Generation of dynamic temporal and spatial concentration gradients using microfluidic devices

This paper describes a microfluidic approach to generate dynamic temporal and spatial concentration gradients using a single microfluidic device. Compared to a previously described method that produced a single fixed gradient shape for each device, this approach combines a simple mixer module with gradient generating network to control and manipulate a number of different gradient shapes. The gradient profile is determined by the configuration of fluidic inputs as well as the design of microchannel network. By controlling the relative flow rates of the fluidic inputs using separate syringe pumps, the resulting composition of the inlets that feed the gradient generator can be dynamically controlled to generate temporal and spatial gradients. To demonstrate the concept and illustrate this approach, examples of devices that generate (1) temporal gradients of homogeneous concentrations, (2) linear gradients with dynamically controlled slope, baseline, and direction, and (3) nonlinear gradients with controlled nonlinearity are shown and their limitations are described.NSF (Grant No. DBI-0138055) U.S. Public Health Service-National Institutes of Health General Medical Science Grant (GM-6605

BC4GO: a full-text corpus for the BioCreative IV GO task

Gene function curation via Gene Ontology (GO) annotation is a common task among Model Organism Database groups. Owing to its manual nature, this task is considered one of the bottlenecks in literature curation. There have been many previous attempts at automatic identification of GO terms and supporting information from full text. However, few systems have delivered an accuracy that is comparable with humans. One recognized challenge in developing such systems is the lack of marked sentence-level evidence text that provides the basis for making GO annotations. We aim to create a corpus that includes the GO evidence text along with the three core elements of GO annotations: (i) a gene or gene product, (ii) a GO term and (iii) a GO evidence code. To ensure our results are consistent with real-life GO data, we recruited eight professional GO curators and asked them to follow their routine GO annotation protocols. Our annotators marked up more than 5000 text passages in 200 articles for 1356 distinct GO terms. For evidence sentence selection, the inter-annotator agreement (IAA) results are 9.3% (strict) and 42.7% (relaxed) in F1-measures. For GO term selection, the IAAs are 47% (strict) and 62.9% (hierarchical). Our corpus analysis further shows that abstracts contain ∼10% of relevant evidence sentences and 30% distinct GO terms, while the Results/Experiment section has nearly 60% relevant sentences and >70% GO terms. Further, of those evidence sentences found in abstracts, less than one-third contain enough experimental detail to fulfill the three core criteria of a GO annotation. This result demonstrates the need of using full-text articles for text mining GO annotations. Through its use at the BioCreative IV GO (BC4GO) task, we expect our corpus to become a valuable resource for the BioNLP research community

Alliance of Genome Resources Portal: unified model organism research platform

The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource

Alliance of Genome Resources Portal: unified model organism research platform

The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project

Differential effects of EGF gradient profiles on MDA-MB-231 breast cancer cell chemotaxis

Chemotaxis, directed cell migration in a gradient of chemoattractant, is an important biological phenomenon that plays pivotal roles in cancer metastasis. Newly developed microfluidic chemotaxis chambers (MCC) were used to study chemotaxis of metastatic breast cancer cells, MDA-MB-231, in EGF gradients of well-defined profiles. Migration behaviors of MDA-MB-231 cells in uniform concentrations of EGF (0, 25, 50, and 100 ng/ml) and EGF (0–25, 0–50, and 0–100 ng/ml) with linear and nonlinear polynomial profiles were investigated. MDA-MB-231 cells exhibited increased speed and directionality upon stimulation with uniform concentrations of EGF. The cells were viable and motile for over 24 h, confirming the compatibility of MCC with cancer cells. Linear concentration gradients of different ranges were not effective in inducing chemotactic movement as compared to nonlinear gradients. MDA-MB-231 cells migrating in EGF gradient of 0–50 ng/ ml nonlinear polynomial profile exhibited marked directional movement toward higher EGF concentration. This result suggests that MDAMB- 231 cancer cell chemotaxis depends on the shape of gradient profile as well as on the range of EGF concentrations. D 2004 Elsevier Inc. All rights reserved.We thank the Concern Foundation and the Whitaker Foundation for supporting this research