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Transposon annotation is a very dynamic field of genomics and various tools assigned to support this bioinformatics endeavor were reported. Genome ARTIST (GA) software was initially developed for mapping artificial transposons mobilized during insertional mutagenesis projects. Now, the new functions of GA_v2 qualify it as an effective companion for mapping and annotation of class II natural transposons in assembled genomes, contigs or sequencing reads.Tabular export of mapping and annotation data for subsequent high-throughput data analysis, the export of a list of flanking sequences around either the coordinates of insertion or around the target site duplications (TSDs) and generation of a consensus sequence for the respective flanking sequences are all key assets of GA_v2.Additionally, we developed two accompanying short scripts that enable the user to annotate transposons existent in assembled genomes and to use various annotation offered by FlyBase for Drosophila melanogaster genome.Herein, we present the applicability of GA_v2 for a preliminary annotation of the class II transposon P-element in the genome of D. melanogaster strain Horezu, Romania, which was sequenced with Nanopore technology in our laboratory. Our results point that GA_v2 is a reliable tool to be integrated in pipelines designed to perform transposon annotation in new sequenced genomes.GA_v2 is open source software compatible with Ubuntu, Mac OS and Windows and is available at https://github.com/genomeartist/genomeartist and at www.genomeartist.ro.

Many of the effects on fitness in population genetics are due not to single locations in the genome, but to the interaction of genetic variants at multiple locations in the genome. Of particular interest are ‘completely epistatic’ interactions, where a combination of genetic variants is required to produce an effect, and the effect cannot occur with any other combination. In diploids, epistasis is strongly connected to meiotic recombination, a process which can both assemble and destroy beneficial combinations of genetic variants. Additionally, epistatic interactions can be hard to detect in empirical studies, and mathematical models of epistasis and recombination are challenging to analyse, so despite their ubiquity epistatic interactions are regularly not considered. As a result, there is little consensus on when high levels of recombination might be expected, or how strongly recombination affects beneficial or deleterious fitness effects controlled by epistatic interactions. We address this question by conducting a meta-analysis and simulations. The meta-analysis used data drawn and curated fromDrosophila melanogasterstudies in Flybase. We extracted studies relating genetic combinations and phenotypically detectable effects on fitness, then analysed the relationship between the rate of recombination and effect on fitness with a statistical model. We also ran simulations under a two-locus Wright-Fisher model with recombination and epistatic selection. The results of both approaches indicated a tendency for genetic combinations with an epistatic effect on fitness to occur in an environment of reduced meiotic recombination. Two possible explanations for this are that the variants controlling such interactions are selected for in regions where there is little recombination, or that such interactions lead to selection for lower rates of recombination in the regions where those variants appear.

Herein we describe the distribution of P{lacW} and P{EP} artificial transposons in the genome of Drosophila melanogaster. A total number of 5,560 P{lacW} and 3,786 P{EP} insertions available on FlyBase were extracted from this database and ordered according to the chromosomal regions they hit. Comparative bioinformatics analysis revealed that the insertion’s patterns are similar for P{lacW} and P{EP}. The two patterns are significantly correlated for chromosomes X, 2L, 2R and 3L, but not for 3R.Fourier analysis revealed a periodic behaviour in the distribution of insertions, which approximates insertional hotspots. Our results raise questions concerning if and how the local chromosomal landscape affects the insertion patterns of different but related transposons.

Insertional mutagenesis experiments performed onDrosophila melanogastermodel often relies on induced mobilization of artificial transposons derived from P mobile element. In an attempt to detect transposition preferences, we accomplished a pilot study concerning the insertional patterns ofP{lacW}andP{EP}constructs in the third chromosome ofD. melanogaster.Our inventory inquiry considered 2177 insertions ofP{lacW}and 1646 insertions ofP{EP}available in FB2016_02 release of FlyBase and revealed insertional hotspots and coldspots in 3L and 3R, but also a preference of both artificial transposons to insert in 3R. The general distribution ofP{lacW}andP{EP}insertions appears to be similar but not identical, probably due to differences in size and molecular architecture of these transposons. Our results may have predictive value for experimental design of insertional mutagenesis screenings, but are also expected to contribute to a better understanding ofPtransposon biology.

