain. Here we present BART Cancer (bartcancer.org), an interactive web resource database to display the putative transcriptional regulators that are responsible for differentially regulated genes in 15 different cancer types in The Cancer Genome Atlas (TCGA). BART Cancer integrates over 10000 gene expression profiling RNA-seq datasets from TCGA with over 7000 ChIP-seq datasets from the Cistrome Data Browser database and the Gene Expression Omnibus (GEO). BART Cancer uses Binding Analysis for Regulation of Transcription (BART) for predicting the transcriptional regulators from the differentially expressed genes in cancer samples compared to normal samples. BART Cancer also displays the activities of over 900 transcriptional regulators across cancer types, by integrating computational prediction results from BART and the Cistrome Cancer database. Focusing on transcriptional regulator activities in human cancers, BART Cancer can provide unique insights into epigenetics and transcriptional regulation in cancer, and is a useful data resource for genomics and cancer research communities.

MYCN Drives a Tumor Immunosuppressive Environment Which Impacts Survival in Neuroblastoma

A wide range of malignancies presents MYCN amplification (MNA) or dysregulation. MYCN is associated with poor prognosis and its over-expression leads to several dysregulations including metabolic reprogramming, mitochondria alteration, and cancer stem cell phenotype. Some hints suggest that MYCN overexpression leads to cancer immune-escape. However, this relationship presents various open questions. Our work investigated in details the relationship of MYCN with the immune system, finding a correlated immune-suppressive phenotype in neuroblastoma (NB) and different cancers where MYCN is up-regulated. We found a downregulated Th1-lymphocytes/M1-Macrophages axis and upregulated Th2-lymphocytes/M2-macrophages in MNA NB patients. Moreover, we unveiled a complex immune network orchestrated by N-Myc and we identified 16 genes modules associated to MNA NB. We also identified a MYCN-associated immune signature that has a prognostic value in NB and recapitulates clinical features. Our signature also discriminates patients with poor survival in non-MNA NB patients where MYCN expression is not discriminative. Finally, we showed that targeted inhibition of MYCN by BGA002 (anti-MYCN antigene PNA) is able to restore NK sensibility in MYCN-expressing NB cells. Overall, our study unveils a MYCN-driven immune network in NB and shows a therapeutic option to restore sensibility to immune cells.

Integrative Epigenomic Analysis of Transcriptional Regulation of Human CircRNAs

Circular RNAs (circRNAs) are evolutionarily conserved and abundant non-coding RNAs whose functions and regulatory mechanisms remain largely unknown. Here, we identify and characterize an epigenomically distinct group of circRNAs (TAH-circRNAs), which are transcribed to a higher level than their host genes. By integrative analysis of cistromic and transcriptomic data, we find that compared with other circRNAs, TAH-circRNAs are expressed more abundantly and have more transcription factors (TFs) binding sites and lower DNA methylation levels. Concordantly, TAH-circRNAs are enriched in open and active chromatin regions. Importantly, ChIA-PET results showed that 23–52% of transcription start sites (TSSs) of TAH-circRNAs have direct interactions with cis-regulatory regions, strongly suggesting their independent transcriptional regulation from host genes. In addition, we characterize molecular features of super-enhancer-driven circRNAs in cancer biology. Together, this study comprehensively analyzes epigenomic characteristics of circRNAs and identifies a distinct group of TAH-circRNAs that are independently transcribed via enhancers and super-enhancers by TFs. These findings substantially advance our understanding of the regulatory mechanism of circRNAs and may have important implications for future investigations of this class of non-coding RNAs.

GTRD: an integrated view of transcription regulation

The Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org/) contains uniformly annotated and processed NGS data related to gene transcription regulation: ChIP-seq, ChIP-exo, DNase-seq, MNase-seq, ATAC-seq and RNA-seq. With the latest release, the database has reached a new level of data integration. All cell types (cell lines and tissues) presented in the GTRD were arranged into a dictionary and linked with different ontologies (BRENDA, Cell Ontology, Uberon, Cellosaurus and Experimental Factor Ontology) and with related experiments in specialized databases on transcription regulation (FANTOM5, ENCODE and GTEx). The updated version of the GTRD provides an integrated view of transcription regulation through a dedicated web interface with advanced browsing and search capabilities, an integrated genome browser, and table reports by cell types, transcription factors, and genes of interest.

