DNPTrapper: an assembly editing tool for finishing and analysis of complex repeat regions

Birth of a new gene on the Y chromosome of Drosophila melanogaster

Contrary to the pattern seen in mammalian sex chromosomes, where most Y-linked genes have X-linked homologs, the Drosophila X and Y chromosomes appear to be unrelated. Most of the Y-linked genes have autosomal paralogs, so autosome-to-Y transposition must be the main source of Drosophila Y-linked genes. Here we show how these genes were acquired. We found a previously unidentified gene (flagrante delicto Y, FDY) that originated from a recent duplication of the autosomal gene vig2 to the Y chromosome of Drosophila melanogaster. Four contiguous genes were duplicated along with vig2, but they became pseudogenes through the accumulation of deletions and transposable element insertions, whereas FDY remained functional, acquired testis-specific expression, and now accounts for ∼20% of the vig2-like mRNA in testis. FDY is absent in the closest relatives of D. melanogaster, and DNA sequence divergence indicates that the duplication to the Y chromosome occurred ∼2 million years ago. Thus, FDY provides a snapshot of the early stages of the establishment of a Y-linked gene and demonstrates how the Drosophila Y has been accumulating autosomal genes.

PINTA: a web server for network-based gene prioritization from expression data

PINTA (available at http://www.esat.kuleuven.be/pinta/; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes based on the differential expression of their neighborhood in a genome-wide protein–protein interaction network. Our strategy is meant for biological and medical researchers aiming at identifying novel disease genes using disease specific expression data. PINTA supports both candidate gene prioritization (starting from a user defined set of candidate genes) as well as genome-wide gene prioritization and is available for five species (human, mouse, rat, worm and yeast). As input data, PINTA only requires disease specific expression data, whereas various platforms (e.g. Affymetrix) are supported. As a result, PINTA computes a gene ranking and presents the results as a table that can easily be browsed and downloaded by the user.

NGSView: an extensible open source editor for next-generation sequencing data

Summary:High-throughput sequencing technologies introduce novel demands on tools available for data analysis. We have developed NGSView (Next Generation Sequence View), a generally applicable, flexible and extensible next-generation sequence alignment editor. The software allows for visualization and manipulation of millions of sequences simultaneously on a desktop computer, through a graphical interface. NGSView is available under an open source license and can be extended through a well documented API.

Availability:http://ngsview.sourceforge.net

Contact:arner@gsc.riken.jp

Genome assembly reborn: recent computational challenges

Research into genome assembly algorithms has experienced a resurgence due to new challenges created by the development of next generation sequencing technologies. Several genome assemblers have been published in recent years specifically targeted at the new sequence data; however, the ever-changing technological landscape leads to the need for continued research. In addition, the low cost of next generation sequencing data has led to an increased use of sequencing in new settings. For example, the new field of metagenomics relies on large-scale sequencing of entire microbial communities instead of isolate genomes, leading to new computational challenges. In this article, we outline the major algorithmic approaches for genome assembly and describe recent developments in this domain.

Computational biology methods and their application to the comparative genomics of endocellular symbiotic bacteria of insects

Comparative genomics has become a real tantalizing challenge in the postgenomic era. This fact has been mostly magnified by the plethora of new genomes becoming available in a daily bases. The overwhelming list of new genomes to compare has pushed the field of bioinformatics and computational biology forward toward the design and development of methods capable of identifying patterns in a sea of swamping data noise. Despite many advances made in such endeavor, the ever-lasting annoying exceptions to the general patterns remain to pose difficulties in generalizing methods for comparative genomics. In this review, we discuss the different tools devised to undertake the challenge of comparative genomics and some of the exceptions that compromise the generality of such methods. We focus on endosymbiotic bacteria of insects because of their genomic dynamics peculiarities when compared to free-living organisms.

ReRep: computational detection of repetitive sequences in genome survey sequences (GSS)

Background

Genome survey sequences (GSS) offer a preliminary global view of a genome since, unlike ESTs, they cover coding as well as non-coding DNA and include repetitive regions of the genome. A more precise estimation of the nature, quantity and variability of repetitive sequences very early in a genome sequencing project is of considerable importance, as such data strongly influence the estimation of genome coverage, library quality and progress in scaffold construction. Also, the elimination of repetitive sequences from the initial assembly process is important to avoid errors and unnecessary complexity. Repetitive sequences are also of interest in a variety of other studies, for instance as molecular markers.

