A cloud-based resource for genome coordinate-based exploration and large-scale analysis of chromosome aberrations and gene fusions in cancer.

Cytogenetic analysis provides important information on the genetic mechanisms of cancer. The Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (Mitelman DB) is the largest catalog of acquired chromosome aberrations, presently comprising >70 000 cases across multiple cancer types. Although this resource has enabled the identification of chromosome abnormalities leading to specific cancers and cancer mechanisms, a large-scale, systematic analysis of these aberrations and their downstream implications has been difficult due to the lack of a standard, automated mapping from aberrations to genomic coordinates. We previously introduced CytoConverter as a tool that automates such conversions. CytoConverter has now been updated with improved interpretation of karyotypes and has been integrated with the Mitelman DB, providing a comprehensive mapping of the 70 000+ cases to genomic coordinates, as well as visualization of the frequencies of chromosomal gains and losses. Importantly, all CytoConverter-generated genomic coordinates are publicly available in Google BigQuery, a cloud-based data warehouse, facilitating data exploration and integration with other datasets hosted by the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC) Resource. We demonstrate the use of BigQuery for integrative analysis of Mitelman DB with other cancer datasets, including a comparison of the frequency of imbalances identified in Mitelman DB cases with those found in The Cancer Genome Atlas (TCGA) copy number datasets. This solution provides opportunities to leverage the power of cloud computing for low-cost, scalable, and integrated analysis of chromosome aberrations and gene fusions in cancer.

National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence.

The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires easy access to large high-quality annotated datasets, which are both representative and diverse. The National Cancer Institute (NCI) Imaging Data Commons (IDC) hosts large and diverse publicly available cancer image data collections. By harmonizing all data based on industry standards and colocalizing it with analysis and exploration resources, the IDC aims to facilitate the development, validation, and clinical translation of AI tools and address the well-documented challenges of establishing reproducible and transparent AI processing pipelines. Balanced use of established commercial products with open-source solutions, interconnected by standard interfaces, provides value and performance, while preserving sufficient agility to address the evolving needs of the research community. Emphasis on the development of tools, use cases to demonstrate the utility of uniform data representation, and cloud-based analysis aim to ease adoption and help define best practices. Integration with other data in the broader NCI Cancer Research Data Commons infrastructure opens opportunities for multiomics studies incorporating imaging data to further empower the research community to accelerate breakthroughs in cancer detection, diagnosis, and treatment. Published under a CC BY 4.0 license.

Evolutionary forces affecting synonymous variations in plant genomes.

Base composition is highly variable among and within plant genomes, especially at third codon positions, ranging from GC-poor and homogeneous species to GC-rich and highly heterogeneous ones (particularly Monocots). Consequently, synonymous codon usage is biased in most species, even when base composition is relatively homogeneous. The causes of these variations are still under debate, with three main forces being possibly involved: mutational bias, selection and GC-biased gene conversion (gBGC). So far, both selection and gBGC have been detected in some species but how their relative strength varies among and within species remains unclear. Population genetics approaches allow to jointly estimating the intensity of selection, gBGC and mutational bias. We extended a recently developed method and applied it to a large population genomic dataset based on transcriptome sequencing of 11 angiosperm species spread across the phylogeny. We found that at synonymous positions, base composition is far from mutation-drift equilibrium in most genomes and that gBGC is a widespread and stronger process than selection. gBGC could strongly contribute to base composition variation among plant species, implying that it should be taken into account in plant genome analyses, especially for GC-rich ones.

Population structure and genetic relationships between Ethiopian and Brazilian Coffea arabica genotypes revealed by SSR markers.

