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Background: Accurate identification of differentially expressed (DE) genes in time course RNA-Seq data is crucial for understanding the dynamics of transcriptional regulatory network. However, most of the available methods treat gene expressions at different time points as replicates and test the significance of the mean expression difference between treatments or conditions irrespective of time. They thus fail to identify many DE genes with different profiles across time. In this article, we propose a negative binomial mixed-effect model (NBMM) to identify DE genes in time course RNA-Seq data. In the NBMM, mean gene expression is characterized by a fixed effect, and time dependency is described by random effects. The NBMM is very flexible and can be fitted to both unreplicated and replicated time course RNA-Seq data via a penalized likelihood method. By comparing gene expression profiles over time, we further classify the DE genes into two subtypes to enhance the understanding of expression dynamics. A significance test for detecting DE genes is derived using a Kullback-Leibler distance ratio. Additionally, a significance test for gene sets is developed using a gene set score. Results: Simulation analysis shows that the NBMM outperforms currently available methods for detecting DE genes and gene sets. Moreover, our real data analysis of fruit fly developmental time course RNA-Seq data demonstrates the NBMM identifies biologically relevant genes which are well justified by gene ontology analysis. Conclusions: The proposed method is powerful and efficient to detect biologically relevant DE genes and gene sets in time course RNA-Seq data. [ABSTRACT FROM AUTHOR]

Background: Snail is a typical transcription factor that could induce epithelial-mesenchymal transition (EMT) and cancer progression. There are some related reports about the clinical significance of snail protein expression in gastric cancer. However, the published results were not completely consistent. This study was aimed to investigate snail expression and clinical significance in gastric cancer. Results: A systematic review of PubMed, CNKI, Weipu, and Wanfang database before March 2015 was conducted. We established an inclusion criterion according to subjects, method of detection, and results evaluation of snail protein. Meta-analysis was conducted using RevMan4.2 software. And merged odds ratio (OR) and 95% CI (95% confidence interval) were calculated. Also, forest plots and funnel plot were used to assess the potential of publication bias. A total of 10 studies were recruited. The meta-analysis was conducted to evaluate the positive rate of snail protein expression. OR and 95% CI for different groups were listed below: (1) gastric cancer and para-carcinoma tissue [OR = 6.15, 95% CI (4.70, 8.05)]; (2) gastric cancer and normal gastric tissue [OR = 17.00, 95% CI (10.08, 28.67)]; (3) non-lymph node metastasis and lymph node metastasis [OR = 0.40, 95% CI (0.18, 0.93)]; (4) poor differentiated cancer, highly differentiated cancer, and moderate cancer [OR = 3.34, 95% CI (2.22, 5.03)]; (5) clinical stage TI + TII and stage TIII + TIV [OR = 0.38, 95% CI (0.23, 0.60)]; (6) superficial muscularis and deep muscularis [OR = 0.18, 95% CI (0.11, 0.31)]. Conclusions: Our results indicated that the increase of snail protein expression may play an important role in the carcinogenesis, progression, and metastasis of gastric cancer. And this result might provide instruction for the diagnosis, therapy, and prognosis of gastric cancer. [ABSTRACT FROM AUTHOR]

Background: For unresectable or metastatic melanoma, first-line ipilimumab has demonstrated long-term survival benefits over a 7-year period. First-line treatment with BRAF inhibitors has demonstrated efficacy in clinical trials with up to 3 years of follow-up. The long-term comparative efficacy and costs of ipilimumab and BRAF inhibitors are unknown. Methods: Patient-level data from 12 clinical studies for ipilimumab were used. Survival data were extracted from included clinical trials for BRAF inhibitors based on a systematic literature review. Different parametric survival models, including exponential, Gompertz, log-normal, and Weibull models, were used to fit reported overall survival (OS) data and to project long-term survival for BRAF inhibitors. Survival benefits were measured in terms of total life-months gained as calculated by the area under the curve of OS Kaplan-Meier curves for the observed ipilimumab data and projected BRAF inhibitor data. Total life-months gained and cumulative costs per life-month gained were compared between ipilimumab and BRAF inhibitors. Results: The systematic literature review identified six randomized-controlled trials of BRAF inhibitors for subsequent analyses. With 7-year follow-up, ipilimumab was associated with a total of 28.5 life-months gained. Based on the Weibull model, the extrapolated total life-months gained for BRAF inhibitors were 26.5 months for dabrafenib, 21.3 months for trametinib, 14.3 months for vemurafenib, and 24.6 months for dabrafenib + trametinib. In sensitivity analyses, extrapolated total life-months gained varied across the three other models, ranging from 13.7 to 36.8 months across therapies. Cumulative costs per life-month gained with ipilimumab decreased steadily over time, while the costs remained constant for BRAF inhibitors due to continuous dosing. By year 3, cumulative costs per life-month gained were the lowest with ipilimumab; by year 7, the costs were $4281 for ipilimumab, compared with $8920 for dabrafenib, $10,211 for trametinib, $11,002 for vemurafenib, and $19,132 for the dabrafenib + trametinib combination therapy. Conclusions: Ipilimumab was associated with a better long-term cost-per-life month compared to BRAF agents. Long-term extrapolation of survival with BRAF agents was uncertain, and showed no evidence of prolonged survival compared to ipilimumab. [ABSTRACT FROM AUTHOR]