Drosophila melanogaster cell lines are important resources for cell biologists. Here, we catalog the expression of exons, genes, and unannotated transcriptional signals for 25 lines. Unannotated transcription is substantial (typically 19% of euchro- matic signal). Conservatively, we identify 1405 novel transcribed regions; 684 of these appear to be new exons of neighboring, oftendistant, genes. Sixty-four percent of genes areexpressed detectably in atleastone line, but only 21%are detected in all lines. Each cell line expresses, on average, 5885 genes, including a common set of 3109. Expression levels vary over several orders of magnitude. Major signaling pathways are well represented: most differentiation pathways are ''off'' andsurvival/growth pathways''on.''Roughly 50%ofthe genesexpressed by eachlinearenot part ofthe commonset, and these show considerable individuality. Thirty-one percent are expressed at a higher level in at least one cell line than in any single developmental stage, suggesting that each line is enriched for genes characteristic of small sets of cells. Most re- markable is that imaginal disc-derived lines can generally be assigned, on the basis of expression, to small territories within developing discs. These mappings reveal unexpected stability of even fine-grained spatial determination. No two cell lines show identical transcription factor expression. We conclude that each line has retained features of an individual founder cell superimposed on a common ''cell line'' gene expression pattern. (Supplemental material is available for this article. The data from this study have been submitted to the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) under accession nos. GSE15596, GSE16269-GSE16290, GSE16321- GSE16322, GSE16325, and GSE18040. All of the microarray data, RNA-seq data, and expression scores for genes and exons are available from the Data Coordination Center of modENCODE (http://modencode.org), and much of the data are also available from the Drosophila Genomic Resources Center (https://dgrc.cgb.indiana.edu/) and FlyBase (http:// flybase.org/).)

RNA-seq was used to generate an extensive map of the Drosophila melanogaster transcriptome by broad sampling of 10 developmental stages. In total, 142.2 million uniquely mapped 64–100-bp paired-end reads were generated on the Illumina GA II yielding 356× sequencing coverage. More than 95% of FlyBase genes and 90% of splicing junctions were observed. Modifications to 30% of FlyBase gene models were made by extension of untranslated regions, inclusion of novel exons, and identification of novel splicing events. A total of 319 novel transcripts were identified, representing a 2% increase over the current annotation. Alternate splicing was observed in 31% of D. melanogaster genes, a 38% increase over previous estimations, but significantly less than that observed in higher organisms. Much of this splicing is subtle such as tandem alternate splice sites.

Background A critical topic of insertional mutagenesis experiments performed on model organisms is mapping the hits of artificial transposons (ATs) at nucleotide level accuracy. Mapping errors may occur when sequencing artifacts or mutations as single nucleotide polymorphisms (SNPs) and small indels are present very close to the junction between a genomic sequence and a transposon inverted repeat (TIR). Another particular item of insertional mutagenesis is mapping of the transposon self-insertions and, to our best knowledge, there is no publicly available mapping tool designed to analyze such molecular events. Results We developed Genome ARTIST, a pairwise gapped aligner tool which works out both issues by means of an original, robust mapping strategy. Genome ARTIST is not designed to use next-generation sequencing (NGS) data but to analyze ATs insertions obtained in small to medium-scale mutagenesis experiments. Genome ARTIST employs a heuristic approach to find DNA sequence similarities and harnesses a multi-step implementation of a Smith-Waterman adapted algorithm to compute the mapping alignments. The experience is enhanced by easily customizable parameters and a user-friendly interface that describes the genomic landscape surrounding the insertion. Genome ARTIST is functional with many genomes of bacteria and eukaryotes available in Ensembl and GenBank repositories. Our tool specifically harnesses the sequence annotation data provided by FlyBase for Drosophila melanogaster (the fruit fly), which enables mapping of insertions relative to various genomic features such as natural transposons. Genome ARTIST was tested against other alignment tools using relevant query sequences derived from the D. melanogaster and Mus musculus (mouse) genomes. Real and simulated query sequences were also comparatively inquired, revealing that Genome ARTIST is a very robust solution for mapping transposon insertions. Conclusions Genome ARTIST is a stand-alone user-friendly application, designed for high-accuracy mapping of transposon insertions and self-insertions. The tool is also useful for routine aligning assessments like detection of SNPs or checking the specificity of primers and probes. Genome ARTIST is an open source software and is available for download at www.genomeartist.ro and at GitHub (https://github.com/genomeartist/genomeartist ). Electronic supplementary material The online version of this article (doi:10.1186/s13100-016-0061-0) contains supplementary material, which is available to authorized users.

