Genomic Taxonomy Research Articles

The next steps in next-gen sequencing of cancer genomes.

The necessary infrastructure to carry out genomics-driven oncology is now widely available and has resulted in the exponential increase in characterized cancer genomes. While a subset of genomic alterations is clinically actionable, the majority of somatic events remain classified as variants of unknown significance and will require functional characterization. A careful cataloging of the genomic alterations and their response to therapeutic intervention should allow the compilation of an "actionability atlas" and the creation of a genomic taxonomy stratified by tumor type and oncogenic pathway activation. The next phase of genomic medicine will therefore require talented bioinformaticians, genomic navigators, and multidisciplinary approaches to decode complex cancer genomes and guide potential therapy. Equally important will be the ethical and interpretable return of results to practicing oncologists. Finally, the integration of genomics into clinical trials is likely to speed the development of predictive biomarkers of response to targeted therapy as well as define pathways to acquired resistance.

Genomic and phenotypic attributes of novel salinivibrios from stromatolites, sediment and water from a high altitude lake.

BackgroundSalinivibrios are moderately halophilic bacteria found in salted meats, brines and hypersaline environments. We obtained three novel conspecific Salinivibrio strains closely related to S. costicola, from Socompa Lake, a high altitude hypersaline Andean lake (approx. 3,570 meters above the sea level).ResultsThe three novel Salinivibrio spp. were extremely resistant to arsenic (up to 200 mM HAsO42−), NaCl (up to 15%), and UV-B radiation (19 KJ/m2, corresponding to 240 minutes of exposure) by means of phenotypic tests. Our subsequent draft genome ionsequencing and RAST-based genome annotation revealed the presence of genes related to arsenic, NaCl, and UV radiation resistance. The three novel Salinivibrio genomes also had the xanthorhodopsin gene cluster phylogenetically related to Marinobacter and Spiribacter. The genomic taxonomy analysis, including multilocus sequence analysis, average amino acid identity, and genome-to-genome distance revealed that the three novel strains belong to a new Salinivibrio species.ConclusionsArsenic resistance genes, genes involved in DNA repair, resistance to extreme environmental conditions and the possible light-based energy production, may represent important attributes of the novel salinivibrios, allowing these microbes to thrive in the Socompa Lake.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-473) contains supplementary material, which is available to authorized users.

Genomic Taxonomy Boost by Lexical Clustering

In the post-genomic era, drawing inferences from multiple massive data sets is a ubiquitous challenge in the computational life sciences. Multiple sequence alignment has played a key role in genomics (and other “omics”) as a means of summarizing and representing relationships between sequences. However, two problems with alignment-based strategies are apparent: the computational expense of constructing alignments and the sensitivity of subsequent analyses to alignment uncertainties. Here we present a novel alignment-free alternative. We use frequency profiles (or n-gram vectors) for sequence comparison, a method inspired by lexical statistics. Such profiles can be used to infer relationships between texts or between biological sequences, and we demonstrate that two statistical techniques – hierarchical clustering (HC) and non-negative matrix factorization (NMF) – provide invaluable insights in both contexts. We present four case studies. First, we show that bigram frequency profiles can be used to reconstruct the ontology of 102,402 PubMed titles selected for their relevance to nine drugs and nine therapeutic proteins. Second, we apply the same methodology to classify 63 protein kinase coding DNA sequences into functional categories, based on trigram frequency profiles. The two major classes (Tyr vs Ser/Thr) are correctly identified. Third, and similarly, we show that Alu subfamilies can be identified in 58,122 Alu sequences, in perfect agreement with the accepted topology of the Alu phylogeny, again based only on trigram frequency profiles. Fourth, we clustered 8,885 human promoters using trigram frequency profiles for ab initio discovery of co-expression networks associated with disease. We demonstrate that “lexical” statistics offers a viable alignment-free approach to identifying and representing structural, functional and evolutionary relationships. We envision that our approach will be applicable to rapid and revealing comparison of whole individual genomes, and will be an important tool for analysis and correlation of “omics” data.