Background: Rates of medication non-adherence in dialysis patients are high, and improving adherence is likely to improve outcomes. Few data are available regarding factors associated with medication adherence in dialysis patients, and these data are needed to inform effective intervention strategies. Methods/design: This is an observational cross-sectional study of a multi-ethnic dialysis cohort from New Zealand, with the main data collection tool being an interviewer-assisted survey. A total of 100 participants were randomly sampled from a single centre, with selection stratified by ethnicity and dialysis modality (facility versus home). The main outcome measure is self-reported medication adherence using the Morisky 8-Item Medication Adherence Scale (MMAS-8). Study data include demographic, clinical, social and psychometric characteristics, the latter being constructs of health literacy, medication knowledge, beliefs about medications, and illness perceptions. Psychometric constructs were assessed through the following survey instruments; health literacy screening questions, the Medication Knowledge Evaluation Tool (Okuyan et al.), the Beliefs about Medication Questionnaire (Horne et al.), the Brief Illness Perception Questionnaire (Broadbent et al.). Using the study data, reliability analysis for internal consistency is satisfactory for the scales evaluating health literacy, medication knowledge, and beliefs about medications, with Chronbach's α > 0.7 for all. Reliability analysis indicated poor internal consistency for scales relating to illness perceptions. MMAS-8 and all psychometric scores are normally distributed in the study data. Discussion: This study will provide important information on the factors involved in medication non-adherence in New Zealand dialysis patients. The resulting knowledge will inform long-term initiatives to reduce medication non-adherence in dialysis patients, and help ensure that they are addressing appropriate and evidence based targets for intervention. [ABSTRACT FROM AUTHOR]

Background: Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development. Results: Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, ∣log(FC)∣ > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets. Conclusions: Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets. [ABSTRACT FROM AUTHOR]

Synergistically integrating multi-layer genomic data at systems level not only can lead to deeper insights into the molecular mechanisms related to disease initiation and progression, but also can guide pathway-based biomarker and drug target identification. With the advent of high-throughput next-generation sequencing technologies, sequencing both DNA and RNA has generated multi-layer genomic data that can provide DNA polymorphism, non-coding RNA, messenger RNA, gene expression, isoform and alternative splicing information. Systems biology on the other hand studies complex biological systems, particularly systematic study of complex molecular interactions within specific cells or organisms. Genomics and molecular systems biology can be merged into the study of genomic profiles and implicated biological functions at cellular or organism level. The prospectively emerging field can be referred to as systems genomics or genomic systems biology. The Mid-South Bioinformatics Centre (MBC) and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and University of Arkansas for Medical Sciences are particularly interested in promoting education and research advancement in this prospectively emerging field. Based on past investigations and research outcomes, MBC is further utilizing differential gene and isoform/exon expression from RNA-seq and co-regulation from the ChiP-seq specific for different phenotypes in combination with protein-protein interactions, and protein-DNA interactions to construct high-level gene networks for an integrative genome-phoneme investigation at systems biology level. [ABSTRACT FROM AUTHOR]

A major goal of metagenomics is to identify and study the entire collection of microbial species in a set of targeted samples. We describe a statistical metagenomic algorithm that simultaneously identifies microbial species and estimates their abundances without using reference genomes. As a trade-off, we require multiple metagenomic samples, usually ≥10 samples, to get highly accurate binning results. Compared to reference-free methods based primarily on k-mer distributions or coverage information, the proposed approach achieves a higher species binning accuracy and is particularly powerful when sequencing coverage is low. We demonstrated the performance of this new method through both simulation and real metagenomic studies. The MetaGen software is available at https://github.com/BioAlgs/MetaGen. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1323-y) contains supplementary material, which is available to authorized users.

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant’s regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1177-3) contains supplementary material, which is available to authorized users.

Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer. [ABSTRACT FROM AUTHOR]

