Somatic mutation and gene expression dysregulation are considered two major tumorigenesis factors. While independent investigations of either factor pervade, studies of associations between somatic mutations and gene expression changes have been sporadic and nonsystematic. Utilizing genomic data collected from 11 315 subjects of 33 distinct cancer types, we constructed MutEx, a pan-cancer integrative genomic database. This database records the relationships among gene expression, somatic mutation and survival data for cancer patients. MutEx can be used to swiftly explore the relationship between these genomic/clinic features within and across cancer types and, more importantly, search for corroborating evidence for hypothesis inception. Our database also incorporated Gene Ontology and several pathway databases to enhance functional annotation, and elastic net and a gene expression composite score to aid in survival analysis. To demonstrate the usability of MutEx, we provide several application examples, including top somatic mutations associated with the most extensive expression dysregulation in breast cancer, differential mutational burden downstream of DNA mismatch repair gene mutations and composite gene expression score-based survival difference in breast cancer. MutEx can be accessed at http://www.innovebioinfo.com/Databases/Mutationdb_About.php.

Expression quantitative trait loci (eQTLs) have been touted as the missing piece that can bridge the gap between genetic variants and phenotypes. Over the past decade, we have witnessed a sharp rise of effort in the identification and application of eQTLs. The successful application of eQTLs relies heavily on their reproducibility. The current eQTL databases such as Genotype-Tissue Expression (GTEx) were populated primarily with eQTLs deriving from germline single nucleotide polymorphisms and normal tissue gene expression. The novel scenarios that employ eQTL models for prediction purposes often involve disease phenotypes characterized by altered gene expressions. To evaluate eQTL reproducibility across diverse data sources and the effect of disease-specific gene expression alteration on eQTL identification, we conducted an eQTL study using 5178 samples from The Cancer Genome Atlas (TCGA). We found that the reproducibility of eQTLs between normal and tumor tissues was low in terms of the number of shared eQTLs. However, among the shared eQTLs, the effect directions were generally concordant. This suggests that the source of the gene expression (normal or tumor tissue) has a strong effect on the detectable eQTLs and the effect direction of the eQTLs. Additional analyses demonstrated good directional concordance of eQTLs between GTEx and TCGA. Furthermore, we found that multi-tissue eQTLs may exert opposite effects across multiple tissue types. In summary, our results suggest that eQTL prediction models need to carefully address tissue and disease dependency of eQTLs. Tissue-disease-specific eQTL databases can afford more accurate prediction models for future studies.

Very short tandem repeats bear substantial genetic, evolutional, and pathological significance in genome analyses. Here, we compiled a census of tandem mono-nucleotide/di-nucleotide/tri-nucleotide repeats (MNRs/DNRs/TNRs) in GRCh38, which we term "polytracts" in general. Of the human genome, 144.4 million nucleotides (4.7%) are occupied by polytracts, and 0.47 million single nucleotides are identified as polytract hinges, i.e., break-points of tandem polytracts. Preliminary exploration of the census suggested polytract hinge sites and boundaries of AAC polytracts may bear a higher mapping error rate than other polytract regions. Further, we revealed landscapes of polytract enrichment with respect to nearly a hundred genomic features. We found MNRs, DNRs, and TNRs displayed noticeable difference in terms of locational enrichment for miscellaneous genomic features, especially RNA editing events. Non-canonical and C-to-U RNA-editing events are enriched inside and/or adjacent to MNRs, while all categories of RNA-editing events are under-represented in DNRs. A-to-I RNA-editing events are generally under-represented in polytracts. The selective enrichment of non-canonical RNA-editing events within MNR adjacency provides a negative evidence against their authenticity. To enable similar locational enrichment analyses in relation to polytracts, we developed a software Polytrap which can handle 11 reference genomes. Additionally, we compiled polytracts of four model organisms into a Track Hub which can be integrated into USCS Genome Browser as an official track for convenient visualization of polytracts.

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type.

Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies.

