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Bioinformatics, as a field, rapidly develops and such development requires the design ofalgorithms and software. RNA-seq provides robust information on RNAs, both alreadyknown and new, hence the increased study of the RNA. Alignment is an important step indownstream analyses and the ability to map reads across splice junctions is a requirement ofan aligner to be suitable for mapping RNA-seq reads. Therefore, the necessity for a standardsplice-aware aligner. STAR, Rsubread and HISAT2 have not been singly studied for thepurpose of benchmarking one of them as a standard aligner for spliced RNA-seq reads. Thisstudy compared these aligners, found to be sensitive to splice sites, with regards to theirsensitivity to splice sites, performance with default parameter settings and the resource usageduring the alignment process. The aligners were matched with featureCounts. The resultsshow that STAR and Rsubread outperform HISAT2 in the aspects of sensitivity and defaultparameter settings. Rsubread was more sensitive to splice junctions than STAR butunderperformed with featureCounts. STAR had a consistent performance, with more demandon the memory and time resource, but showed it could be more sensitive with real data.

Alzheimer’s disease (AD) is an aging-related neurodegenerative disorder with large implications for society and individuals. AD is a multi-factor disorder, with these factors having a direct or indirect correlation with each other. Despite many studies with different aspects on molecular and cellular pathways, there is still no specific treatment for AD. Identification of potential pathogenic factors can be done by transcriptomic studies of differentially expressed genes (DEGs), but the outcomes have been contradictory. Using both bioinformatics and meta-analysis methods can be useful for removing such inconsistencies. A useful and common approach for a better understanding of neurodegenerative disease is to assess its molecular causes, by comparing the gene expression levels in healthy and disease tissues. Next-generation RNA-sequencing is a valuable method for analyzing both coding and non-coding regions of RNA, and it has made it possible to identify differentially expressed genes in large-scale data. The aim of the current study was to get a better understanding of the transcriptional changes in AD models, and identify differentially expressed genes between healthy and AD individuals from the adult mouse brain model as well as detecting AD pathways. In this study, the transcriptomes of purified neuron, astrocyte and microglia cells from mouse brains were analyzed using publicly available RNA-seq datasets. The DEGs were identified for all three mentioned cell types using DESeq2 and EdgeR packages. All statistical analyses were performed by R software and the DEGs detected by DESeq2 and edgeR, respectively, were compared using Venn diagrams. Additionally, analyzing the AD pathway was performed using GOrilla tool for visualizing the enriched gene ontology (GO) terms in the list of ranked genes. From this project, it was found that there were very few significantly DEGs between AD and healthy samples in neuron cells, while there were more DEGs in astrocyte and microglia cells. In conclusion, comparing DESeq2 and egeR packages using Venn diagrams showed a slight advantage of DESeq2 in detection accuracy, since it was able to identify more DEGs than edgeR. Moreover, analyzing AD pathway using GOrilla tool indicated that identified enriched GO terms by each cell type differed from each other. For astrocytes, more enriched GO terms were identified than for microglia cells, while no significant enriched GO terms were detected for neuron cells.

Investigating the immunoglobulin repertoire is a means of understanding the adaptive immune response to infectious disease or vaccine challenge. The data examined are typically generated using high-throughput sequencing on samples of immunoglobulin variable-region genes present in blood or tissue collected from human or animal subjects. The analysis of these large, diverse collections provides a means of gaining insight into the specific molecular mechanisms involved in generating and maintaining a protective immune response. It involves the characterization of distinct clonal populations, specifically through the inference of founding alleles for germline gene segment recombination, as well as the lineage of accumulated mutations acquired during the development of each clone. Germline gene segment inference is currently performed by aligning immunoglobulin sequencing reads against an external reference database and assigning each read to the entry that provides the best score according to the metric used. The problem with this approach is that allelic diversity is greater than can be usefully accommodated in a static database. The absence of the alleles used from the database often leads to the misclassification of single-nucleotide polymorphisms as somatic mutations acquired during affinity maturation. This trend is especially evident with the rhesus macaque, but also affects the comparatively well-catalogued human databases, whose collections are biased towards samples from individuals of European descent. Our project presents novel statistical methods for immunoglobulin repertoire analysis which allow for the de novo inference of germline gene segment libraries directly from next-generation sequencing data, without the need for external reference databases. These methods follow a Bayesian paradigm, which uses an information-theoretic modelling approach to iteratively improve upon internal candidate gene segment libraries. Both candidate libraries and trial analyses given those libraries are incorporated as components of the machine learning evaluation procedure, allowing for the simultaneous optimization of model accuracy and simplicity. Finally, the proposed methods are evaluated using synthetic data designed to mimic known mechanisms for repertoire generation, with pre-designated parameters. We also apply these methods to known biological sources with unknown repertoire generation parameters, and conclude with a discussion on how this method can be used to identify potential novel alleles.