Background: Snail is a typical transcription factor that could induce epithelial-mesenchymal transition (EMT) and cancer progression. There are some related reports about the clinical significance of snail protein expression in gastric cancer. However, the published results were not completely consistent. This study was aimed to investigate snail expression and clinical significance in gastric cancer. Results: A systematic review of PubMed, CNKI, Weipu, and Wanfang database before March 2015 was conducted. We established an inclusion criterion according to subjects, method of detection, and results evaluation of snail protein. Meta-analysis was conducted using RevMan4.2 software. And merged odds ratio (OR) and 95 % CI (95 % confidence interval) were calculated. Also, forest plots and funnel plot were used to assess the potential of publication bias. A total of 10 studies were recruited. The meta-analysis was conducted to evaluate the positive rate of snail protein expression. OR and 95 % CI for different groups were listed below: (1) gastric cancer and para-carcinoma tissue [OR = 6.15, 95 % CI (4.70, 8.05)]; (2) gastric cancer and normal gastric tissue [OR = 17.00, 95 % CI (10.08, 28.67)]; (3) non-lymph node metastasis and lymph node metastasis [OR = 0.40, 95 % CI (0.18, 0.93)]; (4) poor differentiated cancer, highly differentiated cancer, and moderate cancer [OR = 3.34, 95 % CI (2.22, 5.03)]; (5) clinical stage TI + TII and stage TIII + TIV [OR = 0.38, 95 % CI (0.23, 0.60)]; (6) superficial muscularis and deep muscularis [OR = 0.18, 95 % CI (0.11, 0.31)]. Conclusions: Our results indicated that the increase of snail protein expression may play an important role in the carcinogenesis, progression, and metastasis of gastric cancer. And this result might provide instruction for the diagnosis, therapy, and prognosis of gastric cancer.

Background: Understanding the interactions between tumor and the host immune system is critical to finding prognostic biomarkers, reducing drug resistance, and developing new therapies. Novel computational methods are needed to estimate tumor-infiltrating immune cells and understand tumor–immune interactions in cancers. Results: We analyze tumor-infiltrating immune cells in over 10,000 RNA-seq samples across 23 cancer types from The Cancer Genome Atlas (TCGA). Our computationally inferred immune infiltrates associate much more strongly with patient clinical features, viral infection status, and cancer genetic alterations than other computational approaches. Analysis of cancer/testis antigen expression and CD8 T-cell abundance suggests that MAGEA3 is a potential immune target in melanoma, but not in non-small cell lung cancer, and implicates SPAG5 as an alternative cancer vaccine target in multiple cancers. We find that melanomas expressing high levels of CTLA4 separate into two distinct groups with respect to CD8 T-cell infiltration, which might influence clinical responses to anti-CTLA4 agents. We observe similar dichotomy of TIM3 expression with respect to CD8 T cells in kidney cancer and validate it experimentally. The abundance of immune infiltration, together with our downstream analyses and findings, are accessible through TIMER, a public resource at http://cistrome.org/TIMER. Conclusions: We develop a computational approach to study tumor-infiltrating immune cells and their interactions with cancer cells. Our resource of immune-infiltrate levels, clinical associations, as well as predicted therapeutic markers may inform effective cancer vaccine and checkpoint blockade therapies. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1028-7) contains supplementary material, which is available to authorized users.

Background: Neuroblastoma (NB) tumors are well known for their pronounced clinical and molecular heterogeneity. The global gene expression and DNA copy number alterations have been shown to have profound differences in tumors of low or high stage and those with or without MYCN amplification. RNA splicing is an important regulatory mechanism of gene expression, and differential RNA splicing may be associated with the clinical behavior of a tumor. Methods: In this study, we used exon array profiling to investigate global alternative splicing pattern of 47 neuroblastoma samples in stage 1 and stage 4 with normal or amplified MYCN copy number (stage 1-, 4- and 4+). The ratio of exon-level expression to gene-level expression was used to detect alternative splicing events, while the gene-level expression was applied to characterize whole gene expression change. Results: Principal component analysis (PCA) demonstrated distinct splicing pattern in three groups of samples. Pairwise comparison identified genes with splicing changes and/or whole gene expression changes in high stage tumors. In stage 4- compared with stage 1- tumors, alternatively spliced candidate genes had little overlap with genes showing whole gene expression changes, and most of them were involved in different biological processes. In contrast, a larger number of genes exhibited either exon-level splicing, gene-level expression or both changes in stage 4+ versus stage 1- tumors. Those biological processes involved in stage 4- tumors were disrupted to a greater extent by both splicing and transcription regulations in stage 4+ tumors. Conclusions: Our results demonstrated a significant role of alternative splicing in high stage neuroblastoma, and suggested a MYCN-associated splicing regulation pathway in stage 4+ tumors. The identification of differentially spliced genes and pathways in neuroblastoma tumors of different stages and molecular subtypes may be important to the understanding of cancer biology and the discovery of diagnostic markers or therapeutic targets in neuroblastoma. [ABSTRACT FROM AUTHOR]