Information about population structure and genetic relationships within and among wild and brazilian Coffea arabica L. genotypes is highly relevant to optimize the use of genetic resources for breeding purposes. In this study, we evaluated genetic diversity, clustering analysis based on Jaccard's coefficient and population structure in 33 genotypes of C. arabica and of three diploid Coffea species (C. canephora, C. eugenioides and C. racemosa) using 30 SSR markers. A total of 206 alleles were identified, with a mean of 6.9 over all loci. The set of SSR markers was able to discriminate all genotypes and revealed that Ethiopian accessions presented higher genetic diversity than commercial varieties. Population structure analysis indicated two genetic groups, one corresponding to Ethiopian accessions and another corresponding predominantly to commercial cultivars. Thirty-four private alleles were detected in the group of accessions collected from West side of Great Rift Valley. We observed a lower average genetic distance of the C. arabica genotypes in relation to C. eugenioides than C. canephora. Interestingly, commercial cultivars were genetically closer to C. eugenioides than C. canephora and C. racemosa. The great allelic richness observed in Ethiopian Arabica coffee, especially in Western group showed that these accessions can be potential source of new alleles to be explored by coffee breeding programs.

Identification of candidate genes for drought tolerance in coffee by high-throughput sequencing in the shoot apex of different Coffea arabica cultivars.

BACKGROUND: Drought is a widespread limiting factor in coffee plants. It affects plant development, fruit production, bean development and consequently beverage quality. Genetic diversity for drought tolerance exists within the coffee genus. However, the molecular mechanisms underlying the adaptation of coffee plants to drought are largely unknown. In this study, we compared the molecular responses to drought in two commercial cultivars (IAPAR59, drought-tolerant and Rubi, drought-susceptible) of Coffea arabica grown in the field under control (irrigation) and drought conditions using the pyrosequencing of RNA extracted from shoot apices and analysing the expression of 38 candidate genes. RESULTS: Pyrosequencing from shoot apices generated a total of 34.7 Mbp and 535,544 reads enabling the identification of 43,087 clusters (41,512 contigs and 1,575 singletons). These data included 17,719 clusters (16,238 contigs and 1,575 singletons) exclusively from 454 sequencing reads, along with 25,368 hybrid clusters assembled with 454 sequences. The comparison of DNA libraries identified new candidate genes (n = 20) presenting differential expression between IAPAR59 and Rubi and/or drought conditions. Their expression was monitored in plagiotropic buds, together with those of other (n = 18) candidates genes. Under drought conditions, up-regulated expression was observed in IAPAR59 but not in Rubi for CaSTK1 (protein kinase), CaSAMT1 (SAM-dependent methyltransferase), CaSLP1 (plant development) and CaMAS1 (ABA biosynthesis). Interestingly, the expression of lipid-transfer protein (nsLTP) genes was also highly up-regulated under drought conditions in IAPAR59. This may have been related to the thicker cuticle observed on the abaxial leaf surface in IAPAR59 compared to Rubi. CONCLUSIONS: The full transcriptome assembly of C. arabica, followed by functional annotation, enabled us to identify differentially expressed genes related to drought conditions. Using these data, candidate genes were selected and their differential expression profiles were confirmed by qPCR experiments in plagiotropic buds of IAPAR59 and Rubi under drought conditions. As regards the genes up-regulated under drought conditions, specifically in the drought-tolerant IAPAR59, several corresponded to orphan genes but also to genes coding proteins involved in signal transduction pathways, as well as ABA and lipid metabolism, for example. The identification of these genes should help advance our understanding of the genetic determinism of drought tolerance in coffee.

NCI Cancer Research Data Commons: Cloud-Based Analytic Resources.

The NCI's Cloud Resources (CR) are the analytical components of the Cancer Research Data Commons (CRDC) ecosystem. This review describes how the three CRs (Broad Institute FireCloud, Institute for Systems Biology Cancer Gateway in the Cloud, and Seven Bridges Cancer Genomics Cloud) provide access and availability to large, cloud-hosted, multimodal cancer datasets, as well as offer tools and workspaces for performing data analysis where the data resides, without download or storage. In addition, users can upload their own data and tools into their workspaces, allowing researchers to create custom analysis workflows and integrate CRDC-hosted data with their own. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Kim et al., p. 1404.

NCI Cancer Research Data Commons: Lessons Learned and Future State.

