Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available

Computational Modelling of Gene Regulation in Cancer : Coding the noncoding genome

Technological advancements have enabled quantification of processes within and around us. The information stored within our body converts into petabytes of data. Processing and learning from such data requires comprehensive computational programs and software systems. We developed software programs to systematically investigate the process of gene regulation in the human genome. Gene regulation is a complex process where several genomic elements control expression of a gene through recruiting many transcription factor (TF) proteins. The TFs recognize specific DNA sequences known as motifs. DNA mutations in regulatory elements and particularly in TF motifs may cause gene deregulation. Therefore, defining the landscape of regulatory elements and their roles in cancer and complex diseases is of major importance. We developed an algorithm (tfNet) to identify regulatory elements based on transcription factor binding sites. tfNet identified nearly 144,000 regulatory elements in five human cell lines. Investigating the elements we identified TF interaction networks and enrichment of many GWAS SNPs. We also defined the regulatory landscape for other conditions and species. Next, we investigated the role of regulatory elements in cancer. Cancer is initiated and developed by genetic aberrations in the genome. Genetic changes that are present in a cancer genome are obtained through whole genome sequencing technologies. We analyzed somatic mutations that had been detected in 326 whole genomes of liver cancer patients. Our results indicated 907 candidate mutations affecting TF motifs. Genome wide alignment of the mutated motifs revealed a significant enrichment of mutations in a highly conserved position of the CTCF motif. Gene expression analysis exhibited disruption of topologically associated domains in the mutated samples. We also confirmed the mutational pattern in pancreatic, gastric and esophagus cancers. Finally, enrichment of cancer associated gene sets and pathways suggested great role of noncoding mutations in cancer. To systematically analyze DNA mutations in TF motifs, we developed an online database system (funMotifs). Publicly available datasets were collected for thousands experiments. The datasets were integrated using a logistic regression model. Functionality annotations and scores for motifs of 519 TFs were derived. The database allows for identification of variants affecting functional motifs in a selected tissue type. Finally, a comprehensive analysis was performed to identify mutations overlapping functional TF motifs in 37 cancer types. Somatic mutations from a pan-cancer cohort of 2,515 cancer whole genomes were investigated. A significant enrichment of mutations in the CpG site of the CEBPB motif was identified. Overall, 10,806 mutated regulatory elements were identified including 406 highly recurrent ones. Genes associated to the mutated elements were highly enriched for cancer-related pathways. Our analyses provide further insights onto the role of regulatory elements and their impacts on cancer development

Computational Modelling of Gene Regulation in Cancer : Coding the noncoding genome

Technological advancements have enabled quantification of processes within and around us. The information stored within our body converts into petabytes of data. Processing and learning from such data requires comprehensive computational programs and software systems. We developed software programs to systematically investigate the process of gene regulation in the human genome. Gene regulation is a complex process where several genomic elements control expression of a gene through recruiting many transcription factor (TF) proteins. The TFs recognize specific DNA sequences known as motifs. DNA mutations in regulatory elements and particularly in TF motifs may cause gene deregulation. Therefore, defining the landscape of regulatory elements and their roles in cancer and complex diseases is of major importance. We developed an algorithm (tfNet) to identify regulatory elements based on transcription factor binding sites. tfNet identified nearly 144,000 regulatory elements in five human cell lines. Investigating the elements we identified TF interaction networks and enrichment of many GWAS SNPs. We also defined the regulatory landscape for other conditions and species. Next, we investigated the role of regulatory elements in cancer. Cancer is initiated and developed by genetic aberrations in the genome. Genetic changes that are present in a cancer genome are obtained through whole genome sequencing technologies. We analyzed somatic mutations that had been detected in 326 whole genomes of liver cancer patients. Our results indicated 907 candidate mutations affecting TF motifs. Genome wide alignment of the mutated motifs revealed a significant enrichment of mutations in a highly conserved position of the CTCF motif. Gene expression analysis exhibited disruption of topologically associated domains in the mutated samples. We also confirmed the mutational pattern in pancreatic, gastric and esophagus cancers. Finally, enrichment of cancer associated gene sets and pathways suggested great role of noncoding mutations in cancer. To systematically analyze DNA mutations in TF motifs, we developed an online database system (funMotifs). Publicly available datasets were collected for thousands experiments. The datasets were integrated using a logistic regression model. Functionality annotations and scores for motifs of 519 TFs were derived. The database allows for identification of variants affecting functional motifs in a selected tissue type. Finally, a comprehensive analysis was performed to identify mutations overlapping functional TF motifs in 37 cancer types. Somatic mutations from a pan-cancer cohort of 2,515 cancer whole genomes were investigated. A significant enrichment of mutations in the CpG site of the CEBPB motif was identified. Overall, 10,806 mutated regulatory elements were identified including 406 highly recurrent ones. Genes associated to the mutated elements were highly enriched for cancer-related pathways. Our analyses provide further insights onto the role of regulatory elements and their impacts on cancer development

