A VISUAL AID FOR STATISTICIANS AND MOLECULAR BIOLOGISTS WORKING WITH MICROARRAY EXPERIMENTS

The use of microarrays to measure the expression of large numbers of genes simultaneously is increasing in agriculture research. Statisticians are expected to help biologists analyze these large data sets to identify biologically important genes that are differentially regulated in the samples under investigation. However, molecular biologists are often unfamiliar with the statistical methods used to analyze microarrays. Presented here are methods developed to graphically represent microarray data and various types of errors commonly associated with microarrays to help visualize sources of error. Two case studies were used. In case study one, genes differentially regulated when two corn lines, one resistant and one sensitive, were treated with Aspergillus flavus isolate NRRL 3357 or left untreated were investigated. Analyses and images showing 3 types of variation are shown. Genes were ranked according to fold change and re-ranked after adjusting for potential sources of error. In case two, cotton genes differentially regulated in 1-day-old fiber compared to whole ovules or older fibers were investigated. Data and sources of error were imaged as described for case one and genes with significant changes in gene expression were identified

Change of Interest as a Function of Shift in Curricular Orientation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67119/2/10.1177_001316445301300213.pd

Blinded by the bright: A lack of congruence between colour morphs, phylogeography and taxonomy for a cosmopolitan Indo-Pacific butterflyfish, Chaetodon auriga

Aim: We assess genetic differentiation among biogeographical provinces and colour morphs of the threadfin butterflyfish, Chaetodon auriga. This species is among the most broadly distributed butterflyfishes in the world, occurring on reefs from the Red Sea and western Indian Ocean to French Polynesia and Hawai'i. The Red Sea form lacks a conspicuous 'eye-spot' on the dorsal fin, which may indicate an evolutionary distinction. Location: Red Sea, Indian Ocean and Pacific Ocean. Methods: Specimens were obtained at 17 locations (n = 358) spanning the entire range of this species. The genetic data included 669 base pairs of mitochondrial DNA (mtDNA) cytochrome b and allele frequencies at six microsatellite loci. Analysis of molecular variance, structure plots, haplotype networks and estimates of population expansion time were used to assess phylogeographical patterns. Results: Population structure was low overall, but significant and concordant between molecular markers (mtDNA: ΦST = 0.027, P < 0.001; microsatellites: F ST = 0.023, P < 0.001). Significant population-level partitions were only detected at peripheral locations including the Red Sea and Hawai'i. Population expansion events in the Red Sea and Socotra are older (111,940-223,881 years) relative to all other sites (16,343-87,910 years). Main conclusions: We find little genetic evidence to support an evolutionary partition of a previously proposed Red Sea subspecies. The oldest estimate of population expansion in the Red Sea and adjacent Gulf of Aden indicates a putative refuge in this region during Pleistocene glacial cycles. The finding of population separations at the limits of the range, in the Red Sea and Hawai'i, is consistent with peripheral speciation

Scrible: Ultra-Accurate Error-Correction of Pooled Sequenced Reads

Abstract. We recently proposed a novel clone-by-clone protocol for de novo genome sequencing that leverages combinatorial pooling design to overcome the limitations of DNA barcoding when multiplexing a large number of samples on second-generation sequencing instruments. Here we address the problem of correcting the short reads obtained from our sequencing protocol. We introduce a novel algorithm called Scrible that exploits properties of the pooling design to accurately identify/correct sequencing errors and minimize the chance of “over-correcting”. Exper-imental results on synthetic data on the rice genome demonstrate that our method has much higher accuracy in correcting short reads com-pared to state-of-the-art error-correcting methods. On real data on the barley genome we show that Scrible significantly improves the decoding accuracy of short reads to individual BACs.

Large sub-clonal variation in <i>Phytophthora infestans</i> from recent severe late blight epidemics in India

Abstract The population structure of the Phytophthora infestans populations that caused the recent 2013–14 late blight epidemic in eastern India (EI) and northeastern India (NEI) was examined. The data provide new baseline information for populations of P. infestans in India. A migrant European 13_A2 genotype was responsible for the 2013–14 epidemic, replacing the existing populations. Mutations have generated substantial sub-clonal variation with 24 multi-locus genotypes (MLGs) found, of which 19 were unique variants not yet reported elsewhere globally. Samples from West Bengal were the most diverse and grouped alongside MLGs found in Europe, the UK and from neighbouring Bangladesh but were not linked directly to most samples from south India. The pathogen population was broadly more aggressive on potato than on tomato and resistant to the fungicide metalaxyl. Pathogen population diversity was higher in regions around the international borders with Bangladesh and Nepal. Overall, the multiple shared MLGs suggested genetic contributions from UK and Europe in addition to a sub-structure based on the geographical location within India. Our data indicate the need for improved phytosanitary procedures and continuous surveillance to prevent the further introduction of aggressive lineages of P. infestans into the country

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University