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Next Generation Sequencing (NGS) technology is based on cutting DNA into small fragments, and their massive parallel sequencing. The multiple overlapping segments termed “reads” are assembled into a contiguous sequence. To reduce sequencing errors, every genome region should be sequenced several dozen times. This sequencing approach is based on the assumption that genomic DNA breaks are random and sequence-independent. However, previously we showed that for the sonicated restriction DNA fragments the rates of double-stranded breaks depend on the nucleotide sequence. In this work we analyzed genomic reads from NGS data and discovered that fragmentation methods based on the action of the hydrodynamic forces on DNA, produce similar bias. Consideration of this non-random DNA fragmentation may allow one to unravel what factors and to what extent influence the non-uniform coverage of various genomic regions.

Genome annotation is a tedious task that is mostly done by automated methods; however, the accuracy of these approaches has been questioned since the beginning of the sequencing era. Genome annotation is a multilevel process, and errors can emerge at different stages: during sequencing, as a result of gene-calling procedures, and in the process of assigning gene functions. Missed or wrongly annotated genes differentially impact different types of analyses.Here we discuss and demonstrate how the methods of comparative genome analysis can refine annotations by locating missing orthologues. We also discuss possible reasons for errors and show that the second-generation annotation systems, which combine multiple gene-calling programs with similarity-based methods, perform much better than the first annotation tools. Since old errors may propagate to the newly sequenced genomes, we emphasize that the problem of continuously updating popular public databases is an urgent and unresolved one. Due to the progress in genome-sequencing technologies, automated annotation techniques will remain the main approach in the future. Researchers need to be aware of the existing errors in the annotation of even well-studied genomes, such as Escherichia coli, and consider additional quality control for their results. IntroductionSequencing the first complete genome of Haemophilus influenzae in 1995 opened a new page in genome sciences. It took eight more years (till 2003) to increase the number of complete sequenced genomes to 100. This number was doubled by the year 2005, and by 2010 more than 1000 completely sequenced bacterial and archaeal genomes were available at GenBank, with approximately four times this number of genomes in the process of being sequenced. In September of 2009 the GOLD database (Liolios et al., 2008) listed 5902 genome projects; 4242 of these were bacterial genome projects, of which 1154 were listed as complete and 966 in draft. With such a tempo it is obvious that the burden of genome annotation will be assigned mostly to automated methods. However, the accuracy of automated approaches has been questioned since the beginning of the sequencing era (Friedberg, 2006).Genome annotation is a multi-level process that includes prediction not just of coding genes, but also of pseudogenes, promoter regions, direct and inverted repeats, untranslated regions and other genome units. For a comprehensive review of genome and proteome annotation see Reed et al. (2006) and Reeves et al.(2009). In this paper we briefly review the problems associated with identification of coding sequences (CDS) in bacterial and archaeal genomes and demonstrate how comparative genomics can help in the location of missed genes.Bacterial and archaeal genomes, as well as those of some eukaryotic micro-organisms, have the considerable advantage of usually lacking introns, which makes the process of gene boundary identification much easier. Nevertheless bacterial or archaeal gene-calling procedures are not error free. In the absence of introns, it...

Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not sufficient to explain emergence of Z-DNA at different genomic locations. Adding epigenetic and other functional genomic mark-ups to DNA sequence level can help revealing the functional Z-DNA sites. Here we take advantage of the deep learning approach that can analyze and extract information from large volumes of molecular biology data. We developed a machine learning approach DeepZ that aggregates information from genome-wide maps of epigenetic markers, transcription factor and RNA polymerase binding sites, and chromosome accessibility maps. With the developed model we not only verify the experimental Z-DNA predictions, but also generate the whole-genome annotation, introducing new possible Z-DNA regions, which have not yet been found in experiments and can be of interest to the researchers from various fields.

Background: Horizontal gene transfer plays an important role in evolution because it sometimes allows recipient lineages to adapt to new ecological niches. High genes transfer frequencies were inferred for prokaryotic and early eukaryotic evolution. Does horizontal gene transfer also impact phylogenetic reconstruction of the evolutionary history of genomes and organisms? The answer to this question depends at least in part on the actual gene transfer frequencies and on the ability to weed out transferred genes from further analyses. Are the detected transfers mainly false positives, or are they the tip of an iceberg of many transfer events most of which go undetected by current methods?