Education, collaboration, and innovation: intelligent biology and medicine in the era of big data

The Removal of Erythromycin and Its Effects on Anaerobic Fermentation

In view of the problems of antibiotic pollution, anaerobic fermentation technology was adopted to remove erythromycin in this study. The removal of erythromycin and its effects mechanism on anaerobic fermentation were studied, including biogas performance, process stability, substrate degradability, enzyme activity, and microbial communities. The results showed that the removal rates of erythromycin for all tested concentrations were higher than 90% after fermentation. Erythromycin addition inhibited biogas production. The more erythromycin added, the lower the CH₄ content obtained. The high concentration of erythromycin (20 and 40 mg/L) resulted in more remarkable variations of pH values than the control group and 1 mg/L erythromycin added during the fermentation process. Erythromycin inhibited the hydrolysis process in the early stage of anaerobic fermentation. The contents of chemical oxygen demand (COD), NH₄⁺–N, and volatile fatty acids (VFA) of erythromycin added groups were lower than those of the control group. Erythromycin inhibited the degradation of lignocellulose in the late stage of fermentation. Cellulase activity increased first and then decreased during the fermentation and addition of erythromycin delayed the peak of cellulase activity. The inhibitory effect of erythromycin on the activity of coenzyme F₄₂₀ increased with elevated erythromycin concentrations. The relative abundance of archaea in erythromycin added groups was lower than the control group. The decrease in archaea resulted in the delay of the daily biogas peak. Additionally, the degradation rate of erythromycin was significantly correlated with the cumulative biogas yield, COD, pH, and ORP. This study supports the reutilization of antibiotic-contaminated biowaste and provides references for further research.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2020: Scalable techniques and algorithms for computational genomics

In this introduction article, we summarize the 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) conference which was held on August 9-10, 2020 (virtual conference). We then briefly describe the nine research articles included in this supplement issue. ICIBM 2020 hosted four scientific sections covering current topics in bioinformatics, computational biology, genomics, biomedical informatics, among others. A total of 75 original manuscripts were submitted to ICIBM 2020. All the papers were under rigorous review (at least three reviewers), and highly ranked manuscripts were selected for oral presentation and supplement issues. This genomics supplement issue included nine manuscripts. These articles cover methods and applications for single cell RNA sequencing, multi-omics data integration for gene regulation, gene fusion detection from long-read RNA sequencing, gene co-expression analysis of metabolic pathways in cancer, integrative genome-wide association studies (GWAS) of subcortical imaging phenotype in Alzheimer's disease, as well as deep learning methods for protein structure prediction, metabolic pathway membership inference, and horizontal gene transfer (HGT) insertion sites prediction.

The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): conference summary and innovations in genomics

The goal of this editorial is to summarize the 2019 International Conference on Intelligent Biology and Medicine (ICIBM 2019) conference that took place on June 9–11, 2019 in The Ohio State University, Columbus, OH, and to provide an introductory summary of the seven articles presented in this supplement issue. ICIBM 2019 hosted four keynote speakers, four eminent scholar speakers, five tutorials and workshops, twelve concurrent sessions and a poster session, totaling 23 posters, spanning state-of-the-art developments in bioinformatics, genomics, next-generation sequencing (NGS) analysis, scientific databases, cancer and medical genomics, and computational drug discovery. A total of 105 original manuscripts were submitted to ICIBM 2019, and after careful review, seven were selected for this supplement issue. These articles cover methods and applications for functional annotations of miRNA targeting, clonal evolution of bacterial cells, gene co-expression networks that describe a given phenotype, functional binding site analysis of RNA-binding proteins, normalization of genome architecture mapping data, sample predictions based on multiple NGS data types, and prediction of an individual’s genetic admixture given exonic single nucleotide polymorphisms data.

Process Analysis of Anaerobic Fermentation Exposure to Metal Mixtures

Anaerobic fermentation is a cost-effective biowaste disposal approach. During fermentation, microorganisms require a trace amount of metals for optimal growth and performance. This study investigated the effects of metal mixtures on biogas properties, process stability, substrate degradation, enzyme activity, and microbial communities during anaerobic fermentation. The addition of iron (Fe), nickel (Ni), and zinc (Zn) into a copper (Cu)-stressed fermentation system resulted in higher cumulative biogas yields, ammonia nitrogen (NH₄⁺-N) concentrations and coenzyme F₄₂₀ activities. Ni and Zn addition enhanced process stability and acetate utilization. The addition of these metals also improved and brought forward the peak daily biogas yields as well as increased CH₄ content to 88.94 and 86.58%, respectively. Adding Zn into the Cu-stressed system improved the abundance of Defluviitoga, Fibrobacter and Methanothermobacter, the degradation of cellulose, and the transformation of CO₂ to CH₄. The bacterial and archaeal communities were responsible for the degradation of lignocelluloses and CH₄ production during the fermentation process. This study supports the reutilization of heavy metal-contaminated biowaste and provides references for further research on heavy metals impacted anaerobic fermentation.

Genome-wide analysis of spatiotemporal gene expression patterns during floral organ development in Brassica rapa

Flowering is a key agronomic trait that directly influences crop yield and quality and serves as a model system for elucidating the molecular basis that controls successful reproduction, adaptation, and diversification of flowering plants. Adequate knowledge of continuous series of expression data from the floral transition to maturation is lacking in Brassica rapa. To unravel the genome expression associated with the development of early small floral buds (< 2 mm; FB2), early large floral buds (2-4 mm; FB4), stamens (STs) and carpels (CPs), transcriptome profiling was carried out with a Br300K oligo microarray. The results showed that at least 6848 known nonredundant genes (30% of the genes of the Br300K) were differentially expressed during the floral transition from vegetative tissues to maturation. Functional annotation of the differentially expressed genes (DEGs) (fold change ≥ 5) by comparison with a close relative, Arabidopsis thaliana, revealed 6552 unigenes (4579 upregulated; 1973 downregulated), including 131 Brassica-specific and 116 functionally known floral Arabidopsis homologs. Additionally, 1723, 236 and 232 DEGs were preferentially expressed in the tissues of STs, FB2, and CPs. These DEGs also included 43 transcription factors, mainly AP2/ERF-ERF, NAC, MADS-MIKC, C2H2, bHLH, and WRKY members. The differential gene expression during flower development induced dramatic changes in activities related to metabolic processes (23.7%), cellular (22.7%) processes, responses to the stimuli (7.5%) and reproduction (1%). A relatively large number of DEGs were observed in STs and were overrepresented by photosynthesis-related activities. Subsequent analysis via semiquantitative RT-PCR, histological analysis performed with in situ hybridization of BrLTP1 and transgenic reporter lines (BrLTP promoter::GUS) of B. rapa ssp. pekinensis supported the spatiotemporal expression patterns. Together, these results suggest that a temporally and spatially regulated process of the selective expression of distinct fractions of the same genome leads to the development of floral organs. Interestingly, most of the differentially expressed floral transcripts were located on chromosomes 3 and 9. This study generated a genome expression atlas of the early floral transition to maturation that represented the flowering regulatory elements of Brassica rapa.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics with bigger data and wider applications

The sixth International Conference on Intelligent Biology and Medicine (ICIBM) took place in Los Angeles, California, USA on June 10-12, 2018. This conference featured eleven regular scientific sessions, four tutorials, one poster session, four keynote talks, and four eminent scholar talks. The scientific program covered a wide range of topics from bench to bedside, including 3D Genome Organization, reconstruction of large scale evolution of genomes and gene functions, artificial intelligence in biological and biomedical fields, and precision medicine. Both method development and application in genomic research continued to be a main component in the conference, including studies on genetic variants, regulation of transcription, genetic-epigenetic interaction at both single cell and tissue level and artificial intelligence. Here, we write a summary of the conference and also briefly introduce the four high quality papers selected to be published in BMC Genomics that cover novel methodology development or innovative data analysis.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics meets medicine

During June 10–12, 2018, the International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held in Los Angeles, California, USA. The conference included 11 scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics ranging from 3D genome structure analysis and visualization, next generation sequencing analysis, computational drug discovery, medical informatics, cancer genomics to systems biology. While medical genomics has always been a main theme in ICIBM, this year we for the first time organized the BMC Medical Genomics Supplement for ICIBM. Here, we describe 15 ICIBM papers selected for publishing in BMC Medical Genomics.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: summary and innovation in genomics

In this editorial, we first summarize the 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) that was held on December 8–10, 2016 in Houston, Texas, USA, and then briefly introduce the ten research articles included in this supplement issue. ICIBM 2016 included four workshops or tutorials, four keynote lectures, four conference invited talks, eight concurrent scientific sessions and a poster session for 53 accepted abstracts, covering current topics in bioinformatics, systems biology, intelligent computing, and biomedical informatics. Through our call for papers, a total of 77 original manuscripts were submitted to ICIBM 2016. After peer review, 11 articles were selected in this special issue, covering topics such as single cell RNA-seq analysis method, genome sequence and variation analysis, bioinformatics method for vaccine development, and cancer genomics.

An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data

Background

The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development.

Results

In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available.

Conclusions

Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1516-0) contains supplementary material, which is available to authorized users.