Rapid progress of AI technology in the life science area is observed in recent years. Convolutionalneural network (CNN) models were successfully applied for the localization and classification of cellson microscopic images. Induced pluripotent stem cells are one of the most important innovations inbiomedical research and are widely used, e.g. in regenerative medicine, drug screening, and diseasemodeling. However, assessment of cell cultures’ quality requires trained personnel, is timeconsumingand hence expensive. Fluorescence microscope images of human induced pluripotentstem‐hepatocytes (hiPS‐HEPs) derived from three human induced pluripotent stem cell (hiPSC) lineswere taken daily from day 1 until day 22 of differentiation. The cells from day 1 to 14 were classifiedas ´Early differentiation´, and above day 16 as ´Late differentiation´. In this study, it wasdemonstrated that a CNN‐based model can be trained with simple fluorescence microscope imagesof human induced pluripotent stem‐hepatocytes, and then used to predict with high accuracy(96.4%) the differentiation stage of an independent new set of images.

Myocardial infarction (MI) and stroke are the major causes of cardiovascular related morbidities and mortalities around the world. The prevalence of cardiovascular diseases has been increased in last decades and it is vital need of time to investigate this global problem with focus on risk population stratification. The aim of the present study is to investigate the association between individualized personality trait that is neuroticism and risk of MI and stroke has been investigated in a large population-based cohort of UK biobank.375,713 individuals (mean age: 56.24 ± 8.06) were investigated in this longitudinal study and were followed up for seven years to assess the association between neuroticism and risk of MI and stroke incidence. The neuroticism score was assessed by a 12-item questionnaire at baseline, while information related to MI and stroke events was either collected from hospital records and death registries or was self-reported by the participants. Cox proportional hazard regression adjusted for age, gender, BMI, socioeconomic status, lifestyle factors and medical histories for hypertension, diabetes and depression was used. All statistical analyses were performed using R software. In fully adjusted model, a one standard deviation increase in neuroticism score was associated with 1.05-fold increased risk for MI. (HR=1.047(1.009-1.087), p=0.015). However, no significant association was observed between neuroticism score and incident stroke as well as between neuroticism score and overall cardiovascular disease (MI and stroke combined). Results from the present study indicate that neuroticism is a risk factor for MI but not for stroke. These findings suggest that personality traits such as neuroticism may prove to be helpful in efficient risk stratification and pre-clinical diagnosis of individuals at risk for MI.

Pathway enrichment analysis is an approach extensively used when analyzing high throughput data to identify pathways enriched within a group of differentially expressed genes. Furthermore, different methods utilizing the topology of the pathway offer a unique way of analyzing and interpreting gene expression data. These methods usually offer pathway topologies with a limited number of methods and visualization of results. Also, the use of different methods individually and comparison of their results can be very cumbersome, time-consuming and prone to errors due to the need for repeated data conversion and transfer. Packages that offer a common interface to multiple methods are therefore necessary, to provide a uniform way of calling these methods or specifying parameters, and making simultaneous application of the methods easier. In this study topology-based pathway enrichment analysis was performed by using the R packages EnrichmentBrowser and ToPASeq on a time series RNA-Seq data for cardiac hypertrophy in order to compare their usability. Additionally, different topology-based enrichment analysis methods included in the packages were compared with a non-topology-based pathway enrichment analysis method as well as the combination of their results in order to assess biological insights. Regarding usability, the available instructions for how to use both EnrichmentBrowser and ToPASeq were easy to understand and apply in the R workspace. Furthermore, both packages were easy to install and adjust to various parameters. However, ToPASeq returned errors when some parameters other than the default ones were used. Also, one of the differences between the tools was the flexibility of options for visualization and interpretation of the results, where EnrichmentBrowser had clear advantages. Regarding biological insights, the methods SPIA and DEGraph produced significant pathways linked to the phenotype cardiac hypertrophy, with a clear advantage for SPIA that performed well in both tested data setups. Finally, combining results from both SPIA and GSEA (non-topology-based pathway enrichment analysis method) improved individual ranking by increasing confidence in specific target pathways and eliminating irrelevant pathways.

No description available.

Freshwater mussels (Unionida) belong to phylum Mollusca and live in freshwater habitats, such as lakes and rivers. Freshwater mussels have high capacity for water purification and play an important role in calcium recycling. There is not much information about the freshwater mussel genome due to lack of genomic sequences in the database, till now only four species have been sequenced and the only Swedish one is Margaritifera margaritifera. This study aim was to usenanopore sequencing technology to sequence the genomic DNA of a freshwater mussel. The data about the genomic sequence is helpful in identification of their species and give a better understanding towards the genomics and transcriptomics, it also could help in the development of multi-biomarker panels for an early assessment of water pollution. In this study the DNA used was extracted from the foot tissues, and different tissue homogenization methods were tested to find the best approach. The genomic DNA was sequenced by using Oxford nanopore MinION device, and the reads were assembled and polished using multiple software through bioinformatic analysis. The number of reads from sequencing the DNA covered only 13.5x of the estimated genome size of the freshwater mussel, while the required coverage for a complete genome assembly is 20x-25x or higher. Due to low coverage and fragmentation, a partial sequence of the genomic DNA was obtained. This indicates that nanopore sequencing could be used, but additional sequencing runs are needed to get enough coverage to assemble a complete genomic DNA of the freshwater mussel.