Down syndrome (DS) is a genetic disorder with genome dosage imbalances and micro-duplications of human chromosome 21. It is usually associated with a group of serious diseases, including intellectual disabilities, cardiac diseases, physical abnormalities, and other abnormalities. Currently, since there is no cure for human DS, screening and early detection have become the most efficient way for DS prevention. In this study, we used deep learning techniques to build accurate DS prediction/screening models based on the analysis of newly introduced Illumina genotyping array. Specifically, we built chromosome SNP maps based on clinical genotyping data collected by Vanderbilt University Medical Center. Then we proposed a convolutional neural network (CNN) architecture with ten layers and two merged CNN models, which took two input chromosome SNP maps in combination. Our CNN DS prediction/screening model achieved over 99.3% average accuracy, as well as very low false positive and false negative rate, which are critical to disease prediction and screening in medical practice. It also had better performances in terms of all evaluating metrics when compared with three conventional machine-learning algorithms. Finally, we visualized the feature maps and the trained filter weights from intermediate layers of our trained CNN model. We further discussed the advantages of our method and the underlying reasons for its robust performance.

SUMMARY: After the introduction of high-throughput sequencing, genotyping arrays continue to be a viable source for conducting large-scale genetic studies. Currently, Illumina is one of the largest genotyping array manufacturers. One technical issue that has always plagued the post-processing of Illumina genotyping array data is the strand definition. Against convention, Illumina uses their own definition of strand, which is inconsistent with the standard reference forward and reverse definition. This issue has been a major obstacle in the consistency of reporting, meta-analysis and correct interpretation of phenotype association results. To date, the strand issue has not been adequately addressed, prompting us to develop StrandScript, a tool that can convert all genotyping data generated from Illumina genotyping arrays to the reference forward strand. StrandScript works independently of the Illumina array version and is future proof for newer Illumina array designs. Furthermore, StrandScript can examine an Illumina genotyping array manifest file and can detect all problematic SNPs, including SNPs with wrong RS ID and SNPs with mismatched probe sequences. Here, we introduce StrandScript's design and development, and demonstrate its effectiveness using real genotyping data. AVAILABILITY AND IMPLEMENTATION: https://github.com/seasky002002/Strandscript. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The rapid progress in high-throughput sequencing has significantly enriched our capacity for studying the mitochondrial DNA (mtDNA). In addition to performing specific mitochondrial targeted sequencing, an increasingly popular alternative approach is using the off-target reads from exome sequencing to infer mtDNA variants, including single nucleotide polymorphisms (SNPs) and heteroplasmy. However, the effectiveness and practicality of this approach have not been tested. Recently, RNAseq data have also been suggested as a good source for alternative data mining, but whether mitochondrial variants can be detected from RNAseq data has not been validated. We designed a study to evaluate the practicability of mtDNA variant detection using exome and RNA sequencing data. Five breast cancer cell lines were sequenced through mitochondrial targeted, exome, and RNA sequencing. Mitochondrial targeted sequencing was used as the gold standard to compute the validation and false discovery rates of SNP and heteroplasmy detection in exome and RNAseq data. We found that exome and RNA sequencing can accurately detect mitochondrial SNPs. However, the lower false discovery rate makes exome sequencing a better choice for heteroplasmy detection than RNAseq. Furthermore, we examined three alignment strategies and found that aligning reads directly to the mitochondrial reference genome or aligning reads to the nuclear and mitochondrial references genomes simultaneously produced the best results, and that aligning to the nuclear genome first and afterwards to the mitochondrial genome performed poorly. In conclusion, our study provides important guidelines for future studies that intend to use either exome sequencing or RNAseq data to infer mitochondrial SNPs and heteroplasmy.

Recent advances in RNA library preparation methods, platform accessibility and cost efficiency have allowed high-throughput RNA sequencing (RNAseq) to replace conventional hybridization microarray platforms as the method of choice for mRNA profiling and transcriptome analyses. RNAseq is a powerful technique to profile both long and short RNA expression, and the depth of information gained from distinct RNAseq methods is striking and facilitates discovery. In addition to expression analysis, distinct RNAseq approaches also allow investigators the ability to assess transcriptional elongation, DNA variance and exogenous RNA content. Here we review the current state of the art in transcriptome sequencing and address epigenetic regulation, quantification of transcription activation, RNAseq output and a diverse set of applications for RNAseq data. We detail how RNAseq can be used to identify allele-specific expression, single-nucleotide polymorphisms and somatic mutations and discuss the benefits and limitations of using RNAseq to monitor DNA characteristics. Moreover, we highlight the power of combining RNA- and DNAseq methods for genomic analysis. In summary, RNAseq provides the opportunity to gain greater insight into transcriptional regulation and output than simply miRNA and mRNA profiling.