Computational Modelling of Gene Regulation in Cancer : Coding the noncoding genome

Technological advancements have enabled quantification of processes within and around us. The information stored within our body converts into petabytes of data. Processing and learning from such data requires comprehensive computational programs and software systems. We developed software programs to systematically investigate the process of gene regulation in the human genome. Gene regulation is a complex process where several genomic elements control expression of a gene through recruiting many transcription factor (TF) proteins. The TFs recognize specific DNA sequences known as motifs. DNA mutations in regulatory elements and particularly in TF motifs may cause gene deregulation. Therefore, defining the landscape of regulatory elements and their roles in cancer and complex diseases is of major importance. We developed an algorithm (tfNet) to identify regulatory elements based on transcription factor binding sites. tfNet identified nearly 144,000 regulatory elements in five human cell lines. Investigating the elements we identified TF interaction networks and enrichment of many GWAS SNPs. We also defined the regulatory landscape for other conditions and species. Next, we investigated the role of regulatory elements in cancer. Cancer is initiated and developed by genetic aberrations in the genome. Genetic changes that are present in a cancer genome are obtained through whole genome sequencing technologies. We analyzed somatic mutations that had been detected in 326 whole genomes of liver cancer patients. Our results indicated 907 candidate mutations affecting TF motifs. Genome wide alignment of the mutated motifs revealed a significant enrichment of mutations in a highly conserved position of the CTCF motif. Gene expression analysis exhibited disruption of topologically associated domains in the mutated samples. We also confirmed the mutational pattern in pancreatic, gastric and esophagus cancers. Finally, enrichment of cancer associated gene sets and pathways suggested great role of noncoding mutations in cancer. To systematically analyze DNA mutations in TF motifs, we developed an online database system (funMotifs). Publicly available datasets were collected for thousands experiments. The datasets were integrated using a logistic regression model. Functionality annotations and scores for motifs of 519 TFs were derived. The database allows for identification of variants affecting functional motifs in a selected tissue type. Finally, a comprehensive analysis was performed to identify mutations overlapping functional TF motifs in 37 cancer types. Somatic mutations from a pan-cancer cohort of 2,515 cancer whole genomes were investigated. A significant enrichment of mutations in the CpG site of the CEBPB motif was identified. Overall, 10,806 mutated regulatory elements were identified including 406 highly recurrent ones. Genes associated to the mutated elements were highly enriched for cancer-related pathways. Our analyses provide further insights onto the role of regulatory elements and their impacts on cancer development

Comunication skill developing activitie for preschool children

Saskarsmes prasmju veicināšanas iespējas rotaļās jaunākajā pirmsskolas vecuma bērniem Darba autore: Dita Pērkona, Zinātniskais vadītājs: Mārīte Poiša Diplomdarba apjoms: Darbā analizēti saskarsmes jēdzieni, 2 - 3 gadu vecu bērnu attīstības īpatnības un rotaļas nozīmīgums bērnu saskarsmes prasmju veicināšanai. Piedāvātas rotaļas, kas veicina saskarsmes prasmes, kuras tika pārbaudītas praksē. Atslēgas vārdi: 2-3 gadus veci bērni, sadarbība, rotaļas, saskarsme. Pētījuma mērķis: izzināt saskarsmes prasmju veicināšanas iespējas rotaļās jaunākā pirmsskolas vecuma bērniem. Pētījuma uzdevumi: Analizēt pedagogu un psihologu teorijas par bērnu saskarsmi. Veikt bērnu likumsakarības jaunāškajā pirmsskolas vecumā. Pētit saskarmes prasmju veicināšanas iespējas jaunākā pirmsskolas vecuma bērniem rotaļās. Hipotēze: saskarsmes prasmju veicināšana jaunākajā pirmsskolas vecuma bērniem būs veiksmīga, ja pedagogs pārzinās vecumposma saskarsmes prasmju attīstību. Atbilstoši bērnu interesēm un vajadzībām tiek izmantotas daudzveidīgas rotaļas. Pētījuma metodes: Literatūras analizēšana. Pedagoģiskā novērošana. Pedagogu anketēšana. Nepabeigto teikumu metode bērnu vecākiem. Pētījuma bāze: Rīgas X pirmsskolas izglītības iestādes jaunākā grupa.Annotacion Comunication skill developing activitie for preschool children. The author: Dita Pērkona Scientific: Mag. paed. Mārīte Poiša Bachelors scope of work: The work analyzes the concepts of communication, the peculiarities of the development of 2 - 3 year old children and the importance of games for the promotion of children 's communication skills. Suggested games that promote communication skills that were tested in practice. Keywords: 2 – 3 years old children, comunication, collaboration, games. Research objectives: To find out the possibilities of promoting communication skills in the games for younger preschool children. Research tasks: 1.Analyze the theories of pedagogues and psychologists about children's intercourse. 2.Take the regularities of children in the newest preschool age. 3.Study the possibilities of promoting communication skills in toddlers' games in the youngest preschool age. Hypothesis: •Promotion of communication skills for younger preschool children will be successful if the teacher is familiar with the development of age-related communication skills. •Children's communication skills forms more successful when games are used as a means. Methods of research: •Study of Literature; •Teacher observation; •Teachers questionnaires Researh base: Rīga municipality kindergarden X in the youngest group

