NeuroPred is a web application designed to predict cleavage sites at basic amino acid locations in neuropeptide precursor sequences. The user can study one amino acid sequence or multiple sequences simultaneously, selecting from several prediction models and optional, user-defined functions. Logistic regression models are trained on experimentally verified or published cleavage data from mollusks, mammals and insects, and amino acid motifs reported to be associated with cleavage. Confidence interval limits of the probabilities of cleavage indicate the precision of the predictions; these predictions are transformed into cleavage or non-cleavage events according to user-defined thresholds. In addition to the precursor sequence, NeuroPred accepts user-specified cleavage information, providing model accuracy statistics based on observed and predicted cleavages. Neuropred also computes the mass of the predicted peptides, including user-selectable post-translational modifications. The resulting mass list aids the discovery and confirmation of new neuropeptides using mass spectrometry techniques. The NeuroPred application, manual, reference manuscripts and training sequences are available at http://neuroproteomics.scs.uiuc.edu/neuropred.html.

Background: Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival. Methods: A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers. Results: A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. Pik3r1, E2f3, Akr1c3, Csf1, Jag2, Plcg1, Rpl37a, Sod2, Topors, Hras, Mdm2, Camk2g, Fstl1, Il13ra1, Mtap and Tp53 were associated with multiple survival events. Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for Syne1, Pdcd4, Ighg1, Tgfa, Pla2g7, and Paics. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. C2, Egfr, Prkcb, Igf2bp3, and Gdf10 had gender-dependent associations; Sox10, Rps20, Rab31, and Vav3 had race-dependent associations; Chi3l1, Prkcb, Polr2d, and Apool had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death. Conclusions: Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.

Background: General, breed- and diet-dependent associations between feed efficiency in beef cattle and single nucleotide polymorphisms (SNPs) or haplotypes were identified on a population of 1321 steers using a 50 K SNP panel. Genomic associations with traditional two-step indicators of feed efficiency – residual feed intake (RFI), residual average daily gain (RADG), and residual intake gain (RIG) – were compared to associations with two complementary one-step indicators of feed efficiency: efficiency of intake (EI) and efficiency of gain (EG). Associations uncovered in a training data set were evaluated on independent validation data set. A multi-SNP model was developed to predict feed efficiency. Functional analysis of genes harboring SNPs significantly associated with feed efficiency and network visualization aided in the interpretation of the results. Results: For the five feed efficiency indicators, the numbers of general, breed-dependent, and diet-dependent associations with SNPs (P-value < 0.0001) were 31, 40, and 25, and with haplotypes were six, ten, and nine, respectively. Of these, 20 SNP and six haplotype associations overlapped between RFI and EI, and five SNP and one haplotype associations overlapped between RADG and EG. This result confirms the complementary value of the one and two-step indicators. The multi-SNP models included 89 SNPs and offered a precise prediction of the five feed efficiency indicators. The associations of 17 SNPs and 7 haplotypes with feed efficiency were confirmed on the validation data set. Nine clusters of Gene Ontology and KEGG pathway categories (mean P-value < 0.001) including, 9nucleotide binding; ion transport, phosphorous metabolic process, and the MAPK signaling pathway were overrepresented among the genes harboring the SNPs associated with feed efficiency. Conclusions: The general SNP associations suggest that a single panel of genomic variants can be used regardless of breed and diet. The breed- and diet-dependent associations between SNPs and feed efficiency suggest that further refinement of variant panels require the consideration of the breed and management practices. The unique genomic variants associated with the one- and two-step indicators suggest that both types of indicators offer complementary description of feed efficiency that can be exploited for genome-enabled selection purposes.

Motivation: The production of neuropeptides from their precursor proteins is the result of a complex series of enzymatic processing steps. Often, the annotation of new neuropeptide genes from sequence information outstrips biochemical assays and so bioinformatics tools can provide rapid information on the most likely peptides produced by a gene. Predicting the final bioactive neuropeptides from precursor proteins requires accurate algorithms to determine which locations in the protein are cleaved. Results: Predictive models were trained on Apis mellifera and Drosophila melanogaster precursors using binary logistic regression, multi-layer perceptron and k-nearest neighbor models. The final predictive models included specific amino acids at locations relative to the cleavage sites. Correct classification rates ranged from 78 to 100% indicating that the models adequately predicted cleaved and non-cleaved positions across a wide range of neuropeptide families and insect species. The model trained on D.melanogaster data had better generalization properties than the model trained on A. mellifera for the data sets considered. The reliable and consistent performance of the models in the test data sets suggests that the bioinformatics strategies proposed here can accurately predict neuropeptides in insects with sequence information based on neuropeptides with biochemical and sequence information in well-studied species. Contact:rodrgzzs@uiuc.edu Supplementary information: Sequences and cleavage information are available at Bioinformatics online.

Records of mortality during the first year of life of 8,642 lambs from a composite population at the U.S. Meat Animal Research Center were studied using survival and logistic analyses. The traditional logistic approach analyzes the binary response of whether or not a lamb survived until a particular time point, thus disregarding information on the actual age at death. Survival analysis offers an alternative way to study mortality, wherein the response variable studied is the precise age at death while accounting for possible record censoring. Lamb mortality was studied across five periods based on management practices: birth to weaning, birth to 120 d of age, birth to 365 d of age, weaning to 365 d of age, and 120 to 365 d of age. Explanatory variables included in the models were sex, type of birth, age of dam, and whether or not a lamb was raised in a nursery. The survival analysis was implemented using Weibull and Cox proportional hazards models with sire as random effect. The logistic approach evaluated sire, animal, and maternal effects models. Lambs culled during any period were treated as censored in the survival analyses and were assumed alive in the logistic analyses. Similar estimates of the explanatory variables were obtained from the survival and logistic analyses, but the survival analyses had lower standard errors than the logistic analyses, suggesting a slight superiority of the former approach. Heritability estimates were generally consistent across all periods ranging from 0.15 to 0.21 in the Weibull model, 0.12 to 0.20 in the Cox model, 0.08 to 0.11 in the logistic sire model, 0.04 to 0.05 in the logistic animal model, and 0.03 to 0.07 in the maternal effects logistic model. Maternal effects were important in the early stages of lamb life, but the maternal heritability was less than 0.07 in all the stages studied with a negative correlation (−0.86 to −0.61) between direct and maternal effects. The estimates of additive genetic variance indicate that the use of survival analysis estimates in breeding schemes could allow for effective selection against mortality, thereby improving sheep productivity, welfare, and profitability.