Benchmarking of Methods for Genomic Taxonomy

One of the first issues that emerges when a prokaryotic organism of interest is encountered is the question of what it is--that is, which species it is. The 16S rRNA gene formed the basis of the first method for sequence-based taxonomy and has had a tremendous impact on the field of microbiology. Nevertheless, the method has been found to have a number of shortcomings. In the current study, we trained and benchmarked five methods for whole-genome sequence-based prokaryotic species identification on a common data set of complete genomes: (i) SpeciesFinder, which is based on the complete 16S rRNA gene; (ii) Reads2Type that searches for species-specific 50-mers in either the 16S rRNA gene or the gyrB gene (for the Enterobacteraceae family); (iii) the ribosomal multilocus sequence typing (rMLST) method that samples up to 53 ribosomal genes; (iv) TaxonomyFinder, which is based on species-specific functional protein domain profiles; and finally (v) KmerFinder, which examines the number of cooccurring k-mers (substrings of k nucleotides in DNA sequence data). The performances of the methods were subsequently evaluated on three data sets of short sequence reads or draft genomes from public databases. In total, the evaluation sets constituted sequence data from more than 11,000 isolates covering 159 genera and 243 species. Our results indicate that methods that sample only chromosomal, core genes have difficulties in distinguishing closely related species which only recently diverged. The KmerFinder method had the overall highest accuracy and correctly identified from 93% to 97% of the isolates in the evaluations sets.

Genomic Taxonomy of the Genus Prochlorococcus

The genus Prochlorococcus is globally abundant and dominates the total phytoplankton biomass and production in the oligotrophic ocean. The single species, Prochlorococcus marinus, comprises six named ecotypes. Our aim was to analyze the taxonomic structure of the genus Prochlorococcus. We analyzed the complete genomes of 13 cultured P. marinus type and reference strains by means of several genomic taxonomy tools (i.e., multilocus sequence analysis, amino acid identity, Karlin genomic signature, and genome to genome distance). In addition, we estimated the diversity of Prochlorococcus species in over 100 marine metagenomes from all the major oceanic provinces. According to our careful taxonomic analysis, the 13 strains corresponded, in fact, to ten different Prochlorococcus species. This analysis establishes a new taxonomic framework for the genus Prochlorococcus. Further, the analysis of the metagenomic data suggests that, in total, there may only be 35 Prochlorococcus species in the world's oceans. We propose that the dearth of species observed in this study is driven by high selective pressures that limit diversification in the global ocean.

Microbial genomic taxonomy

A need for a genomic species definition is emerging from several independent studies worldwide. In this commentary paper, we discuss recent studies on the genomic taxonomy of diverse microbial groups and a unified species definition based on genomics. Accordingly, strains from the same microbial species share >95% Average Amino Acid Identity (AAI) and Average Nucleotide Identity (ANI), >95% identity based on multiple alignment genes, <10 in Karlin genomic signature, and > 70% in silico Genome-to-Genome Hybridization similarity (GGDH). Species of the same genus will form monophyletic groups on the basis of 16S rRNA gene sequences, Multilocus Sequence Analysis (MLSA) and supertree analysis. In addition to the established requirements for species descriptions, we propose that new taxa descriptions should also include at least a draft genome sequence of the type strain in order to obtain a clear outlook on the genomic landscape of the novel microbe. The application of the new genomic species definition put forward here will allow researchers to use genome sequences to define simultaneously coherent phenotypic and genomic groups.

Genome Sequence of the Marine Bacterium Vibrio campbellii DS40M4, Isolated from Open Ocean Water

Vibrio sp. strain DS40M4 is a marine bacterium that was isolated from open ocean water. In this work, using genomic taxonomy, we were able to classify this bacterium as V. campbellii. Our genomic analysis revealed that V. campbellii DS40M4 harbors genes related to iron transport, virulence, and environmental fitness, such as those encoding anguibactin and vanchrobactin biosynthesis proteins, type II, III, IV, and VI secretion systems, and proteorhodopsin.

Analysis of kinase gene expression patterns across 5681 human tissue samples reveals functional genomic taxonomy of the kinome.

Kinases play key roles in cell signaling and represent major targets for drug development, but the regulation of their activation and their associations with health and disease have not been systematically analyzed. Here, we carried out a bioinformatic analysis of the expression levels of 459 human kinase genes in 5681 samples consisting of 44 healthy and 55 malignant human tissues. Defining the tissues where the kinase genes were transcriptionally active led to a functional genomic taxonomy of the kinome and a classification of human tissues and disease types based on the similarity of their kinome gene expression. The co-expression network around each of the kinase genes was defined in order to determine the functional context, i.e. the biological processes that were active in the cells and tissues where the kinase gene was expressed. Strong associations for individual kinases were found for mitosis (69 genes, including AURKA and BUB1), cell cycle control (73 genes, including PLK1 and AURKB), DNA repair (49 genes, including CHEK1 and ATR), immune response (72 genes, including MATK), neuronal (131 genes, including PRKCE) and muscular (72 genes, including MYLK2) functions. We then analyzed which kinase genes gain or lose transcriptional activity in the development of prostate and lung cancers and elucidated the functional associations of individual cancer associated kinase genes. In summary, we report here a systematic classification of kinases based on the bioinformatic analysis of their expression in human tissues and diseases, as well as grouping of tissues and tumor types according to the similarity of their kinome transcription.