Background: Several proteins that promote angiogenesis are overexpressed in hepatocellular carcinoma (HCC) and have been implicated in disease pathogenesis. Sunitinib has antiangiogenic activity and is an oral multitargeted inhibitor of vascular endothelial growth factor receptors (VEGFRs)-1, -2, and -3, platelet-derived growth factor receptors (PDGFRs)-α and -β, stem-cell factor receptor (KIT), and other tyrosine kinases. In a phase II study of sunitinib in advanced HCC, we evaluated the plasma pharmacodynamics of five proteins related to the mechanism of action of sunitinib and explored potential correlations with clinical outcome.Methods: Patients with advanced HCC received a starting dose of sunitinib 50 mg/day administered orally for 4 weeks on treatment, followed by 2 weeks off treatment. Plasma samples from 37 patients were obtained at baseline and during treatment and were analyzed for vascular endothelial growth factor (VEGF)-A, VEGF-C, soluble VEGFR-2 (sVEGFR-2), soluble VEGFR-3 (sVEGFR-3), and soluble KIT (sKIT).Results: At the end of the first sunitinib treatment cycle, plasma VEGF-A levels were significantly increased relative to baseline, while levels of plasma VEGF-C, sVEGFR-2, sVEGFR-3, and sKIT were significantly decreased. Changes from baseline in VEGF-A, sVEGFR-2, and sVEGFR-3, but not VEGF-C or sKIT, were partially or completely reversed during the first 2-week off-treatment period. High levels of VEGF-C at baseline were significantly associated with Response Evaluation Criteria in Solid Tumors (RECIST)-defined disease control, prolonged time to tumor progression (TTP), and prolonged overall survival (OS). Baseline VEGF-C levels were an independent predictor of TTP by multivariate analysis. Changes from baseline in VEGF-A and sKIT at cycle 1 day 14 or cycle 2 day 28, and change in VEGF-C at the end of the first off-treatment period, were significantly associated with both TTP and OS, while change in sVEGFR-2 at cycle 1 day 28 was an independent predictor of OS.Conclusions: Baseline plasma VEGF-C levels predicted disease control (based on RECIST) and were positively associated with both TTP and OS in this exploratory analysis, suggesting that this VEGF family member may have utility in predicting clinical outcome in patients with HCC who receive sunitinib.Trial Registration: ClinicalTrials.gov: NCT00247676. [ABSTRACT FROM AUTHOR]

Background: Transposons are "jumping genes" that account for large quantities of repetitive content in genomes. They are known to affect transcriptional regulation in several different ways, and are implicated in many human diseases. Transposons are related to microRNAs and viruses, and many genes, pseudogenes, and gene promoters are derived from transposons or have origins in transposon-induced duplication. Modeling transposon-derived genomic content is difficult because they are poorly conserved. Profile hidden Markov models (profile HMMs), widely used for protein sequence family modeling, are rarely used for modeling DNA sequence families. The algorithm commonly used to estimate the parameters of profile HMMs, Baum-Welch, is prone to prematurely converge to local optima. The DNA domain is especially problematic for the Baum-Welch algorithm, since it has only four letters as opposed to the twenty residues of the amino acid alphabet. Results: We demonstrate with a simulation study and with an application to modeling the MIR family of transposons that two recently introduced methods, Conditional Baum-Welch and Dynamic Model Surgery, achieve better estimates of the parameters of profile HMMs across a range of conditions. Conclusions: We argue that these new algorithms expand the range of potential applications of profile HMMs to many important DNA sequence family modeling problems, including that of searching for and modeling the virus-like transposons that are found in all known genomes. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to: i) develop a computational model of promoters of human histone-encoding genes (shortly histone genes), an important class of genes that participate in various critical cellular processes, ii) use the model so developed to identify regions across the human genome that have similar structure as promoters of histone genes; such regions could represent potential genomic regulatory regions, e.g. promoters, of genes that may be coregulated with histone genes, and iii/ identify in this way genes that have high likelihood of being coregulated with the histone genes. Results: We successfully developed a histone promoter model using a comprehensive collection of histone genes. Based on leave-one-out cross-validation test, the model produced good prediction accuracy (94.1% sensitivity, 92.6% specificity, and 92.8% positive predictive value). We used this model to predict across the genome a number of genes that shared similar promoter structures with the histone gene promoters. We thus hypothesize that these predicted genes could be coregulated with histone genes. This hypothesis matches well with the available gene expression, gene ontology, and pathways data. Jointly with promoters of the above-mentioned genes, we found a large number of intergenic regions with similar structure as histone promoters. Conclusions: This study represents one of the most comprehensive computational analyses conducted thus far on a genome-wide scale of promoters of human histone genes. Our analysis suggests a number of other human genes that share a high similarity of promoter structure with the histone genes and thus are highly likely to be coregulated, and consequently coexpressed, with the histone genes. We also found that there are a large number of intergenic regions across the genome with their structures similar to promoters of histone genes. These regions may be promoters of yet unidentified genes, or may represent remote control regions that participate in regulation of histone and histone-coregulated gene transcription initiation. While these hypotheses still remain to be verified, we believe that these form a useful resource for researchers to further explore regulation of human histone genes and human genome. It is worthwhile to note that the regulatory regions of the human genome remain largely un-annotated even today and this study is an attempt to supplement our understanding of histone regulatory regions. [ABSTRACT FROM AUTHOR]