More than ever, scientific progress in cancer research hinges on our ability to combine datasets and extract meaningful interpretations to better understand diseases and ultimately inform the development of better treatments and diagnostic tools. To enable the successful sharing and use of big data, the NCI developed the Cancer Research Data Commons (CRDC), providing access to a large, comprehensive, and expanding collection of cancer data. The CRDC is a cloud-based data science infrastructure that eliminates the need for researchers to download and store large-scale datasets by allowing them to perform analysis where data reside. Over the past 10 years, the CRDC has made significant progress in providing access to data and tools along with training and outreach to support the cancer research community. In this review, we provide an overview of the history and the impact of the CRDC to date, lessons learned, and future plans to further promote data sharing, accessibility, interoperability, and reuse. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Pot et al., p. 1396.

Characterization of adaptation mechanisms in sorghum using a multireference back-cross nested association mapping design and envirotyping.

Identifying the genetic factors impacting the adaptation of crops to environmental conditions is of key interest for conservation and selection purposes. It can be achieved using population genomics, and evolutionary or quantitative genetics. Here we present a sorghum multireference back-cross nested association mapping population composed of 3,901 lines produced by crossing 24 diverse parents to 3 elite parents from West and Central Africa-back-cross nested association mapping. The population was phenotyped in environments characterized by differences in photoperiod, rainfall pattern, temperature levels, and soil fertility. To integrate the multiparental and multi-environmental dimension of our data we proposed a new approach for quantitative trait loci (QTL) detection and parental effect estimation. We extended our model to estimate QTL effect sensitivity to environmental covariates, which facilitated the integration of envirotyping data. Our models allowed spatial projections of the QTL effects in agro-ecologies of interest. We utilized this strategy to analyze the genetic architecture of flowering time and plant height, which represents key adaptation mechanisms in environments like West Africa. Our results allowed a better characterization of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod-sensitive and the region of possible candidate gene Elf3 being photoperiod-insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology-dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the West and Central Africa-back-cross nested association mapping and the presented analytical approach constitute unique resources to better understand adaptation in sorghum with direct application to develop climate-smart varieties.

An initial assessment of linkage disequilibrium (LD) in coffee trees: LD patterns in groups of Coffea canephora Pierre using microsatellite analysis.

BACKGROUND: A reciprocal recurrent selection program has been under way for the Coffea canephora coffee tree for approximately thirty years in the Ivory Coast. Association genetics would help to speed up this program by more rapidly selecting zones of interest in the genome. However, prior to any such studies, the linkage disequilibrium (LD) needs to be assessed between the markers on the genome. These data are essential for guiding association studies. RESULTS: This article describes the first results of an LD assessment in a coffee tree species. Guinean and Congolese breeding populations of C. canephora have been used for this work, with the goal of identifying ways of using these populations in association genetics. We identified changes in the LD along the genome within the different C. canephora diversity groups. In the different diversity groups studied, the LD was variable. Some diversity groups displayed disequilibria over long distances (up to 25 cM), whereas others had disequilibria not exceeding 1 cM. We also discovered a fine structure within the Guinean group. CONCLUSIONS: Given these results, association studies can be used within the species C. canephora. The coffee recurrent selection scheme being implemented in the Ivory Coast can thus be optimized. Lastly, our results could be used to improve C. arabica because one of its parents is closely related to C. canephora.

Differentially expressed genes and proteins upon drought acclimation in tolerant and sensitive genotypes of Coffea canephora.

The aim of this study was to investigate the molecular mechanisms underlying drought acclimation in coffee plants by the identification of candidate genes (CGs) using different approaches. The first approach used the data generated during the Brazilian Coffee expressed sequence tag (EST) project to select 13 CGs by an in silico analysis (electronic northern). The second approach was based on screening macroarrays spotted with plasmid DNA (coffee ESTs) with separate hybridizations using leaf cDNA probes from drought-tolerant and susceptible clones of Coffea canephora var. Conilon, grown under different water regimes. This allowed the isolation of seven additional CGs. The third approach used two-dimensional gel electrophoresis to identify proteins displaying differential accumulation in leaves of drought-tolerant and susceptible clones of C. canephora. Six of them were characterized by MALDI-TOF-MS/MS (matrix-assisted laser desorption-time of flight-tandem mass spectrometry) and the corresponding proteins were identified. Finally, additional CGs were selected from the literature, and quantitative real-time polymerase chain reaction (qPCR) was performed to analyse the expression of all identified CGs. Altogether, >40 genes presenting differential gene expression during drought acclimation were identified, some of them showing different expression profiles between drought-tolerant and susceptible clones. Based on the obtained results, it can be concluded that factors involved a complex network of responses probably involving the abscisic signalling pathway and nitric oxide are major molecular determinants that might explain the better efficiency in controlling stomata closure and transpiration displayed by drought-tolerant clones of C. canephora.

