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Since the eradication of smallpox, there have been increases in poxvirus infections and the emergence of several novel poxviruses that can infect humans and domestic animals. In 2015, a novel poxvirus was isolated from a resident of Alaska. Diagnostic testing and limited sequence analysis suggested this isolate was a member of the Orthopoxvirus (OPXV) genus but was highly diverged from currently known species, including Akhmeta virus. Here, we present the complete 210,797 bp genome sequence of the Alaska poxvirus isolate, containing 206 predicted open reading frames. Phylogenetic analysis of the conserved central region of the genome suggested the Alaska isolate shares a common ancestor with Old World OPXVs and is diverged from New World OPXVs. We propose this isolate as a member of a new OPXV species, Alaskapox virus (AKPV). The AKPV genome contained host range and virulence genes typical of OPXVs but lacked homologs of C4L and B7R, and the hemagglutinin gene contained a unique 120 amino acid insertion. Seven predicted AKPV proteins were most similar to proteins in non-OPXV Murmansk or NY_014 poxviruses. Genomic analysis revealed evidence suggestive of recombination with Ectromelia virus in two putative regions that contain seven predicted coding sequences, including the A-type inclusion protein.

Annotated whole genome sequences of three isolates of the Akhmeta virus (AKMV), a novel species of orthopoxvirus (OPXV), isolated from the Akhmeta and Vani regions of the country Georgia, are presented and discussed. The AKMV genome is similar in genomic content and structure to that of the cowpox virus (CPXV), but a lower sequence identity was found between AKMV and Old World OPXVs than between other known species of Old World OPXVs. Phylogenetic analysis showed that AKMV diverged prior to other Old World OPXV. AKMV isolates formed a monophyletic clade in the OPXV phylogeny, yet the sequence variability between AKMV isolates was higher than between the monkeypox virus strains in the Congo basin and West Africa. An AKMV isolate from Vani contained approximately six kb sequence in the left terminal region that shared a higher similarity with CPXV than with other AKMV isolates, whereas the rest of the genome was most similar to AKMV, suggesting recombination between AKMV and CPXV in a region containing several host range and virulence genes.

The next-generation sequencing technology has generated enormous amount of DNA and RNA sequences that potentially contain volumes of important genetic information, e.g. information on protein-coding genes. The goal of research described in this thesis was to improve prediction of protein-coding genes in newly sequenced genomes by the algorithms and software tools of the GeneMark line. The thesis is divided into three main parts describing i) GeneMarkS-2, ii) GeneMarkS-T, and iii) MetaGeneTack.In prokaryotic genomes, ab initio gene finders can predict genes with high accuracy. However, the error rate is not negligible and largely species-specific. Most errors in gene prediction are made in genes located in genomic regions with atypical GC composition, e.g. genes in pathogenicity islands. We describe a new algorithm GeneMarkS-2 that uses local GC-specific heuristic models for scoring individual ORFs in the first step of analysis. Predicted atypical genes are retained and serve as ‘external’ evidence in subsequent runs of self-training. GeneMarkS-2 also controls the quality of training process by effectively selecting optimal orders of the Markov chain models as well as duration parameters in the hidden semi-Markov model. GeneMarkS-2 has shown significantly improved accuracy compared with other state-of-the-art gene prediction tools.Massive parallel sequencing of RNA transcripts by the next generation technology (RNA-Seq) provides large amount of RNA reads that can be assembled to full transcriptome. We have developed a new tool, GeneMarkS-T, for ab initio identification of protein-coding regions in RNA transcripts. Unsupervised estimation of parameters of the algorithm makes unnecessary several steps in the conventional gene prediction protocols, most importantly the manually curated preparation of training sets. We have demonstrated that the GeneMarkS-T self-training is robust with respect to the presence of errors in assembled transcripts and the accuracy of GeneMarkS-T in identifying proteincoding regions and, particularly, in predicting gene starts compares favorably to other existing methods.Frameshift prediction (FS) is important for analysis and biological interpretation of metagenomic sequences. Reads in metagenomic samples are prone to sequencing errors. Insertion and deletion errors that change the coding frame impair the accurate identification of protein coding genes. Accurate frameshift prediction requires sufficient amount of data to estimate parameters of species-specific statistical models of proteincoding and non-coding regions. However, this data is not available; all we have is metagenomic sequences of unknown origin. The challenge of ab initio FS detection is, therefore, twofold: (i) to find a way to infer necessary model parameters and (ii) to identify positions of frameshifts (if any). We describe a new tool, MetaGeneTack, which uses a heuristic method to estimate parameters of sequence models used in the FS detection algorithm. It was shown on several test sets that the performance of MetaGeneTack FS detection is comparable or better than the one of earlier developed program FragGeneScan.The work presented in this dissertation contributed to the following publications:

In a conventional view of the prokaryotic genome organization promoters precede operons and RBS sites with Shine-Dalgarno consensus precede genes. However, recent experimental research suggesting a more diverse view motivated us to develop an algorithm with improved gene-finding accuracy. We describe GeneMarkS-2, an ab initio algorithm that uses a model derived by self-training for finding species-specific (native) genes, along with an array of pre-computed heuristic models designed to identify harder-to-detect genes (likely horizontally transferred). Importantly, we designed GeneMarkS-2 to identify several types of distinct sequence patterns (signals) involved in gene expression control, among them the patterns characteristic for leaderless transcription as well as non-canonical RBS patterns. To assess the accuracy of GeneMarkS-2 we used genes validated by COG annotation, proteomics experiments, and N-terminal protein sequencing. We observed that GeneMarkS-2 performed better on average in all accuracy measures when compared with the current state-of-the-art gene prediction tools. Furthermore, the screening of ~5,000 representative prokaryotic genomes made by GeneMarkS-2 predicted frequent leaderless transcription in both archaea and bacteria. We also observed that the RBS sites in some species with leadered transcription did not necessarily exhibit the Shine-Dalgarno consensus. The modeling of different types of sequence motifs regulating gene expression prompted a division of prokaryotic genomes into five categories with distinct sequence patterns around the gene starts.