Background: Biclustering of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions/samples. Although several biclustering algorithms have been studied, few are based on rigorous statistical models. Results: We developed a Bayesian biclustering model (BBC), and implemented a Gibbs sampling procedure for its statistical inference. We showed that Bayesian biclustering model can correctly identify multiple clusters of gene expression data. Using simulated data both from the model and with realistic characters, we demonstrated the BBC algorithm outperforms other methods in both robustness and accuracy. We also showed that the model is stable for two normalization methods, the interquartile range normalization and the smallest quartile range normalization. Applying the BBC algorithm to the yeast expression data, we observed that majority of the biclusters we found are supported by significant biological evidences, such as enrichments of gene functions and transcription factor binding sites in the corresponding promoter sequences. Conclusions: The BBC algorithm is shown to be a robust model-based biclustering method that can discover biologically significant gene-condition clusters in microarray data. The BBC model can easily handle missing data via Monte Carlo imputation and has the potential to be extended to integrated study of gene transcription networks. [ABSTRACT FROM AUTHOR]

Background: An important goal of comparative genomics is the identification of functional elements through conservation analysis. Phylo-HMM was recently introduced to detect conserved elements based on multiple genome alignments, but the method has not been rigorously evaluated. Results: We report here a simulation study to investigate the power of phylo-HMM. We show that the power of the phylo-HMM approach depends on many factors, the most important being the number of species-specific genomes used and evolutionary distances between pairs of species. This finding is consistent with results reported by other groups for simpler comparative genomics models. In addition, the conservation ratio of conserved elements and the expected length of the conserved elements are also major factors. In contrast, the influence of the topology and the nucleotide substitution model are relatively minor factors. Conclusion: Our results provide for general guidelines on how to select the number of genomes and their evolutionary distance in comparative genomics studies, as well as the level of power we can expect under different parameter settings. [ABSTRACT FROM AUTHOR]

Background: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. RSVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features. [ABSTRACT FROM AUTHOR]

Background: Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservation present in these protein sequences presumably are due to selective constraints maintaining important but unknown structural mechanisms with some constraints specific to each family and others shared by a larger subset or by the entire superfamily. To exploit these patterns as a source of functional information, we recently devised a statistically based approach called contrast hierarchical alignment and interaction network (CHAIN) analysis, which infers the strengths of various categories of selective constraints from co-conserved patterns in a multiple alignment. The power of this approach strongly depends on the quality of the multiple alignments, which thus motivated development of theoretical concepts and strategies to improve alignment of conserved motifs within large sets of distantly related sequences. Results: Here we describe a hidden Markov model (HMM), an algebraic system, and Markov chain Monte Carlo (MCMC) sampling strategies for alignment of multiple sequence motifs. The MCMC sampling strategies are useful both for alignment optimization and for adjusting position specific background amino acid frequencies for alignment uncertainties. Associated statistical formulations provide an objective measure of alignment quality as well as automatic gap penalty optimization. Improved alignments obtained in this way are compared with PSI-BLAST based alignments within the context of CHAIN analysis of three protein families: Giα subunits, prolyl oligopeptidases, and transitional endoplasmic reticulum (p97) AAA+ ATPases. Conclusion: While not entirely replacing PSI-BLAST based alignments, which likewise may be optimized for CHAIN analysis using this approach, these motif-based methods often more accurately align very distantly related sequences and thus can provide a better measure of selective constraints. In some instances, these new approaches also provide a better understanding of familyspecific constraints, as we illustrate for p97 ATPases. Programs implementing these procedures and supplementary information are available from the authors. [ABSTRACT FROM AUTHOR]

Background: Recurrent non-random genomic alterations are the hallmarks of cancer and the characterization of these imbalances is critical to our understanding of tumorigenesis and cancer progression. Results: We performed array-comparative genomic hybridization (A-CGH) on cDNA microarrays containing 42,000 elements in neuroblastoma (NB). We found that only two chromosomes (2p and 12q) had gene amplifications and all were in the MYCN amplified samples. There were 6 independent non-contiguous amplicons (10.4-69.4 Mb) on chromosome 2, and the largest contiguous region was 1.7 Mb bounded by NAG and an EST (clone: 757451); the smallest region was 27 Kb including an EST (clone: 241343), NCYM, and MYCN. Using a probabilistic approach to identify single copy number changes, we systemically investigated the genomic alterations occurring in Stage 1 and Stage 4 NBs with and without MYCN amplification (stage 1-, 4-, and 4+). We have not found genomic alterations universally present in all (100%) three subgroups of NBs. However we identified both common and unique patterns of genomic imbalance in NB including gain of 7q32, 17q21, 17q23-24 and loss of 3p21 were common to all three categories. Finally we confirm that the most frequent specific changes in Stage 4+ tumors were the loss of 1p36 with gain of 2p24-25 and they had fewer genomic alterations compared to either stage 1 or 4-, indicating that for this subgroup of poor risk NB requires a smaller number of genomic changes are required to develop the malignant phenotype. Conclusions: cDNA A-CGH analysis is an efficient method for the detection and characterization of amplicons. Furthermore we were able to detect single copy number changes using our probabilistic approach and identified genomic alterations specific to stage and MYCN amplification. [ABSTRACT FROM AUTHOR]

Many genomic techniques have been developed to study gene essentiality genome-wide, such as CRISPR and shRNA screens. Our analyses of public CRISPR screens suggest protein interaction networks, when integrated with gene expression or histone marks, are highly predictive of gene essentiality. Meanwhile, the quality of CRISPR and shRNA screen results can be significantly enhanced through network neighbor information. We also found network neighbor information to be very informative on prioritizing ChIP-seq target genes and survival indicator genes from tumor profiling. Thus, our study provides a general method for gene essentiality analysis in functional genomic experiments (http://nest.dfci.harvard.edu). Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0808-9) contains supplementary material, which is available to authorized users.

