SUMMARY Two abundant classes of mobile elements, namely Alu and L1 elements, continue to generate new retrotransposon insertions in human genomes. Estimates suggest that these elements have generated millions of new germline insertions in individual human genomes worldwide. Unfortunately, current technologies are not capable of detecting most of these young insertions, and the true extent of germline mutagenesis by endogenous human retrotransposons has been difficult to examine. Here, we describe new technologies for detecting these young retrotransposon insertions and demonstrate that such insertions indeed are abundant in human populations. We also found that new somatic L1 insertions occur at high frequencies in human lung cancer genomes. Genome-wide analysis suggests that altered DNA methylation may be responsible for the high levels of L1 mobilization observed in these tumors. Our data indicate that transposon-mediated mutagenesis is extensive in human genomes, and is likely to have a major impact on human biology and diseases.

Although many studies have been conducted to identify single nucleotide polymorphisms (SNPs) in humans, few studies have been conducted to identify alternative forms of natural genetic variation, such as insertion and deletion (INDEL) polymorphisms. In this report, we describe an initial map of human INDEL variation that contains 415,436 unique INDEL polymorphisms. These INDELs were identified with a computational approach using DNA re-sequencing traces that originally were generated for SNP discovery projects. They range from 1 bp to 9989 bp in length and are split almost equally between insertions and deletions, relative to the chimpanzee genome sequence. Five major classes of INDELs were identified, including (1) insertions and deletions of single-base pairs, (2) monomeric base pair expansions, (3) multi-base pair expansions of 2-15 bp repeat units, (4) transposon insertions, and (5) INDELs containing random DNA sequences. Our INDELs are distributed throughout the human genome with an average density of one INDEL per 7.2 kb of DNA. Variation hotspots were identified with up to 48-fold regional increases in INDEL and/or SNP variation compared with the chromosomal averages for the same chromosomes. Over 148,000 INDELs (35.7%) were identified within known genes, and 5542 of these INDELs were located in the promoters and exons of genes, where gene function would be expected to be influenced the greatest. All INDELs in this study have been deposited into dbSNP and have been integrated into maps of human genetic variation that are available to the research community.

BackgroundDetection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation.ResultsxMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites.ConclusionsxMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

In this review, we focus on progress that has been made with detecting small insertions and deletions (INDELs) in human genomes. Over the past decade, several million small INDELs have been discovered in human populations and personal genomes. The amount of genetic variation that is caused by these small INDELs is substantial. The number of INDELs in human genomes is second only to the number of single nucleotide polymorphisms (SNPs), and, in terms of base pairs of variation, INDELs cause similar levels of variation as SNPs. Many of these INDELs map to functionally important sites within human genes, and thus, are likely to influence human traits and diseases. Therefore, small INDEL variation will play a prominent role in personalized medicine.

Human genetic variation is expected to play a central role in personalized medicine. Yet only a fraction of the natural genetic variation that is harbored by humans has been discovered to date. Here we report almost 2 million small insertions and deletions (INDELs) that range from 1 bp to 10,000 bp in length in the genomes of 79 diverse humans. These variants include 819,363 small INDELs that map to human genes. Small INDELs frequently were found in the coding exons of these genes, and several lines of evidence indicate that such variation is a major determinant of human biological diversity. Microarray-based genotyping experiments revealed several interesting observations regarding the population genetics of small INDEL variation. For example, we found that many of our INDELs had high levels of linkage disequilibrium (LD) with both HapMap SNPs and with high-scoring SNPs from genome-wide association studies. Overall, our study indicates that small INDEL variation is likely to be a key factor underlying inherited traits and diseases in humans.