Whole Genome Variant Dataset for Enriching Studies across 18 Different Cancers.

Whole genome sequencing (WGS) has helped to revolutionize biology, but the computational challenge remains for extracting valuable inferences from this information. Here, we present the cancer-associated variants from the Cancer Genome Atlas (TCGA) WGS dataset. This set of data will allow cancer researchers to further expand their analysis beyond the exomic regions of the genome to the entire genome. A total of 1342 WGS alignments available from the consortium were processed with VarScan2 and deposited to the NCI Cancer Cloud. The sample set covers 18 different cancers and reveals 157,313,519 pooled (non-unique) cancer-associated single-nucleotide variations (SNVs) across all samples. There was an average of 117,223 SNVs per sample, with a range from 1111 to 775,470 and a standard deviation of 163,273. The dataset was incorporated into BigQuery, which allows for fast access and cross-mapping, which will allow researchers to enrich their current studies with a plethora of newly available genomic data.

SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery.

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.

The 'PUCE CAFE' Project: the first 15K coffee microarray, a new tool for discovering candidate genes correlated to agronomic and quality traits.

BACKGROUND: Understanding the genetic elements that contribute to key aspects of coffee biology will have an impact on future agronomical improvements for this economically important tree. During the past years, EST collections were generated in Coffee, opening the possibility to create new tools for functional genomics. RESULTS: The "PUCE CAFE" Project, organized by the scientific consortium NESTLE/IRD/CIRAD, has developed an oligo-based microarray using 15,721 unigenes derived from published coffee EST sequences mostly obtained from different stages of fruit development and leaves in Coffea Canephora (Robusta). Hybridizations for two independent experiments served to compare global gene expression profiles in three types of tissue matter (mature beans, leaves and flowers) in C. canephora as well as in the leaves of three different coffee species (C. canephora, C. eugenoides and C. arabica). Microarray construction, statistical analyses and validation by Q-PCR analysis are presented in this study. CONCLUSION: We have generated the first 15 K coffee array during this PUCE CAFE project, granted by Génoplante (the French consortium for plant genomics). This new tool will help study functional genomics in a wide range of experiments on various plant tissues, such as analyzing bean maturation or resistance to pathogens or drought. Furthermore, the use of this array has proven to be valid in different coffee species (diploid or tetraploid), drastically enlarging its impact for high-throughput gene expression in the community of coffee research.

RBCS1 expression in coffee: Coffea orthologs, Coffea arabica homeologs, and expression variability between genotypes and under drought stress.

