U87MG Decoded: The Genomic Sequence of a Cytogenetically Aberrant Human Cancer Cell Line

U87MG is a commonly studied grade IV glioma cell line that has been analyzed in at least 1,700 publications over four decades. In order to comprehensively characterize the genome of this cell line and to serve as a model of broad cancer genome sequencing, we have generated greater than 30× genomic sequence coverage using a novel 50-base mate paired strategy with a 1.4kb mean insert library. A total of 1,014,984,286 mate-end and 120,691,623 single-end two-base encoded reads were generated from five slides. All data were aligned using a custom designed tool called BFAST, allowing optimal color space read alignment and accurate identification of DNA variants. The aligned sequence reads and mate-pair information identified 35 interchromosomal translocation events, 1,315 structural variations (>100 bp), 191,743 small (<21 bp) insertions and deletions (indels), and 2,384,470 single nucleotide variations (SNVs). Among these observations, the known homozygous mutation in PTEN was robustly identified, and genes involved in cell adhesion were overrepresented in the mutated gene list. Data were compared to 219,187 heterozygous single nucleotide polymorphisms assayed by Illumina 1M Duo genotyping array to assess accuracy: 93.83% of all SNPs were reliably detected at filtering thresholds that yield greater than 99.99% sequence accuracy. Protein coding sequences were disrupted predominantly in this cancer cell line due to small indels, large deletions, and translocations. In total, 512 genes were homozygously mutated, including 154 by SNVs, 178 by small indels, 145 by large microdeletions, and 35 by interchromosomal translocations to reveal a highly mutated cell line genome. Of the small homozygously mutated variants, 8 SNVs and 99 indels were novel events not present in dbSNP. These data demonstrate that routine generation of broad cancer genome sequence is possible outside of genome centers. The sequence analysis of U87MG provides an unparalleled level of mutational resolution compared to any cell line to date.     

MuPIT interactive: webserver for mapping variant positions to annotated, interactive 3D structures

Mutation position imaging toolbox (MuPIT) interactive is a browser-based application for single-nucleotide variants (SNVs), which automatically maps the genomic coordinates of SNVs onto the coordinates of available three-dimensional (3D) protein structures. The application is designed for interactive browser-based visualization of the putative functional relevance of SNVs by biologists who are not necessarily experts either in bioinformatics or protein structure. Users may submit batches of several thousand SNVs and review all protein structures that cover the SNVs, including available functional annotations such as binding sites, mutagenesis experiments, and common polymorphisms. Multiple SNVs may be mapped onto each structure, enabling 3D visualization of SNV clusters and their relationship to functionally annotated positions. We illustrate the utility of MuPIT interactive in rationalizing the impact of selected polymorphisms in the PharmGKB database, somatic mutations identified in the Cancer Genome Atlas study of invasive breast carcinomas, and rare variants identified in the exome sequencing project. MuPIT interactive is freely available for non-profit use at http://mupit.icm.jhu.edu.     

Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance

The abundance of different SSU rRNA (“16S”) gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly – from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.     

Genome‐wide characterization and expression analysis of genetic variants in sweet orange

Heterozyosity is an important feature of many plant genomes, and is related to heterosis. Sweet orange, a highly heterozygous species, is thought to have originated from an inter‐species hybrid between pummelo and mandarin. To investigate the heterozygosity of the sweet orange genome and examine how this heterozygosity affects gene expression, we characterized the genome of Valencia orange for single nucleotide variations (SNVs), small insertions and deletions (InDels) and structural variations (SVs), and determined their functional effects on protein‐coding genes and non‐coding sequences. Almost half of the genes containing large‐effect SNVs and InDels were expressed in a tissue‐specific manner. We identified 3542 large SVs (>50 bp), including deletions, insertions and inversions. Most of the 296 genes located in large‐deletion regions showed low expression levels. RNA‐Seq reads and DNA sequencing reads revealed that the alleles of 1062 genes were differentially expressed. In addition, we detected approximately 42 Mb of contigs that were not found in the reference genome of a haploid sweet orange by de novo assembly of unmapped reads, and annotated 134 protein‐coding genes within these contigs. We discuss how this heterozygosity affects the quality of genome assembly. This study advances our understanding of the genome architecture of sweet orange, and provides a global view of gene expression at heterozygous loci.     

Protein coding potential of retroviruses and other transposable elements in vertebrate genomes

We suggest an annotation strategy for genes encoded by retroviruses and transposable elements (RETRA genes) based on a set of marker protein domains. Usually RETRA genes are masked in vertebrate genomes prior to the application of automated gene prediction pipelines under the assumption that they provide no selective advantage to the host. Yet, we show that about 1000 genes in four vertebrate gene sets analyzed contain at least one RETRA gene marker domain. Using the conservation of genomic neighborhood (synteny), we were able to discriminate between RETRA genes with putative functionality in the vertebrates and those that probably function only in the context of mobile elements. We identified 35 such genes in human, along with their corresponding mouse and rat orthologs; which included almost all known human genes with similarity to mobile elements. The results also imply that the vast majority of the remaining RETRA genes in current gene sets are unlikely to encode vertebrate functions. To automatically annotate RETRA genes in other vertebrate genomes, we provide as a tool a set of marker protein domains and a manually refined list of domesticated or ancestral RETRA genes for rescuing genes with vertebrate functions.     