Synergistically integrating multi-layer genomic data at systems level not only can lead to deeper insights into the molecular mechanisms related to disease initiation and progression, but also can guide pathway-based biomarker and drug target identification. With the advent of high-throughput next-generation sequencing technologies, sequencing both DNA and RNA has generated multi-layer genomic data that can provide DNA polymorphism, non-coding RNA, messenger RNA, gene expression, isoform and alternative splicing information. Systems biology on the other hand studies complex biological systems, particularly systematic study of complex molecular interactions within specific cells or organisms. Genomics and molecular systems biology can be merged into the study of genomic profiles and implicated biological functions at cellular or organism level. The prospectively emerging field can be referred to as systems genomics or genomic systems biology. The Mid-South Bioinformatics Centre (MBC) and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and University of Arkansas for Medical Sciences are particularly interested in promoting education and research advancement in this prospectively emerging field. Based on past investigations and research outcomes, MBC is further utilizing differential gene and isoform/exon expression from RNA-seq and co-regulation from the ChiP-seq specific for different phenotypes in combination with protein-protein interactions, and protein-DNA interactions to construct high-level gene networks for an integrative genome-phoneme investigation at systems biology level.

A novel normalization method based on the GC content of probes is developed for two-color tiling arrays. The proposed method, together with robust estimates of the model parameters, is shown to perform superbly on published data sets. A robust algorithm for detecting peak regions is also formulated and shown to perform well compared to other approaches. The tools have been implemented as a stand-alone Java program called MA2C, which can display various plots of statistical analysis for quality control., Statistics

Background: Physical activity is essential for physical, mental, and cognitive health. Providing evidence to develop better public health policies to encourage increased physical activity is crucial. Therefore, we developed an in-depth survey as part of the Korea Youth Risk Behavior Survey to assess the current status and determinants of physical activity among Korean adolescents., Methods: We developed an initial version of the questionnaire based on a review of validated questionnaires, recent trends and emerging issues related to adolescent physical activity, and the national public health agenda pertaining to health promotion. Content validity was confirmed by a panel of 10 experts. Face validity was confirmed through focus group interviews with 12 first-year middle school students. The test-retest reliability of the questionnaire was evaluated by administering it twice, approximately two weeks apart, to a sample of 360 middle and high school students. Additionally, the frequency or average number of responses was analyzed in a sample of 600 students who participated in the initial test-retest reliability evaluation of the questionnaire developed in this study., Results: Through item pool generation and content and face validity test, the final 15 questionnaire items were developed across five themes: levels of physical activity, school sports club activities, transportation-related physical activity, physical activity-promoting environments, and factors mediating physical activity. The test-retest reliability ranged from fair to substantial. Results from the newly developed survey reveal that only a minority of adolescents engage in sufficient physical activity, with only 17.2% and 21.5% participating in vigorous and moderate-intensity activities, respectively, for at least five days per week. Among school-based activities, 44.3% of students do not participate in school sports clubs due to reasons including absence of clubs and disinterest in exercise. The major motivators for physical activity are personal enjoyment and health benefits, whereas preferences for other leisure activities and academic pressures are the predominant barriers., Conclusions: This study developed valid and reliable in-depth survey items to assess physical activity among Korean youths. It will hopefully enhance our understanding of adolescent physical activity, offering essential preliminary evidence to inform the development of public health strategies aimed at promoting adolescent health., (© 2024. The Author(s).)

Background: Research on the impact of COVID-19-induced home isolation on the physical fitness of college students is limited. This study aims to compare and analyze the physical fitness test scores of college students in two groups: those who experienced home isolation and those who did not, over three consecutive years after enrolment, to investigate the effects of home isolation on the physical fitness development of Chinese college students., Methods: This comparative study included two longitudinal surveys conducted among medical college students. The participants were divided into an experimental group and a control group. The physical fitness indicators measured included body mass index (BMI), vital capacity (VC), 50-metre run, sit-and-reach, standing long jump, 1000/800-metre runs (males/females), pull-ups (males) and sit-ups (females). Repeated measures analysis of variance (ANOVA) was employed, and the Greenhouse-Geisser correction was applied when Mauchly's assumption of sphericity was violated. Pairwise comparisons were conducted using the Bonferroni method., Results: A total of 6580 students participated in the study, with 3360 students (1490 males, 1870 females) enrolled in 2019 as the experimental group and 3220 students (1326 males, 1894 females) enrolled in 2017 as the control group. All participants completed the physical fitness tests for three consecutive years. The results showed that the experimental group exhibited decreased performance in the 1000-metre and 800-metre runs, and improved performance in the sit-and-reach test. After the end of home isolation, there was an improvement in the performance of the 1000-metre run and 800-metre run, while no significant differences were observed in the trends of the other tested indicators., Conclusion: The findings of this study indicate that the home isolation environment during COVID-19 had a significant impact on the physical fitness of college students, specifically in terms of endurance and flexibility qualities, as well as male BMI. To better prepare for future public health emergencies and mitigate the effects of isolation, teaching students endurance exercises that can be performed at home should be prioritized. Furthermore, physical education programs should be improved to enhance student flexibility., (© 2023. The Author(s).)

