Computational identification of Penaeus monodon microRNA genes and their targets

MicroRNAs (miRNAs) are a distinct class of small non-coding RNAs, ~22 nt long, found in a wide variety of organisms.They play important regulatory roles by silencing gene activities at the post-transcriptional level. In this work, we developeda computational workflow to identify conserved miRNA genes in the 10,536 unique Penaeus monodon expressed sequencetags (ESTs). After removing all simple repeats and coding regions in the ESTs, the workflow uses both the conservationof miRNA sequences and several filters obtained from pre-miRNA secondary structure properties to identify conservedmiRNAs. Finally, we discovered six potential conserved miRNA genes such as mir-4152, mir-466k, miR-32*, lin-4, mir-1346 andmir-4310

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

BACKGROUND: Many genome projects are left unfinished due to complex, repeated regions. Finishing is the most time consuming step in sequencing and current finishing tools are not designed with particular attention to the repeat problem. RESULTS: We have developed DNPTrapper, a shotgun sequence finishing tool, specifically designed to address the problems posed by the presence of repeated regions in the target sequence. The program detects and visualizes single base differences between nearly identical repeat copies, and offers the overview and flexibility needed to rapidly resolve complex regions within a working session. The use of a database allows large amounts of data to be stored and handled, and allows viewing of mammalian size genomes. The program is available under an Open Source license. CONCLUSION: With DNPTrapper, it is possible to separate repeated regions that previously were considered impossible to resolve, and finishing tasks that previously took days or weeks can be resolved within hours or even minutes

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

<p>Abstract</p> <p>Background</p> <p>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 <it>Trypanosoma cruzi </it>consists of more than 50% repeats. These repeats include surface molecule genes, and several other gene families. In the <it>T. cruzi </it>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 <it>T. cruzi </it>genome, it was not clear to what extent this had occurred.</p> <p>Results</p> <p>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 <it>T. cruzi </it>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 <it>T. cruzi </it>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.</p> <p>Conclusion</p> <p>Our results indicate that the number of protein coding sequences and pseudogenes in the <it>T. cruzi </it>genome may be twice the previous estimate. We have constructed a database of the <it>T. cruzi </it>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 <it>Trypanosoma cruzi</it>.</p

CNV-seq, a new method to detect copy number variation using high-throughput sequencing

<p>Abstract</p> <p>Background</p> <p>DNA copy number variation (CNV) has been recognized as an important source of genetic variation. Array comparative genomic hybridization (aCGH) is commonly used for CNV detection, but the microarray platform has a number of inherent limitations.</p> <p>Results</p> <p>Here, we describe a method to detect copy number variation using shotgun sequencing, CNV-seq. The method is based on a robust statistical model that describes the complete analysis procedure and allows the computation of essential confidence values for detection of CNV. Our results show that the number of reads, not the length of the reads is the key factor determining the resolution of detection. This favors the next-generation sequencing methods that rapidly produce large amount of short reads.</p> <p>Conclusion</p> <p>Simulation of various sequencing methods with coverage between 0.1× to 8× show overall specificity between 91.7 – 99.9%, and sensitivity between 72.2 – 96.5%. We also show the results for assessment of CNV between two individual human genomes.</p

MicroTar: predicting microRNA targets from RNA duplexes

BACKGROUND: The accurate prediction of a comprehensive set of messenger RNAs (targets) regulated by animal microRNAs (miRNAs) remains an open problem. In particular, the prediction of targets that do not possess evolutionarily conserved complementarity to their miRNA regulators is not adequately addressed by current tools. RESULTS: We have developed MicroTar, an animal miRNA target prediction tool based on miRNA-target complementarity and thermodynamic data. The algorithm uses predicted free energies of unbound mRNA and putative mRNA-miRNA heterodimers, implicitly addressing the accessibility of the mRNA 3' untranslated region. MicroTar does not rely on evolutionary conservation to discern functional targets, and is able to predict both conserved and non-conserved targets. MicroTar source code and predictions are accessible at , where both serial and parallel versions of the program can be downloaded under an open-source licence. CONCLUSION: MicroTar achieves better sensitivity than previously reported predictions when tested on three distinct datasets of experimentally-verified miRNA-target interactions in C. elegans, Drosophila, and mouse

A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms

We describe a genetic variation map for the chicken genome containing 2.8 million single-nucleotide polymorphisms ( SNPs). This map is based on a comparison of the sequences of three domestic chicken breeds ( a broiler, a layer and a Chinese silkie) with that of their wild ancestor, red jungle fowl. Subsequent experiments indicate that at least 90% of the variant sites are true SNPs, and at least 70% are common SNPs that segregate in many domestic breeds. Mean nucleotide diversity is about five SNPs per kilobase for almost every possible comparison between red jungle fowl and domestic lines, between two different domestic lines, and within domestic lines - in contrast to the notion that domestic animals are highly inbred relative to their wild ancestors. In fact, most of the SNPs originated before domestication, and there is little evidence of selective sweeps for adaptive alleles on length scales greater than 100 kilobases

Correcting errors in shotgun sequences

Sequencing errors in combination with repeated regions cause major problems in shotgun sequencing, mainly due to the failure of assembly programs to distinguish single base differences between repeat copies from erroneous base calls. In this paper, a new strategy designed to correct errors in shotgun sequence data using defined nucleotide positions, DNPs, is presented. The method distinguishes single base differences from sequencing errors by analyzing multiple alignments consisting of a read and all its overlaps with other reads. The construction of multiple alignments is performed using a novel pattern matching algorithm, which takes advantage of the symmetry between indices that can be computed for similar words of the same length. This allows for rapid construction of multiple alignments, with no previous pair-wise matching of sequence reads required. Results from a C++ implementation of this method show that up to 99% of sequencing errors can be corrected, while up to 87% of the single base differences remain and up to 80% of the corrected reads contain at most one error. The results also show that the method outperforms the error correction method used in the EULER assembler. The prototype software, MisEd, is freely available from the authors for academic use

CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates

Abstract Background In current statistical methods for calling differentially expressed genes in RNA-Seq experiments, the assumption is that an adjusted observed gene count represents an unknown true gene count. This adjustment usually consists of a normalization step to account for heterogeneous sample library sizes, and then the resulting normalized gene counts are used as input for parametric or non-parametric differential gene expression tests. A distribution of true gene counts, each with a different probability, can result in the same observed gene count. Importantly, sequencing coverage information is currently not explicitly incorporated into any of the statistical models used for RNA-Seq analysis. Results We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data. Conclusions Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS