Konstantinos Krampis

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community

BackgroundA steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure.ResultsCloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tools functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds.ConclusionsCloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them.

Araport: the Arabidopsis Information Portal

The Arabidopsis Information Portal (https://www.araport.org) is a new online resource for plant biology research. It houses the Arabidopsis thaliana genome sequence and associated annotation. It was conceived as a framework that allows the research community to develop and release ‘modules’ that integrate, analyze and visualize Arabidopsis data that may reside at remote sites. The current implementation provides an indexed database of core genomic information. These data are made available through feature-rich web applications that provide search, data mining, and genome browser functionality, and also by bulk download and web services. Araport uses software from the InterMine and JBrowse projects to expose curated data from TAIR, GO, BAR, EBI, UniProt, PubMed and EPIC CoGe. The site also hosts ‘science apps,’ developed as prototypes for community modules that use dynamic web pages to present data obtained on-demand from third-party servers via RESTful web services. Designed for sustainability, the Arabidopsis Information Portal strategy exploits existing scientific computing infrastructure, adopts a practical mixture of data integration technologies and encourages collaborative enhancement of the resource by its user community.

Analysis of genes underlying soybean quantitative trait loci conferring partial resistance to Phytophthora sojae.

Few quantitative trait loci (QTL) have been mapped for the expression of partial resistance to Phytophthora sojae in soybean and very little is known about the molecular mechanisms that contribute to this trait. Therefore, the objectives of this study were to identify additional QTL conferring resistance to P. sojae and to identify candidate genes that may contribute to this form of defense. QTL on chromosomes 12, 13, 14, 17, and 19, each explaining 4 to 7% of the phenotypic variation, were identified using 186 RILs from a cross of the partially resistant cultivar ‘Conrad’ and susceptible cultivar ‘Sloan’ through composite interval mapping. Microarray analysis identified genes with significant differences in transcript abundances between Conrad and Sloan, both constitutively and following inoculation. Of these genes, 55 mapped to the five QTL regions. Ten genes encoded proteins with unknown functions, while the others encode proteins related to defense or physiological traits. Seventeen genes within the genomic region that encompass the QTL were selected and their transcript abundance was confirmed by quantitative reverse transcription polymerase chain reaction (qRT‐PCR). These results suggest a complex QTL‐mediated resistance network. This study will contribute to soybean resistance breeding by providing additional QTL for marker‐assisted selection as well as a list of candidate genes which may be manipulated to confer resistance.

Infection and genotype remodel the entire soybean transcriptome

BackgroundHigh throughput methods, such as high density oligonucleotide microarray measurements of mRNA levels, are popular and critical to genome scale analysis and systems biology. However understanding the results of these analyses and in particular understanding the very wide range of levels of transcriptional changes observed is still a significant challenge. Many researchers still use an arbitrary cut off such as two-fold in order to identify changes that may be biologically significant. We have used a very large-scale microarray experiment involving 72 biological replicates to analyze the response of soybean plants to infection by the pathogen Phytophthora sojae and to analyze transcriptional modulation as a result of genotypic variation.ResultsWith the unprecedented level of statistical sensitivity provided by the high degree of replication, we show unambiguously that almost the entire plant genome (97 to 99% of all detectable genes) undergoes transcriptional modulation in response to infection and genetic variation. The majority of the transcriptional differences are less than two-fold in magnitude. We show that low amplitude modulation of gene expression (less than two-fold changes) is highly statistically significant and consistent across biological replicates, even for modulations of less than 20%. Our results are consistent through two different normalization methods and two different statistical analysis procedures.ConclusionOur findings demonstrate that the entire plant genome undergoes transcriptional modulation in response to infection and genetic variation. The pervasive low-magnitude remodeling of the transcriptome may be an integral component of physiological adaptation in soybean, and in all eukaryotes.

Genetic variants in root architecture-related genes in a Glycine soja accession, a potential resource to improve cultivated soybean

BackgroundRoot system architecture is important for water acquisition and nutrient acquisition for all crops. In soybean breeding programs, wild soybean alleles have been used successfully to enhance yield and seed composition traits, but have never been investigated to improve root system architecture. Therefore, in this study, high-density single-feature polymorphic markers and simple sequence repeats were used to map quantitative trait loci (QTLs) governing root system architecture in an inter-specific soybean mapping population developed from a cross between Glycine max and Glycine soja.ResultsWild and cultivated soybean both contributed alleles towards significant additive large effect QTLs on chromosome 6 and 7 for a longer total root length and root distribution, respectively. Epistatic effect QTLs were also identified for taproot length, average diameter, and root distribution. These root traits will influence the water and nutrient uptake in soybean. Two cell division-related genes (D type cyclin and auxin efflux carrier protein) with insertion/deletion variations might contribute to the shorter root phenotypes observed in G. soja compared with cultivated soybean. Based on the location of the QTLs and sequence information from a second G. soja accession, three genes (slow anion channel associated 1 like, Auxin responsive NEDD8-activating complex and peroxidase), each with a non-synonymous single nucleotide polymorphism mutation were identified, which may also contribute to changes in root architecture in the cultivated soybean. In addition, Apoptosis inhibitor 5-like on chromosome 7 and slow anion channel associated 1-like on chromosome 15 had epistatic interactions for taproot length QTLs in soybean.ConclusionRare alleles from a G. soja accession are expected to enhance our understanding of the genetic components involved in root architecture traits, and could be combined to improve root system and drought adaptation in soybean.

miR-1207-3p regulates the androgen receptor in prostate cancer via FNDC1/fibronectin

Prostate cancer (PCa) is frequently diagnosed in men, and dysregulation of microRNAs is characteristic of many cancers. MicroRNA-1207-3p is encoded at the non-protein coding gene locus PVT1 on the 8q24 human chromosomal region, an established PCa susceptibility locus. However, the role of microRNA-1207-3p in PCa is unclear. We discovered that microRNA-1207-3p is significantly underexpressed in PCa cell lines in comparison to normal prostate epithelial cells. Increased expression of microRNA-1207-3p in PCa cells significantly inhibits proliferation, migration, and induces apoptosis via direct molecular targeting of FNDC1, a protein which contains a conserved protein domain of fibronectin (FN1). FNDC1, FN1, and the androgen receptor (AR) are significantly overexpressed in PCa cell lines and human PCa, and positively correlate with aggressive PCa. Prostate tumor FN1 expression in patients that experienced PCa-specific death is significantly higher than in patients that remained alive. Furthermore, FNDC1, FN1 and AR are concomitantly overexpressed in metastatic PCa. Consequently, these studies have revealed a novel microRNA-1207-3p/FNDC1/FN1/AR regulatory pathway in PCa.

Non-synonymous variations in cancer and their effects on the human proteome: workflow for NGS data biocuration and proteome-wide analysis of TCGA data

BackgroundNext-generation sequencing (NGS) technologies have resulted in petabytes of scattered data, decentralized in archives, databases and sometimes in isolated hard-disks which are inaccessible for browsing and analysis. It is expected that curated secondary databases will help organize some of this Big Data thereby allowing users better navigate, search and compute on it.ResultsTo address the above challenge, we have implemented a NGS biocuration workflow and are analyzing short read sequences and associated metadata from cancer patients to better understand the human variome. Curation of variation and other related information from control (normal tissue) and case (tumor) samples will provide comprehensive background information that can be used in genomic medicine research and application studies. Our approach includes a CloudBioLinux Virtual Machine which is used upstream of an integrated High-performance Integrated Virtual Environment (HIVE) that encapsulates Curated Short Read archive (CSR) and a proteome-wide variation effect analysis tool (SNVDis). As a proof-of-concept, we have curated and analyzed control and case breast cancer datasets from the NCI cancer genomics program - The Cancer Genome Atlas (TCGA). Our efforts include reviewing and recording in CSR available clinical information on patients, mapping of the reads to the reference followed by identification of non-synonymous Single Nucleotide Variations (nsSNVs) and integrating the data with tools that allow analysis of effect nsSNVs on the human proteome. Furthermore, we have also developed a novel phylogenetic analysis algorithm that uses SNV positions and can be used to classify the patient population. The workflow described here lays the foundation for analysis of short read sequence data to identify rare and novel SNVs that are not present in dbSNP and therefore provides a more comprehensive understanding of the human variome. Variation results for single genes as well as the entire study are available from the CSR website (http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr).ConclusionsAvailability of thousands of sequenced samples from patients provides a rich repository of sequence information that can be utilized to identify individual level SNVs and their effect on the human proteome beyond what the dbSNP database provides.

Extensive Variation in Nuclear Mitochondrial DNA Content Between the Genomes of Phytophthora sojae and Phytophthora ramorum

Fragments of mitochondrial DNA (mtDNA) transferred to the nuclear genome are called nuclear mitochondrial DNAs (NUMTs). We report here a comparison of NUMT content between genomes from two species of the same genus. Analysis of the genomes of Phytophthora sojae and P. ramorum revealed large differences in the NUMT content of the two genomes: 16.27 x 10(-3) and 2.28 x 10(-3)% of each genome, respectively. Substantial differences also exist between the two species in the sizes of the NUMTs found in each genome, with ranges of 20 to 405 bp for P. sojae and 19 to 137 bp for P. ramorum. Furthermore, in P. sojae, fragments from the mitochondrial genes rns, rnl, coxl, and nad (various subunits) are found most frequently, whereas P. ramorum NUMTs most often originate from the cox3, rpsl4, nad4, and nad5 genes. The large differences in the presumptive mtDNA insertions suggest that the insertions occurred subsequent to the divergence of the two species, and this is supported by sequence comparisons among the NUMTs and the mtDNA sequences of the two species. P. sojae mtDNA sequences inserted in the nuclear genome appear to have been altered as a result of insertions, deletions, inversions, and translocations and provide insights into active mechanisms of sequence divergence in this plant pathogen. No clear examples were found of NUMTs forming functional nuclear genes or of NUMTs inserted into exons or introns of any nuclear gene.

Census-based rapid and accurate metagenome taxonomic profiling

BackgroundUnderstanding the taxonomic composition of a sample, whether from patient, food or environment, is important to several types of studies including pathogen diagnostics, epidemiological studies, biodiversity analysis and food quality regulation. With the decreasing costs of sequencing, metagenomic data is quickly becoming the preferred typed of data for such analysis.ResultsRapidly defining the taxonomic composition (both taxonomic profile and relative frequency) in a metagenomic sequence dataset is challenging because the task of mapping millions of sequence reads from a metagenomic study to a non-redundant nucleotide database such as the NCBI non-redundant nucleotide database (nt) is a computationally intensive task. We have developed a robust subsampling-based algorithm implemented in a tool called CensuScope meant to take a ‘sneak peak’ into the population distribution and estimate taxonomic composition as if a census was taken of the metagenomic landscape. CensuScope is a rapid and accurate metagenome taxonomic profiling tool that randomly extracts a small number of reads (based on user input) and maps them to NCBI’s nt database. This process is repeated multiple times to ascertain the taxonomic composition that is found in majority of the iterations, thereby providing a robust estimate of the population and measures of the accuracy for the results.ConclusionCensuScope can be run on a laptop or on a high-performance computer. Based on our analysis we are able to provide some recommendations in terms of the number of sequence reads to analyze and the number of iterations to use. For example, to quantify taxonomic groups present in the sample at a level of 1% or higher a subsampling size of 250 random reads with 50 iterations yields a statistical power of >99%. Windows and UNIX versions of CensuScope are available for download at https://hive.biochemistry.gwu.edu/dna.cgi?cmd=censuscope. CensuScope is also available through the High-performance Integrated Virtual Environment (HIVE) and can be used in conjunction with other HIVE analysis and visualization tools.

Functional Genomics and Bioinformatics of the Phytophthora sojae Soybean Interaction

Oomycete plant pathogens such as Phytophthora species and downy mildews cause destructive diseases in an enormous variety of crop plant species as well as forests and native ecosystems. These pathogens are most closely related to algae in the kingdom Stramenopiles, and hence have evolved plant pathogenicity independently of other plant pathogens such as fungi. We have used bioinformatic analysis of genome sequences and EST collections, together with functional genomics to identify plant and pathogen genes that may be key players in the interaction between the soybean pathogen Phytophthora sojae and its host. In P. sojae, we have identified many rapidly diversifying gene families that encode potential pathogenicity factors including protein toxins, and a class of proteins (avirulence or effector proteins) that appear to have the ability to penetrate plant cells. Transcriptomic analysis of quantitative or multigenic resistance against P. sojae in soybean has revealed that there are widespread adjustments in host gene expression in response to infection, and that some responses are unique to particular resistant cultivars. These observations lay the foundation for dissecting the interplay between pathogen and host genes during infection at a whole-genome level.