Results

We designed and implemented a straightforward pipeline called ReRep, which combines bioinformatics tools for identifying repetitive structures in a GSS dataset. In a case study, we first applied the pipeline to a set of 970 GSSs, sequenced in our laboratory from the human pathogen Leishmania braziliensis, the causative agent of leishmaniosis, an important public health problem in Brazil. We also verified the applicability of ReRep to new sequencing technologies using a set of 454-reads of an Escheria coli. The behaviour of several parameters in the algorithm is evaluated and suggestions are made for tuning of the analysis.

Conclusion

The ReRep approach for identification of repetitive elements in GSS datasets proved to be straightforward and efficient. Several potential repetitive sequences were found in a L. braziliensis GSS dataset generated in our laboratory, and further validated by the analysis of a more complete genomic dataset from the EMBL and Sanger Centre databases. ReRep also identified most of the E. coli K12 repeats prior to assembly in an example dataset obtained by automated sequencing using 454 technology. The parameters controlling the algorithm behaved consistently and may be tuned to the properties of the dataset, in particular to the length of sequencing reads and the genome coverage. ReRep is freely available for academic use at .

Genome assembly forensics: finding the elusive mis-assembly

A collection of software tools is combined for the first time in an automated pipeline for detecting large-scale genome assembly errors and for validating genome assemblies.

We present the first collection of tools aimed at automated genome assembly validation. This work formalizes several mechanisms for detecting mis-assemblies, and describes their implementation in our automated validation pipeline, called amosvalidate. We demonstrate the application of our pipeline in both bacterial and eukaryotic genome assemblies, and highlight several assembly errors in both draft and finished genomes. The software described is compatible with common assembly formats and is released, open-source, at .

Database of Trypanosoma cruzi repeated genes: 20,000 additional gene variants

BACKGROUND

Repeats are present in all genomes, and often have important functions. However, in large genome sequencing projects, many repetitive regions remain uncharacterized. The genome of the protozoan parasite Trypanosoma cruzi consists of more than 50% repeats. These repeats include surface molecule genes, and several other gene families. In the T. cruzi genome sequencing project, it was clear that not all copies of repetitive genes were present in the assembly, due to collapse of nearly identical repeats. However, at the time of publication of the T. cruzi genome, it was not clear to what extent this had occurred.

RESULTS

We have developed a pipeline to estimate the genomic repeat content, where shotgun reads are aligned to the genomic sequence and the gene copy number is estimated using the average shotgun coverage. This method was applied to the genome of T. cruzi and copy numbers of all protein coding sequences and pseudogenes were estimated. The 22,640 results were stored in a database available online. 18% of all protein coding sequences and pseudogenes were estimated to exist in 14 or more copies in the T. cruzi CL Brener genome. The average coverage of the annotated protein coding sequences and pseudogenes indicate a total gene copy number, including allelic gene variants, of over 40,000.

CONCLUSION

Our results indicate that the number of protein coding sequences and pseudogenes in the T. cruzi genome may be twice the previous estimate. We have constructed a database of the T. cruzi gene repeat data that is available as a resource to the community. The main purpose of the database is to enable biologists interested in repeated, unfinished regions to closely examine and resolve these regions themselves using all available shotgun data, instead of having to rely on annotated consensus sequences that often are erroneous and possibly misleading. Five repetitive genes were studied in more detail, in order to illustrate how the database can be used to analyze and extract information about gene repeats with different characteristics in Trypanosoma cruzi.

Hawkeye: an interactive visual analytics tool for genome assemblies

Hawkeye is a new, freely available visual analytics tool for genome assemblies, designed to aid in identifying and correcting assembly errors.

Genome sequencing remains an inexact science, and genome sequences can contain significant errors if they are not carefully examined. Hawkeye is our new visual analytics tool for genome assemblies, designed to aid in identifying and correcting assembly errors. Users can analyze all levels of an assembly along with summary statistics and assembly metrics, and are guided by a ranking component towards likely mis-assemblies. Hawkeye is freely available and released as part of the open source AMOS project http://amos.sourceforge.net/hawkeye.

GRAT--genome-scale rapid alignment tool

Modern alignment methods designed to work rapidly and efficiently with large datasets often do so at the cost of method sensitivity. To overcome this, we have developed a novel alignment program, GRAT, built to accurately align short, highly similar DNA sequences. The program runs rapidly and requires no more memory and CPU power than a desktop computer. In addition, specificity is ensured by statistically separating the true alignments from spurious matches using phred quality values. An efficient separation is especially important when searching large datasets and whenever there are repeats present in the dataset. Results are superior in comparison to widely used existing software, and analysis of two large genomic datasets show the usefulness and scalability of the algorithm.