BACKGROUND: In higher plants, the inhibition of photosynthetic capacity under drought is attributable to stomatal and non-stomatal (i.e., photochemical and biochemical) effects. In particular, a disruption of photosynthetic metabolism and Rubisco regulation can be observed. Several studies reported reduced expression of the RBCS genes, which encode the Rubisco small subunit, under water stress. RESULTS: Expression of the RBCS1 gene was analysed in the allopolyploid context of C. arabica, which originates from a natural cross between the C. canephora and C. eugenioides species. Our study revealed the existence of two homeologous RBCS1 genes in C. arabica: one carried by the C. canephora sub-genome (called CaCc) and the other carried by the C. eugenioides sub-genome (called CaCe). Using specific primer pairs for each homeolog, expression studies revealed that CaCe was expressed in C. eugenioides and C. arabica but was undetectable in C. canephora. On the other hand, CaCc was expressed in C. canephora but almost completely silenced in non-introgressed ("pure") genotypes of C. arabica. However, enhanced CaCc expression was observed in most C. arabica cultivars with introgressed C. canephora genome. In addition, total RBCS1 expression was higher for C. arabica cultivars that had recently introgressed C. canephora genome than for "pure" cultivars. For both species, water stress led to an important decrease in the abundance of RBCS1 transcripts. This was observed for plants grown in either greenhouse or field conditions under severe or moderate drought. However, this reduction of RBCS1 gene expression was not accompanied by a decrease in the corresponding protein in the leaves of C. canephora subjected to water withdrawal. In that case, the amount of RBCS1 was even higher under drought than under unstressed (irrigated) conditions, which suggests great stability of RBCS1 under adverse water conditions. On the other hand, for C. arabica, high nocturnal expression of RBCS1 could also explain the accumulation of the RBCS1 protein under water stress. Altogether, the results presented here suggest that the content of RBCS was not responsible for the loss of photosynthetic capacity that is commonly observed in water-stressed coffee plants. CONCLUSION: We showed that the CaCe homeolog was expressed in C. eugenioides and non-introgressed ("pure") genotypes of C. arabica but that it was undetectable in C. canephora. On the other hand, the CaCc homeolog was expressed in C. canephora but highly repressed in C. arabica. Expression of the CaCc homeolog was enhanced in C. arabica cultivars that experienced recent introgression with C. canephora. For both C. canephora and C. arabica species, total RBCS1 gene expression was highly reduced with WS. Unexpectedly, the accumulation of RBCS1 protein was observed in the leaves of C. canephora under WS, possibly coming from nocturnal RBCS1 expression. These results suggest that the increase in the amount of RBCS1 protein could contribute to the antioxidative function of photorespiration in water-stressed coffee plants.

A high-throughput data mining of single nucleotide polymorphisms in Coffea species expressed sequence tags suggests differential homeologous gene expression in the allotetraploid Coffea arabica.

Polyploidization constitutes a common mode of evolution in flowering plants. This event provides the raw material for the divergence of function in homeologous genes, leading to phenotypic novelty that can contribute to the success of polyploids in nature or their selection for use in agriculture. Mounting evidence underlined the existence of homeologous expression biases in polyploid genomes; however, strategies to analyze such transcriptome regulation remained scarce. Important factors regarding homeologous expression biases remain to be explored, such as whether this phenomenon influences specific genes, how paralogs are affected by genome doubling, and what is the importance of the variability of homeologous expression bias to genotype differences. This study reports the expressed sequence tag assembly of the allopolyploid Coffea arabica and one of its direct ancestors, Coffea canephora. The assembly was used for the discovery of single nucleotide polymorphisms through the identification of high-quality discrepancies in overlapped expressed sequence tags and for gene expression information indirectly estimated by the transcript redundancy. Sequence diversity profiles were evaluated within C. arabica (Ca) and C. canephora (Cc) and used to deduce the transcript contribution of the Coffea eugenioides (Ce) ancestor. The assignment of the C. arabica haplotypes to the C. canephora (CaCc) or C. eugenioides (CaCe) ancestral genomes allowed us to analyze gene expression contributions of each subgenome in C. arabica. In silico data were validated by the quantitative polymerase chain reaction and allele-specific combination TaqMAMA-based method. The presence of differential expression of C. arabica homeologous genes and its implications in coffee gene expression, ontology, and physiology are discussed.

The Road to Sorghum Domestication: Evidence From Nucleotide Diversity and Gene Expression Patterns.

