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Bioinformatics Highlight: Assessing protein mass spectrometry data using Percolator – how to weed out valuable information from the noise

Mass spectrometry (MS) is currently the most effective way to analyze protein on a large scale, and hence one of the most important tools for answering those questions. There are still however difficult challenges in analysing the wealth of data MS-based experiments produce.

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Bioinformatics Highlight: Protein structure prediction — state-of-the-art methods proven in contests

Several scientists in the Bioinformatics community study and develop methods for protein structure prediction.

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Bioinformatics: Computational methods to assess and remedy mapping bias in allele-specific expression and genomic cis-element analysis

Assessing allele-specific expression (ASE) and allele-specific cis-element (ASC) binding or modification from massively parallel sequencing read data is a straightforward way to home in on transcriptional and cis-regulatory variation at the level of single individuals.

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Bioinformatics: Computational optimization of mass spectrometry-based proteomics experiments

Mass spectrometry (MS)-based proteomics is currently the most efficient method for large-scale analysis of protein content in biological mixtures. …

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Bioinformatics: Deep learning in protein structure predictions

Protein structure prediction is fundamental for our understanding of molecular functions in cells. Fundamental for this is the prediction of contact between interacting residues and the evaluation of the quality of protein models. We are developing novel deep learning approaches for both these problems.


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Bioinformatics: Development of automated protein family classification using hidden Markov models for functional characterization of proteins

There is a great need to subdivide large protein families into smaller, homogeneous subfamilies, corresponding to functional entities. Hidden Markov models constitute a powerful technique for such subclassification.

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Bioinformatics: High throughput prediction of disease caused by SNPs

Many diseases are caused by single nuclear polymorphisms (SNPs) that cause an amino acid in a protein to be mutated. However, most SNPs do not cause a disease. Today SNPs are readily detected in large scale studies of individual genetic variation. Here, we want to develop methods for analysis of molecular consequences following these SNPs and thereby aid in the identification of what SNPs are most likely to be disease causing.

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Bioinformatics: Improved scaffolding for genome assembly

A crucial part of genomics is the ability to accurately assemble reads (often short) into larger pieces, so-called contigs. This is still considered difficult and recent results indicate that there is no single assembler that good for all data sets. An important step in genome assembly is scaffolding, in which information about read-pairing is used to connect contigs into larger units.

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Bioinformatics: Improving the power of statistical tests for genetic interactions

This project aims to improve statistical tests for genetic interactions without main effects. These types of interactions, coined epistatic interactions are hypothesized to account for most of the phenotypic variation, and are therefore important to study.

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Bioinformatics: Inferring functional coupling of proteins using next gen sequencing data

Next generation sequencing experiments have a very high throughput producing gigabytes of raw data and setting a big challenge on analytic and processing ability, but in turn have the possibility to produce accurate and abundant data. The main goal of the project has been to try to utilize next generation sequencing data to infer functional coupling in protein interaction networks. The next gen data type used in this project was RNA-Seq.

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