Background: Secondhand smoke (SHS) exposure among adolescents who are still developing can negatively affect their physical and psychological health, including metabolic syndrome (MetS), which is a risk factor for cardiovascular disease. However, the relationship between exposure to SHS and MetS in adolescence has not been evaluated., Methods: A total of 240 subjects aged 13-15 years who were followed up in the Ewha Birth and Growth Study were included in this study. Using the urinary cotinine level, the participants' exposure to SHS was divided into tertiles, and the continuous MetS score (cMetS) and its components were compared among the three groups using a generalized linear model and trend analysis. Univariate and multivariate linear regression analyses were performed. We adjusted for several confounding variables including sex, father's education level, father's current alcohol consumption status, moderate physical activity, and overweight status., Results: The association between cMetS and the urinary cotinine level was not significant. However, the higher the urinary cotinine level, the lower the high-density lipoprotein cholesterol (HDL-C) level. In particular, the significance of the HDL-C level was maintained after adjusting for covariates., Conclusions: This study supports an association between SHS exposure and the components of MetS in adolescents aged 13-15 years, and it suggests the need to address SHS exposure in adolescents to reduce the cardiovascular risk in later life., (© 2023. The Author(s).)

Background: Atelectasis may play a substantial role in the development of pneumonia. However, pneumonia has never been evaluated as an outcome of atelectasis in surgical patients. We aimed to determine whether atelectasis is related to an increased risk of postoperative pneumonia, intensive care unit (ICU) admission and hospital length of stay (LOS)., Methods: The electronic medical records of adult patients who underwent elective non-cardiothoracic surgery under general anesthesia between October 2019 and August 2020 were reviewed. They were divided into two groups: one who developed postoperative atelectasis (atelectasis group) and the other who did not (non-atelectasis group). The primary outcome was the incidence of pneumonia within 30 days after the surgery. The secondary outcomes were ICU admission rate and postoperative LOS., Results: Patients in the atelectasis group were more likely to have risk factors for postoperative pneumonia including age, body mass index, a history of hypertension or diabetes mellitus and duration of surgery, compared with those in the non-atelectasis. Among 1,941 patients, 63 (3.2%) developed postoperative pneumonia; 5.1% in the atelectasis group and 2.8% in the non-atelectasis (P = 0.025). In multivariable analysis, atelectasis was associated with an increased risk of pneumonia (adjusted odds ratio, 2.33; 95% CI: 1.24 - 4.38; P = 0.008). Median postoperative LOS was significantly longer in the atelectasis group (7 [interquartile range: 5-10 days]) than in the non-atelectasis (6 [3-8] days) (P < 0.001). Adjusted median duration was also 2.19 days longer in the atelectasis group (β, 2.19; 95% CI: 0.821 - 2.834; P < 0.001). ICU admission rate was higher in the atelectasis group (12.1% vs. 6.5%; P < 0.001), but it did not differ between the groups after adjustment for confounders (adjusted odds ratio, 1.52; 95% CI: 0.88 - 2.62; P = 0.134)., Conclusion: Among patients undergoing elective non-cardiothoracic surgery, patients with postoperative atelectasis were associated with a 2.33-fold higher incidence of pneumonia and a longer LOS than those without atelectasis. This finding alerts the need for careful management of perioperative atelectasis to prevent or reduce the adverse events including pneumonia and the burden of hospitalizations., Trial Registration: None., (© 2023. The Author(s).)

Aicardi-Goutières syndrome (AGS) is a rare genetic disorder involving the central nervous system and autoimmune abnormalities, leading to severe intellectual and physical disability with poor prognosis. AGS has a phenotype similar to intrauterine viral infection, which often leads to delays in genetic counseling. In this study, we report a case with a prenatal diagnosis of AGS. The first fetal ultrasound detected bilateral lateral ventricle cystic structures, and fetal MRI was performed to identify other signs. The right parietal lobe signal showed cerebral white matter abnormalities, and fetal brain development level was lower than that of normal fetuses of the same gestational age. Whole-exome sequencing revealed that the fetus carried the TREX1:NM&#95;033629.6:exon2:c.294dup:p. C99Mfs*3 variant, suggesting that the c.294dup mutation of the TREX1 gene was the pathogenic mutation site, and the final comprehensive diagnosis was AGS1. In this article, we also reviewed the previous literature for possible phenotypes in the fetus and found that microcephaly and intrauterine growth retardation may be the first and most important markers of the intrauterine phenotype of AGS., (© 2023. The Author(s).)

