Translation initiation site prediction on a genomic scale : beauty in simplicity

Motivation: The correct identification of translation initiation sites (TIS) remains a challenging problem for computational methods that automatically try to solve this problem. Furthermore, the lion's share of these computational techniques focuses on the identification of TIS in transcript data. However, in the gene prediction context the identification of TIS occurs on the genomic level, which makes things even harder because at the genome level many more pseudo-TIS occur, resulting in models that achieve a higher number of false positive predictions. Results: In this article, we evaluate the performance of several 'simple' TIS recognition methods at the genomic level, and compare them to state-of-the-art models for TIS prediction in transcript data. We conclude that the simple methods largely outperform the complex ones at the genomic scale, and we propose a new model for TIS recognition at the genome level that combines the strengths of these simple models. The new model obtains a false positive rate of 0.125 at a sensitivity of 0.80 on a well annotated human chromosome ( chromosome 21). Detailed analyses show that the model is useful, both on its own and in a simple gene prediction setting

Refractive index spectral dependence, Raman and transmission spectra of high-purity 28^{28}Si, 29^{29}Si, 30^{30}Si, and nat^{nat}Si single crystals

Precise measurement of the refractive index of stable silicon isotopes $^{28}$Si, $^{29}$Si, $^{30}$Si single crystals with enrichments above 99.9 at.% and a silicon single crystal $^{nat}$Si of natural isotopic composition is performed with the Fourier-transform interference refractometry method from 1.06 to more than 80 mkm with 0.1 cm$^{-1}$ resolution and accuracy of $2 \times 10^{-5} ... 1 \times 10^{-4}$. The oxygen and carbon concentrations in all crystals are within $5 \times 10^{15}$ cm$^{-3}$ and the content of metal impurities is $10^{-5} ... 10^{-6}$ at.%. The peculiar changes of the refractive index in the phonon absorption region of all silicon single crystals are shown. The coefficients of generalized Cauchy dispersion function approximating the experimental refractive index values all over the measuring range are given. The transmission and Raman spectra are also studied

Probing the pan-genome of Listeria monocytogenes: new insights into intraspecific niche expansion and genomic diversification

Bacterial pathogens often show significant intraspecific variations in ecological fitness, host preference and pathogenic potential to cause infectious disease. The species of Listeria monocytogenes, a facultative intracellular pathogen and the causative agent of human listeriosis, consists of at least three distinct genetic lineages. Two of these lineages predominantly cause human sporadic and epidemic infections, whereas the third lineage has never been implicated in human disease outbreaks despite its overall conservation of many known virulence factors. Here we compare the genomes of 26 L. monocytogenes strains representing the three lineages based on both in silico comparative genomic analysis and high-density, pan-genomic DNA array hybridizations. We uncover 86 genes and 8 small regulatory RNAs that likely make L. monocytogenes lineages differ in carbohydrate utilization and stress resistance during their residence in natural habitats and passage through the host gastrointestinal tract. We also identify 2,330 to 2,456 core genes that define this species along with an open pan-genome pool that contains more than 4,052 genes. Phylogenomic reconstructions based on 3,560 homologous groups allowed robust estimation of phylogenetic relatedness among L. monocytogenes strains. Our pan-genome approach enables accurate co-analysis of DNA sequence and hybridization array data for both core gene estimation and phylogenomics. Application of our method to the pan-genome of L. monocytogenes sheds new insights into the intraspecific niche expansion and evolution of this important foodborne pathogen.https://doi.org/10.1186/1471-2164-11-50

The SEQanswers wiki: a wiki database of tools for high-throughput sequencing analysis

Recent advances in sequencing technology have created unprecedented opportunities for biological research. However, the increasing throughput of these technologies has created many challenges for data management and analysis. As the demand for sophisticated analyses increases, the development time of software and algorithms is outpacing the speed of traditional publication. As technologies continue to be developed, methods change rapidly, making publications less relevant for users. The SEQanswers wiki (SEQwiki) is a wiki database that is actively edited and updated by the members of the SEQanswers community (http://SEQanswers.com/). The wiki provides an extensive catalogue of tools, technologies and tutorials for high-throughput sequencing (HTS), including information about HTS service providers. It has been implemented in MediaWiki with the Semantic MediaWiki and Semantic Forms extensions to collect structured data, providing powerful navigation and reporting features. Within 2 years, the community has created pages for over 500 tools, with approximately 400 literature references and 600 web links. This collaborative effort has made SEQwiki the most comprehensive database of HTS tools anywhere on the web. The wiki includes task-focused mini-reviews of commonly used tools, and a growing collection of more than 100 HTS service providers. SEQwiki is available at: http://wiki.SEQanswers.com/

Genome Assembly Has a Major Impact on Gene Content: A Comparison of Annotation in Two Bos Taurus Assemblies

Gene and SNP annotation are among the first and most important steps in analyzing a genome. As the number of sequenced genomes continues to grow, a key question is: how does the quality of the assembled sequence affect the annotations? We compared the gene and SNP annotations for two different Bos taurus genome assemblies built from the same data but with significant improvements in the later assembly. The same annotation software was used for annotating both sequences. While some annotation differences are expected even between high-quality assemblies such as these, we found that a staggering 40% of the genes (>9,500) varied significantly between assemblies, due in part to the availability of new gene evidence but primarily to genome mis-assembly events and local sequence variations. For instance, although the later assembly is generally superior, 660 protein coding genes in the earlier assembly are entirely missing from the later genome's annotation, and approximately 3,600 (15%) of the genes have complex structural differences between the two assemblies. In addition, 12–20% of the predicted proteins in both assemblies have relatively large sequence differences when compared to their RefSeq models, and 6–15% of bovine dbSNP records are unrecoverable in the two assemblies. Our findings highlight the consequences of genome assembly quality on gene and SNP annotation and argue for continued improvements in any draft genome sequence. We also found that tracking a gene between different assemblies of the same genome is surprisingly difficult, due to the numerous changes, both small and large, that occur in some genes. As a side benefit, our analyses helped us identify many specific loci for improvement in the Bos taurus genome assembly

TopHat: discovering splice junctions with RNA-Seq

Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Reassortant Avian Influenza Virus (H5N1) in Poultry, Nigeria, 2007

Reassortant Influenza Virus (H5N1) in Poultry, Nigeria, 200

Avian Influenza A Virus (H5N1) Outbreaks, Kuwait, 2007

Phylogenetic analysis of influenza A viruses (H5N1) isolated from Kuwait in 2007 show that (H5N1) sublineage clade 2.2 viruses continue to spread across Europe, Africa, and the Middle East. Virus isolates were most closely related to isolates from central Asia and were likely vectored by migratory birds