Prediction of disease-related mutations affecting protein localization

<p>Abstract</p> <p>Background</p> <p>Eukaryotic cells contain numerous compartments, which have different protein constituents. Proteins are typically directed to compartments by short peptide sequences that act as targeting signals. Translocation to the proper compartment allows a protein to form the necessary interactions with its partners and take part in biological networks such as signalling and metabolic pathways. If a protein is not transported to the correct intracellular compartment either the reaction performed or information carried by the protein does not reach the proper site, causing either inactivation of central reactions or misregulation of signalling cascades, or the mislocalized active protein has harmful effects by acting in the wrong place.</p> <p>Results</p> <p>Numerous methods have been developed to predict protein subcellular localization with quite high accuracy. We applied bioinformatics methods to investigate the effects of known disease-related mutations on protein targeting and localization by analyzing over 22,000 missense mutations in more than 1,500 proteins with two complementary prediction approaches. Several hundred putative localization affecting mutations were identified and investigated statistically.</p> <p>Conclusion</p> <p>Although alterations to localization signals are rare, these effects should be taken into account when analyzing the consequences of disease-related mutations.</p

A Beta-mixture model for dimensionality reduction, sample classification and analysis

<p>Abstract</p> <p>Background</p> <p>Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper we propose a beta-mixture model to describe genome-wide methylation patterns based on probe data from methylation microarrays. The model takes dependencies between neighbour probe pairs into account and assumes three broad categories of methylation, low, medium and high. The model is described by 37 parameters, which reduces the dimensionality of a typical methylation microarray significantly. We used methylation microarray data from 42 colon cancer samples to assess the model.</p> <p>Results</p> <p>Based on data from colon cancer samples we show that our model captures genome-wide characteristics of methylation patterns. We estimate the parameters of the model and show that they vary between different tissue types. Further, for each methylation probe the posterior probability of a methylation state (low, medium or high) is calculated and the probability that the state is correctly predicted is assessed. We demonstrate that the model can be applied to classify cancer tissue types accurately and that the model provides accessible and easily interpretable data summaries.</p> <p>Conclusions</p> <p>We have developed a beta-mixture model for methylation microarray data. The model substantially reduces the dimensionality of the data. It can be used for further analysis, such as sample classification or to detect changes in methylation status between different samples and tissues.</p

Integrative genomics and transcriptomics analysis of human embryonic and induced pluripotent stem cells

Peer reviewe

Etiology of acute respiratory disease in fattening pigs in Finland

Background: The objective of our study was to clinically and etiologically investigate acute outbreaks of respiratory disease in Finland. Our study also aimed to evaluate the clinical use of various methods in diagnosing respiratory infections under field conditions and to describe the antimicrobial resistance profile of the main bacterial pathogen(s) found during the study. Methods: A total of 20 case herds having finishing pigs showing acute respiratory symptoms and eight control herds showing no clinical signs suggesting of respiratory problems were enrolled in the study. Researchers visited each herd twice, examining and bleeding 20 pigs per herd. In addition, nasal swab samples were taken from 20 pigs and three pigs per case herd were necropsied during the first visit. Serology was used to detect Actinobacillus pleuropneumoniae (APP), swine influenza virus (SIV), porcine reproductive and respiratory syndrome virus (PRRSV), porcine respiratory coronavirus (PRCV) and Mycoplasma hyopneumoniae antibodies. Polymerase chain reaction (PCR) was used to investigate the presence of porcine circovirus type 2 (PCV2) in serumand SIV in the nasal and lung samples. Pathology and bacteriology, including antimicrobial resistance determination, were performed on lung samples obtained from the field necropsies. Results: According to the pathology and bacteriology of the lung samples, APP and Ascaris suum were the main causes of respiratory outbreaks in 14 and three herds respectively, while the clinical signs in three other herds had a miscellaneous etiology. SIV, APP and PCV2 caused concurrent infections in certain herds but they were detected serologically or with PCR also in control herds, suggesting possible subclinical infections. APP was isolated from 16 (80%) case herds. Marked resistance was observed against tetracycline for APP, some resistance was detected against trimethoprim/sulfamethoxazole, ampicillin and penicillin, and no resistance against florfenicol, enrofloxacin, tulathromycin or tiamulin was found. Serology, even from paired serum samples, gave inconclusive results for acute APP infection diagnosis. Conclusions: APP was the most common cause for acute respiratory outbreaks in our study. SIV, A. suum, PCV2 and certain opportunistic bacteria were also detected during the outbreaks; however, viral pathogens appeared less important than bacteria. Necropsies supplemented with microbiology were the most efficient diagnostic methods in characterizing the studied outbreaks.Peer reviewe