Background: Thirteen essential maternal child health (MCH) commodities, identified by the UN Commission on Life-Saving Commodities for Women and Children, could save the lives of more than 6 million women and children in Low-and-Middle-Income Countries (LMICs) if made available at the point of care. To reduce stockout of those commodities and improve the health supply chains in LMICs, the Electronic TRAcking system for healthcare commodities (E+TRA Health), an all-in-one out-of-box solution, was developed to track and manage medical commodities at lower-level health facilities in rural areas. It aims to support real-time monitoring and decision-making to (1) reduce the time needed to prepare orders, (2) reduce stockout and overstock cases of targeted medical supplies, (3) help improve patient outcomes. In this study, we adopted an integrated approach to analyze the process of information flow, identify and address critical paths of essential supplies associated with maternal health in the Ugandan health system., Methods: We apply system engineering principles and work with community partners in hospitals to develop care process workflow charts (based on essential services) for the lifecycle of maternal health continuum of care. Based on this chart, we develop a cloud-based offline-compatible smart sync platform named "E+TRA Health" to triangulate (1) patient admission, diagnoses, delivery information, testing reports from laboratories, (2) inventory information from main store, stores in MCH unit, and (3) lab, to identify the critical list of medical and laboratory supplies, their lead times for procurement and then generate reports and suggested procurement plans for real time decision-making., Results: The E+TRA Health platform was piloted in two Healthcare Center IV facilities in Uganda over a period of 6 months. The system collected more than 5000 patient records and managed more than 500 types of medicines. The pilot study demonstrated the functionalities of E+TRA Health and its feasibility to sense demand from point of care., Conclusion: E+TRA Health is the first to triangulate supply and demand data from three different departments (main store, lab, and MCH) to forecast and generate orders automatically to meet patient demands. It is capable of generating reports required by Ministry of Health in real time compared to one-week lead-time using paper-based systems. This prompts frontline stakeholders to generate efficient, reliable and sustainable strategic healthcare plans with real time data. This system improves patient outcomes through better commodity availability by sensing true patient demands., (© 2022. The Author(s).)

Background: Chemoattractant is critical to recruitment of osteoclast precursors and stimulates tumor bone metastasis. However, the role of chemoattractant in bone metastasis of colorectal cancer (CRC) is still unclear., Methods: Histochemistry analysis and TRAP staining were utilized to detect the bone resorption and activation of osteoclasts (OCs) after administration of CCL7 neutralizing antibody or CCR1 siRNA. qRT-PCR analysis and ELISA assay were performed to detect the mRNA level and protein level of chemoattractant. BrdU assay and Tunel assay were used to detect the proliferation and apoptosis of osteoclast precursors (OCPs). The migration of OCPs was detected by Transwell assay. Western blots assay was performed to examine the protein levels of pathways regulating the expression of CCL7 or CCR1., Results: OCPs-derived CCL7 was significantly upregulated in bone marrow after bone metastasis of CRC. Blockage of CCL7 efficiently prevented bone resorption. Administration of CCL7 promoted the migration of OCPs. Lactate promoted the expression of CCL7 through JNK pathway. In addition, CCR1 was the most important receptor of CCL7., Conclusion: Our study indicates the essential role of CCL7-CCR1 signaling for recruitment of OCPs in early bone metastasis of CRC. Targeting CCL7 or CCR1 could restore the bone volume, which could be a potential therapeutical target. Video Abstract., (© 2022. The Author(s).)

Background: Shared care is the preferred model for long-term survivorship care by cancer survivors, general practitioners and specialists. However, survivorship care remains specialist-led. A risk-stratified approach has been proposed to select suitable patients for long-term shared care after survivors have completed adjuvant cancer treatment. This study aims to use patient scenarios to explore views on patient suitability for long-term colorectal cancer shared care across the risk spectrum from survivors, general practitioners and specialists., Methods: Participants completed a brief questionnaire assessing demographics and clinical issues before a semi-structured in-depth interview. The interviews focused on the participant's view on suitability for long term cancer shared care, challenges and facilitators in delivering it and resources that would be helpful. We conducted thematic analysis using an inductive approach to discover new concepts and themes., Results: Interviews were conducted with 10 cancer survivors, 6 general practitioners and 9 cancer specialists. The main themes that emerged were patient-centredness, team resilience underlined by mutual trust and stronger system supports by way of cancer-specific training, survivorship care protocols, shared information systems, care coordination and navigational supports., Conclusions: Decisions on the appropriateness of this model for patients need to be made collaboratively with cancer survivors, considering their trust and relationship with their general practitioners and the support they need. Further research on improving mutual trust and operationalising support systems would assist in the integration of shared survivorship care.