Copy number variations of 11 macronuclear chromosomes and their gene expression in Oxytricha trifallax

Genome-wide methylation studies frequently lack adequate controls to estimate proportions of background reads in the resulting datasets. To generate appropriate control pools, we developed technique termed nMETR (non-methylated tag recovery) based on digestion of genomic DNA with methylation-sensitive restriction enzyme, ligation of adapter oligonucleotide and PCR amplification of non-methylated sites associated with genomic repetitive elements. The protocol takes only two working days to generate amplicons for deep sequencing. We applied nMETR for human DNA using BspFNI enzyme and retrotransposon Alu-specific primers. 454-sequencing enabled identification of 1113 nMETR tag sites, of them ~65% were parts of CpG islands. Representation of reads inversely correlated with methylation levels, thus confirming nMETR fidelity. We created software that eliminates background reads and enables to map and annotate individual tags on human genome. nMETR tags may serve as the controls for large-scale epigenetic studies and for identifying unmethylated transposable elements located close to genomic CpG islands.     

Discovering single nucleotide variants and indels from bulk and single-cell ATAC-seq

Genetic variants and de novo mutations in regulatory regions of the genome are typically discovered by whole-genome sequencing (WGS), however WGS is expensive and most WGS reads come from non-regulatory regions. The Assay for Transposase-Accessible Chromatin (ATAC-seq) generates reads from regulatory sequences and could potentially be used as a low-cost ‘capture’ method for regulatory variant discovery, but its use for this purpose has not been systematically evaluated. Here we apply seven variant callers to bulk and single-cell ATAC-seq data and evaluate their ability to identify single nucleotide variants (SNVs) and insertions/deletions (indels). In addition, we develop an ensemble classifier, VarCA, which combines features from individual variant callers to predict variants. The Genome Analysis Toolkit (GATK) is the best-performing individual caller with precision/recall on a bulk ATAC test dataset of 0.92/0.97 for SNVs and 0.87/0.82 for indels within ATAC-seq peak regions with at least 10 reads. On bulk ATAC-seq reads, VarCA achieves superior performance with precision/recall of 0.99/0.95 for SNVs and 0.93/0.80 for indels. On single-cell ATAC-seq reads, VarCA attains precision/recall of 0.98/0.94 for SNVs and 0.82/0.82 for indels. In summary, ATAC-seq reads can be used to accurately discover non-coding regulatory variants in the absence of whole-genome sequencing data and our ensemble method, VarCA, has the best overall performance.     

Targeted assembly of short sequence reads

As next-generation sequence (NGS) production continues to increase, analysis is becoming a significant bottleneck. However, in situations where information is required only for specific sequence variants, it is not necessary to assemble or align whole genome data sets in their entirety. Rather, NGS data sets can be mined for the presence of sequence variants of interest by localized assembly, which is a faster, easier, and more accurate approach. We present TASR, a streamlined assembler that interrogates very large NGS data sets for the presence of specific variants by only considering reads within the sequence space of input target sequences provided by the user. The NGS data set is searched for reads with an exact match to all possible short words within the target sequence, and these reads are then assembled stringently to generate a consensus of the target and flanking sequence. Typically, variants of a particular locus are provided as different target sequences, and the presence of the variant in the data set being interrogated is revealed by a successful assembly outcome. However, TASR can also be used to find unknown sequences that flank a given target. We demonstrate that TASR has utility in finding or confirming genomic mutations, polymorphisms, fusions and integration events. Targeted assembly is a powerful method for interrogating large data sets for the presence of sequence variants of interest. TASR is a fast, flexible and easy to use tool for targeted assembly.     

Development of Genetic Markers in Eucalyptus Species by Target Enrichment and Exome Sequencing

The advent of next-generation sequencing has facilitated large-scale discovery, validation and assessment of genetic markers for high density genotyping. The present study was undertaken to identify markers in genes supposedly related to wood property traits in three Eucalyptus species. Ninety four genes involved in xylogenesis were selected for hybridization probe based nuclear genomic DNA target enrichment and exome sequencing. Genomic DNA was isolated from the leaf tissues and used for on-array probe hybridization followed by Illumina sequencing. The raw sequence reads were trimmed and high-quality reads were mapped to the E. grandis reference sequence and the presence of single nucleotide variants (SNVs) and insertions/ deletions (InDels) were identified across the three species. The average read coverage was 216X and a total of 2294 SNVs and 479 InDels were discovered in E. camaldulensis, 2383 SNVs and 518 InDels in E. tereticornis, and 1228 SNVs and 409 InDels in E. grandis. Additionally, SNV calling and InDel detection were conducted in pair-wise comparisons of E. tereticornis vs. E. grandis, E. camaldulensis vs. E. tereticornis and E. camaldulensis vs. E. grandis. This study presents an efficient and high throughput method on development of genetic markers for family– based QTL and association analysis in Eucalyptus.     

Alternative splicing and genetic diversity: silencers are more frequently modified by SNVs associated with alternative exon/intron borders

With the availability of a large amount of genomic data it is expected that the influence of single nucleotide variations (SNVs) in many biological phenomena will be elucidated. Here, we approached the problem of how SNVs affect alternative splicing. First, we observed that SNVs and exonic splicing regulators (ESRs) independently show a biased distribution in alternative exons. More importantly, SNVs map more frequently in ESRs located in alternative exons than in ESRs located in constitutive exons. By looking at SNVs associated with alternative exon/intron borders (by their common presence in the same cDNA molecule), we observed that a specific type of ESR, the exonic splicing silencers (ESSs), are more frequently modified by SNVs. Our results establish a clear association between genetic diversity and alternative splicing involving ESSs.     

MicroRNA transgene overexpression complements deficiency-based modifier screens in Drosophila

Dosage-sensitive modifier screening is a powerful tool for linking genes to biological processes. Use of chromosomal deletions permits sampling the effects of removing groups of genes related by position on the chromosome. Here, we explore the use of inducible microRNA transgenes as a complement to deficiency-based modifier screens. miRNAs are predicted to have hundreds of targets. miRNA overexpression provides an efficient means to reduces expression of large gene sets. A collection of transgenes was prepared to allow overexpression of 89 miRNAs or miRNA clusters. These transgenes and a set of genomic deficiencies were screened for their ability to modify the bristle phenotype of the cell-cycle regulator minus. Sixteen miRNAs were identified as dominant suppressors, while the deficiency screen uncovered four genomic regions that contain a dominant suppressor. Comparing the genes uncovered by the deletions with predicted miRNA targets uncovered a small set of candidate suppressors. Two candidates were identified as suppressors of the minus phenotype, Cullin-4 and CG5199/Cut8. Additionally, we show that Cullin-4 acts through its substrate receptor Cdt2 to suppress the minus phenotype. We suggest that inducible microRNA transgenes are a useful complement to deficiency-based modifier screens.     

Local gene density predicts the spatial position of genetic loci in the interphase nucleus

Specific chromosomal translocations are hallmarks of many human leukemias. The basis for these translocation events is poorly understood, but it has been assumed that spatial positioning of genes in the nucleus of hematopoietic cells is a contributing factor. Analysis of the nuclear 3D position of the gene MLL, frequently involved in chromosomal translocations and five of its translocation partners (AF4, AF6, AF9, ENL and ELL), and two control loci revealed a characteristic radial distribution pattern in all hematopoietic cells studied. Genes in areas of high local gene density were found positioned towards the nuclear center, whereas genes in regions of low gene density were detected closer to the nuclear periphery. The gene density within a 2 Mbp window was found to be a better predictor for the relative positioning of a genomic locus within the cell nucleus than the gene density of entire chromosomes. Analysis of the position of MLL, AF4, AF6 and AF9 in cell lines carrying chromosomal translocations involving these genes revealed that the position of the normal genes was different from that of the fusion genes, and this was again consistent with the changes in local gene density within a 2 Mbp window. Thus, alterations in gene density directly at translocation junctions could explain the change in the position of affected genes in leukemia cells.     

Normalization, testing, and false discovery rate estimation for RNA-sequencing data

We discuss the identification of genes that are associated with an outcome in RNA sequencing and other sequence-based comparative genomic experiments. RNA-sequencing data take the form of counts, so models based on the Gaussian distribution are unsuitable. Moreover, normalization is challenging because different sequencing experiments may generate quite different total numbers of reads. To overcome these difficulties, we use a log-linear model with a new approach to normalization. We derive a novel procedure to estimate the false discovery rate (FDR). Our method can be applied to data with quantitative, two-class, or multiple-class outcomes, and the computation is fast even for large data sets. We study the accuracy of our approaches for significance calculation and FDR estimation, and we demonstrate that our method has potential advantages over existing methods that are based on a Poisson or negative binomial model. In summary, this work provides a pipeline for the significance analysis of sequencing data.     

Inter‐varietal structural variation in grapevine genomes

Grapevine (Vitis vinifera L.) is one of the world's most important crop plants, which is of large economic value for fruit and wine production. There is much interest in identifying genomic variations and their functional effects on inter‐varietal, phenotypic differences. Using an approach developed for the analysis of human and mammalian genomes, which combines high‐throughput sequencing, array comparative genomic hybridization, fluorescent in situ hybridization and quantitative PCR, we created an inter‐varietal atlas of structural variations and single nucleotide variants (SNVs) for the grapevine genome analyzing four economically and genetically relevant table grapevine varieties. We found 4.8 million SNVs and detected 8% of the grapevine genome to be affected by genomic variations. We identified more than 700 copy number variation (CNV) regions and more than 2000 genes subjected to CNV as potential candidates for phenotypic differences between varieties.