Background: Plasmids play an important role in the dissemination of antibiotic resistance, making their detection an important task. Using whole genome sequencing (WGS), it is possible to capture both bacterial and plasmid sequence data, but short read lengths make plasmid detection a complex problem.Results: We developed a tool named PlasmidSeeker that enables the detection of plasmids from bacterial WGS data without read assembly. The PlasmidSeeker algorithm is based onk-mers and usesk-mer abundance to distinguish between plasmid and bacterial sequences. We tested the performance of PlasmidSeeker on a set of simulated and real bacterial WGS samples, resulting in 100% sensitivity and 99.98% specificity.Conclusion: PlasmidSeeker enables quick detection of known plasmids and complements existing tools that assemble plasmids de novo. The PlasmidSeeker source code is stored on GitHub:https://github.com/bioinfo-ut/PlasmidSeeker.

Papillomaviridae are small dsDNA viruses with a limited coding capacity. To fulfill all of the functional requirements for propagation and spreading, papillomaviruses use double coding and alternative protein isoforms. E8^E2 is an alternative E2 protein isoform that is generated by fusing the short E8 CDS that completely overlaps E1 to the 'hinge' and the DNA-binding region of the E2 protein via alternative transcription/splicing. The papillomaviruses in which E8^E2 mRNA sequences have been described exhibit a sparse phylogenomic distribution. Thus, it is not clear whether E8^E2 is an ancestral protein that has not been described for other papillomavirus types or whether it randomly appears because of the conservation of the E1 protein and occurs only coincidentally. We searched for potential E8 coding sequences in a non-redundant set of papillomaviruses and applied SynPlot2 and an in-house developed algorithm (cRegions) to determine the most plausible of the above-mentioned scenarios. Beginning with 9 experimentally described E8^E2 mRNAs, we predicted the potential E8 CDSs for more than 300 mammalian papillomavirus genomes. According to our analysis, E8^E2 is not a result of E1 coding and represents a protein in its own right, and it most likely has an ancestral origin that precedes the divergence of major mammalian papillomavirus genera.

Almost a century has passed since the discovery of papillomaviruses. A few decades of research have given a wealth of information on the molecular biology of papillomaviruses. Several excellent studies have been performed looking at the long- and short-term evolution of these viruses. However, when and how papillomaviruses originate is still a mystery. In this study, we systematically searched the (sequenced) biosphere to find distant homologs of papillomaviral protein domains. Our data show that, even including structural information, which allows us to find deeper evolutionary relationships compared to sequence-only based methods, only half of the protein domains in papillomaviruses have relatives in the rest of the biosphere. We show that the major capsid protein L1 and the replication protein E1 have relatives in several viral families, sharing three protein domains with Polyomaviridae and Parvoviridae. However, only the E1 replication protein has connections with cellular organisms. Most likely, the papillomavirus ancestor is of marine origin, a biotope that is not very well sequenced at the present time. Nevertheless, there is no evidence as to how papillomaviruses originated and how they became vertebrate and epithelium specific.

Identifying cis-acting elements and understanding regulatory mechanisms of a gene is crucial to fully understand the molecular biology of an organism. In general, it is difficult to identify previously uncharacterised cis-acting elements with an unknown consensus sequence. The task is especially problematic with viruses containing regions of limited or no similarity to other previously characterised sequences. Fortunately, the fast increase in the number of sequenced genomes allows us to detect some of these elusive cis-elements. In this work, we introduce a web-based tool called cRegions. It was developed to identify regions within a protein-coding sequence where the conservation in the amino acid sequence is caused by the conservation in the nucleotide sequence. The cRegion can be the first step in discovering novel cis-acting sequences from diverged protein-coding genes. The results can be used as a basis for future experimental analysis. We applied cRegions on the non-structural and structural polyproteins of alphaviruses as an example and successfully detected all known cis-acting elements. In this publication and in previous work, we have shown that cRegions is able to detect a wide variety of functional elements in DNA and RNA viruses. These functional elements include splice sites, stem-loops, overlapping reading frames, internal promoters, ribosome frameshifting signals and other embedded elements with yet unknown function. The cRegions web tool is available athttp://bioinfo.ut.ee/cRegions/.

Breast cancer (BC) is the leading cause of cancer deaths in women in the world. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with BC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of women according to PRS could be introduced to primary and secondary prevention. Our aim was to revalidate a PRS model and to develop a pipeline for individualizing breast cancer screening. Previously published PRS models for predicting the risk of breast cancer were collected from the literature. Models were validated on the Estonian Biobank (EGC) dataset consisting of 32,548 quality-controlled genotypes with 315 prevalent and 365 incident BC cases and on 249,062 samples in the UK Biobank dataset consisting of 8637 prevalent and 6825 incident cases. The best performing model was selected based on the AUC in prevalent data and independently validated in both incident datasets. Using Estonian BC background information, we performed absolute risk simulations and developed individual risk-based recommendations for prevention. The best-performing PRS included 2803 SNPs. The C-index of the Cox regression model associating BC status with PRS was 0.656 (SE = 0.05) with a hazard ratio of 1.66 (95% confidence interval 1.5 - 1.84) on the incident EGC dataset. The PRS is able to stratify individuals with more than a 3-fold risk increase. The observed 10-year risks of individuals in the 99th percentile exceeded the 1 ^st percentile more than 10-fold. PRS is a powerful predictor of breast cancer risk. Currently, PRS scores are not implemented in routine BC screening. We have developed PRS-based recommendations for personalized primary and secondary prevention and our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations.

Colorectal cancer (CRC) is the second most common cancer in women and third most common cancer in men. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with CRC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of individuals according to PRS could be introduced to primary and secondary prevention. Our aim was to combine risk stratification of a sex-specific PRS model with recommendations for individualized CRC screening. Previously published PRS models for predicting the risk of CRC were collected from the literature. These were validated on the UK Biobank (UKBB) consisting of a total of 458 696 quality-controlled genotypes with 1810 and 1348 prevalent male cases, and 2410 and 1810 incident male and female cases. The best performing sex-specific model was selected based on the AUC in prevalent data and independently validated in the incident dataset. Using Estonian CRC background information, we performed absolute risk simulations and examined the ability of PRS in risk stratifying individual screening recommendations. The best-performing model included 91 SNPs. The C-index of the best performing model in the dataset was 0.613 (SE = 0.007) and hazard ratio (HR) per unit of PRS was 1.53 (1.47 – 1.59) for males. Respective metrics for females were 0.617 (SE = 0.006) and 1.50 (1.44 – 1.58). PRS risk simulations showed that a genetically average 50-year-old female doubles her risk by age 58 (55 in males) and triples it by age 63 (59 in males). In addition, the best performing PRS model was able to identify individuals in one of seven groups proposed by Naber et al. for different coloscopy screening recommendation regimens. We have combined PRS-based recommendations for individual screening attendance. Our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations.