Genomics & Informatics 

About: Genomics & Informatics is an academic journal published by Korea Genome Organization. The journal publishes majorly in the area(s): Gene & Medicine. It has an ISSN identifier of 1598-866X. It is also open access. Over the lifetime, 671 publications have been published receiving 5978 citations. The journal is also known as: genomic science & genome science.

Sample Size and Statistical Power Calculation in Genetic Association Studies

Abstract: A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.

Introns: The Functional Benefits of Introns in Genomes.

Abstract: The intron has been a big biological mystery since it was first discovered in several aspects. First, all of the completely sequenced eukaryotes harbor introns in the genomic structure, whereas no prokaryotes identified so far carry introns. Second, the amount of total introns varies in different species. Third, the length and number of introns vary in different genes, even within the same species genome. Fourth, all introns are copied into RNAs by transcription and DNAs by replication processes, but intron sequences do not participate in protein-coding sequences. The existence of introns in the genome should be a burden to some cells, because cells have to consume a great deal of energy to copy and excise them exactly at the correct positions with the help of complicated spliceosomal machineries. The existence throughout the long evolutionary history is explained, only if selective advantages of carrying introns are assumed to be given to cells to overcome the negative effect of introns. In that regard, we summarize previous research about the functional roles or benefits of introns. Additionally, several other studies strongly suggesting that introns should not be junk will be introduced.

Transposable Elements and Genome Size Variations in Plants

Abstract: Although the number of protein-coding genes is not highly variable between plant taxa, the DNA content in their genomes is highly variable, by as much as 2,056-fold from a 1C amount of 0.0648 pg to 132.5 pg. The mean 1C-value in plants is 2.4 pg, and genome size expansion/contraction is lineage-specific in plant taxonomy. Transposable element fractions in plant genomes are also variable, as low as ~3% in small genomes and as high as ~85% in large genomes, indicating that genome size is a linear function of transposable element content. Of the 2 classes of transposable elements, the dynamics of class 1 long terminal repeat (LTR) retrotransposons is a major contributor to the 1C value differences among plants. The activity of LTR retrotransposons is under the control of epigenetic suppressing mechanisms. Also, genome-purging mechanisms have been adopted to counter-balance the genome size amplification. With a wealth of information on whole-genome sequences in plant genomes, it was revealed that several genome-purging mechanisms have been employed, depending on plant taxa. Two genera, Lilium and Fritillaria, are known to have large genomes in angiosperms. There were twice times of concerted genome size evolutions in the family Liliaceae during the divergence of the current genera in Liliaceae. In addition to the LTR retrotransposons, non-LTR retrotransposons and satellite DNAs contributed to the huge genomes in the two genera by possible failure of genome counter-balancing mechanisms.

Statistical Analysis of Metagenomics Data

Abstract: Understanding the role of the microbiome in human health and how it can be modulated is becoming increasingly relevant for preventive medicine and for the medical management of chronic diseases. The development of high-throughput sequencing technologies has boosted microbiome research through the study of microbial genomes and allowing a more precise quantification of microbiome abundances and function. Microbiome data analysis is challenging because it involves high-dimensional structured multivariate sparse data and because of its compositional nature. In this review we outline some of the procedures that are most commonly used for microbiome analysis and that are implemented in R packages. We place particular emphasis on the compositional structure of microbiome data. We describe the principles of compositional data analysis and distinguish between standard methods and those that fit into compositional data analysis.

Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

Abstract: Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.