http://genomebiology.com The most accessed research articles published by Genome Biology 2012-01-31T00:00:00Z Background: Genomic analysis of high-altitude populations residing in the Andes and Tibet has revealed severalcandidate loci for involvement in high-altitude adaptation, a subset of which have also been shown tobe associated with hemoglobin levels, including EPAS1, EGLN1, and PPARA, which play a role inthe HIF-1 pathway. Here, we have extended this work to high and low altitude populations living inEthiopia for which we have measured hemoglobin levels. We genotyped the Illumina 1M SNP arrayand employed several genome-wide scans for selection and targeted association with hemoglobin levelsto identify genes that play a role in adaptation to high altitude. Results: We have identified a set of candidate genes for positive selection in our high-altitude populationsample, demonstrated significantly different hemoglobin levels between high and low altitudeEthiopians and have identified a subset of candidate genes for selection, several of which also showsuggestive associations with hemoglobin levels. Conclusions: We highlight several candidate genes for involvement in high-altitude adaptation in Ethiopia, including CBARA1, VAV3, ARNT2 and THRB. Although most of these genes have not been identified in previous studies of high-altitude Tibetan or Andean population samples, two of these genes (THRB and ARNT2) play a role in the HIF-1 pathway, a pathway implicated in previous work reported in Tibetan and Andean studies. These combined results suggest that adaptation to high altitude arose independently due to convergent evolution in high-altitude Amhara populations in Ethiopia. http://genomebiology.com/2012/13/1/R1 Laura Scheinfeldt Sameer Soi Simon Thompson Alessia Ranciaro Dawit Wolde Meskel William Beggs Charla Lambert Joseph Jarvis Dawit Abate Gurja Belay Sarah Tishkoff Genome Biology 2012, null:R1 2012-01-20T00:00:00Z doi:10.1186/gb-2012-13-1-r1 Ethiopian highland genetics The genetics of hypoxia selection in an Ethiopian high-altitude population are distinct from Andean and Tibetan genotypes, suggestive of convergent evolution /content/figures/gb-2012-13-1-r1-toc.gif Genome Biology 1465-6906 ${item.volume} R1 2012-01-20T00:00:00Z PDF Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu. http://genomebiology.com/2009/10/3/R25 Ben Langmead Cole Trapnell Mihai Pop Steven Salzberg Genome Biology 2009, null:R25 2009-03-04T00:00:00Z doi:10.1186/gb-2009-10-3-r25 Bowtie: short-read alignment Bowtie: a new ultrafast memory-efficient tool for the alignment of short DNA sequence reads to large genomes. /content/figures/gb-2009-10-3-r25-toc.gif Genome Biology 1465-6906 ${item.volume} R25 2009-03-04T00:00:00Z XML The International Crocodilian Genomes Working Group (ICGWG) will sequence and assemble the American alligator (Alligator mississippiensis), saltwater crocodile (Crocodylus porosus) and Indian gharial (Gavialis gangeticus) genomes. The status of these projects and our planned analyses are described. http://genomebiology.com/2012/13/1/415 John St John Edward Braun Sally Isberg Lee Miles Amanda Chong Jaime Gongora Pauline Dalzell Christopher Moran Bertrand Bed'Hom Arkhat Abzhanov Shane Burgess Amanda Cooksey Todd Castoe Nicholas Crawford Llewellyn Densmore Jennifer Drew Scott Edwards Brant Faircloth Matthew Fujita Matthew Greenwold Federico Hoffmann Jonathan Howard Taisen Iguchi Daniel Janes Shahid Yar Khan Satomi Kohno AP Jason de Koning Stacey Lance Fiona McCarthy John McCormack Mark Merchant Daniel Peterson David Pollock Nader Pourmand Brian Raney Kyria Roessler Jeremy Sanford Roger Sawyer Carl Schmidt Eric Triplett Tracey Tuberville Miryam Venegas-Anaya Jason Howard Erich Jarvis Louis Guillette Jr Travis Glenn Richard Green David Ray Genome Biology 2012, null:415 2012-01-31T00:00:00Z doi:10.1186/gb-2012-13-1-415 Crocodilian genome sequencing An Open Letter describing the project to sequence the genomes of the saltwater crocodile, the American alligator and the Indian gharial /content/figures/gb-2012-13-1-415-toc.gif Genome Biology 1465-6906 ${item.volume} 415 2012-01-31T00:00:00Z XML