An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models

BackgroundCancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.MethodTo develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.ResultWe applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.ConclusionsWe developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.

Genomic taxonomy of Vibrios.

BackgroundVibrio taxonomy has been based on a polyphasic approach. In this study, we retrieve useful taxonomic information (i.e. data that can be used to distinguish different taxonomic levels, such as species and genera) from 32 genome sequences of different vibrio species. We use a variety of tools to explore the taxonomic relationship between the sequenced genomes, including Multilocus Sequence Analysis (MLSA), supertrees, Average Amino Acid Identity (AAI), genomic signatures, and Genome BLAST atlases. Our aim is to analyse the usefulness of these tools for species identification in vibrios.ResultsWe have generated four new genome sequences of three Vibrio species, i.e., V. alginolyticus 40B, V. harveyi-like 1DA3, and V. mimicus strains VM573 and VM603, and present a broad analyses of these genomes along with other sequenced Vibrio species. The genome atlas and pangenome plots provide a tantalizing image of the genomic differences that occur between closely related sister species, e.g. V. cholerae and V. mimicus. The vibrio pangenome contains around 26504 genes. The V. cholerae core genome and pangenome consist of 1520 and 6923 genes, respectively. Pangenomes might allow different strains of V. cholerae to occupy different niches. MLSA and supertree analyses resulted in a similar phylogenetic picture, with a clear distinction of four groups (Vibrio core group, V. cholerae-V. mimicus, Aliivibrio spp., and Photobacterium spp.). A Vibrio species is defined as a group of strains that share > 95% DNA identity in MLSA and supertree analysis, > 96% AAI, ≤ 10 genome signature dissimilarity, and > 61% proteome identity. Strains of the same species and species of the same genus will form monophyletic groups on the basis of MLSA and supertree.ConclusionThe combination of different analytical and bioinformatics tools will enable the most accurate species identification through genomic computational analysis. This endeavour will culminate in the birth of the online genomic taxonomy whereby researchers and end-users of taxonomy will be able to identify their isolates through a web-based server. This novel approach to microbial systematics will result in a tremendous advance concerning biodiversity discovery, description, and understanding.

Molecular Classification of Unknown Primary Cancer

The diagnosis of unknown primary carcinoma is often the result of the failure of light microscopy and immunohistochemistry to elucidate the origin of adenocarcinoma or poorly differentiated carcinoma. Recent advances in gene expression profiling using either reverse transcription polymerase chain reaction (RT-PCR) or microarray have enabled researchers to develop expression profiles unique to a wide variety of well-characterized primary cancers and to compare these unique signatures with those from unknown primary cancers. As the gene expression profile is frequently conserved when the tumor metastasizes, it is often possible to analyze a biopsy specimen and genomically identify its tissue of origin. In fact, the overall accuracy of genomic cancer classification in patients with known primary cancers is 80% to 90%. This new system of molecular classification might be considered as "genomic taxonomy." The genomic classification is then available to the pathologist and clinician to aid in both the patient's diagnosis and treatment planning. The impact of this new technology on patient outcomes is currently under study.

Blastocystis elongation factor-1alpha: genomic organization, taxonomy and phylogenetic relationships.

The elongation factor-1 alpha (EF-1alpha) is a highly conserved ubiquitous protein that is involved in translation and is desirable for use in phylogenetic studies on Blastocystis, an enigmatic intestinal parasite with a contentious taxonomic position. In the present study, a PCR product (BEalpha) that codes for a major part of the coding region of the EF-lalpha protein was amplified. Genome walking experiments together with cloning were implemented to elucidate the 5' and 3' ends of the EF-1alpha gene of the human isolate, Blastocystis hominis C. The genomic organization and the potential transcription factor binding sites of the 5' end of B. hominis C EF-1alpha were identified. A comparative study on the deduced amino acid sequences of BEalpha of 13 Blastocystis isolates from various hosts was done to evaluate the phylogenetic relationship among the species. A phylogenetic reconstruction analysis with other eukaryotic EF-1alpha sequences was carried out to trace the phylogenetic position of Blastocystis among eukaryotic organisms.