Tumor-specific usage of alternative transcription start sites in colorectal cancer identified by genome-wide exon array analysis

<p>Abstract</p> <p>Background</p> <p>Approximately half of all human genes use alternative transcription start sites (TSSs) to control mRNA levels and broaden the transcriptional output in healthy tissues. Aberrant expression patterns promoting carcinogenesis, however, may arise from alternative promoter usage.</p> <p>Results</p> <p>By profiling 108 colorectal samples using exon arrays, we identified nine genes (<it>TCF12, OSBPL1A, TRAK1, ANK3, CHEK1, UGP2, LMO7, ACSL5</it>, and <it>SCIN</it>) showing tumor-specific alternative TSS usage in both adenoma and cancer samples relative to normal mucosa. Analysis of independent exon array data sets corroborated these findings. Additionally, we confirmed the observed patterns for selected mRNAs using quantitative real-time reverse-transcription PCR. Interestingly, for some of the genes, the tumor-specific TSS usage was not restricted to colorectal cancer. A comprehensive survey of the nine genes in lung, bladder, liver, prostate, gastric, and brain cancer revealed significantly altered mRNA isoform ratios for <it>CHEK1, OSBPL1A</it>, and <it>TCF12 </it>in a subset of these cancer types.</p> <p>To identify the mechanism responsible for the shift in alternative TSS usage, we antagonized the Wnt-signaling pathway in DLD1 and Ls174T colorectal cancer cell lines, which remarkably led to a shift in the preferred TSS for both <it>OSBPL1A </it>and <it>TRAK1</it>. This indicated a regulatory role of the Wnt pathway in selecting TSS, possibly also involving TP53 and SOX9, as their transcription binding sites were enriched in the promoters of the tumor preferred isoforms together with their mRNA levels being increased in tumor samples.</p> <p>Finally, to evaluate the prognostic impact of the altered TSS usage, immunohistochemistry was used to show deregulation of the total protein levels of both TCF12 and OSBPL1A, corresponding to the mRNA levels observed. Furthermore, the level of nuclear TCF12 had a significant correlation to progression free survival in a cohort of 248 stage II colorectal cancer samples.</p> <p>Conclusions</p> <p>Alternative TSS usage in colorectal adenoma and cancer samples has been shown for nine genes, and <it>OSBPL1A </it>and <it>TRAK1 </it>were found to be regulated <it>in vitro </it>by Wnt signaling. TCF12 protein expression was upregulated in cancer samples and correlated with progression free survival.</p

PROlocalizer: integrated web service for protein subcellular localization prediction

Subcellular localization is an important protein property, which is related to function, interactions and other features. As experimental determination of the localization can be tedious, especially for large numbers of proteins, a number of prediction tools have been developed. We developed the PROlocalizer service that integrates 11 individual methods to predict altogether 12 localizations for animal proteins. The method allows the submission of a number of proteins and mutations and generates a detailed informative document of the prediction and obtained results. PROlocalizer is available at http://bioinf.uta.fi/PROlocalizer/

A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays

Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge

On Recurrence Time

The amount of the data in the world enlarges all the time and therefore efficient methods are needed for data compression. There are many different algorithms to compress the data. One class of compression algorithms are the Lempel-Ziv algorithms that are closely connected to the recurrence time of the sequence. The recurrence time of the sequence is the number of the characters between the start at the sequence and its following occurrence. Recurrence time has many mathematical properties which are examined in the thesis. Especially the Recurrence time theorem is proved. This theorem gives the basis to use recurrence time as an efficient help in the data compression. When compressing the data different codes are used. This is why the properties of the codes and the using the codes in different cases are also studied. Furthermore, to study these properties, the packings of intervals of integers are important tools. The packing of a interval of integers is a big enoug set of numbers inside the interval.. The special application field of data compression is biological sequences, among other things, DNA sequences. Thus in the thesis recurrence times of DNA-sequences are experimentally studied using the human chromosome 22 as a DNA-sequence. Besides, the recurrence times of DNA-sequences are estimated on the basis of the theorems proved in the thesis. Finally, the experimental recurrence times are compared with the calculated ones and in general, a good agreement is found