Background: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. Results: We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. Conclusions: The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences. http://genomebiology.com/2002/3/7/research/0034 Jo Vandesompele Katleen De Preter Filip Pattyn Bruce Poppe Nadine Van Roy Anne De Paepe Frank Speleman Genome Biology 2002, null:research0034 2002-06-18T00:00:00Z doi:10.1186/gb-2002-3-7-research0034 <p>Using real-time reverse transcription PCR ten housekeeping genes from different abundance and functional classes in various human tissues were evaluated. The conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested.</p> /content/figures/gb-2002-3-7-research0034-toc.gif Genome Biology 1465-6906 ${item.volume} research0034 2002-06-18T00:00:00Z XML High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package. http://genomebiology.com/2010/11/10/R106 Simon Anders Wolfgang Huber Genome Biology 2010, null:R106 2010-10-27T00:00:00Z doi:10.1186/gb-2010-11-10-r106 DEseq DEseq allows the determination of differential expression of read count data from RNA-seq or ChIP-seq experiments /content/figures/gb-2010-11-10-r106-toc.gif Genome Biology 1465-6906 ${item.volume} R106 2010-10-27T00:00:00Z XML Background: Expression quantitative trait loci (eQTLs) are likely to play an important role in the genetics of complex traits; however their functional basis remains poorly understood. Using the HapMap lymphoblastoid cell lines, we combine 1000 Genomes genotypes and an extensive catalogue of human functional elements to investigate the biological mechanisms that eQTLs perturb. Results: We use a Bayesian hierarchical model to estimate the enrichment of eQTLs in a wide variety of regulatory annotations. We find that ~40% of eQTLs occur in open chromatin, and that they are particularly enriched in transcription factor binding sites, suggesting that many directly impact protein-DNA interactions. Analysis of core promoter regions shows that eQTLs also frequently disrupt some known core promoter motifs but, surprisingly, are not enriched in other well-known motifs such as the TATA box. We also show that information from regulatory annotations alone, when weighted by the hierarchical model, can provide a meaningful ranking of the SNPs that are most likely to drive gene expression variation. Conclusions: Our study demonstrates how regulatory annotation and the association signal derived from eQTL-mapping can be combined into a single framework. We used this approach to further our understanding of the biology that drives human gene expression variation, and of the putatively causal SNPs that underlie it. http://genomebiology.com/2012/13/1/R7 Daniel Gaffney Jean-Baptiste Veyrieras Jacob Degner Pique-Regi Roger Athma Pai Gregory Crawford Matthew Stephens Yoav Gilad Jonathan Pritchard Genome Biology 2012, null:R7 2012-01-31T00:00:00Z doi:10.1186/gb-2012-13-1-r7 Regulatory element eQTLs An analysis of eQTLs located in regulatory elements in the human genome /content/figures/gb-2012-13-1-r7-toc.gif Genome Biology 1465-6906 ${item.volume} R7 2012-01-31T00:00:00Z PDF Ultra-deep targeted sequencing (UDT-Seq) can identify subclonal somatic mutations in tumor samples. Early assays' limited breadth and depth restrict their clinical utility. Here, we target 71kb of mutational hotspots in 42 cancer genes. We present novel methods enhancing both laboratory workflow and mutation detection. We evaluate UDT-Seq true sensitivity and specificity (>94% and >99%, respectively) for low prevalence mutations in a mixing experiment and demonstrate its utility using 6 tumor samples. With an improved performance when run on the Illumina Miseq, the UDT-Seq assay is well suited for clinical applications to guide therapy and study clonal selection in heterogeneous samples. http://genomebiology.com/2012/12/12/R124 Olivier Harismendy Richard Schwab Lei Bao Jeff Olson Sophie Rozenzhak Steve Kotsopoulos Stephanie Pond Brian Crain Mark Chee Karen Messer Darren Link Kelly Frazer Genome Biology 2011, null:R124 2011-12-20T00:00:00Z doi:10.1186/gb-2011-12-12-r124 UDT-seq An ultra-deep targeted