MOTIVATION: The transition/transversion (Ti/Tv) ratio and heterozygous/nonreference-homozygous (het/nonref-hom) ratio have been commonly computed in genetic studies as a quality control (QC) measurement. Additionally, these two ratios are helpful in our understanding of the patterns of DNA sequence evolution. RESULTS: To thoroughly understand these two genomic measures, we performed a study using 1000 Genomes Project (1000G) released genotype data (N=1092). An additional two datasets (N=581 and N=6) were used to validate our findings from the 1000G dataset. We compared the two ratios among continental ancestry, genome regions and gene functionality. We found that the Ti/Tv ratio can be used as a quality indicator for single nucleotide polymorphisms inferred from high-throughput sequencing data. The Ti/Tv ratio varies greatly by genome region and functionality, but not by ancestry. The het/nonref-hom ratio varies greatly by ancestry, but not by genome regions and functionality. Furthermore, extreme guanine + cytosine content (either high or low) is negatively associated with the Ti/Tv ratio magnitude. Thus, when performing QC assessment using these two measures, care must be taken to apply the correct thresholds based on ancestry and genome region. Failure to take these considerations into account at the QC stage will bias any following analysis. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Background Characterizing genetic diversity is crucial for reconstructing human evolution and for understanding the genetic basis of complex diseases; however, human population genetics are very complicated. Previously, we proved that based on the Hardy-Weinberg equilibrium, the heterozygous vs. non-reference homozygous single nucleotide polymorphism (SNP) ratio (het/nonref-hom) is two [1]. Later, we found that this ratio is race dependent, with African being the most genetically diverse race and Asian being the most homozygous [2]. This observation prompted us to conduct further study to understand the reasoning behind this diversity.

Background The rapid progress in high throughput sequencing technology has significantly enriched our capability to study mitochondria genomes. Other than performing mitochondria targeted sequencing, an increasingly popular alternative approach is to utilize the off-target reads from exome sequencing to infer mitochondria genomic variants including SNP and heteroplasmy[1-9]. However, the effectiveness and practicality of such an approach has not been tested. Recently, RNAseq data has also been suggested as good source for alternative data mining[10,11], but whether mitochondria variants are minable has not been studied.

Advances in next-generation sequencing (NGS) technologies have greatly improved our ability to detect genomic variants for biomedical research. In particular, NGS technologies have been recently applied with great success to the discovery of mutations associated with the growth of various tumours and in rare Mendelian diseases. The advance in NGS technologies has also created significant challenges in bioinformatics. One of the major challenges is quality control of the sequencing data. In this review, we discuss the proper quality control procedures and parameters for Illumina technology-based human DNA re-sequencing at three different stages of sequencing: raw data, alignment and variant calling. Monitoring quality control metrics at each of the three stages of NGS data provides unique and independent evaluations of data quality from differing perspectives. Properly conducting quality control protocols at all three stages and correctly interpreting the quality control results are crucial to ensure a successful and meaningful study.

MOTIVATION: Exome capture kits have capture efficiencies that range from 40 to 60%. A significant amount of off-target reads are from the mitochondrial genome. These unintentionally sequenced mitochondrial reads provide unique opportunities to study the mitochondria genome. RESULTS: MitoSeek is an open-source software tool that can reliably and easily extract mitochondrial genome information from exome and whole genome sequencing data. MitoSeek evaluates mitochondrial genome alignment quality, estimates relative mitochondrial copy numbers and detects heteroplasmy, somatic mutation and structural variants of the mitochondrial genome. MitoSeek can be set up to run in parallel or serial on large exome sequencing datasets. AVAILABILITY: https://github.com/riverlee/MitoSeek

Insulin secreted by pancreatic islet β-cells is the principal regulating hormone of glucose metabolism. It plays a key role in controlling glucose level in blood. Impairment of the pancreatic islet function may cause glucose to accumulate in the blood, and result in diabetes mellitus. In order to study the cause of dysfunction of pancreatic islets, a multiple-cell model containing healthy and unhealthy cells is proposed based on an existing single cell model. The β-cells in the model are connected through direct electrical connections between neighboring β-cells. The simulation results show that around 20% unhealthy cells in pancreatic islets will disrupt the insulin secretion. This suggests that a small portion of unhealthy cells may have greater effect in the dysfunction of insulin oscillation than expected.