Functional annotation of noncoding mutations in cancer

In a cancer genome, the noncoding sequence contains the vast majority of somatic mutations. While very few are expected to be cancer drivers, those affecting regulatory elements have the potential to have downstream effects on gene regulation that may contribute to cancer progression. To prioritize regulatory mutations, we screened somatic mutations in the Pan-Cancer Analysis of Whole Genomes cohort of 2,515 cancer genomes on individual bases to assess their potential regulatory roles in their respective cancer types. We found a highly significant enrichment of regulatory mutations associated with the deamination signature overlapping a CpG site in the CCAAT/Enhancer Binding Protein beta recognition sites in many cancer types. Overall, 5,749 mutated regulatory elements were identified in 1,844 tumor samples from 39 cohorts containing 11,962 candidate regulatory mutations. Our analysis indicated 20 or more regulatory mutations in 5.5% of the samples, and an overall average of six per tumor. Several recurrent elements were identified, and major cancer-related pathways were significantly enriched for genes nearby the mutated regulatory elements. Our results provide a detailed view of the role of regulatory elements in cancer genomes.Husen M. Umer and Karolina Smolinska contributed equally to the work as firsts authors.</p

Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data

Background: Finding peaks in ChIP-seq is an important process in biological inference. In some cases, such as positioning nucleosomes with specific histone modifications or finding transcription factor binding specificities, the precision of the detected peak plays a significant role. There are several applications for finding peaks (called peak finders) based on different algorithms (e.g. MACS, Erange and HPeak). Benchmark studies have shown that the existing peak finders identify different peaks for the same dataset and it is not known which one is the most accurate. We present the first meta-server called Peak Finder MetaServer (PFMS) that collects results from several peak finders and produces consensus peaks. Our application accepts three standard ChIP-seq data formats: BED, BAM, and SAM. Results: Sensitivity and specificity of seven widely used peak finders were examined. For the experiments we used three previously studied Transcription Factors (TF) ChIP-seq datasets and identified three of the selected peak finders that returned results with high specificity and very good sensitivity compared to the remaining four. We also ran PFMS using the three selected peak finders on the same TF datasets and achieved higher specificity and sensitivity than the peak finders individually. Conclusions: We show that combining outputs from up to seven peak finders yields better results than individual peak finders. In addition, three of the seven peak finders outperform the remaining four, and running PFMS with these three returns even more accurate results. Another added value of PFMS is a separate report of the peaks returned by each of the included peak finders

Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases

Several Genome Wide Association Studies (GWAS) have reported variants associated to immune diseases. However, the identified variants are rarely the drivers of the associations and the molecular mechanisms behind the genetic contributions remain poorly understood. ChIP-seq data for TFs and histone modifications provide snapshots of protein-DNA interactions allowing the identification of heterozygous SNPs showing significant allele specific signals (AS-SNPs). AS-SNPs can change a TF binding site resulting in altered gene regulation and are primary candidates to explain associations observed in GWAS and expression studies. We identified 17,293 unique AS-SNPs across 7 lymphoblastoid cell lines. In this set of cell lines we interrogated 85% of common genetic variants in the population for potential regulatory effect and we identified 237 AS-SNPs associated to immune GWAS traits and 714 to gene expression in B cells. To elucidate possible regulatory mechanisms we integrated long-range 3D interactions data to identify putative target genes and motif predictions to identify TFs whose binding may be affected by AS-SNPs yielding a collection of 173 AS-SNPs associated to gene expression and 60 to B cell related traits. We present a systems strategy to find functional gene regulatory variants, the TFs that bind differentially between alleles and novel strategies to detect the regulated genes