Parallel Deterministic and Stochastic Global Minimization of Functions with Very Many Minima

The optimization of three problems with high dimensionality and many local minima are investigated under five different optimization algorithms: DIRECT, simulated annealing, Spall’s SPSA algorithm, the KNITRO package, and QNSTOP, a new algorithm developed at Indiana University

NetProphet 3: A machine learning framework for transcription factor network mapping and multi-omics integration

MOTIVATION: Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks. RESULTS: We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data. AVAILABILITY AND IMPLEMENTATION: All data and code are available at https://zenodo.org/record/7504131#.Y7Wu3i-B2x8. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Eval: A software package for analysis of genome annotations

SUMMARY: Eval is a flexible tool for analyzing the performance of gene annotation systems. It provides summaries and graphical distributions for many descriptive statistics about any set of annotations, regardless of their source. It also compares sets of predictions to standard annotations and to one another. Input is in the standard Gene Transfer Format (GTF). Eval can be run interactively or via the command line, in which case output options include easily parsable tab-delimited files. AVAILABILITY: To obtain the module package with documentation, go to and follow links for Resources, then Software. Please contact [email protected]

Using Expressing Sequence Tags to Improve Gene Structure Annotation

Finding all gene structures is a crucial step in obtaining valuable information from genomic sequences. It is still a challenging problem, especially for vertebrate genomes, such as the human genome. Expressed Sequence Tags (ESTs) provide a tremendous resource for determining intron-exon structures. However, they are short and error prone, which prevents existing methods from exploiting EST information efficiently. This dissertation addresses three aspects of using ESTs for gene structure annotation. The first aspect is using ESTs to improve de novo gene prediction. Probability models are introduced for EST alignments to genomic sequence in exons, introns, interknit regions, splice sites and UTRs, representing the EST alignment patterns in these regions. New gene prediction systems were developed by combining the EST alignments with comparative genomics gene prediction systems, such as TWINSCAN and N-SCAN, so that they can predict gene structures more accurately where EST alignments exist without compromising their ability to predict gene structures where no EST exists. The accuracy of TWINSCAN_EST and NSCAN_EST is shown to be substantially better than any existing methods without using full-length cDNA or protein similarity information. The second aspect is using ESTs and de novo gene prediction to guide biology experiments, such as finding full ORF-containing-cDNA clones, which provide the most direct experimental evidence for gene structures. A probability model was introduced to guide experiments by summing over gene structure models consistent with EST alignments. The last aspect is a novel EST-to-genome alignment program called QPAIRAGON to improve the alignment accuracy by using EST sequencing quality values. Gene prediction accuracy can be improved by using this new EST-to-genome alignment program. It can also be used for many other bioinformatics applications, such as SNP finding and alternative splicing site prediction

Using several pair-wise informant sequences for de novo prediction of alternatively spliced transcripts

BACKGROUND: As part of the ENCODE Genome Annotation Assessment Project (EGASP), we developed the MARS extension to the Twinscan algorithm. MARS is designed to find human alternatively spliced transcripts that are conserved in only one or a limited number of extant species. MARS is able to use an arbitrary number of informant sequences and predicts a number of alternative transcripts at each gene locus. RESULTS: MARS uses the mouse, rat, dog, opossum, chicken, and frog genome sequences as pairwise informant sources for Twinscan and combines the resulting transcript predictions into genes based on coding (CDS) region overlap. Based on the EGASP assessment, MARS is one of the more accurate dual-genome prediction programs. Compared to the GENCODE annotation, we find that predictive sensitivity increases, while specificity decreases, as more informant species are used. MARS correctly predicts alternatively spliced transcripts for 11 of the 236 multi-exon GENCODE genes that are alternatively spliced in the coding region of their transcripts. For these genes a total of 24 correct transcripts are predicted. CONCLUSION: The MARS algorithm is able to predict alternatively spliced transcripts without the use of expressed sequence information, although the number of loci in which multiple predicted transcripts match multiple alternatively spliced transcripts in the GENCODE annotation is relatively small

Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

MOTIVATION: The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now. RESULTS: We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2. AVAILABILITY AND IMPLEMENTATION: Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Using a Modified Purse Seine to Collect and Monitor Estuarine Fishes

We developed a modified purse seine to sample shallow water estuarine habitats and evaluated the efficacy of using this gear as a tool for monitoring estuarine fish populations in Tampa Bay, Florida. The purse seine (183-m long, 5.2 m deep and 50-mm stretch mesh nylon throughout) was easily deployed and retrieved by a 7 m flat-bottomed, bow-driven boat with a hydraulic wench and aluminum pursing davit. Retention rates of pinfish (Lagodon rhomboides) marked and released into 35 net sets averaged 49% (range 9-100%). Retention rates were not significantly influenced by sets over vegetated and unvegetated bottom types, various water depths from 1-3.3m and sets with and without bycatch. We then used the modified purse seine to sample fishes at 550 randomly selected sites in Tampa Bay from January 1997 to December 1998. Sampled habitats ranged from 1.0 to 3.3 m deep and included seagrass beds and non-vegetated sand or mud bottoms. Benthic, demersal, and pelagic fishes were captured, indicating the purse seine effectively sampled the entire water column. A wide size range of fishes was collected including pre-recruitment sizes of several economically important species. The ability of purse seines to fish independent of adjacent shorelines allowed us to sample nearshore waters that included large expanses of sea grass meadow

Optimization of Gene Prediction via More Accurate Phylogenetic Substitution Models

Determining the beginning and end positions of each exon in each protein coding gene within a genome can be difficult because the DNA patterns that signal a gene’s presence have multiple weakly related alternate forms and the DNA fragments that comprise a gene are generally small in comparison to the size of the genome. In response to this challenge, automated gene predictors were created to generate putative gene structures. N SCAN identifies gene structures in a target DNA sequence and can use conservation patterns learned from alignments between a target and one or more informant DNA sequences. N SCAN uses a Bayesian network, generated from a phylogenetic tree, to probabilistically relate the target sequence to the aligned sequence(s). Phylogenetic substitution models are used to estimate substitution likelihood along the branches of the tree. Although N SCAN’s predictive accuracy is already a benchmark for de novo HMM based gene predictors, optimizing its use of substitution models will allow for improved conservation pattern estimates leading to even better accuracy. Selecting optimal substitution models requires avoiding overfitting as more detailed models require more free parameters; unfortunately, the number of parameters is limited by the number of known genes available for parameter estimation (training). In order to optimize substitution model selection, we tested eight models on the entire genome including General, Reversible, HKY, Jukes-Cantor, and Kimura. In addition to testing models on the entire genome, genome feature based model selection strategies were investigated by assessing the ability of each model to accurately reflex the unique conservation patterns present in each genome region. Context dependency was examined using zeroth, first, and second order models. All models were tested on the human and D. melanogaster genomes. Analysis of the data suggests that the nucleotide equilibrium frequency assumption (denoted as πi) is the strongest predictor of a model’s accuracy, followed by reversibility and transition/transversion inequality. Furthermore, second order models are shown to give an average of 0.6% improvement over first order models, which give an 18% improvement over zeroth order models. Finally, by limiting parameter usage by the number of training examples available for each feature, genome feature based model selection better estimates substitution likelihood leading to a significant improvement in N SCAN’s gene annotation accuracy