Using a computational model, we simulated mitochondrial deoxynucleotide metabolism and mitochondrial DNA replication. Our results indicate that the output from the mitochondrial salvage enzymes alone is inadequate to support a mitochondrial DNA replication duration of as long as 10 hours. We find that an external source of deoxyribonucleoside diphosphates or triphosphates (dNTPs), in addition to those supplied by mitochondrial salvage, is essential for the replication of mitochondrial DNA to complete in the experimentally observed duration of approximately 1 to 2 hours. For meeting a relatively fast replication target of 2 hours, almost two-thirds of the dNTP requirements had to be externally supplied as either deoxyribonucleoside di- or triphosphates, at about equal rates for all four dNTPs. Added monophosphates did not suffice. However, for a replication target of 10 hours, mitochondrial salvage was able to provide for most, but not all, of the total substrate requirements. Still, additional dGTPs and dATPs had to be supplied. Our analysis of the enzyme kinetics also revealed that the majority of enzymes of this pathway prefer substrates that are not precursors (canonical deoxyribonucleosides and deoxyribonucleotides) for mitochondrial DNA replication, such as phosphorylated ribonucleotides, instead of the corresponding deoxyribonucleotides. The kinetic constants for reactions between mitochondrial salvage enzymes and deoxyribonucleotide substrates are physiologically unreasonable for achieving efficient catalysis with the expected in situ concentrations of deoxyribonucleotides.

The activity of polymerase γ is complicated, involving both correct and incorrect DNA polymerization events, exonuclease activity, and the disassociation of the polymerase:DNA complex. Pausing of pol-γ might increase the chance of deletion and depletion of mitochondrial DNA. We have developed a stochastic simulation of pol-γ that models its activities on the level of individual nucleotides for the replication of mtDNA. This method gives us insights into the pausing of two pol-γ variants: the A467T substitution that causes PEO and Alpers syndrome, and the exonuclease deficient pol-γ (exo(-)) in premature aging mouse models. To measure the pausing, we analyzed simulation results for the longest time for the polymerase to move forward one nucleotide along the DNA strand. Our model of the exo(-) polymerase had extremely long pauses, with a 30 to 300-fold increase in the time required for the longest single forward step compared to the wild-type, while the naturally occurring A467T variant showed at most a doubling in the length of the pauses compared to the wild-type. We identified the cause of these differences in the polymerase pausing time to be the number of disassociations occurring in each forward step of the polymerase.

Insulin secreted by pancreatic islet β-cells is the principal regulating hormone of glucose metabolism. Disruption of insulin secretion may cause glucose to accumulate in the blood, and result in diabetes mellitus. Although deterministic models of the insulin secretion pathway are available, the stochastic aspect of the biological pathway has not been explored. As a first step in this direction, we present a hybrid model of the insulin secretion pathway, in which the delayed rectifying K+channels are treated as stochastic events. Simulation results of our hybrid model demonstrate that our model not only can reproduce the bursts of electrical activity as the deterministic model does, but also can be used to predict the magnitude of the total number of the delayed rectifying K+channels per cell needed in order to prevent the function of this pathway from disruption by stochastic effects. The coupling effect of multiple cells is also studied based on the hybrid model, which shows the synchronization behavior of the cells.

Nucleoside analogs used in antiretroviral treatment have been associated with mitochondrial toxicity. The polymerase-gamma hypothesis states that this toxicity stems from the analogs' inhibition of the mitochondrial DNA polymerase (polymerase-gamma) leading to mitochondrial DNA (mtDNA) depletion. We have constructed a computational model of the interaction of polymerase-gamma with activated nucleoside and nucleotide analog drugs, based on experimentally measured reaction rates and base excision rates, together with the mtDNA genome size, the human mtDNA sequence, and mitochondrial dNTP concentrations. The model predicts an approximately 1000-fold difference in the activated drug concentration required for a 50% probability of mtDNA strand termination between the activated di-deoxy analogs d4T, ddC, and ddI (activated to ddA) and the activated forms of the analogs 3TC, TDF, AZT, FTC, and ABC. These predictions are supported by experimental and clinical data showing significantly greater mtDNA depletion in cell culture and patient samples caused by the di-deoxy analog drugs. For zidovudine (AZT) we calculated a very low mtDNA replication termination probability, in contrast to its reported mitochondrial toxicity in vitro and clinically. Therefore AZT mitochondrial toxicity is likely due to a mechanism that does not involve strand termination of mtDNA replication.