Transcription factors (TFs) are essential regulators of gene expression, and mutated TF genes have been shown to cause numerous human genetic diseases. Yet to date, no single, comprehensive database of human TFs exists. In this work, we describe the collection of an essentially complete set of TF genes from one depiction of the human ORFeome, and the design of a microarray to interrogate their expression. Taking 1468 known TFs from TRANSFAC, InterPro, and FlyBase, we used this seed set to search the ScriptSure human transcriptome database for additional genes. ScriptSure's genome-anchored transcript clusters allowed us to work with a nonredundant high-quality representation of the human transcriptome. We used a high-stringency similarity search by using BLASTN, and a protein motif search of the human ORFeome by using hidden Markov models of DNA-binding domains known to occur exclusively or primarily in TFs. Four hundred ninety-four additional TF genes were identified in the overlap between the two searches, bringing our estimate of the total number of human TFs to 1962. Zinc finger genes are by far the most abundant family (762 members), followed by homeobox (199 members) and basic helix-loop-helix genes (117 members). We designed a microarray of 50-mer oligonucleotide probes targeted to a unique region of the coding sequence of each gene. We have successfully used this microarray to interrogate TF gene expression in species as diverse as chickens and mice, as well as in humans.

The functional classification of genes on a genome-wide scale is now in its infancy, and we make a first attempt to assess existing methods and identify sources of error. To this end, we compared two independent efforts for associating proteins with functions, one implemented by FlyBase and the other by PANTHER at Celera Genomics. Both methods make inferences based on sequence similarity and the available experimental evidence. However, they differ considerably in methodology and process. Overall, assuming that the systematic error across the two methods is relatively small, we find the protein-to-function association error rate of both the FlyBase and PANTHER methods to be

The Gene Ontology Consortium (Gene Ontology Consortium 2000) provides a standardized vocabulary for the annotation of gene attributes, which fall into the three general categories of molecular function, biological process, and cellular component. Organism-specific databases such as FlyBase (FlyBase Consortium 2002), Saccharomyces Genome Database (SGD; Cherry et al. 1998), Mouse Genome Database (MGD; Blake et al. 2002), and WormBase (Stein et al. 2001), have codeveloped this vocabulary, and have used it to annotate genes with the attributes that the biomedical literature asserts that they hold. These databases are incomplete because there are genes whose attributes are not yet all known, and because there is literature that has not yet been digested by the database curators. In such cases it is useful to have a prediction of whether a gene has a certain attribute. Such predictions can help to make the databases more complete (and consequently more useful to researchers) by directing curators toward literature that they may have overlooked. Also, predictions that are not presently supported by the literature provide new hypotheses that may be tested experimentally. A variety of approaches for predicting Gene Ontology (GO) attributes have been attempted. Natural language processing was used in Raychaudhuri et al. (2002) to automate the curator's task of extracting gene–attribute associations from literature abstracts. Others have assigned attributes to genes on the basis of microarray data (Hvidsten et al. 2001) or protein folds (Schug et al. 2002). These approaches are especially valuable for assigning attributes to genes with otherwise unknown function. But once some attributes of a gene are known, statistical patterns among the annotations themselves can be useful for predicting additional attributes. In this paper, we model the probabilistic relationships between the GO annotations using two approaches, one based on decision trees and the other based on Bayesian networks. We assess the models using cross-validation on the SGD and FlyBase databases. We also manually assess 100 of those gene–attribute associations that the models indicate are likely to hold but that have not been annotated in the databases.