Native African cereals (sorghum, millets) ensure food security to millions of low-income people from low fertility and drought-prone regions of Africa and Asia. In spite of their agronomic importance, the genetic bases of their phenotype and adaptations are still not well-understood. Here we focus on Sorghum bicolor, which is the fifth cereal worldwide for grain production and constitutes the staple food for around 500 million people. We leverage transcriptomic resources to address the adaptive consequences of the domestication process. Gene expression and nucleotide variability were analyzed in 11 domesticated and nine wild accessions. We documented a downregulation of expression and a reduction of diversity both in nucleotide polymorphism (30%) and gene expression levels (18%) in domesticated sorghum. These findings at the genome-wide level support the occurrence of a global reduction of diversity during the domestication process, although several genes also showed patterns consistent with the action of selection. Nine hundred and forty-nine genes were significantly differentially expressed between wild and domesticated gene pools. Their functional annotation points to metabolic pathways most likely contributing to the sorghum domestication syndrome, such as photosynthesis and auxin metabolism. Coexpression network analyzes revealed 21 clusters of genes sharing similar expression patterns. Four clusters (totaling 2,449 genes) were significantly enriched in differentially expressed genes between the wild and domesticated pools and two were also enriched in domestication and improvement genes previously identified in sorghum. These findings reinforce the evidence that the combined and intricated effects of the domestication and improvement processes do not only affect the behaviors of a few genes but led to a large rewiring of the transcriptome. Overall, these analyzes pave the way toward the identification of key domestication genes valuable for genetic resources characterization and breeding purposes.

NCI Imaging Data Commons.

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data. We achieve this goal by colocating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research. SIGNIFICANCE: This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.

Enteropathogen Resource Integration Center (ERIC): bioinformatics support for research on biodefense-relevant enterobacteria.

ERIC, the Enteropathogen Resource Integration Center (www.ericbrc.org), is a new web portal serving as a rich source of information about enterobacteria on the NIAID established list of Select Agents related to biodefense-diarrheagenic Escherichia coli, Shigella spp., Salmonella spp., Yersinia enterocolitica and Yersinia pestis. More than 30 genomes have been completely sequenced, many more exist in draft form and additional projects are underway. These organisms are increasingly the focus of studies using high-throughput experimental technologies and computational approaches. This wealth of data provides unprecedented opportunities for understanding the workings of basic biological systems and discovery of novel targets for development of vaccines, diagnostics and therapeutics. ERIC brings information together from disparate sources and supports data comparison across different organisms, analysis of varying data types and visualization of analyses in human and computer-readable formats.

Transcriptional Regulation of Sorghum Stem Composition: Key Players Identified Through Co-expression Gene Network and Comparative Genomics Analyses.

Most sorghum biomass accumulates in stem secondary cell walls (SCW). As sorghum stems are used as raw materials for various purposes such as feed, energy and fiber reinforced polymers, identifying the genes responsible for SCW establishment is highly important. Taking advantage of studies performed in model species, most of the structural genes contributing at the molecular level to the SCW biosynthesis in sorghum have been proposed while their regulatory factors have mostly not been determined. Validation of the role of several MYB and NAC transcription factors in SCW regulation in Arabidopsis and a few other species has been provided. In this study, we contributed to the recent efforts made in grasses to uncover the mechanisms underlying SCW establishment. We reported updated phylogenies of NAC and MYB in 9 different species and exploited findings from other species to highlight candidate regulators of SCW in sorghum. We acquired expression data during sorghum internode development and used co-expression analyses to determine groups of co-expressed genes that are likely to be involved in SCW establishment. We were able to identify two groups of co-expressed genes presenting multiple evidences of involvement in SCW building. Gene enrichment analysis of MYB and NAC genes provided evidence that while NAC SECONDARY WALL THICKENING PROMOTING FACTOR NST genes and SECONDARY WALL-ASSOCIATED NAC DOMAIN PROTEIN gene functions appear to be conserved in sorghum, NAC master regulators of SCW in sorghum may not be as tissue compartmentalized as in Arabidopsis. We showed that for every homolog of the key SCW MYB in Arabidopsis, a similar role is expected for sorghum. In addition, we unveiled sorghum MYB and NAC that have not been identified to date as being involved in cell wall regulation. Although specific validation of the MYB and NAC genes uncovered in this study is needed, we provide a network of sorghum genes involved in SCW both at the structural and regulatory levels.

Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens.

BACKGROUND: The Enteropathogen Resource Integration Center (ERIC; http://www.ericbrc.org) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as Escherichia coli and Salmonella spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process. DESCRIPTION: We have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc.) and over 70% for relations (gene/gene product to role, etc). This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application. CONCLUSION: Our Text Mining application is available online on the ERIC website (http://www.ericbrc.org/portal/eric/articles). The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations systems.