sequencing assay detects low prevalence mutations in Illumina MiSeq and GAII clinical datasets generated from heterogeneous tumor samples /content/figures/gb-2011-12-12-r124-toc.gif Genome Biology 1465-6906 ${item.volume} R124 2011-12-20T00:00:00Z PDF We propose a method for predicting splice graphs that enhances curated gene models using evidence from RNA-Seq and EST alignments. Results obtained using RNA-Seq experiments in Arabidopsis thaliana show that predictions made by our SpliceGrapher method are more consistent with current gene models than predictions made by TAU and Cufflinks. Furthermore, analysis of plant and human data indicates that the machine learning approach used by SpliceGrapher is useful for discriminating between real and spurious splice sites, and can improve the reliability of detection of alternative splicing. SpliceGrapher is available for download at http://SpliceGrapher.sf.net. http://genomebiology.com/2012/13/1/R4 Mark Rogers Julie Thomas Anireddy Reddy Asa Ben-Hur Genome Biology 2012, null:R4 2012-01-31T00:00:00Z doi:10.1186/gb-2012-13-1-r4 SpliceGrapher A tool that uses a priori information to optimize alternative splicing prediction from RNA-seq data /content/figures/gb-2012-13-1-r4-toc.gif Genome Biology 1465-6906 ${item.volume} R4 2012-01-31T00:00:00Z PDF An elegant, genome-wide approach to define the precise DNA sequences bound by transcription factors has been developed by Rhee and Pugh. http://genomebiology.com/2012/13/1/139 Eric Mendenhall Bradley Bernstein Genome Biology 2012, null:139 2012-01-27T00:00:00Z doi:10.1186/gb-2012-13-1-139 DNA-protein interactions Mendenhall and Bernstein highlight ChIP-exo, which is an elegant, genome-wide approach to define the precise DNA sequences bound by transcription factors /content/figures/gb-2012-13-1-139-toc.gif Genome Biology 1465-6906 ${item.volume} 139 2012-01-27T00:00:00Z XML Background: Cannabis sativa has been cultivated throughout human history as a source of fiber, oil and food, and for its medicinal and intoxicating properties. Selective breeding has produced cannabis plants for specific uses, including high-potency marijuana strains and hemp cultivars for fiber and seed production. The molecular biology underlying cannabinoid biosynthesis and other traits of interest is largely unexplored. Results: We sequenced genomic DNA and RNA from the marijuana strain Purple Kush using shortread approaches. We report a draft haploid genome sequence of 534 Mb and a transcriptome of 30,000 genes. Comparison of the transcriptome of Purple Kush with that of the hemp cultivar 'Finola' revealed that many genes encoding proteins involved in cannabinoid and precursor pathways are more highly expressed in Purple Kush than in 'Finola'. The exclusive occurrence of Δ9-tetrahydrocannabinolic acid synthase in the Purple Kush transcriptome, and its replacement by cannabidiolic acid synthase in 'Finola', may explain why the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC) is produced in marijuana but not in hemp. Resequencing the hemp cultivars 'Finola' and 'USO-31' showed little difference in gene copy numbers of cannabinoid pathway enzymes. However, single nucleotide variant analysis uncovered a relatively high level of variation among four cannabis types, and supported a separation of marijuana and hemp. Conclusions: The availability of the Cannabis sativa genome enables the study of a multifunctional plant that occupies a unique role in human culture. Its availability will aid the development of therapeutic marijuana strains with tailored cannabinoid profiles and provide a basis for the breeding of hemp with improved agronomic characteristics. http://genomebiology.com/2011/12/10/R102 Harm van Bakel Jake Stout Atina Cote Carling Tallon Andrew Sharpe Timothy Hughes Jonathan Page Genome Biology 2011, null:R102 2011-10-20T00:00:00Z doi:10.1186/gb-2011-12-10-r102 The cannabis genome The genome and transcriptome of marijuana, and comparative analysis with resequenced hemp, explains the genetic basis of psychoactivity /content/figures/gb-2011-12-10-r102-toc.gif Genome Biology 1465-6906 ${item.volume} R102 2011-10-20T00:00:00Z XML