ADRB1 was identified as a potential biomarker for breast cancer by the co-analysis of tumor mutational burden and immune infiltration

Breast cancer (BRCA) has traditionally been considered as having poor immunogenicity and is characterized by relatively low tumor mutational burden (TMB). Improving immunogenicity may improve the response to clinical immunotherapy of BRCA. However, the relationship between TMB, immune infiltration, and prognosis in BRCA remains unclear. We aimed to explore their interrelations and potential biomarkers. In this study, based on somatic mutation data of BRCA from The Cancer Genome Atlas (TCGA), patients were categorized into high and low TMB groups utilizing the TMB values. CIBERSOFT algorithm indicated significant infiltration of activated partial immune cells in high TMB group. Besides, ADRB1 had been identified as a prognosis-related immune gene in the mutant genes by the combination of the ImmPort database and the univariate Cox analysis. ADRB1 mutation was associated with lower TMB and manifested a satisfactory clinical prognosis. Various database applications (Gene Set Enrichment Analysis, Tumor IMmune Estimation Resource, Connectivity Map, KnockTF) supported the selection of treatment strategies targeting ADRB1. In conclusion, TMB was not an independent prognostic factor for BRCA and high TMB was more likely to activate a partial immune response. ADRB1 was identified as a potential biomarker and may provide new insights for co-therapy of BRCA.

Integrating binding and expression data to predict transcription factors combined function

BACKGROUND

Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction.

RESULTS

We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor's targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets.

CONCLUSION

We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors.

Characterization of super-enhancer-associated functional lncRNAs acting as ceRNAs in ESCC

Long noncoding RNAs (lncRNAs) have important regulatory roles in cancer biology. Although some lncRNAs have well-characterized functions, the vast majority of this class of molecules remains functionally uncharacterized. To systematically pinpoint functional lncRNAs, a computational approach was proposed for identification of lncRNA-mediated competing endogenous RNAs (ceRNAs) through combining global and local regulatory direction consistency of expression. Using esophageal squamous cell carcinoma (ESCC) as model, we further identified many known and novel functional lncRNAs acting as ceRNAs (ce-lncRNAs). We found that most of them significantly regulated the expression of cancer-related hallmark genes. These ce-lncRNAs were significantly regulated by enhancers, especially super-enhancers (SEs). Landscape analyses for lncRNAs further identified SE-associated functional ce-lncRNAs in ESCC, such as HOTAIR, XIST, SNHG5, and LINC00094. THZ1, a specific CDK7 inhibitor, can result in global transcriptional downregulation of SE-associated ce-lncRNAs. We further demonstrate that a SE-associated ce-lncRNA, LINC00094 can be activated by transcription factors TCF3 and KLF5 through binding to SE regions and promoted ESCC cancer cell growth. THZ1 downregulated expression of LINC00094 through inhibiting TCF3 and KLF5. Our data demonstrated the important roles of SE-associated ce-lncRNAs in ESCC oncogenesis and might serve as targets for ESCC diagnosis and therapy.

The 27th annual Nucleic Acids Research database issue and molecular biology database collection

The 2020 Nucleic Acids Research Database Issue contains 148 papers spanning molecular biology. They include 59 papers reporting on new databases and 79 covering recent changes to resources previously published in the issue. A further ten papers are updates on databases most recently published elsewhere. This issue contains three breakthrough articles: AntiBodies Chemically Defined (ABCD) curates antibody sequences and their cognate antigens; SCOP returns with a new schema and breaks away from a purely hierarchical structure; while the new Alliance of Genome Resources brings together a number of Model Organism databases to pool knowledge and tools. Major returning nucleic acid databases include miRDB and miRTarBase. Databases for protein sequence analysis include CDD, DisProt and ELM, alongside no fewer than four newcomers covering proteins involved in liquid-liquid phase separation. In metabolism and signaling, Pathway Commons, Reactome and Metabolights all contribute papers. PATRIC and MicroScope update in microbial genomes while human and model organism genomics resources include Ensembl, Ensembl genomes and UCSC Genome Browser. Immune-related proteins are covered by updates from IPD-IMGT/HLA and AFND, as well as newcomers VDJbase and OGRDB. Drug design is catered for by updates from the IUPHAR/BPS Guide to Pharmacology and the Therapeutic Target Database. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been revised, updating 305 entries, adding 65 new resources and eliminating 125 discontinued URLs; so bringing the current total to 1637 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.